SBHD 2014 Program Book - Systems Biology of Human Disease 2014

Transcription

INTERNATIONAL CONFERENCE ON
JUNE
17–19
2014
sbhd2014.org
SPONSORED BY:
THE JOSEPH B. MARTIN CONFERENCE CENTER
HARVARD MEDICAL SCHOOL
77 AVENUE LOUIS PASTEUR
BOSTON, MA 02115
Contents
Welcome . . . . . . . . .
Schedule . . . . . . . . .
Tuesday, June 17 . .
Wednesday, June 18
Thursday, June 19 .
Awards . . . . . . . . . .
Attendee List . . . . . . .
Speaker Abstracts . . . .
Tuesday, June 17 . .
Wednesday, June 18
Thursday, June 19 .
Poster Abstracts . . . . .
General Information . . .
Floor Plans . . . . .
HMS Campus Map .
Area Street Map . .
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2
International Conference on Systems Biology of Human Disease
Welcome
June 2014
Dear Colleagues,
We are pleased to welcome you to the 2014 International Conference on Systems Biology
of Human Disease (SBHD), a conference organized by the Council for Systems Biology
in Boston (CSB2 ; www.csb2.org). This year’s three-day program includes platform talks,
talks chosen from submitted abstracts, lightning talks, poster sessions and award presentations. We are particularly pleased by the large number of poster submissions and hope
you spend some time to attend the poster sessions.
The 2014 SBHD meeting is supported by the newly established Harvard Program in
Therapeutic Sciences (HiTS). HiTS engages clinicians and scientists from multiple universities and Harvard affiliated hospitals in the application of systems biology approaches
to therapeutic mechanisms and drug evaluation, with the ultimate goal of improving how
existing treatments are personalized to individual patients and how drugs are developed
for unmet medical needs. Achieving these goals will require deep understanding of cell
signaling networks, disease processes and medicinal chemistry – the focus of the Laboratory of Systems Pharmacology – and fundamental improvements in the processes
used to evaluate drugs via clinical trials – the focus of our Regulatory Sciences Program.
As part of this latter effort we are sponsoring a special symposium “Challenges in Drug
Evaluation and Regulatory Science: Applications to Multiple Sclerosis” on Thursday
afternoon (June 19). We hope you will join us to hear how challenges arising from “adverse clinical events” were tackled by investigators close to the program that developed
Alemtuzumab (Lemtrada) as a treatment for multiple sclerosis.
This meeting would not be possible without the continuing alliance between HMS and its
European partners, including the Helmholtz Alliance on Systems Biology
(www.helmholtz.de/systemsbiology), DKFZ and BioQuant. In 2015 SBHD will once again
be held in Heidelberg, after which it will return to Boston in 2016. Dates for future conferences will be announced during the proceedings, posted at www.csb2.org and shared by
email (if you are a current or past SBHD attendee).
We are grateful for Merrimack Pharmaceuticals’ continuing sponsorship of the CSB2 Prize
in Systems Biology. This prize, which recognizes independent junior investigators who
have exhibited exceptional promise, will be awarded in 2014 to Chris Bakal of the London
Institute for Cancer Research. In addition, Chroma Technology Corp. has generously
sponsored the first Anne Heidenthal Prize for Fluorescence Research awarded to
Bernd Bodenmiller of the University of Zürich. Birgit Schoeberl from Merrimack and Georg
Draude and Paul Millman from Chroma will present the awards prior to the awardees’ talks
Boston, MA
June 17-19, 2014
3
Welcome
on June 18.
As always, we welcome any ideas you might have for improving this conference. We
are particularly aware that we have fewer social events than the Heidelberg-based versions of this meeting and we are trying to figure out how we can afford it in future years.
Organizing an event such as SBHD requires an enormous amount of effort by a talented
team, and we would like to thank Chris Bird, Laura Maliszewski, Jeremy Muhlich and
Joleen Pugliese, as well as members of the Organizing Committee (see below). We hope
that you have an enjoyable and exciting meeting, and look forward to seeing you in Germany in 2015.
Yours sincerely,
Peter Sorger
Roland Eils
SBHD 2014 Organizing Committee
Peter Sorger, Harvard Medical School, Boston, USA
Roland Eils, DKFZ, Heidelberg, Germany
Bree Aldridge, Tufts, Cambridge, USA
Leonidas Alexopoulos, National Technical University of Athens, Greece
Philipp Bastiaens, MPI Dortmund, Germany
Pascal Braun, TU München, Munich, Germany
Suzanne Gaudet, Dana Farber Cancer Institute, Boston, USA
Alexander Hoffmann, UC San Diego, San Diego, USA
Ursula Klingmüller, DKFZ, Heidelberg, Germany
Avi Ma’ayan, Mount Sinai Medical Center, New York, USA
Uwe Sauer, ETH Zürich, Switzerland
Birgit Schoeberl, Merrimack Pharmaceuticals, Cambridge, USA
Luis Serrano, CRG Barcelona, Spain
SBHD 2014 Local Organizing Team
Chris Bird, Harvard Medical School, Boston, USA
Laura Maliszewski, Harvard Medical School, Boston, USA
Jeremy Muhlich, Harvard Medical School, Boston, USA
Joleen Pugliese, Harvard Medical School, Boston, USA
Funds to support this meeting were generously provided by the Harvard Program in Therapeutic Science (HiTS), DKFZ, BioQuant, Hemlholtz Association, and SystemsX.ch. Sup4
Welcome
port for travel costs for German students was provided by the German Federal Ministry of
Education and Research (BMBF).
We thank Birgit Schoeberl and Merrimack Pharmaceuticals, sponsor of the CSB2 Prize
in Systems Biology and Paul Millman, Head of Chroma Technology Corp., sponsor of
the Anne Heidenthal Prize for Fluorescence Research.
Thanks also to Ulrike Conrad and Jan Eufinger for their assistance with planning and
coordination of the meeting.
Boston, MA
June 17-19, 2014
5
Welcome
Some important things to keep in mind:
Speakers:
• Please keep an eye on the podium timer and do not go over your allotted time.
• Please bring your presentations or computers to the attention of AV staff at the front
of the room well before the session begins.
Everyone:
• Please do not stand or sit in the aisles — there is sufficient seating for everyone.
• Please use the microphone when asking questions of the speakers.
• Public wireless access is available in all the conference rooms. Please speak to an
AV staff member if you experience any difficulties.
• In case of an emergency:
– Please evacuate the room by following the nearest exit signs.
– Upon exiting, head to the conference center assembly area (see map on facing
page).
– The assembly area is the MERCK Parking lot that is located to the left of the
building as you exit from the main entrance.
– Please report to the person wearing the green vest and holding “The Conference Center at Harvard Medical” sign.
– Await further instructions.
Drink tickets for the poster sessions:
• Tickets in the back of your badge are for drinks during the poster session. You have
two for each night. Non-alcoholic drinks do not require a ticket. You must be at least
21 years of age to order and/or consume drinks containing alcohol.
6
Welcome
Boston, MA
June 17-19, 2014
7
Welcome
8
Schedule
Schedule
Tuesday, June 17, 2014
Registration and continental breakfast
7:45 – 8:15 AM
Welcome remarks
8:15 – 8:30 AM
Roland Eils, German Cancer Research Center (DKFZ)
Prenatal Environmental Exposure Induces Epigenetic Reprogramming with Consequences for Disease Risk Later in Children’s Life
8:30 – 9:10 AM
John Albeck, University of California, Davis
Linking the Dynamics of Kinase, Transcriptional, and Metabolic
Networks in Single Cells
9:10 – 9:50 AM
Franziska Michor, Dana-Farber Cancer Institute
Evolution of the Cancer Genome
9:50 – 10:30 AM
Coffee break
10:30 – 10:50 AM
Dirk Drasdo, INRIA Paris / IZBI Leipzig
10:50 – 11:30 AM
How Quantitative Modeling can Inform on Disease Pathogenesis:
Lessons from Liver
Leonidas Alexopoulos, National Technical University of Athens
11:30 – 12:10 PM
Tackling Cartilage Degeneration using Systems Biology and
Biomechanics Approach
Lunch (bagged lunch provided)
12:10 – 1:00 PM
Grégoire Altan-Bonnet, Memorial Sloan-Kettering Cancer Center
Single-cell Analysis of the Dysregulation of Antigen Signaling in
Chronic Lymphocytic Leukemia (CLL)
1:00 – 1:40 PM
Olga Troyanskaya, Princeton University
Cell-lineage and Tissue-specific View of Human Disease on the
Whole-genome Scale
1:40 – 2:20 PM
Kathryn Miller-Jensen, Yale University
Analysis of Single-cell Secretion Reveals a Role for Paracrine Signaling in Coordinating Macrophage Response to TLR4 Stimulation
(Selected speaker from poster abstracts – See poster #1)
2:20 – 3:00 PM
10
Tuesday, June 17
Coffee break
3:00 – 3:20 PM
Ariel Kniss, Georgia Tech
Developing Microfluidic and Experimental Tools to Interrogate Intracellular T Cell Signaling using a Frequency Response Analysis
Approach
3:20 – 4:00 PM
Lightning talks
4:00 – 4:40 PM
Six one-slide, five-minute presentations by selected presenters
from the Tuesday poster session:
Loice Chingozha # 5
Jia-Ren Lin
# 9
Jette Strasen
# 13
Evan Daugharthy # 7
Dimitris Messinis # 11
Chun-Chao Wang # 15
Tuesday poster session and refreshments
Adjourn
Boston, MA
4:40 – 6:30 PM
6:30 PM
June 17-19, 2014
11
Schedule
Wednesday, June 18, 2014
8:00 – 8:25 AM
Welcome remarks
8:25 – 8:30 AM
Christopher Sassetti, University of Massachusetts Medical School
Systems Genetic Approaches to Understand Tuberculosis Pathogenesis
8:30 – 9:10 AM
Douglas Lauffenburger, Massachusetts Institute of Technology
Multi-Scale Systems Analysis of Cell-Cell Communication and
Signaling in Complex Inflammatory Disease
9:10 – 9:50 AM
Markus Covert, Stanford University
High-sensitivity Measurements of Multiple Kinase Activities in Live
single Cells
9:50 – 10:30 AM
Coffee break
10:30 – 10:50 AM
AJ Marian Walhout, University of Massachusetts Medical School
Interspecies Systems Biology: Nutritional Networks
10:50 – 11:30 AM
Bernd Bodenmiller, University of Zürich
11:30 – 12:10 PM
Recipient of the Anne Heidenthal Prize for Fluorescence Research – sponsored by Chroma Technology Corp.
Analysis of Single Cell Networks through Time and Space by Mass
Cytometry
12:10 – 1:00 PM
Kevin Janes, University of Virginia
Linking Signal-transduction and Gene-expression Networks by
Statistical Modeling
1:00 – 1:40 PM
Chris Bakal, The Institute of Cancer Research, London
Recipient of the CSB2 Prize in Systems Biology – sponsored
by Merrimack Pharmaceuticals
How Signaling Regulates Cell Shape, and how Cell Shape Regulates Signaling
1:40 – 2:20 PM
Ursula Klingmüller, German Cancer Research Center (DKFZ)
Unraveling the Risk of Epo in Lung Cancer
2:20 – 3:00 PM
12
Wednesday, June 18
Coffee break
3:00 – 3:20 PM
Sara Dempster, AstraZeneca
Approaches for Integrative Analysis of Pharmacology and
Genome-wide Molecular Profiling Data Applied to a Large Scale
Screen of Cancer Cell Lines
3:20 – 4:00 PM
Lightning talks
4:00 – 4:40 PM
Six one-slide, five-minute presentations by selected presenters
from the Wednesday poster session:
Jia-Yun Chen # 4
Yan Kou
# 8
Miles Miller
# 12
Kelly (Wen Li) Chen
# 6
Carlos Lopez
# 10
Lakshmi Venkatraman # 14
Wednesday poster session and refreshments
Adjourn
Boston, MA
4:40 – 6:30 PM
6:30 PM
June 17-19, 2014
13
Schedule
Thursday, June 19, 2014
8:00 – 8:25 AM
Welcome remarks
8:25 – 8:30 AM
Mohammed AlQuraishi, Harvard Medical School
From Genomes to Molecular Phenomes
8:30 – 9:10 AM
Tim van Opijnen, Boston College
Host-genetics and Fluid Bacterial Genomes Make Virulence and
Drug-tolerance Context Dependent
9:10 – 9:50 AM
Birgit Schoeberl, Merrimack Pharmaceuticals
A Systems Biology Approach to Drug Development: Clinical Testing of Biomarkers for the anti-ErbB3 Antibody MM-121
9:50 – 10:30 AM
Coffee break
10:30 – 10:50 AM
Sabrina Spencer, Stanford University
Single-cell Dynamics of the Proliferation-quiescence Decision
10:50 – 11:30 AM
Luis Serrano Pubul, Center for Genomic Regulation, Barcelona
11:30 – 12:10 PM
Structure-energy-based Predictions, Competition and Network
Modelling of ERbMAPK Signalling, and its Implication in RASopathy and Cancer Missense Mutations
Adjourn
14
12:10 – 1:00 PM
1:00 PM
Awards
Awards
The CSB2 Prize in Systems Biology
sponsored by Merrimack Pharmaceuticals
This prize is awarded to Chris Bakal, Ph.D., Leader of the Dynamical Cell Systems Team
within the Division of Cancer Biology at the The Institute of Cancer Research. Chris Bakal
was chosen for his contributions to the development and implementation of new statistical
methods to investigate signals regulating cellular behaviors by analyzing features of cells
captured by imaging. In his earlier work, he showed how single-cell measurements of
JNK activity under a multitude of genetic perturbations allowed the inference of a JNK
signaling network. In the past year, the power of these new statistical methods was highlighted in a publication by his group in Nature Cell Biology. The statistical methods they
developed enabled a quantitative description of cell shape complexity, showing that cells
only take on a small set of discrete shapes, as well as identification of key regulators of
cell shape heterogeneity. In all of his work, Chris Bakal combines the power of single-cell
observations by microscopy with that of statistics to gain new insights in signal transduction and cell biology.
The Anne Heidenthal Prize for Fluorescence Research
sponsored by Chroma Technology Corp.
Professor Bernd Bodenmiller is the first awardee of the “Anne Heidenthal Prize,” which is
being generously sponsored by Chroma Technology Corp. The prize is awarded annually
in memory of Anne Neumeyr-Heidenthal, a molecular and cell biologist at Chroma Technology Corp. The Anne Heidenthal Prize recognizes promising young scientisits who are
advancing the field of quantitative biology through the use of fluorescence and advanced
imaging technologies. Bernd Bodenmiller is the SNSF Assistant Professor for Quantitative Biology in the Institute of Molecular Life Sciences at the University of Zürich. As a
postdoctoral fellow with Gary Nolan, Bernd was instrumental in the development of mass
cytometry as means to perform high multiplicity profiling of human immune cells. In his
own lab, Bernd has led the development of novel methods for imaging cells and tissues
using mass cytometry, a revolutionary approach to single-cell analysis.
16
Previous Awardees
2013
James Locke, Cambridge University, Cambridge UK
2012
Hana El-Samad, UCSF University of California, San Francisco
2011
Hang Lu, Georgia Institute of Technology
2010
Marcus Covert, Stanford University
Melissa Kemp, Georgia Tech & Emory University
2009 Ernest Fraenkel, Massachusetts Institute of Technology
Nathanael Gray, Dana Farber Cancer Institute
2008
Gavin MacBeath, Harvard University
Aneil Mallavarapu, Harvard Medical School
Boston, MA
June 17-19, 2014
17
Awards
18
Speaker Abstracts
Speaker Abstracts
Prenatal Environmental Exposure Induces Epigenetic Reprogramming with Consequences for Disease Risk Later
in Children’s Life
Roland Eils
German Cancer Research Center (DKFZ)
Environmental factors can cause persistent perturbations of regulatory pathways with the
consequence of altered disease susceptibility. Prenatal exposure to environmental factors may increase the disease risk later in children’s life. It is currently discussed that
the transient effect of environmental exposure early in life can be preserved by epigenetic mechanisms that may silence or activated disease relevant regulatory pathways in
a persistent way. Here, we studied genome-wide, environmentally induced epigenetic
changes and their functional relevance for disease risks later in life within a longitudinal mother-child birth cohort. Among others, we studied gene environment interactions
induced by maternal smoking during pregnancy. By integrated analysis of longitudinal
whole genome bisulfite sequencing, ChIP-sequencing and RNA sequencing we unraveled a genome-wide epigenetic stable reprogramming converging upon transcriptional
enhancers. We found a number of disease related pathways deregulated, among them
Wnt signaling, which is involved in the airway inflammatory response to cigarette smoke
in smoking mothers as well as their newborn children. An association between epigenetic
reprogramming of genes within the Wnt signaling pathway already at time of birth and the
development of impaired lung function later in children’s life can be shown.
36
Tuesday, June 17
Linking the Dynamics of Kinase, Transcriptional, and Metabolic Networks in Single Cells
John Albeck
University of California, Davis
As single-cell technologies expand, it is becoming clear that many cellular signaling events
are very dynamic, necessitating a time-lapse approach. I will present our work on the
single-cell kinetics of two kinases - ERK and AMPK - that play key roles in the response
to targeted cancer therapies aimed at disrupting cellular growth, proliferation, and homeostasis. Induced by growth factor stimulation, ERK activity is a central controller of transcription factors involved in oncogenesis, including Myc, Fra-1, and Egr-1. We show that
at the single-cell level, each of these factors interprets ERK dynamics differently, leading
to a diversity of cellular states within a genetically homogeneous population. ERK pathway inhibitors, now being evaluated for use in multiple cancers, modulate ERK dynamics
differentially and redefine the repertoire of cellular states in unique ways. AMPK responds
to cellular energy deprivation, and we show that direct inhibition of glycolysis results in a
strikingly regular modulation of AMPK activity and metabolic state. In contrast, PI3K and
mTOR inhibitors, another key class of targeted therapies, lead to highly disordered disruption of metabolic dynamics. Together these findings underscore the concept that, despite
the chemically specific of modern targeted cancer therapies, their usefulness may be limited by the highly variable kinetics that they induce within cellular populations, resulting in
sub-optimal, heterogeneous responses.
Boston, MA
June 17-19, 2014
37
Speaker Abstracts
Evolution of the Cancer Genome
Franziska Michor
Dana-Farber Cancer Institute
38
Tuesday, June 17
How Quantitative Modeling can Inform on Disease Pathogenesis: Lessons from Liver
Dirk Drasdo
INRIA Paris / IZBI Leipzig
Systems biology has opened up new ways of understanding disease processes based
on close iterations between experimentation and mathematical modelling. So far, its
focus has mainly been on molecular processes. The combination of modern imaging
modalities with image processing and analysis (Hammad et. al. Arch. Toxicol. 2014;
Hoehme and Drasdo, Bioinformatics 2010), and mathematical models opens up a promising new approach towards a quantitative understanding of pathologies and of disease
processes that includes the multicellular tissue level. For illustration we will consider three
examples of interdisciplinary approaches integrating biological models and mechanisms
of processes contributing to disease progression at various scales within mathematical
modelling frameworks. In the first example a multi-cellular spatial temporal model predicts within a systems biology approach a previously not recognized and subsequently
validated order principle underlying liver regeneration after drug-induced damage, as it occurs for example after overdosing acetaminophen (paracetamol) (Hoehme et. al., PNAS,
2010). The second example will present a mathematical model integrating information
from the spatial temporal model of the first example with the chemical reactions known to
detoxify liver from ammonia in health liver during the destruction and subsequent regeneration process, that succeeded to indicate the lack of an important reaction (Schliess et.
al., Hepatology, 2014). Experiments triggered by this model prediction led to finding of a
so far unrecognized good candidate reaction that might be clinically utilized in case of hyperammonemia. The final example will address the spatial-temporal molecular control of
the regeneration process within a mechanistic multi-scale model spanning the molecular,
cellular, tissue and body scale. The tissue model involved in each of these examples represents each cell individually as biophysical entities and is hence able to integrate equally
physical and biological information. The example demonstrates that multi-cellular models
are so far able to falsify hypotheses and guide towards the most informative experimental
design.
Boston, MA
June 17-19, 2014
39
Speaker Abstracts
Tackling Cartilage Degeneration using Systems Biology
and Biomechanics Approach
Leonidas Alexopoulos, Stavroula Samara, Ioannis N Melas, Elisavet Chatzopoulou, Theodore
Sakalleropoulos, Alexander Mitsos, Zoe Dailiana, Panagoula Kollia
National Technical University of Athens
Arthritis is the leading cause of disability affecting one of every six people. The most
common type of arthritis is osteoarthritis (OA), a painful disease, in which cartilage loses
its mechanical integrity. It is evident that OA is not just a natural cause of aging but an
imbalance of signaling mechanisms that leads to cartilage degeneration. Two different
approaches are proposed to study OA: Systems Biology and Biomechanics. The systems biology approach uses high throughput proteomics and an optimization formulation
in order to: i) screen compounds and identify major catabolic players for cartilage and ii)
identify signaling mechanisms responsible for cartilage degeneration. The biomechanics
approach is used to quantify the mechanical properties of cartilage on a custom multi
indentation device. The combination of those approaches links a tissue phenotype (degeneration) with the signaling network and sheds a light into possible treatments of OA.
Methods: Systems Biology: Chondrocytes are isolated and treated with a diverse set of
90 catabolic and anabolic stimuli. 18 phopshoproteins and 60 cytokines are measured
using multiplex technology (Luminex) with custom multiplex assays (ProtATonce). Data
are normalized and analyzed using PLSR, PCA, clustering algorithm, and integer linear
programming formulation. Signaling pathways were constructed. Biomechanics: A multiindentation device than handles up to 24 cartilage disk explants is built and an analytical,
linear elastic model and a biphasic model is used to quantify Young’s modulus, Poisson’s
ratio, and hydraulic permeability.
Results: The biomechanics approach quantifies the mechanical degeneration of the tissue whereas the systems biology approach identifies pathways responsible for cartilage
degeneration such as the known IL1a, IL1b, and TNFa but also the less known TLR stimuli. Closer look into the pathways constructed by the ILP formulation identify possible
ways of reversing the loss of mechanical properties via targeting of the NFkB and possibly the p38 pathway.
Conclusions: The combination of Systems Biology and Biomechanics was able to identify
new players and related pathways to cartilage degeneration. Our approach sheds a light
into possible treatments of osteoarthritis and suggests novel therapeutic interventions of
a disease with strong medical, economic, and social impact.
40
Tuesday, June 17
Single-cell Analysis of the Dysregulation of Antigen Signaling in Chronic Lymphocytic Leukemia (CLL)
Carly G. Ziegler, M. Lia Palomba & Grégoire Altan-Bonnet
Memorial Sloan-Kettering Cancer Center
Chronic Lymphocytic Leukemia (CLL) is characterized by a perturbed B-cell receptor mediated signaling machinery, driving B cell accumulation in the blood and lymphoid organs.
We applied phosphatase inhibition to activate and probe ex vivo the signaling response
of human primary B cells with single cell resolution. We found that B cells (from CLL
patients or from healthy donors) rapidly present a bimodal response in proximal kinase
phosphorylation. Using drug inhibitors, we validated the existence of a proximal positive
feedback in kinase activation, whose existence is supported by the observed hysteresis in BCR signaling. We then documented systematically the kinetics of phosphorylation/dephosphorylation within the BCR pathway to build and constrain a universal model
of BCR signaling. Our model predicted that all B cells (either from healthy donors or CLL
patients) respond equivalently, once normalized for varied degrees of receptor clustering,
as quantified by super-resolution microscopy. Our study highlights the role of tonic signaling in CLL B cells, and a potential defect in negative selection as a major driving event for
oncogenesis in CLL.
Boston, MA
June 17-19, 2014
41
Speaker Abstracts
Cell-lineage and Tissue-specific View of Human Disease
on the Whole-genome Scale
Olga Troyanskaya
Princeton University
An immense molecular complexity forms the foundation of human disease. Our goal is to
interpret and distill this complexity through accurate modeling of molecular networks and
pathways, particularly those in which malfunction promotes the emergence of complex
human disease. Although cell-lineage-specific gene expression and function underlie the
development, function, and maintenance of diverse cell types within an organism, highthroughput data are rarely resolved with respect to specific cell lineages. In this talk, I will
focus on our recent work developing integrative approaches that leverage functional genomics data collections to study how cellular pathways function in diverse cell types. Our
work accounts for cell-lineage specificity in integrated models of gene expression and
protein networks, including predictions of tissue-specific and cell-lineage-specific gene
expression in human with accuracies higher than those of high-throughput experimental
studies. I will also discuss how integrated analysis of functional genomics data can be
leveraged to study tissue-lineage-specific protein function and interactions and to identify
genes involved in disease in a way complementary to quantitative genetics approaches.
42
Tuesday, June 17
Analysis of Single-cell Secretion Reveals a Role for Paracrine Signaling in Coordinating Macrophage Response to
TLR4 Stimulation
Qiong Xue, Yao Lu, Markus Eisele, Nafeesa Khan, Rong Fan, Kathryn Miller-Jensen
Yale University
Cell populations produce reliable biological responses despite significant levels of cellto-cell heterogeneity. These biological responses often involve intermediate extracellular
signals in which cells secrete and respond to the same factor. The propagation of intermediate signals by extracellular signaling has a significant impact on the collective cellpopulation response, but the contribution of autocrine versus paracrine signaling remains
difficult to analyze. Here we combined multiplexed, nanowell single-cell secretion measurements with cell-population data to distinguish the role of paracrine signaling in shaping the inflammatory cytokine secretion profile in a monocytic cell line (U937) and primary
human monocyte-derived macrophages (MDMs) in response to toll-like receptor 4 (TLR4)
stimulation with lipopolysaccharide (LPS). Loss of paracrine signaling upon single-cell isolation in nanowells significantly reduced secretion of a subset of LPS-stimulated cytokines,
including interleukin-6 (IL-6) and IL-10, in both U937 and primary human MDM cells. Using graphical Gaussian modeling of LPS-stimulated single-cell data, we identified tumor
necrosis factor-α (TNF-α) and IL-1β as central nodes in the LPS-stimulated paracrinesignaling network, which was further validated experimentally. In both U937 and MDM
single cells, a small fraction of cells accounted for the majority of the total TNF-α output
in the cell population, but paracrine signaling from this small subset amplified secretion
and reduced variability in the downstream dependent signals. Our results demonstrate
that a small subpopulation of high-responding cells appears to drive the innate immune
response in macrophages through paracrine signaling. Thus, paracrine signaling results
in a coordinated population response that is absent in isolated single cells, underscoring
the importance of inter-cellular signaling in determining cellular behaviors.
Boston, MA
June 17-19, 2014
43
Speaker Abstracts
Developing Microfluidic and Experimental Tools to Interrogate Intracellular T Cell Signaling using a Frequency
Response Analysis Approach
Ariel Kniss, Loice Chingozha, Hang Lu, Melissa Kemp
Georgia Institute of Technology
Introduction: T cells are part of the adaptive immune response that recognize and fight
pathogens in the body. T cell activation is a complex process resulting in the induction
of a proliferative response, encoded by the dynamics of multiple signaling cascades and
reliant on second messenger molecules such as calcium and hydrogen peroxide. Frequency response analysis (FRA), developed in control engineering, has been previously
allowed for inference of cellular signaling network structure but has been experimentally
difficult to apply to suspension T cells. We have designed a FRA experimental and computational platform for future use in determining the dominant feedback controls present
in T cell signaling.
Materials and Methods: To develop the experimental precision required for this approach,
we use a microfluidic device capable of applying an oscillatory chemical cue as an input
signal while enabling visualization of T cells through time. In this work we apply H2 O2
at a range of driving frequencies to Jurkat T cells while monitoring cytoplasmic calcium
concentration. We then perform spectral analysis of the single cell calcium traces to determine the output signal response of the system.
Results: Calcium signaling is found to respond differently to 100µM H2 O2 at specific driving frequencies of 16.7mHz (1 min period), 8.3mHz (2 min period), and 2.8mHz (6 min
period). With the single cell spectral analysis, subpopulations of cells emerge in response
to the same driving frequency and between different experiments. Cells are entrained to
frequencies below 8.3mHz but attenuate H2 O2 signals above 16.7mHz.
Conclusion: With the use of microfluidic technology and initial spectral analysis, we have
determined that T cells have a cutoff frequency between 1 and 2 minutes when interrogated with oscillatory H2 O2 stimulus. This suggests downstream transcriptional effects
are dependent on the frequency of environmental cues, with high frequency signals filtered out as potential noise.
44
Wednesday, June 18
Systems Genetic Approaches to Understand Tuberculosis Pathogenesis
Christopher Sassetti
University of Massachusetts Medical School
Mycobacterium tuberculosis is an obligate human pathogen that infects a significant fraction of the world’s population. Throughout its coevolution with humans, both host and
pathogen have imposed direct selective pressures on each other. As a result, the competition between M. tuberculosis and the immune system can be largely viewed as an
interaction between two genomes. Based on this concept, we are working to understand
the mechanisms underlying tuberculosis disease progression through the identification of
intra- and inter-species genetic interaction (GI) networks. Bacterial GIs can be mapped
at a genome-wide scale using high-density transposon mutagenesis followed by deep sequencing. Networks defined during infection reflect the physiology of the bacterium in this
specific environment and can be used to delineate condition-specific pathway structure.
We have used this to understand complex adaptations to the host, such as carbon acquisition and redox homeostasis. By subjecting bacterial libraries to selection in mice with
systematically-varied genomes, inter-species GIs can also be identified, which associate
bacterial adaptations to the immune functions that impose the corresponding pressure.
We are exploring this approach as a new strategy to discover and functionally characterize host susceptibility loci.
Boston, MA
June 17-19, 2014
45
Speaker Abstracts
Multi-Scale Systems Analysis of Cell-Cell Communication
and Signaling in Complex Inflammatory Disease
Douglas Lauffenburger
Massachusetts Institute of Technology
Complex inflammatory diseases such as arthritis, diabetes, and endometriosis involve
interactions of immune system cells with tissue cells via molecular communication processes, and resulting disease consequently arises from dysregulated signaling network
activities governing pathological cell behaviors. We are employing combined experimental/computational systems biology approaches to understand these communication and
signaling processes at multiple scales, integrating across multiple intracellular pathways,
multiple extracellular factors, and multiple cell types.
46
Wednesday, June 18
High-sensitivity Measurements of Multiple Kinase Activities in Live single Cells
Markus Covert
Stanford University
Increasing evidence has shown that population dynamics are qualitatively different from
single cell behaviors. Reporters to probe dynamic, single cell behaviors are desirable, yet
relatively scarce. Here we describe an easy-to-implement and generalizable technology
to generate reporters of kinase activity for individual cells. Our technology converts phosphorylation into a nucleocytoplasmic shuttling event that can be measured by epifluorescence microscopy. Our reporters reproduce kinase activity for multiple types of kinases,
and allow for calculation of active kinase concentrations via a mathematical model. Using this technology, we made several experimental observations that had previously been
technically unfeasible, including stimulus-dependent patterns of c-Jun N-Terminal Kinase
(JNK) and Nuclear Factor kappa B (NF-κB) activation. We also measured JNK, p38 and
ERK activities simultaneously, finding that p38 regulates the peak number, but not the intensity, of ERK fluctuations. Our approach opens the possibility of analyzing a wide range
of kinase-mediated processes in individual cells.
Boston, MA
June 17-19, 2014
47
Speaker Abstracts
Interspecies Systems Biology: Nutritional Networks
AJ Marian Walhout
University of Massachusetts Medical School
How does our diet affect our physiology? And what are the networks involved in interpreting nutritional state and executing systems level phenotypic outputs?
We have discovered that physiology of nematode C. elegans is dramatically different,
depending on which bacterial diet it consumes.
We pioneered a novel ‘interspecies systems biology’ approach by which we started to
delineate the networks involved in both the worm and the bacteria.
These networks are directly relevant not only to human inborn errors of metabolism, but
may also illuminate the biology of complex diseases such as type 2 diabetes and obesity.
48
Wednesday, June 18
Analysis of Single Cell Networks through Time and Space
by Mass Cytometry
Bernd Bodenmiller
University of Zürich
Recipient of the Anne Heidenthal Prize for Fluorescence Research – sponsored
by Chroma Technology Corp.
Tumors are heterogeneous assemblies of multiple cell types that interact and communicate with each other to achieve disease states. Many aspects of tumor development to
metastasis depend on these cell interactions in unique microenvironments. Especially signaling networks, forming the core of cellular decision making, are controlled by cell-to-cell
communication. To study and understand the decision making in the microenvironments,
single cell analysis technologies are needed that allow to measure cell type, signaling
network state and other cellular processes with spatial resolution.
Mass cytometry is a recent single cell mass spectrometry approach that enables to measure up to 100 proteins and their modifications simultaneously using isotopically pure rare
earth metals as reporters on antibodies. Previously, only cells in suspension could be analyzed by mass cytometry, and thus essential information on cell location and cell-to-cell
interactions was lost. We have now coupled immunocytochemical and immunohistochemical methods with high-resolution laser ablation to mass cytometry. The approach now
enables the simultaneous imaging of up to 100 proteins and phosphorylation sites at a
sub-cellular resolution. We use mass cytometry to study the signaling networks activated
during the epithelial-mesenchymal transition (EMT), a process driving the formation of
metastasis, in model systems and within their native microenvironment in human breast
cancer tumors.
Imaging mass cytometry will enable the analysis into how cellular assemblies generate
phenotypes in health and disease and will support the transition of medicine towards individualized molecularly targeted therapies.
Boston, MA
June 17-19, 2014
49
Speaker Abstracts
Linking Signal-transduction and Gene-expression Networks
by Statistical Modeling
Kevin Janes
University of Virginia
Changes in cell state are initiated by environmental stimuli and executed by a combination of signaling and regulated gene expression. In this talk, we propose a data-driven
approach to examine the interface between the effector proteins of cell signaling and the
transcription factors that trigger gene expression. We built a tri-linear statistical model
that connects time-varying, multiparameter measurements of signaling with dynamic expression clusters obtained by microarray profiling. The model allowed us to link signaling
and transcriptional pathways that were jointly regulated by crosstalk from proinflammatory
cytokines and growth factors. We will discuss ongoing experiments that seek to test the
hypothesis that – for one such transcriptional cluster that is stimulated by tumor necrosis
factor (TNF) and antagonized by insulin – crosstalk occurs through novel GSK3-mediated
phosphorylation of the endodermal transcription factor GATA6.
50
Wednesday, June 18
How Signaling Regulates Cell Shape, and how Cell Shape
Regulates Signaling
Chris Bakal
The Institute of Cancer Research, London
Recipient of the CSB2 Prize in Systems Biology – sponsored by Merrimack Pharmaceuticals
The primary goal of the Bakal laboratory is to understand the generation of cell form
during migration. Our laboratory’s philosophy is that a comprehensive understanding of
cell migration in both health and disease can only occur if we ascertain the architecture and dynamics of signaling networks that regulate, and spatiotemporally co-ordinate,
the specific morphogenetic processes underpinning cell migration. Towards the aim of
mapping the signaling networks that regulate cell shape, and how their dynamical behavior results in cell migration, we utilize two approaches. Firstly, we generate datasets
that describe single cell shape following systematic gene perturbation using RNA interference (RNAi). We have shown that datasets describing cell shape following gene depletion can be used to: i) rapidly characterize how individual genes contribute to the cell
shape changes required for migration; ii) map networks of functional interactions between
these genes. Secondly, we perform high-throughput screens where we quantify single cell
shape and signaling activity across diverse cell lines in the absence of perturbation, and
exploit naturally occurring phenotypic heterogeneity to describe quantitative and predictive relationships between cell morphogenesis and signaling dynamics. Importantly, we
believe that rigorous computational analysis of datasets describing cell shape will generate new systems-levels insights into cell shape and motility beyond the role of individual
genes or the description of signaling networks.
Here I will discuss how single cell imaging experiments and statistical analyses have
revealed a novel role for morphological heterogeneity in the regulation of the inflammatory response in breast cancer cells, and show how morphological heterogeneity is an
evolvable process regulated by signaling networks.
Boston, MA
June 17-19, 2014
51
Speaker Abstracts
Unraveling the Risk of Epo in Lung Cancer
Ursula Klingmüller
German Cancer Research Center (DKFZ)
The hormone erythropoietin (Epo) has been widely used for the treatment of anemia.
However, in the context of chemotherapy related anemia its safety is controversially discussed. We establish a detailed dynamic pathway model of Epo-mediated JAK-STAT
signaling in primary erythroid progenitor cells and adapt this to Epo-induced JAK-STAT
signaling in lung cancer cells. Systematic analysis and comparison of model parameters
reveals that negative feedback regulation is severely altered in the cancer cells providing
a possibility to selectively perturb Epo-induced signaling in lung cancer. Furthermore,
we utilize our dynamic pathway model of Epo-EpoR interaction to identify erythropoiesis
stimulating agents (ESA) that due to their specific binding properties might provide a safer
treatment option in lung cancer. In summary, our approach demonstrates that through
rigorously quantitative dynamic pathway models we can contribute to design improved
treatment options for patients.
52
Wednesday, June 18
Approaches for Integrative Analysis of Pharmacology and
Genome-wide Molecular Profiling Data Applied to a Large
Scale Screen of Cancer Cell Lines
Sara Dempster, Jonathan Dry
AstraZeneca
AstraZeneca has partnered with the Wellcome Trust Sanger Institute to screen the AstraZeneca oncology portfolio across a diverse panel of 1000 cancer cell lines. The cell
lines have been profiled by gene expression arrays, whole exome sequencing, and array
CGH. The dataset provides a unique opportunity to identify novel molecular markers of
drug sensitivity and resistance in pre-clinical models. We are integrating the datasets to
identify novel molecular markers to inform hypotheses for patient segmentation. For example, one goal is to identify molecular features that are associated with differences in
specificity and efficacy of AZ compounds that target the same pathways. Here, we focus
on the application of methods that will support these goals. Because there are hundreds
of thousands of molecular features, we need to reduce the dimensionality by grouping
features likely to have redundant functional impact in order to improve the power of our
analysis. To address this challenge, we are exploring approaches that leverage known
biological pathways and networks such as HotNet and NBS as well as unsupervised clustering approaches such as iCluster Plus. We will present different approaches for applying
the HotNet algorithm to the data, show initial results, and propose next steps.
Boston, MA
June 17-19, 2014
53
Speaker Abstracts
From Genomes to Molecular Phenomes
Mohammed AlQuraishi
Harvard Medical School
Large-scale genomic and proteomic initiatives have respectively generated thousands of
genome sequences and millions of protein-protein binding measurements, yet it remains
difficult to determine the effects of mutations on protein networks. We have developed
an analytical framework that integrates and reconciles these distinct data modalities to
model the human SH2-phosphoprotein network in normal and cancer cells, and experimentally validated novel predicted interactions and the effects of cancer mutations on
binding affinity. Our analysis indicates that cancer mutations perturb SH2 networks in
a bimodal fashion depending on tissue type, surgically rewiring individual interactions to
disrupt connected sub-networks in one mode, and bluntly disrupting the function of one
signaling protein in the other mode. The effects of cancer mutations also vary by protein
type, frequently resulting in gain of new interactions in phosphoproteins, and almost exclusively loss of interactions in SH2 domains. Our analytical framework represents a new
approach to the interpretation of genomic data that synthesizes genetics and biophysics.
54
Thursday, June 19
Host-genetics and Fluid Bacterial Genomes Make Virulence and Drug-tolerance Context Dependent
Tim van Opijnen
Boston College
In the last decade the increased availability of complete bacterial genomes has demonstrated a distinction between a species’ core-genome (the pool of genes that is shared by
all members of a species) and its pan-genome (the global gene repertoire of a species).
This implies that, even within species, genomes are fluid and new elements are easily
integrated into the genomic network. This plasticity greatly expands the possible geneticinteraction space and consequential phenotypes. To reduce such complexity the environment and the genetic background are often experimentally controlled, which ignores that
in natural environments are rarely constant, that genomic content is subject to change
and that in effect genotype-phenotype relationships are highly context dependent.
Here we focus on the bacterial pathogen Streptococcus pneumoniae and show the importance of the host-environment as well as bacterial genomic content for survival, disease
induction and drug tolerance. S. pneumoniae is a human nasopharyngeal commensal
and respiratory pathogen. It triggers pneumococcal pneumonia, meningitis, and septicemia, resulting in around 1 million deaths annually among children under 5 years of
age, and another approximately 0.5 million deaths among the immunocompromised and
the elderly. This enormous disease burden places S. pneumoniae among the most important bacterial pathogens worldwide.
By exploring different pneumococcal strains with genome-wide transposon mutagenesis
coupled to massive parallel sequencing (Tn-seq), expression experiments, genetic interaction mapping and droplet-based microfluidics, we show that the genetic background
drastically alters genotype-phenotype relationships – including essentiality of genes – and
has a large impact on the way the genome functions, affecting diverse processes such as
metabolism, drug tolerance and virulence.
Besides genomic content, host genetics confounds bacterial virulence. Children with
sickle cell disease (SCD) have an extremely high risk of fatal pneumococcal infection,
yet the reason for this increased risk is largely unknown. By performing whole genome
sequencing on over 300 pneumococcal strains isolated from SCD patients over the last 20
years, we determined that gene content has changed and that a shift towards non-vaccine
serotypes has taken place while invasive capacity has been retained. Additionally, a
murine SCD model coupled to Tn-seq identified specific streptococcal genes that directly
Boston, MA
June 17-19, 2014
55
Speaker Abstracts
link immune system deficiencies and different availability of micronutrients in the SCD
host, to an increase in bacterial virulence. Moreover, vaccination experiments demonstrate that the SCD host may be unable to raise a sufficient immune response due to loss
of potential antigens that are dispensable for infection in high-risk individuals.
These studies highlight the importance of identifying context-dependent phenotypes on
a broader scale. Applying our functional genomics framework will not only provide to
help unravel fundamental principles of genetic networks resulting from natural variation
but for bacteria it means we will be able to better predict virulence, and drug-tolerance of
individual strains on a species wide-level.
56
Thursday, June 19
A Systems Biology Approach to Drug Development: Clinical Testing of Biomarkers for the anti-ErbB3 Antibody
MM-121
Birgit Schoeberl
Merrimack Pharmaceuticals
Using a Systems Biology approach we identified ErbB3 as the most critical activator of
phosphoinositide 3-kinase (PI3K) signaling within the ErbB signaling network. Based on
this insight Merrimack Pharmaceuticals designed a monoclonal antibody, MM-121, which
blocks HRG (heregulin) and BTC (betacellulin) induced signaling. (Schoeberl et al., 2009)
In order to understand which patients would respond to MM-121, we identified a set of five
preclinically defined biomarkers (EGFR, ErbB2, ErbB3, HRG and BTC) that allowed us to
distinguish between responding vs. non-responding cancer cell lines grown as xenografts.
Recently, MM-121 completed three Phase 2 clinical trials to determine if patients with advanced malignancies would derive benefit from the addition of MM-121 to their standard
therapy. Here we will describe the result of translating the preclinical biomarker findings
into the clinical development of MM-121.
Among 464 patients who participated in our ovarian, breast and lung clinical trials we
obtained HRG and BTC measurements by RNA-ISH and RT-PCR and quantitative receptor expression by qIHC of ErbB3, EGFR and HER2 in a large fraction of the patients. Of
the five biomarkers, the most predictive of response was HRG mRNA: patients with detectable HRG in pre-treatment biopsies or high HRG in archived tissue blocks responded
poorly to standard-of-care therapy and benefited most from MM-121. In addition, benefit
was largely restricted to patients with low ErbB2 (<200,000 receptors per cell) which we
had predicted preclinically. In fact, the insight that the MM-121 activity is limited by high
HER2 levels was the reason for Merrimack to design MM-111, a ultra-potent ErbB3 inhibitor in HER2 amplified tumors.
In summary, we found that across the different Phase 2 trials heregulin seems to be a
biomarker for poor response to standard of care therapy in HER2 low tumors and a potential predictor of clinical benefit from MM-121 in late-stage ovarian, lung, and breast
cancers which is in accordance to our preclinical studies. To our knowledge, this is the
first example of a drug designed and clinically tested based on Systems Biology insights.
1. Schoeberl, B., Pace, E. a, Fitzgerald, J. B., Harms, B. D., Xu, L., Nie, L., Nielsen, U. B.
(2009). Therapeutically targeting ErbB3: a key node in ligand-induced activation of the
ErbB receptor-PI3K axis. Science Signaling, 2(77), ra31. doi:10.1126/scisignal.2000352
Boston, MA
June 17-19, 2014
57
Speaker Abstracts
Single-cell Dynamics of the Proliferation-quiescence Decision
Sabrina Spencer
Stanford University
Tissue homeostasis in metazoans is regulated by transitions of cells between quiescence
and proliferation. The hallmark of proliferating populations is progression through the cell
cycle, which is driven by Cyclin-dependent kinase (CDK) activity. I will discuss our recent
development of a live-cell sensor for CDK2 activity and the finding that proliferating cells
bifurcate into two populations as they exit mitosis. Some cells immediately commit to the
next cell cycle by building up CDK2 activity from an intermediate level, while other cells
lack CDK2 activity and enter a transient state of quiescence. This bifurcation is directly
controlled by the CDK inhibitor p21 and is regulated by mitogens during a restriction window at the end of the previous cell cycle. Thus, cells decide at the end of mitosis to either
start the next cell cycle by immediately building up CDK2 activity or to enter a transient
G0-like state by suppressing CDK2 activity.
58
Thursday, June 19
Structure-energy-based Predictions, Competition and Network Modelling of ERbMAPK Signalling, and its Implication in RASopathy and Cancer Missense Mutations
Luis Serrano Pubul
Center for Genomic Regulation, Barcelona
Different cell types share many of their signaling molecules yet can respond differently
to the same stimuli, through ways not fully understood. One hypothesis is that differences
in protein concentrations could change signaling outputs if there is competition at a critical network branching point. Here we analysed the extent of competing interactions in
the ErbB signaling network. We extended the latest high-confidence network, identified
domains and linear sequence motifs mediating binary interactions, and their affinities.
Using three-dimensional structures the network was dissected into compatible and mutually exclusive interactions. We experimentally determined protein abundances for a large
fraction of the 198 network nodes in three cell types, and uncovered a high degree of competition. In a combined experimental and computational network modelling approach we
showed for one competing node that protein level perturbations remodeled downstream
signaling flows. Our results suggest that protein level variation at competing nodes contributes to cell type-specific signaling responses. Using the 3D reconstructed network
we investigated a group of developmental disorders caused by by germline mutations in
15 genes encoding proteins of the Ras/mitogen activated protein kinase (MAPK) pathway frequently involved in cancer. Using the protein design algorithm FoldX, we predict
that most of the missense mutations with destabilizing energies are in structural regions
that control the activation of proteins, and only a few are predicted to compromise protein folding. We find a trend in which energy changes are higher for cancer compared to
RASopathy mutations. In summary, we suggest that quantitative rather than qualitative
network differences determine the phenotypic outcome of RASopathy compared to cancer mutations. Our work shows the power of combining network analysis, with structural
information and protein design in dissecting cell signaling and in disease.
Boston, MA
June 17-19, 2014
59
Speaker Abstracts
60
Poster Abstracts
Poster Abstracts
Alphabetical list of poster presenters
Presenter
Orfeas S. Aidonopoulos
Ozan Alkan
Matthew Anderson
Vinayagam
Arunachalam
Gnanaprakash
Balasubramanian
Joerg Bartel
François Bertaux
Benjamin Boucher
Madison Brandon
Leslie Brick
John Burke
Alexzandrea Buscarello
Jaime Campos
Natalie Catlett
Jia-Yun Chen
Kelly W. Chen
Loice Chingozha
Michael Chou
Giovanni Ciriello
Pau Creixell
Kristina D’Agostino
Evan Daugharthy
Sara Dempster
Gautam Dey
Madeline Diekmann
Sulav Duwal
Markus R. Eisele
Ana Finzel Pérez
Andres Florez
Michael Flossdorf
Juan Fuxman Bass
Avishai Gavish
Miriam Gutschow
Kevin Haigis
Tim Heinemann
Frank Stefan Heldt
Heinrich Huber
Junguk Hur
62
Title
A method for parsing clinical outcomes and combining them with phopshoproteomic
and genomic data for predicting drug efficacy: application in hepatocellular carcinoma
Model-guided target identification for synergistic combination therapies in the DNA
damage response pathway
Modelling the dynamics at the interface between autophagy and apoptosis
Controllability of protein interaction network identifies human disease genes
Epstein-Barr virus Integrome and Infectome of the Human Genome by D-ViSioN
(Detection of Integration of Virus(s) by SingletoN(s))
The human blood metabolome-transcriptome interface
A dynamic view on cell-to-cell variability in protein levels is required to investigate the
molecular mechanisms behind non-genetic resistance in TRAIL-induced apoptosis
Characterization of genetic interaction classes in a metazoan reveals the modular
organisation of genetic interactomes and suggests prioritizing strategies for
combinatorial GWAS in human
Concise Functional Enrichment of Ranked Gene Lists
An Exploratory Analysis of Shared Genetic Effects between Brain Oscillations and
DSM-IV Alcohol Dependence
Systems Pharmacology: Enabling early quantitative decisions in pharma from start of
lead identification to clinical trials
Sequence Analysis of the mt-COXII Gene in Crassostrea virginica: Application to
Human Disease
Systemic analysis of expression data of glioblastoma multiforme patients
OpenBEL - A platform for capture, integration, and application of biological knowledge
Dosage of Dyrk1a shifts cells within a p21-cyclin D1 signaling map to control the
decision to enter the cell cycle
Systems analysis of cytokine-mediated multicellular cross-talk in normal and
inflammatory conditions
Microfluidic high-throughput platforms for live-cell imaging and transcriptional
quantification in immune cells at single cell resolution
Discovery of kinase motifs and prediction of target substrates using the ProPeL
method
Emerging landscape of oncogenic signatures across human cancers
Decoding Network-Attacking Mutations in Cancer
Sequencing and Analysis of the mt-ATP6 Gene in Crassostrea virginica
In Situ Sequencing: A Multi-Plex Multi-Omic Multi-Scale Technology
Approaches for Integrative Analysis of Pharmacology and Genome-wide Molecular
Profiling Data Applied to a Large Scale Screen of Cancer Cell Lines
Phylogenetic profiling reveals novel functional modules in the human genome
Integration of multi-leveled -omics data for cancer network analysis.
PK-PD Modelling of the Reverse Transcriptase Inhibitor Tenofovir and Quantification
of its Prophylactic Efficacy against HIV-1 Infection
Cell-to-Cell Communication Modulates the TNF-a and IL-6 Response to TLR4
Stimulation in Macrophages: A Computational Approach
The role of p53 in metabolic regulation and response to cellular perturbations
The mechanistic role of MYCN in driving cell cycle progression in Neuroblastoma
T cell immune responses generate diversity through linear cell-fate progression rather
than asymmetric cell divisions
Human transcription factor network evolution and rewiring in disease
Robust pattern formation in the Drosophila eye disc
Multiple stimuli converge to influence transcription factor dynamics.
Cytokine signaling in Alzheimer’s disease
Robust DNA Repair through Collective Rate Control
A multiscale model of influenza A virus infection that elucidates the treatment with
direct-acting antivirals
Modeling NOS3 signaling as markers for NO-bioavailability, ROS generation and
cGMP-metabolism in cardiac pathophysiology
Systems pharmacology analysis of drug-induced peripheral neuropathy
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Presenter
Martine A Jaworski
Enes Karaboga
S. Jordan Kerns
Antje Kettelhake
Christina Kiel
Daniel Kirouac
Bettina Knapp
Ariel Kniss
Fabian Konrath
Anil Korkut
Yan Kou
Erika Kuchen
Natasha Kumar
Young Kwon
Kasper Lage
Anastasiya Lapytsko
Dooyoung Lee
Victor Li
Jia-Ren Lin
Carlos Lopez
Roy Malka
Paula A. Marin Zapata
Steve Martin
Linas Mazutis
Ioannis N. Melas
Dimitris E Messinis
Aaron Meyer
Miles Miller
Kathryn Miller-Jensen
Shekhar Mishra
Janina Mothes
Steven C. Neier
Ian Overton
John Paasch
Christian Priesnitz
Stephen Ramsey
Andreas Raue
Erzsébet Ravasz Regan
Boston, MA
Title
A systems biology approach to the GABAergic contribution to Juvenile Myoclonic
Epilepsy
Application of S-pyocins to eradicate Pseudomonas aeruginosa biofilms
Detecting and Remembering Gut Dysfunction in Biomimetic Microfluidic Devices with
Living Bacterial Diagnostics
Systems Biology Evaluation of Hepatocellular Carcinoma Metabolism
Structure-energy-based predictions and network modelling of RASopathy and cancer
missense mutations
In silico identification of biomarker-optimized treatment strategies in HER2+ cancer
Inter-individual effects mask the molecular signature of psoriasis and eczema
Developing microfluidic and experimental tools to interrogate intracellular T cell
signaling using a frequency response analysis approach
Identification of new IkappaBalpha complexes by an iterative experimental and
mathematical modeling approach
Network models of signaling and drug response in melanoma
Enrichment vectors identify glioblastoma multiforme patient subgroups and suggest
molecular mechanisms and potential treatments
MYCN and cellular decisions in neuroblastoma
TASK-2 channels contribute to pH sensitivity of retrotrapezoid nucleus chemoreceptor
neurons
The Hippo Signaling Pathway Interactome
Functional interpretation of genome-wide association signals in arrhythmia using
protein networks of cardiac ion channels
Auto-inhibition stabilizes delayed negative feedback in biochemical systems
A systems biology model for the coagulation network in non-bleeding state describes
baseline activity of clotting
Molecular ties between the cell cycle and differentiation in embryonic stem cells
A Quantitative Drug-Target Relationship Network for Probing Cellular Response and
Poly-pharmacology of Small Molecular Kinase Inhibitors
Exploring how cells commit to apoptotic or necrotic cell-death.
In Vivo Volume and Hemoglobin Dynamics of Red Blood Cells
Mathematical modelling of the regulation and degradation of the transcription factor
EB
A systems biology model of the coagulation network in bleeding state reveals
differences in behaviors of biomarkers in response to perturbations.
Barcoding thousands of single cells in a single tube by droplet microfluidics
Identification of deregulated signaling pathways in Multiple Sclerosis based on gene
expression data
Construction of a drug-induced phosphoprotein/cytokine dataset in clinical samples
for Multiple Sclerosis
The AXL Receptor is a Sensor of Ligand Spatial Heterogeneity
Single-cell pharmacokinetic modeling of a clinically approved nanoparticle allows
prediction of nano-therapeutic drug delivery to tumor microvasculature and associated
macrophages
Analysis of single-cell secretion reveals a role for paracrine signaling in coordinating
macrophage response to TLR4 stimulation
Reversible Bile Transport : A Cholesterol trader in Human Body
Dynamic variability of NF-kappaB signal transduction: A mechanistic model
Novel multiplex analysis of protein complexes in signaling networks reveals signatures
that distinguish T-cell responses to antigen
A map of functionally coherent binding for Snail and Twist transcription factors in fly
mesoderm development informs regulation of epithelial remodelling and oncogenic
Notch
A Targeted Therapy - So, what is it targeting? A Case Study of the On-Target Effect
on Waldenstrom’s and the Off-Target Effect on Platelet Count and What One May Tell
Us About the Other
Metabolomic characterization and analysis of liver-specific functions of an in vitro
fibrosis model
Network topological characteristics aid in identifying causal genes for atherosclerosis
The Effect of Higher-Order Receptor Clusters on TRAIL Induced Apoptotic Signaling
Organ-specific stochastic phenotype switching is required for endothelial health
June 17-19, 2014
No.
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Poster Abstracts
Presenter
Faisal Reza
Edward Rietman
Jens Roat Kultima
Jenny Rouka
Assieh Saadatpour
Anne Sadewasser
Theodore
Sakellaropoulos
MN Salleh
Somponnat
Sampattavanich
Shimon Sapir
Sibaji Sarkar
Ulf Schmitz
Amitabh Sharma
Ashwini Kumar
Sharma
Sriganesh Srihari
Steven N. Steinway
Edward Stites
Jette Strasen
Madhuresh Sumit
Jaeyoung Sung
Jaeyun Sung
Christian Tokarski
Stepan Tymoshenko
Lakshmi Venkatraman
Anita Voigt
Astrid Wachter
Allon Wagner
Chun-Chao Wang
Ruisheng Wang
Kathleen Wilkie
Jennifer Wilson
Victor Wong
Levi Wood
Xianfang Xia
Nilgun Yilmaz
Carissa L. Young
64
Title
Treating monogenic human diseases with evolving computationally designed mutagenic
triplex-forming and recombinagenic donor DNA molecules
Topological Measures of Protein-Protein Interactions And Implications For Cancer
Therapy
MOCAT: a metagenomics assembly and gene prediction toolkit
Identification of novel CD2AP SH3 interaction partners by combining crystallography,
bioinformatic tools and peptide arrays
Characterizing heterogeneity in leukemic cells using single cell gene expression
analysis
Characterization of the protein signatures of permissive vs. non-permissive influenza A
virus infections in human host cells by quantitative proteomic analysis
Training of signaling pathways to phosphoproteomic data via hybrid node/edge
optimization using a mixed integer programming formulation
Study on the Clinical Significance of c-FLIP and JUN-B Expression in Psoriatic Lesion
by using Quantitative RT-PCR and Tissue Microarray
Pulsatile FoxO3a translocation in response to growth factors is controlled jointly by AKT
and ERK pathways
Speech abnormalities in Parkinson’s disease reflect a major disturbance in
sensorimotor servomechanisms
Systems Biology Approach to Determine the Efficacy of Methylation by DNA-Methyl
Transferase1 in Cancer Cells
Comprehensive identification of cooperative microRNA target regulation through an
integrative workflow
Diseaseome 2.0: Uncovering disease-disease relationships through the human
interactome
Proximity of metabolic and cancer causing genes in the genome leads to metabolic
remodeling in cancers.
Harnessing DNA Damage Repair Pathways in Breast Cancer Therapy: a Synthetic
Lethality Paradigm
Network modeling reveals key features of epithelial-to-mesenchymal transition dynamics
Ras mutant-specific response to upstream inhibition
TGFb Signaling in Living Cells
Delineating G-protein coupled receptor (GPCR)-linked oscillatory signaling mechanisms
through single cell microfluidic analysis and computational modeling
A new stochastic kinetics approach to quantitative description of intracellular networks
coupled to hidden cell environment
Global metabolic interaction network of the human gut microbiota
Hepatic Response to Refeeding - From Ketone Bodies to Lipids
Reconstruction and in silico analysis of metabolism in the apicomplexan parasite
Toxoplasma gondii
Systems-biology of Poly(I:C) induced apoptosis signaling
Assessment of the human gut microbiome in response to temporal and colorectal
cancer-associated variability
High-Throughput Proteomic and Transcriptomic Data Integration based on MS/MS and
RNA-Seq Data using Prior Pathway Knowledge
Drug efficacy and side effects are strongly associated with its ability to reverse gene
expression abnormalities in a mouse model of dyslipidemia
A dynamic regulatory circuit in single breast epithelial cells and basal-like
premalignancies
Network-based association of hypoxia-responsive genes with cardiovascular diseases
Classifying Cancer Types for Treatability Using PPI Network Structure
Network algorithms dicover dysregulated pathways in RNAi screens
Cell-to-cell variability in NF-κB activation and chromatin at the HIV promoter contribute
to noise in the reactivation of latent HIV
In Vivo Systems Analysis Identifies Cytokine Drivers of Neurodegeneration in
Alzheimer’s Disease
Exploring the cellular responses to short pulses of TNF
A Multiscale-Modeling Approach Towards Understanding the Biological Response to
Stress
Elucidating Mechanisms of Liver Metastasis: an All-Human Microphysiological Model to
Investigate Disease Progression & Therapies
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#1 : Analysis of single-cell secretion reveals a role for
paracrine signaling in coordinating macrophage response
to TLR4 stimulation
Qiong Xue1 , Yao Lu1 , Markus Eisele1,2 , Nafeesa Khan1 , Rong Fan1 , Kathryn Miller-Jensen1
1
2
Yale University, New Haven, CT
University of Stuttgart, Stuttgart, Germany
Cell populations produce reliable biological responses despite significant levels of cellto-cell heterogeneity. These biological responses often involve intermediate extracellular
signals in which cells secrete and respond to the same factor. The propagation of intermediate signals by extracellular signaling has a significant impact on the collective cellpopulation response, but the contribution of autocrine versus paracrine signaling remains
difficult to analyze. Here we combined multiplexed, nanowell single-cell secretion measurements with cell-population data to distinguish the role of paracrine signaling in shaping the inflammatory cytokine secretion profile in a monocytic cell line (U937) and primary
human monocyte-derived macrophages (MDMs) in response to toll-like receptor 4 (TLR4)
stimulation with lipopolysaccharide (LPS). Loss of paracrine signaling upon single-cell isolation in nanowells significantly reduced secretion of a subset of LPS-stimulated cytokines,
including interleukin-6 (IL-6) and IL-10, in both U937 and primary human MDM cells. Using graphical Gaussian modeling of LPS-stimulated single-cell data, we identified tumor
necrosis factor-α (TNF-α) and IL-1β as central nodes in the LPS-stimulated paracrinesignaling network, which was further validated experimentally. In both U937 and MDM
single cells, a small fraction of cells accounted for the majority of the total TNF-α output
in the cell population, but paracrine signaling from this small subset amplified secretion
and reduced variability in the downstream dependent signals. Our results demonstrate
that a small subpopulation of high-responding cells appears to drive the innate immune
response in macrophages through paracrine signaling. Thus, paracrine signaling results
in a coordinated population response that is absent in isolated single cells, underscoring
the importance of inter-cellular signaling in determining cellular behaviors.
Boston, MA
June 17-19, 2014
65
Poster Abstracts
#2 : Approaches for Integrative Analysis of Pharmacology and Genome-wide Molecular Profiling Data Applied
to a Large Scale Screen of Cancer Cell Lines
Sara Dempster, Jonathan Dry
AstraZeneca Pharmaceuticals LP, Waltham, MA
AstraZeneca has partnered with the Wellcome Trust Sanger Institute to screen the AstraZeneca oncology portfolio across a diverse panel of 1000 cancer cell lines. The cell
lines have been profiled by gene expression arrays, whole exome sequencing, and array
CGH. The dataset provides a unique opportunity to identify novel molecular markers of
drug sensitivity and resistance in pre-clinical models. We are integrating the datasets to
identify novel molecular markers to inform hypotheses for patient segmentation. For example, one goal is to identify molecular features that are associated with differences in
specificity and efficacy of AZ compounds that target the same pathways. Here, we focus
on the application of methods that will support these goals. Because there are hundreds
of thousands of molecular features, we need to reduce the dimensionality by grouping
features likely to have redundant functional impact in order to improve the power of our
analysis. To address this challenge, we are exploring approaches that leverage known
biological pathways and networks such as HotNet and NBS as well as unsupervised clustering approaches such as iCluster Plus. We will present different approaches for applying
the HotNet algorithm to the data, show initial results, and propose next steps.
66
#3 : Developing microfluidic and experimental tools to interrogate intracellular T cell signaling using a frequency
response analysis approach
Ariel Kniss1 , Loice Chingozha2 , Hang Lu1,2 , Melissa Kemp1
1
The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and
Emory University
2
School of Chemical and Biomolecular Engineering, Georgia Institute of Technology
Introduction: T cells are part of the adaptive immune response that recognize and fight
pathogens in the body. T cell activation is a complex process resulting in the induction
of a proliferative response, encoded by the dynamics of multiple signaling cascades and
reliant on second messenger molecules such as calcium and hydrogen peroxide. Frequency response analysis (FRA), developed in control engineering, has been previously
allowed for inference of cellular signaling network structure but has been experimentally
difficult to apply to suspension T cells. We have designed a FRA experimental and computational platform for future use in determining the dominant feedback controls present
in T cell signaling.
Materials and Methods: To develop the experimental precision required for this approach,
we use a microfluidic device capable of applying an oscillatory chemical cue as an input
signal while enabling visualization of T cells through time. In this work we apply H2 O2
at a range of driving frequencies to Jurkat T cells while monitoring cytoplasmic calcium
concentration. We then perform spectral analysis of the single cell calcium traces to determine the output signal response of the system.
Results: Calcium signaling is found to respond differently to 100µM H2 O2 at specific driving frequencies of 16.7mHz (1 min period), 8.3mHz (2 min period), and 2.8mHz (6 min
period). With the single cell spectral analysis, subpopulations of cells emerge in response
to the same driving frequency and between different experiments. Cells are entrained to
frequencies below 8.3mHz but attenuate H2 O2 signals above 16.7mHz.
Conclusion: With the use of microfluidic technology and initial spectral analysis, we have
determined that T cells have a cutoff frequency between 1 and 2 minutes when interrogated with oscillatory H2 O2 stimulus. This suggests downstream transcriptional effects
are dependent on the frequency of environmental cues, with high frequency signals filtered out as potential noise.
Boston, MA
June 17-19, 2014
67
Poster Abstracts
#4 : Dosage of Dyrk1a shifts cells within a p21-cyclin D1
signaling map to control the decision to enter the cell cycle
Jia-Yun Chen1,2 , Jia-Ren Lin1,2 , Feng-Chiao Tsai1 , Tobias Meyer1
1
2
Department of Chemical and Systems Biology, Stanford University, Stanford, CA
Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA
Mammalian cells have a remarkable capacity to compensate for heterozygous gene loss
or extra gene copies. One exception is Down syndrome (DS) where a third copy of chromosome 21 mediates neurogenesis defects and lowers the frequency of solid tumors.
Here we combine live-cell imaging and single-cell analysis to show that increased dosage
of chromosome 21-localized Dyrk1a steeply increases G1 cell cycle duration through
direct phosphorylation and degradation of cyclin D1 (CycD1). DS-derived fibroblasts
showed analogous cell cycle changes that were reversed by Dyrk1a inhibition. Furthermore, reducing Dyrk1a activity increased CycD1 expression to force a bifurcation, with
one subpopulation of cells accelerating proliferation and the other arresting proliferation
by co-stabilizing CycD1 and the CDK inhibitor p21. Thus, dosage of Dyrk1a repositions
cells within a p21-CycD1 signaling map, directing each cell to either proliferate or to follow
two distinct cell cycle exit pathways characterized by high or low CycD1 and p21 levels.
68
#5 : Microfluidic high-throughput platforms for live-cell
imaging and transcriptional quantification in immune cells
at single cell resolution
Loice Chingozha1 , Cheng Zhu2,3,4 , Hang Lu1,3
1
Georgia Institute of Technology, School of Chemical and Biomolecular Engineering, Atlanta, GA
Georgia Institute of Technology, School of Mechanical Engineering, Atlanta, GA
3
Interdisciplinary Program in Bioengineering, Georgia Institute of Technology, Atlanta, GA
4
Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta,
GA
2
Analyzing immune T cell response to stimulation requires assays that are highly sensitive, provide real-time measurements, and enable single-cell analysis. The inherent cell
heterogeneity that can have pathological consequences necessitates assays that allow
for single-cell resolution in a high-throughput manner. Here we developed a microfluidic
platform for (1) single cell handling, (2) performing cell dynamic stimulation with simultaneous live-cell imaging, (3) immunofluorescence for surface marker expression and
cytokine secretion characterization, and (4) single-cell Fluorescence In Situ Hybridization (smFISH) for quantification of single mRNA transcripts at a single cell resolution for
hundreds of cells in a single device with reduced reagent use. smFISH is a quantitative
method to characterize mRNA transcripts in cells; it uses multiple short, complementary
oligonucleotides, and therefore is accurate to single transcript level. Our device enables
cell stimulation via soluble stimulation with temporal patterns or stimulation with surfaceanchored molecules, while simultaneously performing fluorescence-based live-cell imaging. Subsequent downstream characterization of RNA transcription using smFISH and immunofluorescence characterization at single cell resolution enables us to correlate early
time events with the subsequent late time functional response in the same cell with a
throughput of a thousand cells per chip. Therefore, this technique will contribute greatly
to systems understanding cellular functional response under system perturbations such
as immunological cell response to viral transcription in infected hosts. Overall, our design provides a tool for studying stochasticity underlying gene expression, and will enable
advancement in single-cell immunophenotyping and transcriptomics.
Boston, MA
June 17-19, 2014
69
Poster Abstracts
#6 : Systems analysis of cytokine-mediated multicellular
cross-talk in normal and inflammatory conditions
Kelly W. Chen1 , Jason Velazquez1 , Linda G. Griffith1 , Rebecca Carrier2 , Douglas A. Lauffenburger1
1
2
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA
Department of Chemical Engineering, Northeastern University, MA
The human gut is the largest immune organ in the body. Intestinal homeostasis is tightly
regulated by the coordinated actions of a multitude of cell types, including enterocytes,
goblet cells and immune cells. However, a quantitative understanding of how these cellular constituents communicate and integratively contribute to overall tissue function is
lacking. Given the complexity of intercellular cross-talk, conventional approaches are no
longer sufficient, and improved understanding necessitates multivariate experimental design and systems-level analysis.
To this end, we have developed an immune-competent human intestinal model, which
incorporates representative cellular components of the mucosal environment (enterocyte,
goblet cells, macrophages/ dendritic cells). To investigate the contribution of each cell type
to the overall cytokine milieu of the microenvironment, cellular composition in the intestinal model was varied systematically (e.g., +/-goblet cells, +/- immune cells) under normal
and perturbed conditions (e.g., LPS, microbes, drugs). The secreted signals (cytokines,
chemokines, growth factors) were measured using multiplex assays. Multivariate modeling techniques (e.g., partial least-squares regression) were used to quantitatively relate
soluble signaling profiles to tissue-level phenotypes and functions (e.g., barrier function,
mucus production, enterocyte differentiation), thereby enabling the identification of key
soluble mediators that contribute to divergent cell responses (normal versus inflamed).
The predictive models generated from this study can guide hypothesis generation regarding cytokine-mediated multicellular interactions and their implications for tissue-level
phenotypes/functions.
Our experimental approach, which combines physiological relevant in vitro culture platforms and computational strategies, has broad applicability in advancing fundamental
research in mucosal immunology and drug development.
70
#7 : In Situ Sequencing: A Multi-Plex Multi-Omic MultiScale Technology
Evan Daugharthy1,2,3 , Je-Hyuk Lee1,2 , Richard Terry2 , Jonathan Scheiman1,2 , Paul Tillberg4,5 ,
Fei Chen4,5 , Benjamin Pruitt2 , Brian Turczyk2 , Reza Kalhor1,2 , John Aach1 , Ed Boyden4,5 ,
George Church1,2,4
1
2
3
4
5
Department of Genetics, Harvard Medical School, Boston MA
Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston MA
Department of Systems Biology, Harvard Medical School, Boston MA
MIT Media Lab, Cambridge MA
Departments of Biological Engineering Brain and Cognitive Sciences, Cambridge MA
Biological systems exhibit meaningful spatial heterogeneity over 15 orders of magnitude
in scale and in an even larger space of genetic information and material compositions.
Unfortunately, in situ methods such as fluorescent in situ hybridization and immunohistochemistry are low-throughput, have low-multiplexity, and have quantitativity and spatial
measurement scales limited by the physics of light and electronic photosensitivity. We
have recently demonstrated massively multiplex fluorescent in situ RNA sequencing, in
which stably cross-linked cDNA amplicons are sequenced within a biological sample. Using 30-base reads from 8102 genes in situ, we examined RNA expression and localization
in human primary fibroblasts with a simulated wound-healing assay. Moreover, our platform enables massively parallel detection of genetic elements; simultaneous measurements of the genome, transcriptome, and proteome; and programmable spatial analysis
scales from single cell to whole organism. As a tool for systems biologists, these quantitative measurements can illuminate the molecular-genetic foundation of human infirmities
to be addressed with genome engineering and other precisely targetted therapies.
Boston, MA
June 17-19, 2014
71
Poster Abstracts
#8 : Enrichment vectors identify glioblastoma multiforme
patient subgroups and suggest molecular mechanisms
and potential treatments
Yan Kou1 , Qiaonan Duan1 , Neil Clark1 , Jann Sarkaria2 , James Gallo1 , Avi Ma’ayan1
1
Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, Icahn
School of Medicine at Mount
2
Department of Radiation Oncology, Mayo Clinic College of Medicine, Rochester, MN 55905
Glioblastoma multiforme (GBM) is the most common and aggressive malignant primary
brain tumor. Although treated with a combination of chemotherapy, radiation, surgery and
anti-tumor drugs, the typical survival for GBM patients is less than 15 months. Furthermore the diverse response of individuals to the various treatment regimens poses intriguing questions for biologists about the underlying molecular mechanisms. Four molecular subtypes of glioblastoma have been identified by clustering patient tumor’s signature
genes expression. Nonetheless, these subtypes neither suggest underlying molecular
mechanisms nor provide direct suggestion to the most effective potential treatments that
would benefit patients belonging to a certain subtype. We developed a novel patient classification method by systematically integrating data from large ongoing cancer genomic
projects such as The Cancer Genome Atlas, Cancer Genome Project as well as data that
we processed from ENCODE, ChEA, the Roadmap Epigenomics, and Mouse Genome
Informatics Mammalian Phenotypes. By applying gene set enrichment analyses on the
up-regulated genes obtained from each glioblastoma patient, we constructed an enrichment vector clustering of patients. We show that classification of GBM patients based on
the enrichment vectors is able to identify 9 clusters with 3 having significant difference
in clinical outcome. Next, we investigated the correlation between the patient subgroups
and 40 glioblastoma cell lines and we found cell lines that share similar enrichment vector
profiles with specific patient clusters. Finally, using the drug sensitivity data from CGP
we created a tripartite network connecting patient clusters, glioblastoma cell lines and
drugs to schematically reveal potential drugs that can benefit clusters of cancer patients.
Without any prior knowledge, we predicted that bleomycin, a glycopeptide inhibitor currently undergoing Phase I trial, as a drug that would benefit the patient group with the
poorest prognosis. The tripartite network also suggest the drugs NVP-TAE684 and OSI906 studied in other cancer types as potentially effective for specific glioblastoma patient
clusters.
72
#9 : A Quantitative Drug-Target Relationship Network for
Probing Cellular Response and Poly-pharmacology of Small
Molecular Kinase Inhibitors
Jia-Ren Lin, Marc Hafner, Mario Niepel, Gabriel Berriz, Peter Sorger
Laboratory of Systems Pharmacology, Harvard Medical School
Systems pharmacology has been thrived over the past decade, given the huge amount of
data gather from large-scale drug screens and functional genomics studies (Hopkins,
2008). Furthermore, the accumulated drug-target interactions has been summarized
in databases like DrugBank and been broadly used in drug discovery. However, the
lack of quantitative data sources or network tools hinders the overall understanding on
drug action in network pharmacology. To fulfill this need, we constructed a computable
drug-target networks using the aggregated information from PubChem bioassay database
(Wang et al., 2013). This focused data-set contained around 200 drugs using in cancer
therapy. From the network analysis of quantitative target-drug interactions, we have been
able not only to identify different sub-clusters among drugs with the same nominal targets
but also reveals the underlying biological networks of different drugs. In addition, we used
these information to compute the specificity or “dirtiness” of drugs and confirmed these
predictions experimentally using KinomeScan. Finally, the similarity of drugs from their
target networks are correlated with the drug sensitivity matrix in NCI60 panel, suggesting
the network information can be used to predict drug action in vivo. Altogether, our computational platform here provides researchers with a useful tool to probe poly-pharmacology
in cancers and a comprehensive view of mutli-drugs multi-target networks in systems
biology.
1. Hopkins, A. L. (2008). Network pharmacology: the next paradigm in drug discovery.
Nature chemical biology, 4(11), 682–90. doi:10.1038/nchembio.118
2. Wang, Y., Suzek, T., Zhang, J., Wang, J., He, S., Cheng, T., . . . Bryant, S. H. (2013).
PubChem BioAssay: 2014 update. Nucleic acids research, (September 2013), 1–8.
doi:10.1093/nar/gkt978
Boston, MA
June 17-19, 2014
73
Poster Abstracts
#10 : Exploring how cells commit to apoptotic or necrotic
cell-death.
Michael Irvin, Patrice Wagner, Sandra Zinkel, Carlos Lopez
Vanderbilt University School of Medicine
Necrosis has recently emerged as a programmed cell-death alternative to cellular apoptosis. Programmed necrosis is important in the pathology of a number of human diseases
including myocardial infarct, inflammatory bowel diseases, stroke, and neurodegeneration. Death receptor mediated signaling can induce either apoptotic or necrotic cell death
and thus represents an ideal system to understand the mechanistic origins of cellular
decisions processes. This work uses novel mathematical modeling approaches and experimental data, to explore mechanistic hypotheses about cell death decisions between
apoptosis or necrosis. We explore on results from the Zinkel lab that demonstrate that
the pro-apoptotic protein Bid, inhibits and modulates Rip1-driven programmed necrosis.
Given that Bid and RIP1 function within a complex network of biological signals, we explore the systems-level behavior of protein interactions that could regulate necrosis or
apoptosis signals. We generate mathematical models, calibrated to experimental data,
which describe biochemical interactions that can modulate apoptosis or necrosis outcome. Our apoptosis-necrosis reaction model (ANRM) extends previous apoptosis mathematical models with new necrosis pathway interactions to describe a comprehensive cell
death mechanistic framework. ANRM can simulate cellular processes that lead to either
apoptosis or necrosis cell fates based only on initial protein concentrations. The ANRM
biochemical interaction topology has been calibrated to experimental data using parameters from multiple cells including Jurkat, and Hematopoietic Progenitor cells. We find that
the best fits to experimental data occur when a regulatory step for Bid is accentuated. In
our work, anti-necrotic activity of Bid required regulation with kinetic rate four orders of
magnitude different than generic values thus implying, from a modeling perspective, that
this interaction is fundamental to cell commitment to either form of cell death.
74
#11 : Construction of a drug-induced phosphoprotein/cytokine
dataset in clinical samples for Multiple Sclerosis
Vaia Pliaka1,2 , Dimitris E Messinis1,2,3 , Theodore Sakellaropoulos3 , Ekaterina Kotelnikova4 ,
Tomas Olsson5 , Jesper Tegner6 , Roland Martin7 , Dimitris Tzeranis1,3 , Friedemann Paul8 ,
Elena Schwartz9 , Ilya Mazo9 , Sophia Stamatatou1 , Mar Masso10 , Albert Zamora10 , Pablo
Villoslada4 , Leonidas G Alexopoulos1,3 , Julio Saez-Rodiguez11 , Marti Bernardo-Faura11 ,
Ioannis N Melas1,3,11 , Jose Manuel Mas12 , Laura Artigas12
1
ProtATonce Ltd, Athens, Greece
equal contributor
3
National Technical University of Athens, Greece
4
IDIBAPS – Hospital Clinic of Barcelona, Spain
5
Department of Neurology, Karolinska Institutet, Sweden
6
Unit of Computational Medicine, Karolinska Institutet, Sweden
7
University of Zurich, Switzerland
8
NeuroCure Clinical Research Center, Charité University Medicine Berlin, Berlin, Germany
9
Ariadne Diagnostic, Rockville, MD, US
10
Bionure Farma SL, Barcelona, Spain
11
European Molecular Biology Laboratory, European Bioinformatics Institute, UK
12
Anaxomics Biotech, Barcelona, Spain
2
Multiple Sclerosis (MS) is an autoimmune disease that affects the brain and spinal cord.
An estimated 2,500,000 people around the world have MS and there is not yet a cure for
the disease. Even though significant progress is currently being made in MS research, the
pathogenesis of the disease has not been comprehensively understood. A great number
of pathological mechanisms responsible for the disease have been described, involving
genes & proteins altering many processes & pathways. By understanding how current
MS therapies work in biological networks, more effective therapies can be designed. On
this front, the CombiMS consortium (combims.eu) is developing computational and experimental tools to improve the therapeutic options of MS in the future.
A milestone in this consortium is to evaluate how current and “promising” MS drugs work
at the signaling level in different patient populations. Peripheral blood mononuclear cells
(PBMCs) from 255 donors were collected by several European medical centers. The
PBMCs were plated in 96 well plates and 20 stimuli & drugs were applied. Using custom
multiplex assays, 17 phosphoproteins were measured at 5 & 25 minutes and 22 cytokines
at 24h post stimulus. To ensure the highest possible data quality, a kit was developed to
include all reagents needed in order to isolate PBMCs from 1 donor and then plate, stimulate and lyse them. Sample collection controls were applied in the stimuli set to evaluate
Boston, MA
June 17-19, 2014
75
Poster Abstracts
errors in sample processing and 2 custom xMAP bead sets were used to evaluate errors
and instrument measuring variability in the bead-based ELISA procedure.
Phosphoproteomic and cytokine data will be combined with SNP data and clinical profiles
(i.e. responders, non-responders, therapeutic intervention) in a computational framework
which will help to understand MS more thoroughly and systematically. As a first step,
the phosphoproteomic dataset will be used for the construction of a detailed map of the
signaling pathway differences between MS and healthy donors, which can help generate
a model of MS pathogenesis and improve our understanding of the disease.
76
#12 : Single-cell pharmacokinetic modeling of a clinically
approved nanoparticle allows prediction of nano-therapeutic
drug delivery to tumor microvasculature and associated
macrophages
Miles Miller1 , Suresh Gadde2 , Christina Pfirschke1 , Rainer Kohler1 , Ashley Laughney1 ,
Mikael Pittet1 , Omid Farokhzad2 , Ralph Weissleder1
1
MGH Center for Systems Biology, Harvard Medical School
Laboratory of Nanomedicine and Biomaterials, Brigham and Women’s Hospital (BWH), Harvard Medical
School
2
Nanoparticle encapsulation for targeted and controlled therapeutic delivery holds promise
for a variety of drugs, yet it is has been generally difficult to ascertain at high resolution how such therapeutic nanoparticles (TNP) function in vivo, especially in patients.
Magnetic nanoparticles (MNP) such as the clinically approved ferumoxytol (Feraheme)
represent attractive magnetic resonance imaging agents to select and monitor patients
for TNP-based drug treatment, yet the compartmental tumoral distribution and possible
co-localization with TNP is unknown. Here we used single cell resolution intravital imaging to address both questions. We visualize MNP in tumor microvasculature and show
sustained uptake into tumor-associated-macrophages within minutes. When compared
against a model TNP (poly(D,L-lactic-co-glycolic acid)-b-poly(ethylene glycol); PLGA-bPEG) that is based on several clinically tested therapeutic formulations, we show excellent correspondence between the two nanoparticles in the microvasculature and within
tumor associated macrophages. Computational non-linear finite-element modeling of NP
transport enables predictive quantification of TNP distribution based on imaging data, and
is used to identify key parameters governing overall intratumoral NP accumulation. These
approaches yield insight into in vivo drug action, especially as it relates to enhanced permeability and retention (EPR) effects thought to be critical for TNP efficacy. Furthermore,
the studies form the basis for clinical approaches to select patients into TNP trials based
on MNP imaging.
Boston, MA
June 17-19, 2014
77
Poster Abstracts
#13 : TGFb Signaling in Living Cells
Jette Strasen1 , Uddipan Sarma2 , Stefan Legewie2 , Alexander Loewer1
1
2
Max-Delbrueck-Center (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
Institute of Molecular Biology (IMB), Mainz, Germany
The TGFβ pathway is a multi-functional signaling system regulating cellular processes
ranging from proliferation and migration to differentiation and cell death. Upon ligand
binding, SMAD proteins translocate to the nucleus and activate numerous genes. While
many components of the TGFβ pathway have been identified, we are still challenged to
understand how its activation is translated into distinct cellular responses. We hypothesize that the information is encoded in stimulus- and context-specific dynamics of SMAD
translocation. We therefore aimed to quantify long-term SMAD dynamics and to identify
the network interactions that shape them. As the cellular response to a given stimulus
often varies even in genetically identical cells, we focused on measuring pathway activity in single cells. By combining fluorescent reporter cell lines with live-cell microscopy
and automated image analysis, we monitored the cytoplasmic to nuclear translocation
of SMADs with high temporal and spatial resolution in hundreds of individual cells. Our
experiments demonstrated that pathway activity can be divided into a first synchronous
phase of SMAD translocation, followed by a second response that shows high variability
in the extent and duration of nuclear accumulation. The amplitude of the first response is
sensitive to low TGFβ concentration, while the duration of the second response encodes
information at high ligand doses. Perturbation experiments indicated that both receptor
internalization or degradation and transcriptional feedbacks contribute to shaping the dynamic response. We are now interested in further characterizing memory in the system
to understand how information is encoded. To guide this analysis, we are developing a
mathematical model that reflects signaling dynamics both at the population and single
cell level. Our study will provide a deeper understanding of the molecular mechanisms
regulating TGFβ signaling and open opportunities to modulate it in diseased cells.
78
#14 : Systems-biology of Poly(I:C) induced apoptosis signaling
Lakshmi Venkatraman, Joel Beaudouin, Roland Eils
Department for Bioinformatics and Functional Genomics,German Cancer Research Centre, BioQuant, Heidelberg, Germany
Cellular apoptosis is one of the immune response mechanisms used by cells to counteract
the presence of foreign DNA/RNAbrought by viruses1 . Although inflammatory response to
virus entry is well documented, a comprehensive study of the apoptosis signaling initiated
by virus is currently absent. In this study using a combination of biochemical methods
and live cell imaging we aim to construct a mathematical model of virus-induced apoptosis signaling. Addition of the dsRNA mimetic, polyinosinic:polycytidylic acid (poly(I:C)),
both dose and time dependently increased apoptosis in HeLa cells. However we observed cellular variability in apoptotic response, with only a fraction of cells dying and
with a strong variability in the timing. Overexpression of Toll-like receptor3 (TLR3) or
pretreatment with the protein synthesis inhibitor, cycloheximide increased sensitivity to
poly(I:C)-induced apoptosis. To analyze the cellular variability in apoptosis, fluorescent
caspase probes for the initiator caspase-8 and the effector caspase-3 are imaged in living
cells over time. Interestingly in cells treated with poly(I:C) a time-lag between caspase-8
activation and caspase-3 activation was observed. Presence of thresholds in the signaling pathway leading to caspase-8 activation could potentially explain cellular variability in
apoptotic response. To analyze the cause of this threshold, upstream signaling molecules
like TLR3 and mitochondrial membrane protein MAVS are perturbed in the presence of
poly(I:C). Kinetics obtained from experiments are further utilized to create a mathematical
model of casp8 activation and apoptosis. A quantitative model detailing the apoptosis
signaling initiated after poly(I:C) induction would be beneficial in analyzing the potential
role of poly(I:C) in targeted cancer therapy2 .
1. Trends in Microbiology (1999), 7,160.
2. Nature Biotechnology (2014), 32,182.
Boston, MA
June 17-19, 2014
79
Poster Abstracts
#15 : A dynamic regulatory circuit in single breast epithelial cells and basal-like premalignancies
Chun-Chao Wang1 , Sameer Bajikar1 , Leen Jamal1 , Kristen Atkins2 , Kevin Janes1
1
2
Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
Department of Pathology, University of Virginia, Charlottesville, Virginia
Basal-like carcinoma is a subtype of breast cancer that is characterized by poor prognosis and high intratumor heterogeneity. In basal-like breast epithelia, we have identified
two anti-correlated gene-expression programs that arise among single extracellular matrix
(ECM)-attached cells during organotypic 3D culture. The first program contains multiple
TGFβ-related genes including TGFBR3, and its heterogeneous induction is critical to suppress ductal branching. The second program contains JUND together with the basal-like
marker, KRT5. Homogenizing JUND expression in single cells leads to 3D acini with
bridged lumina that are similar to cribiform ductal carcinoma in situ. TGFBR3 and JUND
form a circuit that is interconnected via four negative-feedback loops, which give rise to
spontaneous damped oscillations in 3D culture. The TGFBR3–JUND circuit is remarkably
conserved in basal-like premalignant lesions that heterogeneously express KRT5, suggesting that asynchronous circuit dynamics are active in this patient subset. We further
show that the circuit is strongly dependent on ECM engagement, as detachment leads
to a rewiring that is triggered by RPS6 dephosphorylation and maintained by juxtacrine
signaling from tenascin C. Importantly, disruption of tenascin C substantially inhibits intraductal colonization of basal-like breast cancer cells in vivo. Breast tumor heterogeneity
need not stem from partial basal-like differentiation and could instead reflect dynamic
toggling of individual cells between expression states that are not cell autonomous.
80
#16 : A method for parsing clinical outcomes and combining them with phopshoproteomic and genomic data
for predicting drug efficacy: application in hepatocellular carcinoma
Orfeas S. Aidonopoulos1,2 , Ioannis N. Melas3 , Theodore Sakellaropoulos1 , Leonidas G.
Alexopoulos1
1
2
3
Dept. of Mechanical Engineering, National Technical University of Athens, Greece
Dept. of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece
European Molecular Biology Lab, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
Systems biology has become an essential component of drug discovery, attempting to
combine experimental data with computational modeling to capture different levels of cellular function (such as signaling, transcription, regulation) and integrate them in predictive
models. These models are then used to best understand the drug mode of action (MOA),
identify new targets and predict clinical drug efficacy and toxicity. In this study, we tried to
identify signaling pathways related to drug efficacy in one of the most common malignancies worldwide, hepatocellular carcinoma (HCC). Particularly, gene expression data were
collected for various HCC cell lines treated with anticancer compounds and a feature
selection method was applied to identify genes predictive of the drugs’ clinical efficacy.
For labeling each sample we constructed a graphical user interface that parses clinical
trials databases for clinical outcomes containing the respective compound. Then, using
the extracted data as an input to a pathway construction algorithm, we were able to infer
signaling networks on the proteomic level that best fit the measured gene expression signatures. Finally, a high-throughput phosphoproteomic dataset was used to validate model
predictions.
Boston, MA
June 17-19, 2014
81
Poster Abstracts
#17 : Modelling the dynamics at the interface between autophagy and apoptosis
Matthew Anderson, Virginie Betin, Robert Szalai, Jon Lane
University of Bristol, Bristol, UK
The dynamics involved in the cross-talk between apoptosis and autophagy has only
recently come under the scrutiny of the scientific community, despite being key in the
trade-off between cell death and survival, a crucial consideration for DNA-damaged cells.
Using a comprehensive extant model of apoptosis as a framework, this study will combine known protein-protein interactions from the scientific literature with new experimental
measurements of basal protein concentrations in order to construct a network model linking apoptosis and autophagy. Key elements of this relationship, such as mitochondrial
fission and fusion, cytochrome c storage and release, and the numerous protein interactions involved in the cross-talk, will all be included in the completed model. Once tested
experimentally and calibrated sufficiently, this model will be used to make predictions of
system dynamics in order to answer key questions that will then be tested in the lab.
The primary goal will be to assess how variation in mitochondrial structure influences the
apoptotic and autophagic pathways, and how the activation thresholds of these pathways
control the effects of targeted protein perturbations, the latter of which is crucial in the
development of methods of disease treatment.
82
#18 : Model-guided target identification for synergistic
combination therapies in the DNA damage response pathway
Andreas Raue, Shinji Oyama, Daryl Drummond, Birgit Schoeberl, Ozan Alkan
Merrimack Pharmaceuticals Inc.
Most commonly used anti-cancer therapies involve the systemic administration of antiproliferative and DNA damaging agents. Unfortunately, the dose of many chemotherapy
agents leads to toxic side effects in many healthy tissues as well. In addition, systemic
administration of such chemotherapy agents leads to accumulation of DNA mutations
that may contribute to development of secondary cancers. The mechanism and pathways maintaining the genome stability and integrity are shared with the features of cell
cycle regulation, which is deregulated in most human cancers. Loss of some elements of
DNA damage response (DDR) or cell cycle pathways may be compensated by increased
activity of other elements and therefore can cause resistance to DNA damaging therapies.
Our goal is to gain mechanistic understanding of DNA damage response pathway topology and to develop a predictive computational model of the involved mechanisms. The
model will help rationally design therapies targeting the DDR pathway. In particular, the
discovery of synergistic and potentiating effects of drug combinations using standard-ofcare drugs, e.g. topoisomerase inhibitors such as doxorubicin and irinotecan/SN38, and
existing or novel DDR targeting modulators/inhibitors are the main focus of this analysis.
Furthermore, we hope to develop strategies that may overcome resistance. Finally, a
model-based companion diagnostic strategy for the novel therapy is developed.
Boston, MA
June 17-19, 2014
83
Poster Abstracts
#19 : Epstein-Barr virus Integrome and Infectome of the
Human Genome by D-ViSioN (Detection of Integration of
Virus(s) by SingletoN(s))
Gnanaprakash Balasubramanian1 , Barbara Hutter1 , Roland Eils2 , Benedikt Brors1
1
Computational Oncology Group, B080 Theoretical Bioinformatics, German Cancer Research Center
(DKFZ), Heidelberg, Germany
2
B080 Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
Viruses are causal agents in various debilitating diseases including certain cancers. Globally 12% of the diagnosed malignancies have been associated with an oncovirus. Characterizing the viral infectious agent associated with cancer is of great importance in treating
this dreadful disease. We have developed a method named D-ViSioN (Detection of Integration of Virus(s) by SingletoN(s)) that predicts viral integration sites based on Next
Generation Sequencing. Our method also gives an overview of the associated viral infectome. D-ViSioN can be applied as a cancer pathogen discovery tool to decipher novel
viral associations. We have applied this method to the whole genome sequencing data
of Epstein Barr virus (EBV) transformed immortalized lymphoblastic cells. We have successfully obtained the EBV integrome and infectome of the cell line. Our method correctly
detects the experimentally known integration sites of EBV. Genes affected by viral integration are involved in various basic cell processes like cell cycle control, cytoskeleton
remodeling, vesicular traffic and cell communication. Thus, D-ViSioN gives a glimpse of
the process of cell immortalization and might give valuable clues to decipher the phenomenon of oncogenic transformation.
84
#20 : The human blood metabolome-transcriptome interface
Joerg Bartel1 , Jan Krumsiek1 , Katharina Schramm2 , Jerzy Adamsky3 , Christian Gieger4 ,
Christian Herder5 , Annette Peters6 , Wolfgang Rathmann7 , Michael Roden5 , Konstantin
Strauch4 , Karsten Suhre8,9 , Gabi Kastenmüller8 , Holger Prokisch2 , Fabian Theis1,10
1
Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Institute of Human Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
3
Institute of Experimental Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
4
Institute of Genetic Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
5
Institute for Clinical Diabetology, German Diabetes Center, 40225 Düsseldorf, Germany
6
Institute of Epidemiology II, Helmholtz Zentrum München, 85764 Neuherberg, Germany
7
Institute for Biometry and Epidemiology, German Diabetes Center, 40225 Düsseldorf, Germany
8
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
9
Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, P.O. BOX 24144, Doha,
Qatar
10
Department of Mathematics, TU Munich, D-80333 Munich, Germany
2
Simultaneous analysis of cross-sectional multi-omics data from large population studies
enables a comprehensive characterization of subtle regulatory interactions on a physiological scale. In this study, we systematically analyzed the relationship between fasting
serum metabolomics and whole blood transcriptomics data from 712 individuals of the
German KORA F4 cohort.
Correlation-based analysis identified significant associations between 522 transcripts and
114 metabolites summarized in an integrated network, which we subsequently call the
‘human blood metabolome-transcriptome interface’ (BMTI). Integration of these associations with the most recent genome-scale human metabolic network revealed a clear
tendency towards higher correlations between metabolites and mRNAs which are closely
connected in a pathway. Moreover, functional category-based aggregation of network
nodes enables the interpretation of the large amount of inferred cross-omics associations,
and allowed us to further confirm the biological validity of the BMTI. Inspection of subpathways of the resulting aggregated network uncovered both well-known mechanisms within
a metabolic pathway, like the ‘carnitine-shuttle’, and novel information on the interplay between different biological processes like the immune system and monoacylglycerols. In a
final step, we mapped associations to serum LDL, HDL and triglycerides onto the BMTI
and the aggregated network, thereby identifying highly co-regulated network regions and
differentially affected biological processes.
Boston, MA
June 17-19, 2014
85
Poster Abstracts
We constructed a comprehensive gene-metabolite network derived from human whole
blood transcriptomics. Results from association analysis with intermediate phenotypes
like LDL, HDL and triglycerides demonstrated the utility of this network for future investigations in combination with disease-related phenotypes.
86
#21 : A dynamic view on cell-to-cell variability in protein levels is required to investigate the molecular mechanisms behind non-genetic resistance in TRAIL-induced
apoptosis
François Bertaux, Szymon Stoma, Dirk Drasdo, Gregory Batt
INRIA Paris-Rocquencourt
Selective triggering of extrinsic apoptosis in cancer cells is a promising therapeutic strategy. But it faces many challenges, such as non-genetic resistance mechanisms, either
natural (fractional killing) or acquired (decrease of efficiency as treatment is repeated).
Recent single-cell, time-lapse, molecular-level measurements improved our quantitative
understanding of extrinsic apoptosis: a better knowledge of its biochemistry was acquired
and the crucial role of cell-to-cell differences in protein levels in determining cell fate variability was revealed. This knowledge was successfully formalized into mathematical models. But we still miss a comprehensive and quantitative picture of the molecular events
allowing for cell survival and driving the evolution of resistance in surviving cells. Such
understanding could lead to improved timing and dosage of drug administrations and reveal molecular targets to decrease resistance.
Here we argue that mathematical models of extrinsic apoptosis used so far are fundamentally limited to investigate the mechanisms behind non-genetic resistance, because
they account for cell-to-cell variability only in a static manner, by assuming randomly distributed initial protein levels. They ignore that protein levels fluctuate in single cells. Because those fluctuations can overlap with apoptotic signaling and are likely involved in the
evolution of resistance in surviving cells, it appears necessary to model protein fluctuations, and not only their consequences on the initial variability.
We developed a generic approach to merge in a systematic and principled manner signal
transduction models with stochastic gene expression models. We describe its application
to an established kinetic model of TRAIL-induced apoptosis [Spencer et al, 2009] and we
show that it markedly increased model prediction capabilities: one obtains a mechanistic
explanation of observations on fractional killing and non-trivial predictions of the temporal
evolution of cell resistance. We discuss implications of our results, notably the predicted
key role of Flip and Mcl1 and how they challenge the role of survival pathways.
Boston, MA
June 17-19, 2014
87
Poster Abstracts
#22 : Characterization of genetic interaction classes in a
metazoan reveals the modular organisation of genetic interactomes and suggests prioritizing strategies for combinatorial GWAS in human
Benjamin Boucher1,2,3 , Anna Lee4,5 , Michael Hallett6,7,8 , Sarah Jenna1,2,3
1
2
3
4
5
6
7
8
Department of Chemistry, Université du Québec à Montréal, Montréal, Québec, Canada
Pharmaqam, Université du Québec à Montréal, Montréal, Québec, Canada
Biomed, Université du Québec à Montréal, Montréal, Québec, Canada
The Donnelly Centre, University of Toronto, Ontario, Canada
Department of Molecular Genetics, University of Toronto, Ontario, Canada
McGill Centre for Bioinformatics, McGill University, Montréal, Québec, Canada
School of Computer Science, McGill University, Montréal, Québec, Canada
Rosalind and Morris Goodman Cancer Centre, McGill University, Montréal, Québec, Canada
The challenge of accurately predicting genetic interactions (GIs) in human contributes
to the difficulty of identifying genetic biomarkers in genome-wide association studies
(GWAS). Prioritization of SNP pairs through integrative genomic approaches has the potential to overcome the computational challenge of testing every pair with combinatorial
GWAS. Several in silico approaches using weighted data integration have been employed
to predict GIs in the nematode Caenorhabditis elegans. Interestingly, while studies in
yeast revealed the existence of a modular organization within the GI network, predictive tools targeting GIs in C. elegans still consider GIs as homogenous. To improve the
performances of predictive tools for GIs, we further investigated the structure of the GI
interactome in C. elegans.
In this study, we identified six classes of GIs, called C1-C6. These classes are characterized by distinctive properties such as their relationship with protein-protein interaction
modules, the molecular and/or biological functions of interacting genes as well as the essentiality and pleiotropy of these genes. Importantly, we identified two different classes
of functional gene-modules: 1) specialized modules, a protein-module-centric class with
a low pleiotropic index and 2) pleiotropic modules, a protein-module-independent class
with a high pleiotropic index. Additionally, genes within these modules interact through
different classes of GIs: C4 and C5 for specialized modules and C1 and C2 for pleiotropic
modules. Moreover, these modules are coordinated through their interactions with “organizer” genes and also by gene-module connections found in C3 and C6. Considering
this organization and the expected evolutionary constraint applied on each class of identified GIs, we propose here a strategy to prioritize SNP combinations in GWAS to more
efficiently identify modifier genes/SNPs.
88
#23 : Concise Functional Enrichment of Ranked Gene Lists
Claus Kadelka1,2 , Madison Brandon3,4 , T.M. Murali5,6
1
2
3
4
5
6
Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA
Department of Mathematics, Virginia Tech, Blacksburg, VA
Center for Quantitative Medicine, University of Connecticut Health Center, Farmington, CT
Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT
Department of Computer Science, Virginia Tech, Blacksburg, VA
ICTAS Center for Systems Biology of Engineered Tissues, Virginia Tech, Blacksburg, VA
Background: Advances in molecular biology have led to the broad availability of genomewide expression data as well as the development of gene interaction databases and numerous statistical algorithms to organize and extract meaning from, resp., such large data
sets. Standard techniques used in genome-wide expression profiling output long lists of
genes, typically ranked by differential expression level. Gene set enrichment algorithms
that simultaneously analyze the expression and the interaction of all genes have been
developed to reveal overall trends, previously hidden in the data.
Motivation:Most gene set enrichment methods work only in a binary mode; that is, either a gene is considered differentially expressed or not. This view disregards the various
levels of differential expression, which could be used to obtain a better explanation of the
data. Other common shortcomings include high redundancy and non-specificity of the
explanatory set, which is common among methods that analyze explanatory terms on an
individual basis such as by calculating the hypergeometric p-value, which is biased toward
terms that annotate many genes. To our knowledge, all gene set enrichment methods exhibit at least one of these deficiencies.
Results: We present a new gene enrichment algorithm that neither disregards important
information by operating in binary mode, nor shares any of the common shortcomings of
other enrichment methods.
Boston, MA
June 17-19, 2014
89
Poster Abstracts
#24 : Systems Pharmacology: Enabling early quantitative decisions in pharma from start of lead identification
to clinical trials
John Burke, Joshua Apgar, Andrew Sutherland
Applied BioMath, LLC, Winchester, MA
Systems Pharmacology modeling has been used successfully in Pharma. Here we show
several case studies that have saved an estimated $295M by enabling earlier quantitative
decisions. The cases include: feasibility assessments; prediction of best-in-class drug
properties to enable screening, improve therapeutic window, or define clinical candidate
selection criteria; and first in human dose predictions. In each case, systems approaches
enabled decisions that traditional methods could not address as easily or as early in
the pipeline. Unlike traditional PK/PD models, systems pharmacology models leverage
known biophysical interactions and integrate data from a variety of sources (in vitro, in
vivo, and clinical). Systems pharmacology models act as a central repository of data and
knowledge of human pathomechanisms, allowing for the exploration of hypotheses that
cannot be fully tested prior to dosing patients.
These case studies show the potential of systems pharmacology approaches to enable
earlier quantitative decision making with the end goal of reducing late stage attrition and
delivering premier therapeutics to address unmet medical need.
90
#25 : An Exploratory Analysis of Shared Genetic Effects
between Brain Oscillations and DSM-IV Alcohol Dependence
Leslie Brick1 , Valerie Knopik2,3 , Rohan Palmer2,3
1
2
3
Division of Behavioral Genetics, Department of Psychiatry at Rhode Island Hospital
Division of Behavioral Genetics, Department of Psychiatry at Rhode Island Hospital
Department of Psychiatry and Human Behavior, Brown University
The lack of replication of genetic effects across studies of alcohol dependence (AD) has
prompted increased utility of endophenotypes (EPs) that better reflect biological differences indicative of susceptibility to AD. By definition, EPs are more proximal to the underlying mechanisms of a behavior and are more heritable (i.e., h2 [> 0.70] effects). Brain
oscillations are regarded as candidate EPs for AD because they may stem from regulatory genes that influence neural functioning by controlling neurochemical processes. To
the extent that brain oscillations reflect differences in neuronal functioning, we explored
the additive effect of common single nucleotide polymorphisms (SNPs) on the phenotypic covariance between measures of brain electrophysiology (eyes-closed resting EEG,
P3 amplitude, frontal theta power, and parietal delta power) and DSM-IV alcohol dependence. Analyses were conducted using ∼1300 unrelated individuals of European descent
from the Collaborative Study on the Genetics of Alcoholism. Genome-wide Complex Trait
Analysis was used to estimate the SNP-heritability (h2 SNP ) of each measure and their
genetic covariance with AD while correcting for ancestral background and demographics
(when appropriate). h2 SNP effects varied across oscillation measures , ranging from zero
to 0.85, with the greatest effects seen on theta-band oscillations. Phenotypic correlations
between AD and brain oscillations were modest (<0.20). Bivariate SNP-correlations for
brain oscillations were not significant (e.g., rg = 0.27 between theta-band oscillation and
AD) with large standard errors. Although only preliminary and in need of replication with
a much larger sample, these findings suggest that common SNPs differentially contribute
to brain oscillations; however large standard errors preclude clear conclusions about associations between brain oscillations and AD in this sample.
Boston, MA
June 17-19, 2014
91
Poster Abstracts
#26 : Sequence Analysis of the mt-COXII Gene in Crassostrea virginica: Application to Human Disease
Alexis Bernstein, Alexzandrea Buscarello, Bryanna Dellaripa, Mary Disbrow, Siobhan
Fennell, Ashley Heidtmann, Gabrielle Hummel, Margaret Mirabella, Alexandra Zoarski,
Mary Jane Paolella
Sacred Heart Academy; Hamden, CT
This project targets the genome of Crassostrea virginica (Cv), the Eastern Oyster, because of the organism’s abundance, commercial value, and ecological contribution. The
mitochondrial gene, COXII, (Cytochrome c Oxidase, subunit II), plays a significant role
in the respiratory chain of cellular respiration. This gene has been observed in significantly higher levels in human breast carcinomas than in fibroadenomas, suggesting an
association between COXII and breast cancer, of particular relevance in an all-female
school. The live oysters were obtained from NOAA, National Oceanic and Atmospheric
Administration, located in Milford, Connecticut. Students extracted genomic DNA from
the oysters’ adductor muscles utilizing two methods: FTA card technology and Qiagen
DNeasy Kits; results were verified by agarose gel electrophoresis. It was necessary to
quantitate the samples, using both the Nanodrop and Biophotometer, to obtain a target
concentration of 50-100ng in preparation for PCR, the Polymerase Chain Reaction. Due
to the length of the gene, students designed original, overlapping primers to maximize
the result. These oligos were properly resuspended to produce stock solutions and then
appropriately diluted for working solutions. Students designed PCR protocols, purified the
products using the spin column method and quantitated to attain a target concentration
of 3-10ng for DNA sequencing . Students completed cycle sequencing using Big Dye
Terminators. Their subsequent extension products were purified via an ethanol protocol which produced lyophilized pellets. In preparation for DNA Sequencing, the pellets
were resuspended using 25ul of HiDi Formamide. Samples were then sequenced on the
school-owned ABI Prism 310 Genetic Analyzer. The sequences were then analyzed using
a variety of bioinformatics tools including NCBI, Ensembl, and UCSC Genome Browser.
The results of this project were submitted to Genbank.
92
#27 : Systemic analysis of expression data of glioblastoma multiforme patients
Jaime Campos, Lars Kaderali
Institute for Medical Informatics and Biometry. TU Dresden. Dresden, Germany
Glioblastoma multiforme is the most aggressive malignant brain tumor. Due to its localization there is a high difficulty on its analysis and testing. It has been hypothesized that they
may be different subgroups of patients, which, however, have been difficult to study due
to the disease’s prognosis and characteristics. This study looks for the unique molecular
relationships in different glioblastoma subgroups with the goal to find the key elements in
their operation, which will allow us to classify patients and suggest promising drug targets.
Expression data for 400 patients was used to classify them into 4 different subgroups with
similar characteristic than those presented in literature. This information is aggregated
with KEGG pathways and protein-protein interaction (PPI) networks. This integrated information was used to find enriched pathways and to study the differences in the PPI
networks between subgroups.
Enrichment of the KEGG pathways showed inverse activation levels in a number of pathways in 3 subgroups. The forth subgroup presented enriched pathways related to neural
functions. Centrality measurements of the integrated PPI networks with the differential
expression values recovered the usual suspects in cancer, such as TP53. Additionally,
a number of interesting elements were found that could have a central role in each subgroup.
Our analysis presents novel enrichment profiles and allows us to study the effect of different expression profiles in the context of protein interactions. In addition, it opens new
questions, about the relationships between different glioblastoma patient subgroups. In
future work, we will integrate additional information on molecular profiles and regulatory
networks, creating a unified representation of different levels of biological regulation in
glioblastoma multiforme.
Boston, MA
June 17-19, 2014
93
Poster Abstracts
#28 : OpenBEL - A platform for capture, integration, and
application of biological knowledge
Natalie Catlett
Selventa, Cambridge, MA
OpenBEL is an open source platform for managing biological knowledge, comprised of
the Biological Expression Language (BEL), a knowledgebase platform, and an application ecosystem. OpenBEL provides the ability to capture biological observations, such
as molecular responses to genetic and pharmacologic perturbations, in a network format
suitable for reasoning applications. BEL is a human-readable language designed to be
easy for life scientists to learn and use. The BEL knowledgebase platform compiles BEL
knowledge into cohesive, computable networks. BEL knowledge networks can be used
for systems biology research in a broad range of biomedical research areas, for the interpretation of large-scale data sets and modeling cellular responses. Specific uses include
assessment of disease and drug target biology, biomarker development, understanding
drug toxicity, and evaluating translatability to model organisms. More information about
OpenBEL, including documentation, example knowledge, and technical specifications can
be found through the OpenBEL Portal at www.openbel.org. Support for OpenBEL is kindly
provided by the OpenBEL Consortium hosted by the Linux Foundation.
94
#29 : Discovery of kinase motifs and prediction of target
substrates using the ProPeL method
Michael Chou1 , Sladjana Prisic2 , Joshua Lubner3 , George Church1 , Robert Husson2 ,
Daniel Schwartz3
1
2
3
Department of Genetics, Harvard Medical School, Boston, MA
Division of Infectious Diseases, Children’s Hospital Boston, Boston, MA
Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT
Identification of kinase substrates is essential to understanding the role kinases play in
normal and disease states. Kinases discriminate between potential substrates in part
by recognizing linear sequence “motifs” of amino acids surrounding the phosphorylated
residue. Current techniques for discovering kinase motifs are often expensive and labor intensive. The Proteomic Peptide Library (ProPeL) method is a novel approach to
discover kinase specificity motifs de novo. Briefly, human kinases are expressed in E.
coli and allowed to phosphorylate bacterial proteins. After enzymatic digestion of the
bacterial lysate, tandem mass spectrometry is performed and motifs are extracted from
the identified peptide sequences using the authors’ pLogo and motif-x software. The
ProPeL methodology successfully recapitulated the known and diverse motifs for human
basophilic Protein Kinase A (PKA) and acidophilic Casein Kinase II (CK II). We then used
the motifs determined using the ProPeL methodology to directly predict with high accuracy, potential target substrates of each kinase in the human proteome using the authors’
scan-x program. We thus demonstrate the validity of the found motifs and the approach
to search for potential substrates.
Boston, MA
June 17-19, 2014
95
Poster Abstracts
#30 : Emerging landscape of oncogenic signatures across
human cancers
Giovanni Ciriello, Martin Miller, Bulent Arman Aksoy, Yasin Senbabaoglu, Nikolaus Schultz,
Chris Sander
Computational Biology Program, Memorial Sloan Ketering Cancer Center
Cancer therapy is challenged by the diversity of molecular implementations of oncogenic
processes and by the resulting variation in therapeutic responses. Projects such as The
Cancer Genome Atlas (TCGA) provide molecular tumor maps in unprecedented detail.
The interpretation of these maps remains a major challenge. Here we distilled thousands
of genetic and epigenetic features altered in cancers to ∼500 selected functional events
(SFEs). Using this simplified description, we derived a hierarchical classification of 3,299
TCGA tumors from 12 cancer types. The top classes are dominated by either mutations
(M class) or copy number changes (C class). This distinction is clearest at the extremes
of genomic instability, indicating the presence of different oncogenic processes. The full
hierarchy shows functional event patterns characteristic of multiple cross-tissue groups
of tumors, termed oncogenic signature classes. Targetable functional events in a tumor
class are suggestive of class-specific combination therapy. These results may assist in
the definition of clinical trials to match actionable oncogenic signatures with personalized
therapies.
96
#31 : Decoding Network-Attacking Mutations in Cancer
Pau Creixell1 , Erwin M. Schoof1 , Craig D. Simpson1 , Nevena Zivanovic2 , Lara Perryman3 ,
Antonio Palmeri4 , Agata Wesolowska-Andersen5 , Hiroaki Itamochi6 , Janine Erler3 , Bernd
Bodenmiller2 , Benjamin E. Turk7 , Rune Linding1
1
Cellular Signal Integration Group (C-SIG), Center for Biological Sequence Analysis (CBS), DTU, DK-2800,
Lyngby, Denmark.
2
Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich,
Switzerland.
3
Biotech Research Innovation Centre (BRIC), Copenhagen University (KU), DK-2200 Copenhagen, Denmark.
4
Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, 00133 Rome,
Italy.
5
Functional Human Variation Group, Center for Biological Sequence Analysis (CBS), DTU, DK-2800, Lyngby, Denmark.
6
Department of Obstetrics and Gynecology, Tottori University School of Medicine, 36-1 Nishicho, Yonago
683-8504, Japan.
7
Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA.
Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling processes. While thousands of mutations have been identified, mostly by
genome-wide sequencing, systematic interpretation of their role in cancer and impact on
cellular information processing is presently missing. Here, we propose a computational
approach (ReKINect) to identify mutations attacking signaling networks. We demonstrate
six types of network-attacking mutations (NAMs) including changes in kinase modulation, network rewiring as well as the genesis and extinction of specific phosphorylation
sites. Through global, quantitative analysis of the exomes and (phospho-)proteomes of
five ovarian cancer cell lines we identify and validate numerous NAMs. Finally, we explore
the entire cancer genome repertoire and predict hundreds of NAMs affecting kinase and
SH2 driven signaling. Our approach is scalable with the complexity of cancer genomes
and cell signaling, and can be readily applied in personalized precision medicine.
Boston, MA
June 17-19, 2014
97
Poster Abstracts
#32 : Sequencing and Analysis of the mt-ATP6 Gene in
Crassostrea virginica
Maria Beecher, Kristina D’Agostino, Katherine Donohue, Marija Jukic, Emily Mancini,
Kanita Mote, Samantha Sansone, Emily Smith, Mary Jane Paolella
Sacred Heart Academy; Hamden, CT
The mitochondrial ATP6 Synthase gene in the Crassostrea genus is the focus of this study.
The gene is 683 base pairs in length and vital to cellular respiration’s transport chain. If
mutations occur in humans, it causes neuropathies and difficulties with locomotion and vision. Recent studies concerning variations of this gene have been found to result in Leigh
Syndrome, a progressive brain disorder. Mutations in human mitochondrial ATP6 are also
implicated in some epithelial ovarian tumor subtypes. Crassostrea virginica, (Cv), Eastern Oysters, were provided by the National Marine Fisheries Laboratory in Milford, CT. Cv
was chosen as the target organism because of its commercial relevance to Long Island
Sound and its accessibility as a live organism. Invertebrate DNA was extracted from the
adductor muscle utilizing two methods: FTA cards and Qiagen DNeasy Kits. Genomic
DNA was then applied to low percentage agarose gels for confirmation of results. To prepare for PCR, the Polymerase Chain Reaction, DNA was quantitated using the Nanodrop
and the Biophotometer to obtain the target concentration of 50-100ng. The class, who
also prepared all stock and working solutions, then designed overlapping primers and the
PCR protocol. Successfully amplified PCR products of the mt-ATP6 gene were later purified and quantitated for target concentrations of 3-10ng prior to DNA sequencing. The
students performed all sequencing reactions and purification of all extension products
to produce samples of pellet DNA. These were subsequently resuspended for utilization
in the school-owned ABI Prism 310 Genetic Analyzer, a single capillary automated sequencer. Once analyzed using bioinformatics tools: NCBI, Ensembl, and UCSC Genome
Browser, the sequences will be submitted for inclusion in Genbank as additions to the 24
sequences previously published by Sacred Heart Academy students.
98
#33 : Phylogenetic profiling reveals novel functional modules in the human genome
Gautam Dey, Ariel Jaimovich, Akiko Seki, Tobias Meyer
Chemical and Systems Biology, Stanford University, Stanford, CA
A significant fraction of human genes remain uncharacterized, posing a major challenge
for both medicine and basic cell biology. While we rely heavily on phenotypic screens and
high-throughput genetic interaction mapping to discover novel functions, the informationrich phylogenetic history of genes is often overlooked. We have employed phylogenetic profiles, correlated patterns of joint ortholog presence and absence across multiple
genomes, to generate a map of functional interactions and predictions for hundreds of
uncharacterized genes.
Despite considerable success as a tool for bacterial genomics, phylogenetic profiling in
eukaryotes has been hampered by a limited pool of sequenced genomes and evolutionary trends that include widespread functional divergence of duplication-derived paralogs.
Taking advantage of the recent explosion in the number of fully sequenced species, we
have addressed these challenges with a novel algorithm, profiling 30,000 ortholog groups
across 177 eukaryotic genomes. Benchmarked using curated datasets from the literature, these profiles can be filtered into around 300 functional modules containing over
2000 genes, representing the core set of interactions that persist across species separated by millions of years of evolution. In addition, many clusters map to specific subsets
of highly conserved signaling networks, highlighting the surprisingly modular architecture
of pathways ranging from chromatin remodeling and splicing regulation to cholesterol
biosynthesis.
Importantly, the map also provides targeted, experimentally tractable predictions for over
200 uncharacterized, highly conserved genes. We have focused initially on a subset of 30
genes linked either to primary cilium formation or to a poorly characterized cluster containing the actin-nucleating WASH complex. Our experiments so far have validated the
overwhelming majority of predictions, underscoring the broad utility of this resource for
targeted gene function discovery.
Boston, MA
June 17-19, 2014
99
Poster Abstracts
#34 : Integration of multi-leveled –omics data for cancer
network analysis.
Madeline Diekmann1,2 , Jonas Behr1,2 , Niko Beerenwinkel1,2
1
2
Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
SIB Swiss Institute of Bioinformatics, Basel, Switzerland
Heterogeneity of cancer within and between patients poses profound challenges for data
analysis and treatment. However, since all tumors need to acquire certain biological capabilities, we may expect a reduction of this variability when looking for functional implications of genetic variations. Specifically, we are interested in identifying genes that are
affected similarly in a large fraction of samples. The activities of these genes may depend on various mechanisms, including genetic variations, changes in transcriptional and
translational regulation, or post translational modification.
High-throughput measurements of the genome, transcriptome, proteome, and phosphoproteome allow us to observe such features at an unprecedented level of detail. However,
all measurements have limited sensitivity, significant technical variability and biases, and
therefore illuminate the whole picture only partially. We attempt to integrate all these
sources of information in a unified framework using biological networks as prior information. Accounting for uncertainty in all sources of information, we seek to identify the most
likely protein activity status for all genes in a given sample. Furthermore, our model provides estimated gene activity on a continuous scale.
We develop this method in the context of a mouse model for hepatocellular carcinoma,
where all mentioned measurements are present. We utilize a network structure learned
on public pathway databases to estimate gene activity as a combination of functions of all
available data like RNA expression, protein abundance, or phosphorylation. Moreover, we
also accommodate multi-leveled data on the DNA level by learning how factors such as
structure, relative location, frequency and conservation of SNPs and CNVs are important
for gene activity.
100
#35 : PK-PD Modelling of the Reverse Transcriptase Inhibitor Tenofovir and Quantification of its Prophylactic Efficacy against HIV-1 Infection
Sulav Duwal1 , Christof Schütte1,2 , Max von Kleist1
1
2
Department of Mathematics and Computer Science, Free University Berlin, Berlin, Germany
Zuse Institute Berlin, Berlin, Germany
Despite tremendous efforts, the HIV epidemics continues to expand and an effective vaccine remains to be developed. However, prophylactic strategies recently showed promise
in reducing the risk of HIV onward transmission. One such strategy, pre-exposure prophylaxis (PrEP), targets uninfected persons at risk of infection. The key component of
the strategy is the pro-drug tenofovir-disoproxil fumarate (TDF), which has recently been
approved by the FDA for use in PrEP. However, several yet unknown factors may affect
the efficacy of TDF as a part of PrEP strategies.
To delineate some of these factors, we developed a pharmacokinetic model for TDF and
its active anabolite tenofovir-disphosphate (TFV-DP) and coupled it with a HIV dynamics
model. Our PK-PD predictions were subsequently validated with available data. Using
a hybrid stochastic-deterministic simulation approach, we estimated the prophylactic efficacy against HIV-1 infections for (i) daily TDF-based PrEP, (ii) one week TDF started
either shortly before, or – after viral exposure and (iii) a single dose oral TDF taken before
viral challenge (sd-PrEP). The prophylactic efficacy of TDF showed a negative correlation
with the number of transmitted viruses for all evaluated regimens. Once daily TDF-based
PrEP with 300 mg could prevent approx. 80% infections, in agreement with clinical data
(TDF-only sub-study of Partners PrEP ) and was relatively unaffected by poor adherence.
However, the efficacies of event-driven prophylaxis (strategies ii-iii) were limited by a slow
accumulation of TFV-DP. Finally, we derived and validated an analytic expression for the
concentration vs. prophylactic effect, which summarized important parameters that affect
PrEP-efficacy, such as the number of transmitted viruses etc. As TDF showed modest efficacy for the event-driven prophylaxis, in future the prophylactic efficacy of other antivirals
NRTIs, NNRTIs, INIs etc. should be explored for that purpose.
Boston, MA
June 17-19, 2014
101
Poster Abstracts
#36 : Cell-to-Cell Communication Modulates the TNF-a
and IL-6 Response to TLR4 Stimulation in Macrophages:
A Computational Approach
Markus R. Eisele1,2 , Qiong Xue1 , Kathryn Miller-Jensen1,3
1
2
3
Department of Biomedical Engineering, Yale Universtiy, New Haven, Conneticut
Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany
Department of Molecular, Cellular Developmental Biology, Yale University, New Haven, Conneticut
Cell responses are mediated by intermediate signals that are secreted and sensed by
the same cells and are subject to significant cell-to-cell heterogeneity. We recently analyzed the single-cell responses of primary human monocyte-derived macrophage (MDM)
cells to lipopolysaccharide (LPS) stimulation using a high-throughput multiplexed singlecell secretomic assay, and the results showed a high variation in single-cell responses.
TNF-α secretion after 20 hours was increased approximately 10-fold compared to population experiments, while in contrast IL-6 secretion was significantly lowered. We hypothesized that the propagation of intermediate secreted signals via cell-to-cell interactions
might account for such differences between single cells and cells in a population. To
explore this question, a computational ODE model was built using the experimental results in order to study the TNF-α and IL-6 response to LPS stimulation in single cells
and small cell populations. The model reconstructs NFκB activation, followed by TNF-α
secretion through LPS stimulation. TNF-α secretion can activate a second wave of cytokines, which is characterized in the model through IL-6, which can stimulate its own
secretion through a strong positive feedback loop. Our simulation shows that this positive
feedback is not sufficiently activated in single cells. Only cells in a population are able
to activate a reliable paracrine feedback loop leading to a more homogenous response
to LPS. Our results suggest that more than ten cells are needed to create a response
similar to cell-population experiments. With the aid of our simulations we are able to provide an explanation regarding the signaling constraints of single cells. We suggest that
isolated cells lack important paracrine feedback loops and that these feedback loops are
necessary for coordinated innate immune activation following LPS stimulation.
102
#37 : The role of p53 in metabolic regulation and response
to cellular perturbations
Ana Finzel Pérez, Fabian Bindel, Christin Zasada, Guido Mastrobuoni, Stefan Kempa,
Alexander Loewer
Berlin Institute for Medical Systems Biology, Max-Delbrueck-Center, Berlin, Germany
P53 has been called “cellular gatekeeper” as it mediates the cellular response to a wide
range of cellular stress signals. Upon stress, p53 is activated and stabilized by covalent modifications, showing different dynamics depending on the stimulus. It activates or
represses hundreds of target genes regulating repair and cell fate. However, the correlation between p53 dynamics and temporal activation of target genes is not well known.
To better understand how information transmitted by p53 is decoded, we have created
fluorescent reporter cell lines for p53 and several target genes. This allows us to simultaneously monitor in individual cells p53 dynamics and target gene expression using time
lapse fluorescent microscopy. We treated these cells with chemotherapeutic drugs and
applied specific perturbations to identify network nodes that are critical for information
decoding. Using this approach, we observed that inhibiting DNA-PK led to an increase
in amplitude and width of the first p53 pulse and corresponding changes in target gene
expression.
As recent studies have highlighted a role for p53 in steering metabolic fluxes, we also
performed pulsed Stable Isotope Resolved Metabolomics and Quantitative Proteomics to
systematically study how p53 signaling affects metabolic pathways. In contrast to previous
reports, we did not observe a major effect of p53 on the central carbon metabolism in unstressed conditions. We are now focusing on p53’s role in regulating metabolic changes
upon genotoxic stress. By applying different chemotherapeutic drugs and specific inhibitors, we aim to identify key metabolic processes that contribute to p53’s stimulusdependent control of cell fate.
By understanding how changes in p53 dynamics affect the strength and timing of target gene expression and how this in turn affects cellular regulatory networks such as
metabolism, we will be able to specifically modulate the p53 response in order to change
cell fate, which is important in the context of cancer therapy.
Boston, MA
June 17-19, 2014
103
Poster Abstracts
#38 : The mechanistic role of MYCN in driving cell cycle
progression in Neuroblastoma
Andres Florez1 , Tatjana Ryl1 , Frank Westermann2 , Thomas Höfer1
1
2
Division of Theoretical Systems Biology (DKFZ), Heidelberg, Germany
Division of Tumor Genetics (DKFZ), Heidelberg, Germany
Neuroblastoma is a common extracranial tumor in children. It has been found that MYCN
oncogene is an important prognosis marker in 30% of the tumors. Additional studies have
found that MYCN exerts a control in the Rb-E2F1 network but the exact mechanism remains still unclear. The work by Yao et. al, has shown that E2F1 expression exhibits a
bistable switch response. This translates into a controlled and irreversible commitment
of cells into cell cycle upon certain threshold of growth factor stimulation. To investigate
the effects of MYCN on the Rb-E2F1 switch, Neuroblastoma cell line (IMR5/75) with a
Tet-inducible system for MYCN knockdown was used, and this cells line was transduced
with GFP promoter construct for E2F1 and the cell cycle indicator FUCCI (Cdt1) for G1
phase determination. Steady state experiments show that cells accumulate in the G1
phase late upon MYCN knockdown. Mapping the E2F1 behavior to G1 early/late phases
shows that E2F1 promoter activation is heterogeneous with cells exhibiting different E2F1
levels in G1 early and late. However the knockdown condition shows that cells failed to
increase E2F1 in G1 late as compared to the control. Further qPCR validation of sorted
cells by E2F1-GFP intensities confirms correlation with E2F1 mRNA levels. A mathematical model was developed to integrate these results, suggesting an important role of Rb
phosphorylation in preventing cells entering into S phase. Combination of mathematical
modeling and single cell experiments will lead to a better understanding of the Rb-E2F1
network and potential prediction of new drug targets based on MYCN actions.
104
#39 : T cell immune responses generate diversity through
linear cell-fate progression rather than asymmetric cell divisions
Michael Flossdorf1,2 , Veit Buchholz3 , Patricia Graef3 , Dirk Busch3 , Thomas Höfer1,2
1
2
3
German Cancer Research Center, Heidelberg, Germany
BioQuant Center, University of Heidelberg, Heidelberg, Germany
Technische Universität München (TUM), Munich, Germany
Upon infection, naive antigen-specific cytotoxic T cells expand vigorously and give rise
to a population of short-lived effector and long-lived memory cells. Conflicting models
have been proposed that suggest either of these subsets to be a precursor of the other
or attribute their generation to asymmetrically dividing naive cells. To gain insight into the
mechanism that underlies T cell diversification we combine stochastic population modeling with large scale model discrimination based on single cell in vivo fate mapping data.
Our computational framework allows for stochastic differentiation and proliferation decisions of individual cells and incorporates both symmetric and asymmetric cell division.
Building on this framework, we find, first, that asymmetric cell divisions of the activated
naive T cells play a negligible role and, second, that phenotypic diversity is instead generated through linear cell-fate progression: Naive cytotoxic T cells give rise to slowly proliferating, long-lived subsets from which rapidly proliferating, short-lived subsets emerge.
Critical predictions of this linear differentiation model have been validated in subsequent
experiments. Third, we find that recall responses initiated by resting memory T cells recapitulate the primary response. We believe that our theoretical framework will be of wider
applicability to the study of cell-fate decisions in proliferating cell populations.
Boston, MA
June 17-19, 2014
105
Poster Abstracts
#40 : Human transcription factor network evolution and
rewiring in disease
Juan Fuxman Bass1,2 , Nidhi Sahni3,4,5 , Akihiro Mori1,2 , Shaleen Shrestha1,2 , Numana Bhat1,2 ,
Song Yi3,4,5 , Aurian Garcia-Gonzalez1,2 , David Hill3,4,5 , Marc Vidal3,4,5 , Albertha Walhout1,2
1
Program in Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts,
USA
2
Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts,
USA
3
Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts, USA
4
Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
5
Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA.
Gene regulatory networks comprising protein-DNA interactions (PDIs) between transcription factors (TFs) and regulatory DNA regions play a critical role in development, physiology and can go awry in a variety of human diseases. In recent years, genome-wide
TF occupancy profiles determined by chromatin immunoprecipitation (ChIP) greatly expanded our view of the genomic regions occupied by individual TFs in particular cell
types. However, only a minority of the ∼1500 human TFs have been assayed by ChIP,
and only in a limited number of cell types. As a consequence, systems-level questions
relating to TF function and evolution have been difficult to address. Here we use genecentered enhanced yeast one-hybrid (eY1H) assays to interrogate the binding of more
than a thousand full-length human TFs to regulatory genomic regions in parallel. Using
this approach we describe the first gene-centered human enhancer network, involving
2,274 PDIs between 269 enhancers and 276 TFs. Analysis of this network reveals the
evolutionary path of paralogs following gene duplication, as well as pairs of TFs that are
redundant or that have opposing functions. We find that highly connected TFs are more
frequently essential for viability or associated with human disease. Finally, we provide a
proof-of-principle to show how eY1H assays can provide insights into disease-associated
regulatory network rewiring that results from mutations in TFs or in regulatory sequences.
This work greatly expands our knowledge about TF function and evolution and will provide
a valuable resource to study regulatory perturbations such as those identified in genomewide association studies.
106
#41 : Robust pattern formation in the Drosophila eye disc
Avishai Gavish, Ben-Zion Shilo, Naama Barkai
Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel.
cz.kebrt.html2latex.NoItemException: Can’t find specified config item cedil Periodic patterns emerge during the development of multiple tissues and organs. How those patterns
are generated in a robust way is still an open question. The Drosophila eye is comprised of
∼750 eye units, named ommatidia whose crystalline order is defined during eye disc patterning through a dynamic process involving a traveling activation wave sweeping across
the disc, followed by a lateral-inhibition based refinement. Using mathematical modeling,
we confirm that lateral inhibition can generate patterns in the absence of spatial inhomogeneity, but find that it fails to do so in the presence of noise. We describe the basis for
this breakdown and show that robustness is retrieved when a short-range diffusible activator is assumed. Experimentally, we identify this missing activator as Scabrouscedil; a
previously implicated inhibitor. We further predict and verify that robustness is improved
by the action of the morphogenic furrow, which effectively reduces distance between cells.
We argue that mechanisms of periodic pattern formation are largely constrained by the
need to buffer spatial and temporal heterogeneities.
Boston, MA
June 17-19, 2014
107
Poster Abstracts
#42 : Multiple stimuli converge to influence transcription
factor dynamics.
Miriam Gutschow, Jacob Hughey, Markus Covert
Department of Bioengineering, Stanford University, Stanford, CA
A major challenge in understanding complex cellular signaling networks is deciphering
how multiple signals can converge on one protein and be translated into a host of specific
responses. Many innate immune system signaling cascades interact with transcription
factor NF-κB, which in turn produces downstream effects specifically targeted to its stimuli. Our overall goal was to assess the combinatorial effects of multiple ligands converging
upon NF-κB signaling. First, we stimulated cells with either Tumor Necrosis Factor (TNF)
or bacterial lipopolysaccharide (LPS) at a range of concentrations spanning several orders of magnitude, and followed this with pairwise combinations of concentrations of both
stimuli. We then observed the nuclear translocation dynamics of the NF-κB response in
individual cells using high-throughput, live-cell imaging. Finally, we measured cytokine
production in cells stimulated with representative pairwise combinations of the two ligands. We found that a greater proportion of cells were activated under the combined
stimuli, however, the maximum NF-κB activation intensity did not increase when the signals overlapped. More surprisingly, we found that the effects of each individual stimulus
could be used to predict the pairwise combinations as a simple linear combination. We
also show through cytokine profiling, that despite the linear integration of the signaling
dynamics, cells exposed to both pathogen and cytokine signals can exhibit synergistic
and antagonistic downstream cytokine production, depending on the concentrations and
timing of the two stimuli.
108
#43 : Cytokine signaling in Alzheimer’s disease
Levi Wood1,2 , Douglas Lauffenburger2 , Kevin Haigis1
1
2
Molecular Pathology Unit, Massachusetts General Hospital
Department of Biological Engineering, Massachusetts Institute of Technology
Alzheimer’s disease (AD) and related dementias are estimated to afflict more than 35 million people worldwide. The traditional approach to developing AD therapeutics has been
to intervene in the formation of the amyloid beta (Aβ) plaques that are the pathologic
hallmark of disease onset and progression (i.e., the amyloid hypothesis). Nevertheless,
phase III clinical trials of therapies based on the amyloid hypothesis have either shown no
improvement, or else have accelerated cognitive decline in patients with mild-to-moderate
AD, suggesting that formation of Aβ plaques reflects only one portion of complex AD
pathogenesis. Since the initial and ongoing physiologic response to Aβ involves microglial
and astrocyte immune responses, we hypothesized that glial cytokines may play an important role in AD pathogenesis. Using partial least squares regression (PLSR) analysis
to integrate cytokine protein expression data with pathologic disease state, we were able
to identify a signature that reliably distinguished postmortem AD brain tissues from nondemented control brains (94% correctly predicted). Integration of our cytokine signaling
data into a prior knowledge network of AD disease pathogenesis revealed a cluster of
up-regulated cytokines in human AD tissues that may impinge upon multiple aspects of
pathogenesis, including Aβ production, Tau hyper-phosphorylation, and neuronal death.
Multifactorial application of these cytokines to primary neuron cultures revealed cytokines
that (1) directly reduced viability and (2) reduced viability only when applied together with
Aβ. These results illustrate the multi-faceted nature of AD pathogenesis and the need
to study it in a multi-factorial manner in order to identify novel therapeutic targets. The
cytokine signature derived from this work will serve as a basis for understanding complex
signaling interactions in AD and for designing new therapeutic approaches for intervening
in the disease.
Boston, MA
June 17-19, 2014
109
Poster Abstracts
#44 : Robust DNA Repair through Collective Rate Control
Tim Heinemann1,2 , Paul Verbruggen1,3 , Erik Manders3 , Gesa von Bornstaedt1,2 , Roel van
Driel3 , Thomas Höfer1,2
1
Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), 69120 Heidelberg,
Germany
2
BioQuant Center, 69120 Heidelberg, Germany
3
Swammerdam Institute for Life Sciences, University of Amsterdam, 1090GE Amsterdam, The Netherlands
DNA repair is indispensible for the cellular protection against environmental and endogenous damaging agents. This is reflected in the increased susceptibility to cellular aging
and cancer development as a consequence of impaired repair. Functional repair is carried out by enzymatic macromolecular complexes that assemble at specific sites on the
chromatin fiber. How these molecular machineries and their constituent parts faithfully
regulate the repair process and the repair rate in particular is poorly understood.
Here we quantify nucleotide-excision DNA repair (NER) in mammalian cells and find that,
despite the pathway’s molecular complexity, repair effectively obeys slow first-order kinetics. Theoretical analysis indicates that these kinetics are not due to a single rate-limiting
step. Rather, first-order kinetics emerge from the interplay of rapidly and reversibly assembling repair proteins, stochastically distributing DNA lesion repair over a broad time
period. Based on this mechanism, the model predicts that the repair proteins collectively
control the repair rate. Exploiting natural cell-to-cell variability, we corroborate this prediction for the lesion-recognition factors XPC and XPA.
Our findings provide a rationale for the emergence of slow time scales in chromatinassociated processes from fast molecular steps and suggest that collective rate control
might be a widespread mode of robust regulation in DNA repair and transcription.
110
#45 : A multiscale model of influenza A virus infection that
elucidates the treatment with direct-acting antivirals
Frank Stefan Heldt1 , Timo Frensing1 , Udo Reichl1,2
1
2
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
Chair of Bioprocess Engineering, Otto-von-Guericke University, Magdeburg, Germany
Influenza A viruses are human, respiratory pathogens that cause 3 to 5 million cases
of severe illness and up to 500,000 deaths each year. Yet, there are currently only two
classes of antiviral drugs licensed for treatment and resistant virus strains have already
emerged. To identify novel targets for antiviral therapy, we constructed a multiscale model
of influenza A virus infection. It accounts for the key steps of intracellular viral replication
such as virus entry, viral genome and mRNA synthesis, and virus release. The model
combines this information with infection dynamics on the extracellular level, i.e., the transmission of virions between host cells and the kinetics of virus-induced apoptosis. This
integrated modeling approach allows us to reproduce a variety of experimental data at the
single cell and the cell population level, and to predict the most promising targets for antiviral agents. In particular, we find that interference with the viral polymerase represents
a very efficient treatment strategy because it interrupts the auto-catalytic mechanism of
viral RNA synthesis. By contrast, targeting the steps of virus entry primarily delays virus
spreading but does not protect host cells from infection in vitro. In addition, we demonstrate that cell death dynamics can strongly affect treatment success. Hence, multiscale
modeling provides a systems-level understanding of viral infection and therapy, and is
therefore an ideal platform to include further levels of complexity such as the immune
response and between-host transmission.
Boston, MA
June 17-19, 2014
111
Poster Abstracts
#46 : Modeling NOS3 signaling as markers for NO-bioavailability,
ROS generation and cGMP-metabolism in cardiac pathophysiology
Heinrich Huber1 , Michele Andreazzi1 , Melissa Swinnen1 , Peter Pokreisz1 , Stefan Janssens1,2
1
2
Department of Cardiovascular Sciences, KU Leuven, 3000 Leuven, Belgium
Dep. of Cardiovascular Medicine, University Hospital Leuven, 3000 Leuven, Belgium
Endothelial NO synthase (eNOS; NOS3) is key to reduce malignant reactive oxygen
species and for regulating cardiovascular homeostasis by guaranteeing bioavailability of
nitric oxide (NO) and cyclic GMP (cGMP). Deregulated eNOS signaling has been associated with pressure-overload-induced heart failure and dilated cardiomyopathies, and
can partially result from reduced availability of its co-factor tetrahydrobiopterin (BH4) or
from inhibition of its downstream soluble guanylate cyclase or pathological upregulation of
phosphodiesterases (PDEs). Recent findings suggest that eNOS-related signaling eNOSmediated signaling is heavily dependent on the cell and tissue-specific molecular context
and pose the need for a holistic and quantitative systems analysis.
We here provide the first systems analysis of eNOS-regulated signaling. We therefore
will analyze in house gene expression data of eNOS-pathway proteins in heart, brain
and liver endothelium, and evaluate the tissue-specific robustness of eNOS signaling to
small pathological aberrations in gene expression. Having previously identified a role of
elevated phosphodiesterase 5 (PDE5) after increased cardiac afterload [1], we will analyze how differential expression patterns of PDE5 in mice and patients would translate
into changes of cyclic guanosine monophosphate cGMP levels and activation of downstream protein kinase G-related signaling. We will investigate how pathological changes
in PDE5 and BH4 availability may change activation of downstream NOS signaling from
ultra-sensitivity (requiring a threshold for activation) to a hyper-reactive response. Finally,
we will discuss how we will apply our model as potential tool for assessing the likelihood
of cardiotoxic injury for patients treated with the chemotherapeutic drug doxorubicin in line
with our ongoing clinical studies.
1. Vandenwijngaert S, Pokreisz P et al PLoS One, 2013. 8(3): p. e58841.
112
#47 : Systems pharmacology analysis of drug-induced
peripheral neuropathy
Junguk Hur1 , Abra Guo2 , Wei-Yin Loh3 , Eva Feldman1 , Jane Bai4
1
2
3
4
Department of Neurology, University of Michigan, Ann Arbor, Michigan 48109
College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, Michigan 48109
Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706
Office of Clinical Pharmacology, U.S. Food and Drug Administration, Silver Spring, Maryland 20993
Background: Drug-induced peripheral neuropathy (DIPN) is a side-effect of many lifesaving drugs, often leading to dose reduction or regimen modification that may comprise
clinical outcomes. The underlying mechanisms are not fully understood, and no predictive
tool for DIPN is available. Here, we employed a systems pharmacology approach to identify the proteins/genes significantly associated with clinical incidence and severity of DIPN.
Results: Using text-mining and expert curation, we identified 234 neuropathy-inducing
drugs (NIDs) from drug labels and online resources (Drugs@FDA, DailyMed, and SIDER).
A pharmacological network consisting of NIDs and their drug-targets was constructed
by referencing DrugBank, and extended by adding intermediators having protein-protein
or genetic interactions with the drug-targets. Comparison against 1,000 random networks identified 230 DIPN-associated intermediators, significantly enriched with apoptosis and stress-response genes. Transcriptome analysis of neural progenitor cells was performed by leveraging the Library of Integrated Network-based Cellular Signatures (LINCS)
database. The transcriptomic changes caused by NIDs were highly similar to those by
shRNA perturbation of the genes in the ErbB signaling pathway. We also collected neuropathy incidence and severity from drug labels and literature, and built a regression-tree
model of DIPN based on the drug-targets and intermediators. Those NIDs whose targets
interacted with both MYC and PAF15 were associated with a neuropathy incidence of
38.1%, while drugs interacting only with MYC had an incidence of 2.9%.
Conclusions: A systems pharmacology analysis, integrating of multiple bio-/chem-informatics
approaches, suggests that the ErbB signaling pathway and PAF15 may play an important
role in DIPN.
Boston, MA
June 17-19, 2014
113
Poster Abstracts
#48 : A map of functionally coherent binding for Snail and
Twist transcription factors in fly mesoderm development
informs regulation of epithelial remodelling and oncogenic
Notch
Essafi Abdelkader1 , Sims Andrew1 , Owen Jeremy2,3 , Heale Bret1 , Ford Matthew1 , Lubbock Alexander1 , Overton Ian1
1
2
3
MRC Human Genetics Unit, University of Edinburgh, Edinburgh UK
Department of Systems Biology, Harvard Medical School, Boston, USA
University of Cambridge, Cambridge, UK
Cell identity is governed by gene expression, regulated by transcription factor binding
at cis-regulatory modules (CRMs). Genome-scale assignment of functional targets is
challenging and many CRMs likely remain uncharacterised. We developed and rigorously benchmarked network biology approaches to address these key limitations in understanding gene expression control and applied these novel tools to predict functional
targets for the transcription factors Snail and Twist. Analysis of multiple independent
datasets in fly mesoderm development found considerable unanticipated direct regulation. Predicted Snail and Twist functional targets substantially overlapped with modifiers
of the Notch pathway identified by genetic, RNAi screens and also identified novel factors in processes that control Notch signalling. Notch is critical for cell fate decisions
across development, linking to several cancers and genetic disorders. Snail and Twist are
canonical Epithelial to Mesenchymal Transition (EMT) transcription factors, reactivation
of a programme resembling EMT is a credible mechanism for key aspects of metastasis. Unsupervised clustering of microarray data for orthologues of functional Snail and
Twist targets predicted by our algorithm stratified 2999 primary breast cancers by subtype. The aggressive, basal/triple-negative subtype has EMT characteristics and is driven
by Notch signalling. Our integrative analysis identified conserved functional targets of
EMT transcription factors in fly development as new players in basal-like breast cancers,
including poorly characterised transcription factors, membrane receptors, splicing factors,
vesicle trafficking proteins and chromatin modifiers. This work therefore suggests novel
regulators and effectors of oncogenic Notch output relevant to the biology of cancer stem
cells and metastasis. Ectopic expression of four poorly characterised predicted functional
targets showed significant effects on breast cancer cell invasion.
114
#49 : A systems biology approach to the GABAergic contribution to Juvenile Myoclonic Epilepsy
Martine A Jaworski1 , Nick Tokarew2 , Yamile Wasslen3
1
2
3
Carleton University Health Services, Carleton University, Ottawa ON Canada
Department of Biochemistry, Cancer Research Institute, Ottawa Hospital, Ottawa ON Canada
PhD, Ottawa ON Canada
Juvenile Myoclonic Epilepsy (JME) is the most common form of Genetic Generalized
Epilepsy (GGE). Whole exome sequencing (WES) and other genetic studies show JME is
genetically heterogeneous (eg Heinzen 2012 and OMIM EJM1-9). A JME mouse model
shows abnormalities in the GABAergic system (Pal 2010; Velisek 2011); drugs enhancing
GABAergic function are effective in some JME patients.
Purpose: To review protein-disrupting mutations of pre- and postsynaptic GABAergic proteins in JME patients.
Methods: Using systems biology, we integrated in silico data from JME studies of genetics/genomics (family studies, WES, and/or genotyping, targetted gene sequencing),
as well as transcriptomics, proteomics, and synaptomics of GABAergic synapses.
Results: 1. Multiple mutations in the presynaptic GABAergic vesicle life cycle occur from
GABA synthesis to vesicular exocytosis and endocytosis. 2. Ionotropic and metabotropic
GABAergic neuroreceptors are mutated, but mutations of their interactants in trafficking,
post-translational modification, receptor desensitization, scaffolding, and endocytosis are
equally numerous. 3. WES and PolyPhen-2 (Heinzen 2012) predict some deleterious
mutations of AA residues at protein-protein, protein-lipid or protein-small molecule interaction sites within the GABAergic system. 4. JME mutations in GABAergic synapses
target strategically placed protein complexes which modulate the Excitatory-Inhibitory (EI) balance in the motor system (eg. the putamen).
Conclusions: 1. A subset of protein mutations in JME are interconnected via presynaptic and post-synaptic GABAergic pathways, converging to form a highly dynamic view
of GABAergic synaptic networks. 2. Disruptive mutations in some JME patients involve
proteins in GABAergic systems which directly, or indirectly, are the targets of AED, thus
allowing a preliminary linkage of genomics, proteomics and therapeutics. 3. Additional
knowledge of these networks at different levels of analysis may assist in understanding
patient response to AED, and for patient stratification in trials of AED.
Boston, MA
June 17-19, 2014
115
Poster Abstracts
#50 : Application of S-pyocins to eradicate Pseudomonas
aeruginosa biofilms
Enes Karaboga, Suphan Bakkal
Sabanci University, Istanbul / Turkey
Pseudomonas aeruginosa (Pa) is a gram-negative opportunistic pathogen, which is associated with life-threatening hospital-acquired and community-acquired infections occurred
in urinary tract, skin, eye, ear, and lungs. Antipseudomonal antibiotics are the most potent
arsenals to treat Pa infections. However, Pa infections are still among the most urgent and
prevalent bacterial infections due to their elevated antibiotic resistance. Especially if Pa
strains form biofilms, antibiotic resistance can be up to 1,000 times more compared to the
antibiotic resistance observed in planktonic bacteria. There is an obvious need for novel
therapies for the treatment of Pa infections. Recently, several studies performed on pyocins, proteinaceous bacterial toxins produced by Pa, to explore the potential of pyocins
as novel antibiotics to treat Pa infections. Similar to other members of the bacteriocin family, pyocins mostly kill strains of the related species. There are three types of pyocins: S,
R, and F pyocins. S-type pyocins are high molecular weight proteins and RF-type pyocins
resemble bacteriophage tails. In this study, we applied four S-pyocins (S1, S2, S3, AP41)
on Pa biofilms separately and in combination with six commonly used antibiotics (Tobramycin, Gentamicin, Colistin, Piperacillin, Ceftazidime, Ciprofloxacin) to determine the
efficacy of S-pyocins on Pa biofilms to eradicate Pa biofilms and if there is synergy and/or
antagonism between S-pyocins and antibiotics. We created Pa biofilms using Calgary
Biofilm Device (CBD) and determined minimum biofilm eradication concentration (MBEC)
of each antibiotics and S-pyocins separately and in combination. We expect to observe
similar efficacy and also synergy between S-pyocins and antibiotics. This will indicate the
potential use of S-pyocins as alternative drugs where Pa biofilms show high resistance to
antibiotics.
116
#51 : Systems Biology Evaluation of Hepatocellular Carcinoma Metabolism
Antje Kettelhake1 , Guido Mastrobuoni2 , Chris Bielow2 , Ines Rudolph1 , Olga Vvedenskaya1,2 ,
Matthias Pietzke2 , Stefan Kempa2 , Thorsten Cramer1
1
Charité Campus Virchow-Clinic, Augustenburger Platz 1, 13353 Berlin, Germany
Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine, Robert-Rössle-Straße
10, 13125 Berlin
2
Hepatocellular carcinoma (HCC) is one of the most common tumor types and shows constantly rising incidence in the western world. Therapeutic options are limited since HCCs
are robustly resistant to conventional treatment. Detailed understanding of the molecular
HCC pathogenesis is a fundamental prerequisite to design innovative and effective forms
of therapy. Deregulated cellular energetics represents an emerging hallmark of cancer.
Previous work by us and others pointed towards a functional importance of glycolysis and
the central carbon metabolism (CCM) for HCC malignancy in vitro and in vivo. To further
analyze this, we decided to apply a systems biology approach integrating proteomic and
metabolomic data from a murine HCC model system. Shotgun proteomics analyses revealed a distinct CCM pattern in murine HCC as glycolysis enzyme isoforms with high
glucose affinity were found significantly upregulated. Strikingly, all CCM-regulating proteins downstream of GAPDH were downregulated compared to healthy liver. Results from
metabolomics studies suggest high turn-over rates of early-glycolysis intermediates but
a decelerated TCA cycle. Chemical inhibition of the glycolytic pathway pointed towards
robust anti-proliferative efficacy of this approach. In summary, our data display distinct
metabolic reprogramming of a murine HCC model with translation into an effective experimental therapy. Whether similar results can be obtained in human HCCs is currently
being evaluated by us.
Boston, MA
June 17-19, 2014
117
Poster Abstracts
#52 : Structure-energy-based predictions and network modelling of RASopathy and cancer missense mutations
Christina Kiel1,2 , Luis Serrano1,3,4
1
EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), Dr. Aiguader 88,
08003 Barcelona, Spain
2
Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
3
Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
4
Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, 08010 Barcelona,
Spain
The Ras/MAPK syndromes (‘RASopathies’) are a class of developmental disorders caused
by germline mutations in 15 genes encoding proteins of the Ras/mitogen activated protein
kinase (MAPK) pathway frequently involved in cancer. Little is known about the molecular
mechanisms underlying the differences in mutations of the same protein causing either
cancer or RASopathies. Here we shed light on 956 RASopathy and cancer missense mutations by combining protein network data with mutational analyses based on 3D structures. Using the protein design algorithm FoldX, we predict that most of the missense
mutations with destabilizing energies are in structural regions that control the activation of
proteins, and only a few are predicted to compromise protein folding. We find a trend in
which energy changes are higher for cancer compared to RASopathy mutations. Through
network modelling we show that partly compensatory mutations in RASopathies result in
only minor downstream pathway deregulation compared to cancer. In summary, we suggest that quantitative rather than qualitative network differences determine the phenotypic
outcome of RASopathy compared to cancer mutations.
118
#53 : In silico identification of biomarker-optimized treatment strategies in HER2+ cancer
Daniel Kirouac, Jinyan Du, Johanna Lahdenranta, Ulrik Nielson, Charlotte McDonagh
Merrimack Pharmacetuicals
The use of individually tailored multi-drug combinations has been touted as a solution to
improve the success of anti-cancer drug therapy, but approaches to rationally design such
regimens are lacking. Herein, we combine functional proteomics and semi-mechanistic
network modelling to identify strategies for treating HER2+ cancers. First, to characterize
the molecular and functional diversity of the disease we performed a systematic profiling
study across a panel of 20 HER2+, but otherwise heterogeneous cancer cell lines. In
vitro video microscopy and LuminexTM bead-based quantitative proteomics were used
to monitor cell growth and signaling responses to AKT (MK2206) and MEK (tremitinib)
inhibitor treatment matrices. We find that HER2+ cell lines can be functionally classified
by PI3K/AKT vs. MAPK/ERK pathway dependence, and this dependence predicted by a
combination of protein measurements comprising cell surface receptors and a cell cycle
regulator. PI3K and MAPK pathway suppression was found to increase the expression of
multiple receptor tyrosine kinases, thereby buffering the effect of inhibitor treatments, and
in some cases increasing tumor cell proliferation in subsets of cells.
We next utilized a previously developed a multi-scale model of HER2+ cancer, linking
cell signaling networks to proliferation and survival. Growth inhibitory responses to combinations of drugs targeting the ErbB signaling network were simulated across a diverse
population of synthetic tumors. Co-targeting the HER2-HER3 dimer with a combination of
Herceptin, lapatininb, and MM-111 was more effective on average than any other 3-drug
combination considered. Resistance was largely confined to cells harboring activating
mutations within the PI3K (PIK3CA) or MAPK (KRAS) pathways. Together, our study
identified potential biomarkers, drug combinations and resistance mechanisms in HER2+
cancers, revealing strategies for optimizing the treatment of these patients.
Boston, MA
June 17-19, 2014
119
Poster Abstracts
#54 : Inter-individual effects mask the molecular signature of psoriasis and eczema
Bettina Knapp1 , Maria Quaranta2 , Natalie Garzorz3 , Martina Mattii2 , Venu Pullabhatla4 ,
Davide Pennino3 , Christian Andres3 , Claudia Traidl-Hoffmann3 , Andrea Cavani5 , Fabian J.
Theis1,6 , Johannes Ring3 , Carsten B. Schmidt-Weber2 , Stefanie Eyerich2 , Kilian Eyerich3
1
Institute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany
ZAUM - Center of Allergy and Environment, Technische Universität and Helmholtz Center Munich, DZL,
Munich, Germany
3
Department of Dermatology and Allergy, Technische Universität Munich, Munich, Germany
4
Division of Genetics and Molecular Medicine, King’s College London School of Medicine, Guy’s Hospital,
UK
5
Laboratory of experimental immunology, IDI-IRCCS, Rome, Italy
6
Department of Mathematics, Technische Universität Munich, Garching, Germany
2
Psoriasis and eczema are two prevalent inflammatory diseases where the underlying
molecular mechanisms are not yet fully understood and the primary cause is still under debate. Due to the incomplete picture of the underlying pathogenesis, the specific
therapies given for the two diseases are mainly based on empiric clinical studies rather
than on targeting a specific gene or biological process. Previous attempts to use gene
expression measurements to study psoriasis and eczema were limited by the high interindividual variability based e.g. upon gender and age. We present gene expression data
from patients affected by both psoriasis and eczema simultaneously. Using a principal
component analysis on the data we show that the inter-individual differences mask the
differences between psoriasis and eczema. If we take this into account and normalize
each patient’s gene expression pattern of diseased tissue against healthy tissue, we reveal intra-individual effects. Thereby, we are able to identify the molecular signatures of
both diseases and to provide a comprehensive understanding of the disease pathogenesis on a single gene level but also in a broader context using a gene set enrichment
analysis on the identified genes. The molecular signature of psoriasis and eczema were
furthermore used to define a diagnostic tool that allows to classify the two diseases. We
used an independent cohort of eczema and psoriasis patients and separated them into
a training and a test set. For each patient, real-time PCR data was collected for 15
significantly regulated genes identified in the microarray analysis. Using support vector
machines on the training data we established a classifier which diagnosed all patients of
the test set correctly and also identified initially misdiagnosed or clinically undifferentiated
patients. In future work, we will use the data not only to classify psoriasis and eczema but
also to develop new therapeutic targets in a personalized manner.
120
#55 : Identification of new IkappaBalpha complexes by
an iterative experimental and mathematical modeling approach
Fabian Konrath1 , Johannes Witt2 , Thomas Sauter3 , Dagmar Kulms4
1
Mathematical Modeling of Cellular Processes, Max-Delbrueck-Center for Molecular Medicine Berlin-Buch,
Berlin, Germany
2
Institue for System Dynamics, University Stuttgart, Stuttgart, Germany
3
Life Sciences Research Unit, University of Luxembourg, Luxembourg, Luxembourg
4
Experimental Dermatology, TU Dresden, Dresden, Germany
The transcription factor nuclear factor kappa B (NF-κB) plays a fundamental role in numerous cellular processes including apoptosis as well as inflammatory and pro-proliferative
processes. Therefore, the occurrence of aberrant functionalities in the complex regulatory network of NF-κB can lead to the development of severe diseases. The NF-κB
inhibitor IκBα is a crucial component of this network that sequesters the transcription factor in the cytosol by masking its nuclear localization signal. Consequently, degradation
of NF-κB-bound IκBα causes the activation of NF-κB and thus transcription of its target
genes. By iterative model refinement and model inspired experimentation we analysed
orthovanadate-induced IκBα degradation which occurred without affecting NF-κB activity.
The model’s prediction of NF-κB free IκBα complexes that contain stabilizing IKK subunits led to the design of in vitro experiments revealing the existence of a yet unknown
IκBα:IKKγ complex. Together with the fact that other but canonical processes may affect the cellular IκBα status the uncovered IκBα complex might play a role in preventing
undesired NF-κB activity.
Boston, MA
June 17-19, 2014
121
Poster Abstracts
#56 : Network models of signaling and drug response in
melanoma
Anil Korkut, Weiqing Wang, Evan Molinelli, Arman Aksoy, Emek Demir, Chris Sander
Computational Biology Center, MSKCC, New York, NY
Systematic prediction of cellular response to combinatorial perturbations is a central goal
in biology. To reach this goal, we developed a combined experimental-computational systems biology method, called perturbation biology. Using this strategy, we build network
models of drug response in RAF inhibitor resistant melanoma cells and predict the response of the cells to previously untested drug combinations. First, we systematically
perturb cancer cells with a set of targeted drugs singly and in paired combinations. Next,
we measure (phospho) proteomic and phenotypic (e.g. cell cycle arrest, cellular viability)
response profiles to the perturbations. High throughput (phospho)proteomic data is collected using an antibody-based proteomics platform called ZeptoMARK reverse arrays.
The response profiles serve as the training set to infer network models that quantitatively
link proteomic and phenotypic changes. We infer non-linear differential equation based
models of cellular signaling using statistical physics algorithms. Based on the derived
network models, we can rapidly predict the proteomic and phenotypic response profiles
to untested drug perturbations. Experimental tests of the predictions have led to two hypotheses. (1) Inhibition of polo-like kinase (PLK1) is effective in melanoma cell lines and,
at reduced concentration, may be a useful component of combination therapy in RAFiresistant melanoma. (2) Co-targeting of the c-MYC and ERK pathways has a synergistic
response that may potentially overcome RAFi resistance in melanoma cells.
122
#57 : MYCN and cellular decisions in neuroblastoma
Erika Kuchen1 , Nathalie Harder2 , Tatjana Ryl1 , Chunxuan Shao1 , Andres Florez1 , Emma
Bell3 , Karl Rohr2 , Frank Westermann3 , Thomas Höfer1
1
2
3
Theoretical Systems Biology, German Cancer Research Center, Heidelberg, Germany
Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany
Neuroblastoma Genomics, German Cancer Research Center, Heidelberg, Germany
MYCN amplification, occurring in 20% of cases of the solid child tumour neuroblastoma,
is associated with drug resistant relapse and poor prognosis. MYCN has paradoxical effects in the cell: promoting cell cycle progression and sensitising cells to apoptosis. To
better understand the role of MYCN in cell fate decisions, time-lapse imaging was used
to quantify cell behaviours and attributes in populations grown under a variety of conditions using MYCN-regulatable cell lines. By combining microscopy data and mathematical
modelling we aim to get a mechanistic understanding of the heterogeneity in cell-fate decisions within neuroblastoma populations. Live cell imaging revealed a large heterogeneity
in interdivision times and apoptotic events within both populations of MYCN overexpressing and MYCN knock-down cells. However, MYCN increased the fraction of proliferating
cells and their cell cycle speed, allowing cells to evade quiescence driven by growth factor
depletion. This indicates that MYCN expression level controls the switching between cycling and non-cycling states of the cell cycle. In addition, within-population variability was
compared based on individual cells and their progeny. Within cell lineages sibling cells
demonstrated a high similarity in their fates and attributes. This correlation deteriorated
quickly in inter-generation comparisons. Taken together, our data shows that division dynamics are MYCN dependent, but retain a heterogeneity that is partly inherited. Sibling
similarity suggests that divisions occur overall symmetrically, with daughter cell behaviour
being partly governed by the state of the mother cell. Combining mathematical modelling
and experiments, we are further investigating the nature of this control.
Boston, MA
June 17-19, 2014
123
Poster Abstracts
#58 : TASK-2 channels contribute to pH sensitivity of retrotrapezoid nucleus chemoreceptor neurons
Natasha Kumar1 , Sheng Wang1 , Najate Benamer2,3 , Sebastien Zanella4 , Yingtang Shi1 ,
Michelle Bevengut4 , David Penton2 , Patrice Guyenet1 , Florian Lesage3 , Christian Gestreau4 ,
Barhanin Jacques2 , Douglas Bayliss1
1
2
3
4
Department of Pharmacology, UVA, Charlottesville, VA, USA
Universite de Nice-Sophia Antipolis, Centre National de la Recherche Scientifique (CNRS), Nice, France
Laboratories of Excellence, Ion Channel Science and Therapeutics, France
Aix-Marseille-Universite, CNRS, Marseille, France
Central control of respiration is impaired in breathing disorders such as Congenital Central Hypoventilation Syndrome (CCHS) and central apnea. Phox2b-expressing neurons
of the retrotrapezoid nucleus (RTN) function as central respiratory chemoreceptors; they
are directly activated by CO2/H+, via an unidentified pH-sensitive background K+ channel, to drive breathing. Also, TASK2 (K2P5, an alkaline-activated background K+ channel)
expressing RTN neurons are eliminated in a mouse model for human CCHS. Here, we
test the effect of TASK2 (K2P5, an alkaline-activated background K+ channel) channel
deletion on the pH sensitivity of RTN neurons and on CO2 modulation of central respiratory output. For patch clamp recordings in brainstem slices, individual RTN neurons
were identified by GFP expression (driven by the Phox2b promoter) or β-galactosidase
(from the gene trap used for TASK2 deletion). Whereas 95% of RTN cells from control
mice were pH sensitive, only 56% from TASK2-/- mice were classified as pH sensitive;
the remaining cells were pH insensitive (44%). The alkaline-activated background K+
currents were reduced in amplitude in pH-sensitive RTN neurons from TASK2-/- mice but
were absent from pH-insensitive cells. TASK2 was strongly expressed in 63% of Phox2bexpressing RTN neurons (by X-gal staining), and in 85% (by a more sensitive scPCR
approach). Using an in situ working heart–brainstem preparation, we found diminished
inhibition of phrenic burst amplitude by alkalization in TASK2-/- mice, with apneic threshold shifted to higher pH levels. We conclude that TASK2 channels represent a molecular
substrate for pH sensing in RTN respiratory chemoreceptor neurons.
124
#59 : The Hippo Signaling Pathway Interactome
Young Kwon1 , Arunachalam Vinayagam1 , Xiaoyun Sun2 , Noah Dephoure3 , Steven Gygi3 ,
Pengyu Hong2 , Norbert Perrimon1
1
Department of Genetics and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA
02115
2
Department of Computer Science, Volen Center for Complex Systems, Brandeis University, Waltham, MA
02454
3
Department of Cell Biology, Harvard Medical School, Boston, MA 02115
The Hippo pathway controls metazoan organ growth by regulating cell proliferation and
apoptosis. Many components have been identified, but our knowledge of the composition
and structure of this pathway is still incomplete. Using existing pathway components as
baits, we generated by Mass Spectrometry a high-confidence Drosophila Hippo proteinprotein interaction network (Hippo-PPIN) consisting of 153 proteins and 204 interactions.
Depletion of 67% of the proteins by RNAi regulated the transcriptional co-activator Yorkie
(Yki) either positively or negatively. We selected for further characterization a new member of the alpha-arrestin family, Leash, and show that it promotes degradation of Yki
through the lysosomal pathway. Given the importance of the Hippo pathway in tumor
development, the Hippo-PPIN will contribute to our understanding of this network in both
normal growth and cancer.
Boston, MA
June 17-19, 2014
125
Poster Abstracts
#60 : Functional interpretation of genome-wide association signals in arrhythmia using protein networks of cardiac ion channels
Kasper Lage1,2,3 , Alicia Lundby4 , Elizabeth Rossin1,2,3 , Paul de Bakker1,2,3 , Chris NewtonCheh1,2,3 , Jesper Olsen4
1
2
3
4
Harvard Medical School
Massachusetts General Hospital
The Broad Institute
Novo Nordisk Foundation Center for Protein Research
Genome-wide association studies (GWAS) have identified thousands of loci associated
with complex traits, but it is challenging to pinpoint causal genes in these loci and to
exploit subtle association signals to gain biological insight. We used tissue-specific quantitative interaction proteomics to map a network of five cardiac ion channels involved in
the Mendelian disorder long QT syndrome (LQTS). We integrated the ion channel network
with GWAS loci from the corresponding common complex trait, QT interval variation, to
identify candidate genes that were subsequently confirmed in Xenopus laevis oocytes and
zebrafish. We used the ion channel network to filter weak GWAS signals by identifying
single nucleotide polymorphisms (SNPs) in proximity to genes in the network supported
by strong proteomic evidence. Three SNPs passing this filter reached genome-wide significance after replication genotyping in a follow up cohort of 17,500 individuals. Overall,
we present a general strategy to functionally interpret GWAS loci, to integrate common
and rare genetic variation involved in a particualr phenotype, and to systematically filter
subtle association signals using quantitative protein interaction networks derived from the
relevant tissue.
1. Lundby A, Rossin E, et al., Accepted in Nature Methods
2. Arking D, Pulit S, et al., Accepted in Nature Genetics
126
#61 : Auto-inhibition stabilizes delayed negative feedback
in biochemical systems
Anastasiya Lapytsko, Jörg Schaber
Institute of Experimental Internal Medicine, Otto von Guericke University, Magdeburg, Germany
Negative feedback control is a fundamental and ubiquitous feature in biological systems that can serve several objectives like, e.g., stabilizing the abundance of biochemical
species, inducing oscillations, modifying response times and mediating adaptation. Another feature of biological systems is time delay between a signal and its response, which
appears because of the time needed to transcribe and translate biochemical information
into cellular compounds. In conjunction with time delay negative feedback may lead to
oscillatory behavior that might be inappropriate in cellular systems mediating adaptive responses.
In order to mitigate sustained oscillations in three-dimensional signal-response systems
it was suggested to couple fast and slow negative feedbacks [1-2]. We generalized this
idea, where we introduced explicit time delay between signal and response that allows
replacing intermediate signal transduction processes by their duration. For this we developed generic models of a cellular adaptation response using two-dimensional systems
of delay differential equations and analyzed the dependence of their steady state properties on the kind of employed feedbacks. We showed that stable oscillations arise in
cellular integral feedback systems due to a Hopf bifurcation. We also show that transient
auto-inhibitory feedbacks can stabilize the adaptation response in terms of suppressing
oscillatory behavior [3]. Thus, our general approach allows to study the nature of both
oscillatory as well as adaptive behavior in cellular systems, which is demonstrated with
examples from osmo-adaptation in yeast and p53 and NF-κB oscillations in mammalian
cells. Our theoretical framework can be used to engineer synthetic cellular modules, in
which oscillatory behavior can be fine-tuned.
1. Nguyen, L.K., (2012), J. R. Soc. Interface, 9, 1998.
2. Schaber, J., et al., (2013), J. R. Soc. Interface, 11, 20130971.
3. Lapytsko, A., et al., Submitted.
Boston, MA
June 17-19, 2014
127
Poster Abstracts
#62 : A systems biology model for the coagulation network in non-bleeding state describes baseline activity of
clotting
Dooyoung Lee1 , Satyaprakash Nayak2 , Steven Martin2 , Anne Heatherington1 , Paolo Vicini3 ,
Fei Hua1
1
2
3
BioTherapeutic Clinical RD, Pfizer Inc, Cambridge, MA, USA
Pharmacometrics, Global Clinical Pharmacology, Pfizer Inc, Cambridge, MA, USA
Pharmacokinetics, Dynamics and Metabolism , Pfizer Inc, San Diego, CA, USA
Coagulation is a crucial process to cease bleeding in a damaged vessel and depends
on the interplay of various clotting factors. The clotting process becomes fully activated
by exposing tissue factor (TF) in response to a vascular injury. Congenital deficiency
of factors VIII or IX leads to hemophilia, a bleeding disorder in which patients bleed for
prolonged periods after an injury. It is known that low level clotting activity exists in the
normal, non-bleeding state (i.e., “baseline” conditions) as evidenced by detectable levels
of prothrombin fragment 1+2 (F1+2), D-dimer and thrombin-antithrombin III complex (TAT)
in healthy volunteers. However, previous coagulation models in a bleeding state have not
accounted for the baseline conditions of the clotting system. The goal of this work is
to understand how the baseline clotting activity impacts the coagulation process during
bleeding. A non-bleeding baseline model of the human coagulation network in vivo
128
#63 : Molecular ties between the cell cycle and differentiation in embryonic stem cells
Victor Li, Marc Kirschner
Harvard Medical School, Dept. of Systems Biology
Attainment of the differentiated state during the final stages of somatic cell differentiation is closely tied to cell cycle progression. Much less is known about the role of the
cell cycle at very early stages of embryonic development. Here we show that molecular
pathways involving the cell cycle can be engineered to strongly affect embryonic stem cell
differentiation at early stages in vitro. Strategies based on perturbing these pathways can
shorten the rate and simplify the lineage path of ES differentiation. These results make
it likely that pathways involving cell proliferation intersect at various points with pathways
that regulate cell lineages in embryos and demonstrate that this knowledge can be used
profitably to guide the path and effectiveness of cell differentiation of pluripotent cells.
Boston, MA
June 17-19, 2014
129
Poster Abstracts
#64 : In Vivo Volume and Hemoglobin Dynamics of Red
Blood Cells
Roy Malka1,2 , Francisco Feijó Delgado3 , Scott R. Manalis3,4 , John M. Higgins1,2
1
2
3
4
Center for Systems Biology and Department of Pathology, Massachusetts General Hospital
Department of Systems Biology, Harvard Medical School
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology
Department of Biological Engineering, Massachusetts Institute of Technology
Human red blood cells (RBCs) lose ∼30% of their volume and ∼20% of their hemoglobin
(Hb) content during their ∼100-day lifespan in the bloodstream. We combine theory with
single-cell characteristics to investigate the impact of vesiculation on the reduction in volume and Hb. We show that vesicle shedding alone is sufficient to explain membrane
losses but not volume or Hb losses. We use dry mass measurements of human RBCs to
validate the models and to propose that additional unknown mechanisms control volume
and Hb reduction and are responsible for ∼90% of the observed reduction.
130
#65 : Barcoding thousands of single cells in a single tube
by droplet microfluidics
Allon Klein1,2 , Linas Mazutis2,3 , Ilke Akartuna2,3 , David Weitz3 , Marc Kirschner1
1
2
3
Department of Systems Biology, Harvard Medical School, Boston, MA
equal contribution
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
Among the central goals of single cell analysis are the identification of rare cell states,
as well as cell sub-populations and their population hierarchy. For this goal, a system
must be able to profile a representative sample of the target cell population, preferably
consisting of hundreds or thousands of cells. There is a difficult trade-off between analyzing many cells and many individual mRNA molecules per cell. A few methods address
the accessible “middle ground” of assaying up to 100-1000 cells, but this scale is not sufficient for many of the goals of single cell analysis. Here, we describe our progress in
developing a technique for profiling transcriptome of at least 1,000 individual cells per single run. For this purpose, we have developed droplet microfluidic approach that delivers
more than 100.000 unique DNA barcodes to individual cells in a single tube. We are using
barcoded DNA primers coated on a bead that are co-encapsulated with single-cells. The
advantage is that beads essentially can be used as independent reagent facilitating their
handling and delivery to single-cells. Sample encapsulation, collection and processing
is rapid and takes less than one hour for thousands of cells. After barcoding, the cDNA
material from all cells is combined for bulk processing. The sensitivity and scale of the
method is primarily limited by the depth of sequencing. Our presentation reviews technical innovations, assays of accuracy and sensitivity and our primary results of barcoding
mixture of cells we have achieved to date.
Boston, MA
June 17-19, 2014
131
Poster Abstracts
#66 : Identification of deregulated signaling pathways in
Multiple Sclerosis based on gene expression data
Ioannis N. Melas, Francesco Iorio, Julio Saez-Rodriguez
European Molecular Biology Lab, European Bioinformatics Institute EMBL-EBI, Cambridge, UK.
The construction of extensive gene expression datasets measuring cell response to perturbations is common practice these past years, in an attempt to characterize the cell’s
signaling mechanisms, identify pathways that are deregulated in disease, and suggest
novel drug targets. Thus, there is active research to develop methodologies to leverage
these data. More specifically, one problem of interest is the use of gene expression data
to identify deregulated pathways in a specific cell type. As gene expression is regulated
by the activation patterns of transcription factors, and this is turn is regulated by signaling
pathways, one could take advantage of changes in the gene expression level caused by
a perturbations such as a drug or a disease, and backtrack all the way into the signaling level, pinpointing the proteins deregulated by that perturbation causing the observed
changes in gene expression.
In this work, we have developed an approach based on an Integer Linear Programming
formulation for the identification of signaling networks that best fit a measured gene expression signature. In more detail, assuming we are given (i) a Prior Knowledge Network (PKN) which represents the protein connectivity according to literature, (ii) prior
knowledge of transcription regulation, and (iii) a gene expression dataset capturing how
a cell/tissue type of interest responds on the gene expression level; we identify subsets
of the PKN and the regulon that most probably yielded the measured gene signatures.
As a case study we have computed signaling models for PBMCs and identified pathways
that are strongly deregulated in Multiple Sclerosis. Our findings include major players of
MS as well as underreported proteins that could play a role in the disease and constitute
potential drug targets.
132
#67 : The AXL Receptor is a Sensor of Ligand Spatial Heterogeneity
Aaron Meyer1,2 , Ceridwen Riley1 , Frank Gertler2,3 , Douglas Lauffenburger1,2,3
1
2
3
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge MA
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge MA
Department of Biology, Massachusetts Institute of Technology, Cambridge MA
The AXL receptor is a TAM (Tyro3, AXL, MerTK) receptor tyrosine kinase (RTK) important in blood clotting, viral infection, innate immune response and cell clearance, deregulated in many human carcinomas. While the immediate cognate ligand-receptor complex
(Gas6-AXL) structure is known, studies examining ligand-mediated signaling often provide paradoxical results. Therefore, a detailed, mechanistic picture of AXL activation, and
thus quantitative understanding of the nature and contexts of ligand-mediated signaling,
has not emerged. Employing quantitative biochemistry and deterministic modeling we
show that AXL operates to sense local spatial heterogeneity in ligand concentration, due
to an unusual dichotomy between ligand-dependent and ligand-independent signaling.
We experimentally validate diverse model predictions concerning this behavior. The results demonstrate that AXL functions distinctly from other RTK families, and this surprising
insight will be vital for the design of AXL-targeted therapeutic intervention.
Boston, MA
June 17-19, 2014
133
Poster Abstracts
#68 : Reversible Bile Transport : A Cholesterol trader in
Human Body
Shekhar Mishra, Pramod R. Somvanshi, K.V. Venkatesh
Department of Chemical Engineering, Indian Institute of Technology Bombay
Several factors spanning from genetic to lifestyle components are responsible for defective cholesterol homeostasis leading to hypercholesterolemia. A system level analysis
of the cholesterol regulatory network insights into the control mechanisms that regulate
cholesterol homeostasis. A multiscale mechanistic model forms an essential basis for
a control systems analysis of cholesterol processing in the human body. We have developed an integrated kinetic model to quantify the whole body cholesterol homeostasis.
Using this model, we studied the effect of rate perturbations in several regulatory modules that are responsible for cholesterol homeostasis. One of the modules which exerts
a significant control over cholesterol levels is the hepato-intestine bile transport system.
Physiologically, bile plays a dual role, as it is responsible for consuming cholesterol for
its synthesis and acts as a carrier for cholesterol absorption and transport between the
organs. Thereby it controls the import and export processes of cholesterol in the human
body. This, implies the necessity of a fine balance of bile salt transport in cholesterol
homeostasis. It was evident from our analysis of the perturbation of hepato-intestine reversible bile transport that an increase or decrease in the rate of bile salt transport from
intestine to liver or its reverse respectively, decreased total plasma cholesterol.To assess
the effectiveness of these transport rates, we performed simulations in a case of induced
Familial Hypercholesterolemia, which is caused by mutations in the LDLR gene. A 2fold change in the bile salt transport restored the total plasma cholesterol level from 6.5
mmol/L to 5.5 mmol/L. This suggests that the bile transport control strategy can be effectively used to enhance the cholesterol excretion flux thereby lowering the excess cholesterol levels. Thus, the bile transport module presents an alternative therapeutic method
to control hypercholesterolemia.
134
#69 : Dynamic variability of NF-kappaB signal transduction: A mechanistic model
Janina Mothes, Dorothea Busse, Bente Kofahl, Jana Wolf
Max Delbrück Center, Berlin, Germany
NF-κB (p65/p50) is a central transcription factor in mammalian cells and drives the expression of a variety of different genes. Aberrant regulation of NF-κB is associated with
chronic inflammation, asthma, neurodegenerative diseases and cancer. Therefore, NFκB is tightly regulated to ensure a correct activation of different gene expression profiles.
In the absence of extracellular signals, NF-κB is inactive in the cytoplasm where it is
sequestered by inhibitory κB (IκB) proteins including IκBα, IκBβ and IκB. Upon stimulation, e.g. TNFα, IκB kinases (IKKs) are activated inducing the phosphorylation and
subsequent degradation of IκB proteins. Unbound NF-κB translocates into the nucleus
and regulates target gene expression including genes encoding for IκB isoforms or the
zinc finger protein A20. Newly synthesised IκB binds nuclear NF-κB leading to an export
of the NF-κB-IκB-complex to the cytoplasm. A20 also inhibits NF-κB signalling by inhibiting pathway components upstream of IKK. This leads to an additional negative feedback.
Although the dynamics of NF-κB activation is a subject of intensive research, it still remains an open question how NF-κB encodes gene regulatory information. In this context
it is of special interest if the variability of the NF-κB response observed in single cells may
encode different cellular outcomes. By applying mathematical modelling and bifurcation
analyses, we show that NF-κB is capable of showing a great range of different dynamics,
from steady states to sustained oscillations. We identified the total NF-κB concentration
and the transcriptional efficiency of NF-κB on IκBα as two critical parameters that determine the NF-κB dynamics. The variation of the total NF-κB concentration might be caused
by cell to cell variability. The regulation of the transcriptional effect of NF-κB on IκBα, e.g.
by co-factors, might be an active way of the cell to regulate the NF-κB dynamics.
Boston, MA
June 17-19, 2014
135
Poster Abstracts
#70 : A systems biology model of the coagulation network in bleeding state reveals differences in behaviors of
biomarkers in response to perturbations.
Satyaprakash Nayak1 , Dooyoung Lee2 , Sunita Patel-Hett3 , Debra D. Pittman3 , Paolo Vicini4 ,
Anne Heatherington2 , Steve Martin1 , Fei Hua2
1
2
3
4
Pharmacometrics, Global Clinical Pharmacology, Pfizer, Cambridge MA
Biotherapeutics, Clinical Research, Pfizer, Cambridge MA
Biotherapeutics RD, Pfizer, Cambridge MA
Pharmacokinetics, Dynamics and Metabolism - New Biological Entity, Pfizer, LaJolla CA
Blood coagulation is an important process responsible for maintaining hemostasis. The
blood coagulation system is a complex and tightly regulated network of pro- and anticoagulation mechanisms which work together to initiate rapid generation of the main coagulation protein, thrombin, in response to a vascular injury or trauma and its subsequent
inhibition after the blood loss has been stopped. In this work, we present a computational
model of coagulation formed by combining three different models from the literature which
was then numerically fit to match in-house ex vivo thrombin generation data. The model
was optimized to fit 26 different Thrombin Generation Assay (TGA) experiments and activated Partial Thromboplastin Time (aPTT) in normal or hemophilic plasma simultaneously
using a global optimization method. Our simulations show that biomarkers such as lag
time, peak thrombin and area under the curve (AUC or Endogenous Thrombin Potential)
of the TGA profile and aPTT show varying degree of sensitivity to changes in coagulation
factors. For example, increasing the levels of the potent anti-coagulant anti-Thrombin III
(ATIII) in the simulations changes the peak value and AUC for thrombin with little effect
on the lag time. Similarly, varying the levels of active protein C (APC) had a pronounced
effect on changing the TGA profile in hemophilia A plasma, but it didn’t lead to a change
in aPTT. The response of endpoints depended on whether the level of an activated factor or its respective zymogen form was varied and the type of plasma, e.g., factor VIII or
IX deficient plasma (simulating hemophilia A or B) that was utilized. In conclusion, our
systems model predicted differences in sensitivity of biomarkers in response to various
perturbations of the coagulation network. These results can serve as a useful guide in
selecting the sensitive endpoints to measure in a clinical study and provide mechanistic
understanding of the relative changes in biomarkers.
136
#71 : Novel multiplex analysis of protein complexes in signaling networks reveals signatures that distinguish T-cell
responses to antigen
Steven C. Neier1 , Stephen E.P. Smith1 , Jason P. Sinnwell2 , Zhenjun Chen3 , Tessa R.
Davis1 , Wendy K. Nevala4 , Elizabeth Ann L. Enninga4 , Scott R. Burrows5 , James McCluskey3 ,
Svetomir N. Markovic4 , Jeanette E. Eckel-Passow2 , Claudia Neuhauser6 , Adam G. Schrum1
1
Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN
Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic,
Rochester, MN
3
Department of Microbiology and Immunology, Institute for Infection and Immunity, University of Melbourne,
Australia
4
Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, MN
5
QIMR Berghofer Medical Research Institute and QIMR Berghofer Centre for Immunotherapy and Vaccine
Development, Australia
6
University of Minnesota Informatics Institute, Minneapolis MN
2
As central mediators of signal transduction, protein-protein interactions (PPI) are thought
to coordinate cellular responses through the formation of extensive networks. We hypothesized that measuring PPI network activity in human T cells would reveal immune
activation versus tolerogenic signaling profiles. We have designed and mounted a new
technological application, multiplex immunoprecipitation detected by flow cytometry (MIF),
to empirically measure a significant subset of the signaling PPI network in T-cells. The approach captures protein complexes onto microspheres through immunoprecipitation, and
uses fluorescent probes to detect co-associated proteins. A panel of distinct microsphere
bead types (Luminex xMAPTM ) allows for capturing many distinct complexes simultaneously. Currently, MIF is capable of 253 unique pair-wise measurements between 22 different proteins that participate in T cell antigen receptor signaling. We have applied MIF
to distinguish agonist (pro-immune) versus antagonist (pro-tolerance) activation of the Tcell antigen receptor. To analyze this innovative dataset, we developed new statistical
approaches that utilize the full fluorescence distribution and correct for technical variability and multiple hypothesis testing. We compared our approaches to existing ones. Our
results favor the use of a novel, empirically derived alpha cut-off correction, and we propose two optimized analytical approaches for MIF data. Furthermore, composite network
signatures have been identified by principle component analysis, while the application of
other dimensionality reduction and clustering methods continue to be explored. In summary, MIF is providing new insight into the network patterns of shared protein complexes
as they translate distinct signals into T-cell function.
Boston, MA
June 17-19, 2014
137
Poster Abstracts
#72 : A Targeted Therapy – So, what is it targeting? A
Case Study of the On-Target Effect on Waldenstrom’s and
the Off-Target Effect on Platelet Count and What One May
Tell Us About the Other
John Paasch
Winsilico Chelmsford MA
Deriving science from a single person case study is a challenging and some may say
questionable task. While the data used for the analysis presented here is from a single
person’s response to a pill that is a covalent inhibitor of Bruton’s tyrosine kinase (Btk),
the person was a part of a very early and highly successful Clinical Trial (CT) of this new
and novel drug. Waldenstrom’s macroglobulinemia (WM) is a rare form of non-Hodgkins
Lymphoma. It is diagnosed by the presence of lymphoplasmacytic cells in the bone marrow and a monoclonal IgM found in the blood. The WM B-cells demonstrate somatic
hypermutation of their antibody but do not execute Class Switch Recombination hence
the excessive production of IgM. Unique to this set of data, apart from the sparse, nontime coherent samples is the degree of the dynamic response to the initial on-off protocol.
This lends itself to analysis and hypothesis generation. While the on-target goal of the
drug was to control the person’s WM, there was an off-target effect to the platelet count
(PC). Both parameters went down when the drug was taken but rose when the drug was
briefly stopped. This happened through two cycles of that protocol. The person was then
switched to a continuous, lower level dose for the remainder of the CT. The focus of this
treatment was addressing the intended disease. The side effects, not always of severe
level, are not often analyzed and if so the results are not presented unless they are critical
or life threatening. I will report the analysis followed to take this data and extrapolate from
related lab work reported by others to create a hypothesis of what have been happening,
PBPK, to create the undesired off-target, PD, effect to the PC. The intent of this effort is to
show that it may be possible to further the understanding of the intended mechanism of
action of this drug by using the results seen in other cell types within the blood cell group
and identify areas that could be explored further.
138
#73 : Mathematical modelling of the regulation and degradation of the transcription factor EB
Marin Zapata Paula A.1 , Jünge Anja1 , Brady Nathan R.2 , Hamacher-Brady Anne1
1
2
Lysosomal Systems Biology, German Cancer Research Center (DKFZ) and Bioquant
Systems Biology of Cell Death Mechanisms, German Cancer Research Center (DKFZ) and Bioquant
Autophagy is a catabolic process in which cytosolic components are engulfed by doublemembrane vesicles, and subsequently degraded after fusion with lysosomes. At the transcriptional level autophagy is largely regulated by the recently characterized transcription
factor EB (TFEB), which translocates to nucleus in response to metabolic and lysosomal stresses. In this work we combine computational and experimental approaches to
study the regulation of TFEB, focusing on spatial and temporal degradation events. By
integrating literature knowledge with sequence similarity from related transcription factors,
we developed different competing ODE-models for the TFEB regulatory network, which
satisfy reported findings. These models were compared against each other and against
our time course Western blot data of endogenous TFEB. Obtained results allowed us to
discard some of the models, since they were unable to reproduce the experimental data.
Importantly, some of the remaining models implemented novel regulatory links based on
sequence similarity, and thus suggest a narrowed experimental validation strategy. Taken
together this work exemplifies how mathematical modeling can drive experimental hypothesis testing, and reduce focus to a set of possible regulatory models.
Boston, MA
June 17-19, 2014
139
Poster Abstracts
#74 : Metabolomic characterization and analysis of liverspecific functions of an in vitro fibrosis model
Christian Priesnitz1 , Roman Liebe2 , Yeda Kaminski1 , Frank Lammert3 , Fozia Noor1
1
Biochemical Engineering Institute, Saarland University, Saarbruecken, Germany
Molecular hepatology and alcohol associated diseases, Medical faculty, Heidelberg University, Mannheim,
Germany
3
University Hospital of Saarland University, Homburg, Germany
2
Fibrosis is characterized by an excessive formation and deposition of fibrotic connective tissue and occurs as a result of inflammation or tissue damage in the liver. Factors
leading to the development of fibrosis are among others TGF-ßwhich is secreted by various cell types in the liver upon tissue damage. TGF-ßis known to be a major pro-fibrotic
cytokine that induces epithelial-mesenchymal transition (EMT) of hepatocytes in vitro.
Furthermore, it plays critical roles in the onset and progression of liver fibrosis in vivo.
Most of the research in this field is still conducted using animal models. We compared
two mouse strains, namely C57/BL6 and DBA/2J which are commonly used in the field
of fibrosis research. We applied a metabolomics approach since the metabolome represents the integrated phenotype of regulation on the genome, transcriptome and proteome
level. We analyzed hepatocytes of both strains regarding their central carbon metabolism
and liver-specific functions, such as urea and albumin production and CYP450 activity,
under control conditions as well as in the presence of TGF-ß. The analysis of the viable cell numbers and LDH release over time as well as the liver specific functions show
clear strain-dependent differences in the susceptibility of hepatocytes in vitro to TGF-ß.
We observed a significant decrease in the viable cell number within 5 days in culture
and an earlier onset of dedifferentiation in DBA/2J hepatocytes as compared to C57/BL6
hepatocytes. However, we did not detect any TGF-ßspecific signature in the central carbon metabolism. This shows that the central carbon metabolism in contrast to most liver
specific functions is robust against TGF-ßexposure. In combination with proteome and
transcriptome data, these results provide a valuable basis for strain selection for studies
focusing on the systematic in-depth understanding of fibrosis and fibrosis related diseases as well as research focusing on detection of early biomarkers in the fibrosis related
disease
140
#75 : Network topological characteristics aid in identifying causal genes for atherosclerosis
Holly Arnold1,2 , Stephen Ramsey1,2
1
2
Department of Biomedical Sciences, Oregon State University, Corvallis, OR
School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR
A central problem in bioinformatics is leveraging molecular profiling data, in conjunction
with prior knowledge (e.g., the global protein interaction network) to predict key molecules
that initiate or regulate pathogenesis. Understanding the molecular basis of pathogenesis
of atherosclerosis is both vital for uncovering new therapies and an appealing test-case
for investigating how lists of molecules with known roles in the disease (e.g., from mouse
gene-targeting studies and from rare disease-causing mutations) can be used to guide
a computational strategy for predicting disease genes. We hypothesized that the location and connectedness of a protein within the global protein interaction network (i.e.,
the local and global network topology) can be used, in conjunction with molecular profiling data from plaque, to predict genes that are involved in atherosclerosis in the context
of a machine-learning algorithm. To test this hypothesis we identified 222 genes with
probable causal roles in atherosclerosis, from the primary research literature, the NCBI
OMIM database, and the JAX MGI database, as a training set of molecules (“positive
cases”). As features for the machine learning, we used eight topological characteristics
extracted from the protein interaction network as well as data from transcriptome profiling
studies of plaque. Using the Random Forest machine-learning algorithm and a ten-fold
cross-validation strategy, we investigated the prediction performance (area under the full
sensitivity-vs.-FPR curve) of the algorithm for different subsets of features and the relative
benefit of each feature for prediction performance. We found that as a predictive feature,
shortest-paths proximity to a positive case (a known disease gene) is highly beneficial
for accurately predicting disease genes and that it strongly outperforms all the other features. Our results indicate the importance and the potential of network-based approaches
for prioritizing candidate disease genes.
Boston, MA
June 17-19, 2014
141
Poster Abstracts
#76 : The Effect of Higher-Order Receptor Clusters on
TRAIL Induced Apoptotic Signaling
Raue Andreas1,2 , Chai Diana1 , Hudson Hannah1 , Tam Eric1 , Schoeberl Birgit1
1
2
Merrimack Pharmaceuticals, Cambridge, MA, USA
University of Freiburg, Freiburg, Germany
The trimeric TNF-related apoptosis-inducing ligand (TRAIL) is an endogenous ligand that
binds to trimeric death receptors (DR4/5). TRAIL is known to induce apoptosis mainly in
malignant cells while normal cells remain unaffected. This makes TRAIL a most interesting target for cancer therapy.
We investigate the effects of ligand valency on the degree of Caspase-8 activation and
apoptosis induction. As a model system we use the semi-sensitive cell line DU145 and
multivalent anti-DR5 fibronectin domains that can potentially form higher-order receptor
clusters. In order to disentangle the effects of receptor clusters of different size, a dynamic
model of ligand-receptor binding and Caspase-8 activation is coupled to a model of cell
growth and cell death. The dynamic model is calibrated using quantitative experimental
data of ligand on cell binding, Caspase-8 activity and cell viability.
Our results show that higher-order receptor clusters do amplify apoptotic signaling. The
model predicts that receptor clusters of size three and six are more abundant than others,
indicating the special role of preformed receptor trimers. However, Caspase-8 activation
only increases after receptor clusters of size larger than three. On the cell population
level, the model can quantitatively predict the induction of apoptosis in this cell line. This
work is an important step towards an integrative model of TRAIL induced apoptosis that
finally aims at the understanding of heterogeneity both on a single cell level across cell
lines.
142
#77 : Organ-specific stochastic phenotype switching is
required for endothelial health
Lei Yuan1,2 , Gary C. Chan1,2 , David Beeler1,2 , Lauren Janes1,2 , Katherine C. Spokes1,2 ,
Anahita Mojiri3 , William J. Adams4 , Tracey Sciuto4 , Guillermo Garcia-Cardeña5 , Grietje
Molema6 , Nadia Jahroudi3 , Philip A. Marsden7 , Ann Dvorak4 , Erzsébet Ravasz Regan1,2 ,
William C. Aird1,2
1
Center for Vascular Biology Research, Beth Israel Deaconess Medical Center
Harvard Medical School, Boston MA
3
Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
4
Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA
5
Center for Excellence in Vascular Biology, Department of Pathology, Brigham and Women’s Hospital,
Boston, MA
6
Department of Pathology and Medical Biology, Medical Biology Section, University Medical Center Groningen
7
Department of Medical Biophysics, University of Toronto, Ontario, Canada
2
Among unicellular organisms, stochastic phenotype switching is a documented strategy
for survival. These populations hedge their bets: while the majority of their cells are
adapted to their present environment, a minority remains poised to thrive under drastically
different conditions. Bet hedging has also been described in metazoan cells, primarily in
vitro. However, its role in tissue homeostasis has yet to be established. Here we show
that von Willebrand factor (vWF) is expressed in a spatially heterogeneous manner in a
small fraction of capillary endothelial cells in the heart, skeletal muscle, lung and brain. To
test whether this fraction is dynamically maintained, we generated transgenic mice that
allow for direct comparison between snapshots of vWF mRNA expression (vWF+/LacZ ) and
its cumulative expression over time (vWF+/Cre crossed with a LacZ reporter line, leading
to permanent LacZ expression in transiently vWF+ cells). We have found that vWF mosaic patterns are dynamic, in that vWF expression stochastically toggles ON/OFF over
time. By contrast, expression in the aorta and liver is static in time. Mosaic vWF heterogeneity is also present in cultured primary endothelial cells from multiple vascular beds,
and it is stochastically reconstituted in clonally expanded populations. We show that the
ON/OFF bimodality of vWF expression is due to an epigenetic switch, the ON/OFF states
of which require low/high DNA methylation of the vWF promoter. Stochastic transitions
between active/silenced vWF transcription were accompanied by dynamically changing
DNA methylation, resulting in its heterogeneity among clonal cells. Finally, vWF-/- mice
demonstrated extensive endothelial cell damage in capillaries of the heart, but not kidney or aorta. Taken together, these findings suggest that dynamic mosaism of vWF expression is functionally relevant and represents represent a novel, adaptive homeostatic
Boston, MA
June 17-19, 2014
143
Poster Abstracts
mechanism tailored to specific organs.
144
#78 : Treating monogenic human diseases with evolving
computationally designed mutagenic triplex-forming and
recombinagenic donor DNA molecules
Faisal Reza, Peter M. Glazer
Yale University, School of Medicine, New Haven, Connecticut 06520, U.S.A.
Endogenous human genome targeting and editing in an efficient and specific manner
are technological challenges, particularly in development and translational settings, with
significant foreseeable impacts on healthcare. The use of synthetic molecules to confer
safe and effective therapeutic changes to the human genome is sought in translational
medicine. Targeting and editing one or more genes in an efficient and specific manner
remains a challenge in addressing monogenic human diseases.
To address these challenges, we have developed and applied designed triplex-forming
molecules, composed of synthetic oligo- or peptide- nucleic acids, to target and elevate the DNA repair and recombination machinery in human progenitor cells by forming
triplex structures within duplex chromosomal and episomal DNA. Once elevated, we have
developed and applied designed recombinagenic donor DNA molecules as homologydependent repair templates to co-opt this DNA repair and recombination machinery to
introduce edits into the genome.
Safety and efficacy is achieved by leveraging the performance profile of the cell’s own endogenous DNA repair machinery in concert with these sequence-specific and localizingin-tandem molecules. Progenitor cells drugged with these designed molecules for triplexformation and for recombination, and primed with chemical cell modulators, safely and
effectively target and edit the genome, which are then propagated to cellular progeny, for
physiological gene correction, induction, and regulation.
This triplex-forming and recombinagenic molecular technology is developed to remediate the underlying genetic causes of human diseases, such as hemoglobinopathies, and
has a well-positioned technology profile and potential for human systems and synthetic
biology as well as translational medicine.
Boston, MA
June 17-19, 2014
145
Poster Abstracts
#79 : Topological Measures of Protein-Protein Interactions
And Implications For Cancer Therapy
Sebastien Benzekry1 , Edward Rietman2 , Jack Tuszynski3 , Giannoula Klement2
1
2
3
Institut de Mathematiques de Bordeaus, Bordeaux, France
Newman-Lakka Institute, Tufts University School of Medicine, Boston, MA 02111
Department of Oncology, University of Alberta, Edmonton, Alberta, Canada T6G 1Z2
A correlation between the complexity of cancer protein-protein interaction networks and
5-year survival of cancer patients has been shown (Breitkreutz et al., 2012; Takemoto et
al., 2013). Here we extend that work to include two related topological measures. We
first show a linear correlation of a topological measure with 5-yr survival and then we
show that inhibition of specific proteins in the network, as suggested interpolation on the
survival curve, may actually lead to improved survival. This new approach may prove
applicable to selecting drug targets for cancer therapy.
1. Breitkreutz D, Hlatky L, Rietman E, Tuszynski JA. Molecular signaling network complexity is correlated with cancer patient survivability. Proc Nat Acad U S A. 2012 Jun
5;109(23):9209-12.
2. Takemoto K, Kihara K. Modular organization of cancer signaling networks is associated with patient survivability. Biosystems 2013 113(3):149-154.
146
#80 : Identification of novel CD2AP SH3 interaction partners by combining crystallography, bioinformatic tools and
peptide arrays
Jenny Rouka1 , Philip C. Simister1 , Melanie Janning1,2 , Joerg Kumbrink3 , Tassos Konstantinou1 ,
Tobias Kroger4 , Joao R.C. Muniz4 , Moin Saleem5 , Stefan Knapp4 , Frank Von Delft4 , Nicola
O’Reilly6 , Stephen Taylor7 , Rudolf Volkmer8 , Kathrin Kirsch3 , Stephan M. Feller1
1
2
3
4
5
6
7
8
Biological Systems Architecture Group, Department of Oncology, WIMM, University of Oxford, Oxford, UK
UKE Hamburg, Department of Oncology/Hematology, Hamburg, Germany
Boston University Medical College, Department of Biochemistry, Boston, MA, USA
Structural Genomics Consortium, Oxford, UK
Bristol Heart Institute, University of Bristol, Bristol, UK
Cancer Research UK Peptide Synthesis Group, London Research Institute, London, UK
Computational Biology Research Group, University of Oxford, Oxford, UK
Charité, Institute of Medical Immunology, Berlin, Germany
CD2AP is a member of the CD2AP/CIN85 family adaptors and involved in several cellular processes, such as kidney podocyte development and actin-mediated membrane
trafficking. It may also be involved in tumorogenesis. It contains three SH3 domains
whose binding properties and interaction partners remain largely unexplored.
The CD2AP SH3 interaction with the novel partner Rab5-activating GEF RIN3 was studied extensively by isothermal titration calorimetry (ITC), peptide scanning arrays, mutagenesis studies and X-ray crystallography. Mapping of the interaction regions showed that
human RIN3 contains two binding sites for the CD2AP SH3 domains. From these studies,
the CD2AP SH3 preferred binding motif P-x-P/A-x-x-R emerged. Two crystal structures
(1.6 Åand 1.2 Å) of the SH3-1 and SH3-2 domains in complex with RIN3 epitope I and
II respectively revealed that these residues serve as anchoring points. With the aid of
bioinformatics tools, this motif was used to conduct a peptide array based screen for additional signalling partner candidates. One of the hits was the Arf-GAP ARAP1. ITC data
indicate that the three SH3 domains differentially recognise three ARAP1 epitopes, with
the first ARAP1 epitope binding to SH3-2 in the nanomolar range. A crystal structure (1.6
Å) of the SH3-2 domain in complex with the first ARAP1 epitope uncovered two additional
anchoring residues that extend beyond the PPII helical region of the binding epitope.
The CD2AP - ARAP1 interaction was confirmed in podocytes and cancer cells at the endogenous protein level. Even though RIN3 and ARAP1 are also involved in membrane
trafficking, a direct link to CD2AP had not been reported before. Further candidates from
the peptide array analyses were also investigated by ITC.
Boston, MA
June 17-19, 2014
147
Poster Abstracts
In conclusion, this study led to the elucidation of the CD2AP SH3 domain binding signatures and the identification of putative novel binding partners. The intriguing CD2AP ARAP1 interaction warrants further investigations.
148
#81 : Characterizing heterogeneity in leukemic cells using single cell gene expression analysis
Assieh Saadatpour1,2 , Guoji Guo1,3 , Stuart Orkin1,4,5 , Guo-Cheng Yuan1,2
1
2
3
4
5
Dana-Farber Cancer Institute
Harvard School of Public Health
Boston Children’s Hospital
Boston Children’s Hospital
Howard Hughes Medical Institute
Characterization of cancer heterogeneity is of immense importance with significant clinical
implications. Recent advances in single-cell gene expression analysis have paved the way
to identify rare cell types and to dissect potential intra-cancer cellular hierarchy.Here, we
apply two complementary approaches to characterize the heterogeneity within leukemic
cells at the single-cell resolution using the MLL-AF9 driven mouse model of acute myeloid
leukemia (AML). We started with fluorescence-activated cell sorting (FACS) analysis with
seven surface markers, and extended our study by using a multiplexing quantitative polymerase chain reaction (qPCR) approach to assay the transcriptional profile of a panel of
175 carefully selected genes in leukemic cells at single cell level. We employed an integrative set of computational tools to identify the cellular hierarchy within leukemic cells and
to investigate transcriptional network changes. Our analysis showed striking heterogeneity among the leukemic cells. Mapping to the hematopoietic cellular hierarchy identified
two distinct subtypes of leukemic cells; one similar to granulocyte/monocyte progenitor
(GMP) phenotype and the other to macrophage and dendritic cells (DCs).Network analysis further revealed similarities as well as organizational differences between the GMP
and leukemia networks. Overall, the combined analysis presented in this study pinpoints
previously unrecognized heterogeneity within leukemic cells and provides insights into the
molecular signatures of AML.
Boston, MA
June 17-19, 2014
149
Poster Abstracts
#82 : Characterization of the protein signatures of permissive vs. non-permissive influenza A virus infections
in human host cells by quantitative proteomic analysis
Anne Sadewasser1 , Katharina Paki1 , Katrin Eichelbaum2 , Matthias Selbach2 , Thorsten
Wolff1
1
2
Div. of Influenza viruses and other Respiratory Viruses, Robert Koch-Institute, Seestr. 10, 13353 Berlin
Max-Delbrück-Center for Molecular Medicine, Robert-Roessle-Str. 10, 13125 Berlin
Influenza virus infections are the major cause for respiratory disease in humans, which
affect all age groups and result in extensive global mortality and morbidity, as well as
substantial economic costs. Human and most avian influenza A virus (IAV) strains differ
largely in their replication efficiency in and activation of human cells despite successful
cell entry. We hypothesize that the distinct outcome of an infection with a given virus strain
is determined by the differential interplay between specific host and viral factors, which
remain to be defined in their entity.
By mass spectrometry-based quantitative proteomics we aim to characterize the sets
of cellular and viral factors whose abundance is specifically up- or down-regulated in permissive and non-permissive IAV infection, respectively. Our analysis involves a “Spike-in
SILAC (stable isotopic labeling by amino acids in cell culture)” approach in human A549
cells that are highly permissive for a seasonal H3N2 strain, but restrict replication of an
avian H3N2 virus. This allows the description and comparison of changes in the host cell
proteomes in response to infections with these two viruses.
The analysis resulted in the identification of a distinct set of cellular factors influenced
by viral infection. Most identified cellular and viral proteins were regulated in a similar
way for both virus strains, but also candidates with distinct changes in permissive and
non-permissive infection were found. Ongoing studies involve the validation of the detected proteins and further bioinformatic and functional analyses to elucidate their roles in
IAV infection and their potential contribution to the species barrier. In conclusion, we will
present initial results of a comprehensive analysis on the complex host pathogen interaction network by using the efficient tools and methods of system biology.
150
#83 : Training of signaling pathways to phosphoproteomic
data via hybrid node/edge optimization using a mixed integer programming formulation
Theodore Sakellaropoulos, Leonidas G Alexopoulos
National Technical University of Athens
Biological networks have always been an integral part of Systems Biology’s effort to model
mathematically the biological processes of the cell. Despite the vast amount of identified
interactions stored in databases, modeling the cell behavior is not a straightforward task
since it is heavily dependent on the biological content in which the cell resides. In this
work, we tried to couple the prior knowledge available in the pathway databases with experimental data describing the response of epithelial cells to certain stimuli perturbations
in order to create a cell-specific network depicting the signal transduction mechanism of
the cell. To this end, we utilized the data available for the recent SBV Improver Species
Translation Challenge. In particular, starting from the reference network provided for the
challenge we augmented it with reactions derived from literature to represent our prior
knowledge about the connectivity of the proteins of interest. Signal transduction in the
network was modeled using Boolean logic as reactions represented Boolean gates between nodes. We then formulated an mixed integer linear program that sought to modify
this network by removing nodes and reactions in order to better describe the experimental
data at hand. Each modification was associated to a cost. Edges that appear in multiple
databases as well as nodes measured in the dataset required stronger evidence to be
removed. Finally, extra constrains were added to the problem to guarantee that the final
topology would be a directed acyclic graph, a structure that would allow us to infer causal
relationships among the involved compounds.
Boston, MA
June 17-19, 2014
151
Poster Abstracts
#84 : Study on the Clinical Significance of c-FLIP and
JUN-B Expression in Psoriatic Lesion by using Quantitative RT-PCR and Tissue Microarray
MN Salleh1 , HI Lai1 , N Shamsudin2 , WK Wan Ramli1 , MI Mustafa1
1
2
Faculty of Biomedical and Health Sciences, University of Selangor,
Dermatology Unit, Department of Medicine, Faculty of Medicine and Health Sciences, University Putra
Psoriasis is a chronic T cell-mediated inflammatory skin disease. Studies have shown
that angiogenesis plays an important role in the pathogenesis of psoriasis. In the present
study, we investigated the involvement of c-FLIP and Jun-B protein expression in the possible mechanisms of psoriasis. The hyperproliferation of keratinocytes observed in psoriasis prompted us to evaluate c-FLIP and Jun-B expression on biopsies collected from
involved and uninvolved skin of 42 samples with active plaque-type psoriasis with respect
to healthy skin. We analyzed the expression of C-FLIP and Jun-B at transcript and protein levels by quantitative RT-PCR and tissue microarray based immunohistochemistr. We
demonstrated the expression of both genes c-FLIP and Jun-B in a hyperproliferative skin
condition not related to neoplastic transformation. Interestingly, we observed that c-FLIP
and Jun-B is not expressed in healthy skin, but it becomes detectable in non-lesional
areas and it is even more expressed in lesional psoriatic skin. In addition, we found
that Jun-B expression is correlated to the rate of keratinocyte proliferation and activation
compared to c-FLIP. Hence, our observations indicate Jun-B as a new possible player, involved in the development and/or maintenance of the hyperproliferative state of psoriatic
keratinocytes. Our study confirms distinct prognostic relevance of c-FLIP and Jun-B protein expression levels in the psoriasis patients and identifies an association of high Jun-B
levels with elevated expression of Jun-B target genes and markers for infiltrating immune
cells.
152
#85 : Pulsatile FoxO3a translocation in response to growth
factors is controlled jointly by AKT and ERK pathways
Somponnat Sampattavanich1 , Bernhard Steiert1,2,3 , Bernhard Kramer1,4 , John Albeck5 ,
Peter Sorger1
1
Institute of Physics, University of Freiburg, Germany
3
Freiburg Center for Systems Biology, University of Freiburg, Germany
4
Division of Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
5
College of Biological Sciences, University of California, Davis
2
FOXO factors serve as important gate keepers of cell growth although it is unclear how
mitogenic signaling along the AKT and ERK signaling cascades get integrated by FOXO
proteins. Using a panel of RTK ligands that exhibit differing activation levels of ERK
and AKT pathways, we demonstrated here that both AKT and ERK signaling cascades
uniquely contribute to the FoxO3a translocation dynamics. We first showed that the relative FoxO3a phosphorylation at ERK-specific versus AKT-specific sites (S294/S253) correspond well with the relative ERK/AKT phosphorylation activity by the different RTK
ligands. Using both immunostaining and live cell imaging, we then showed that AKTdominating ligands promote persistent cytosolic localization of FoxO3a whereas ERKdominating ligands promote transient nuclear-to-cytosolic translocation of FoxO3a initially,
and later exhibit pulsatile FoxO3a translocation. Therapeutic inhibition of MEK phosphorylation does not affect the early non-stationary FoxO3a translocation but reduce the late
pulsatile response. Investigation of the FoxO3a/AKT/ERK relationship in different breast
cancer cell lines reveals alternative role of ERK pathway on FoxO3a dynamics, by suppressing the activation of AKT on FoxO3a nuclear-to-cytosolic translocation. Collectively,
our study reveals the sophisticated dynamics of FoxO3a translocation by different RTK
ligands and emphasizes the role of FoxO3a as an important integrative node of both AKT
and ERK signaling cascades.
Boston, MA
June 17-19, 2014
153
Poster Abstracts
#86 : Speech abnormalities in Parkinson’s disease reflect
a major disturbance in sensorimotor servomechanisms
Shimon Sapir
Department of Communication Disorders, University of Haifa, Haifa, Israel
Parkinson’s disease (PD) is a progressive and highly debilitating disease of the central
nervous system, affecting the basal ganglia and their cortical and subcortical networks.
Nearly all individuals with PD have voice and speech abnormalities, collectively termed
hypokinetic dysarthria (HKD). The HKD is typically characterized by a weak and hoarse
voice, reduced prosodic intonation, reduced articulatory movements, manifested as imprecise production of vowels, consonants, and other phonemes, and a tendency to accelerate (festinate), thus resulting in mumbled speech (Sapir et al., 2007). Until recently,
these speech abnormalities have been attributed to dopamine deficiency and muscle
rigidity. However, research and clinical studies indicate that HKD is a comlex and dynamic
disorder, involving multiple factors, most of these are related to deficits in sensorimotor
servomechanisms (Sapir, 2014). This poster will present two patients with PD and HKD
to illustrate the speech abnormalities and the effects of behavioral treatment designed to
improve motor speech control through optimization of servomechanisms and vocal vigilance.
1. Sapir, S. (2014, in press). Multiple factors are involved in the dysarthria associated with Parkinson’s disease: A review with implications for clinical practice and
research. Journal of Speech Language and Hearing Research.
2. Sapir, S., Spielman, J., Ramig, L., Story, B., & Fox, C. (2007). Effects of intensive
voice treatment (the Lee Silverman Voice Treatmen (LSVT)) on vowel articulation in
dysarthric individuals with idiopathic Parkinson’s disease: acoustic and perceptual
findings. Journal of Speech, Language, and Hearing Research, 50, 899-912.
154
#87 : Systems Biology Approach to Determine the Efficacy of Methylation by DNA-Methyl Transferase1 in Cancer Cells
Eric Samorodnitsky, Emily Ghosh, Sahana Majumder, Sibaji Sarkar
Cancer Center, Department of Medicine, Boston University School of Medicine, Boston, MA
Epigenetic regulation of gene silencing involves both histone deacetylation-methylation
and DNA CpG island methylation. Maintenance of DNA methylation is mediated by DNA
methyl transferase 1 (DNMT1). DNMT1 expression level varies with stages of cell cycle
in normal cells but it is highly expressed in cancer cells. DNMT1 methylates the new DNA
strand after replication, at the sites complimentary to the hemimethylated mother strand
to maintain a constant methylated status when and after the cell division. Promoter region
methylation silences many tumor suppressor genes in cancer cells. We have shown that
re-expression of tumor suppressor genes by using histone deacetylase inhibitors could be
exploited to develop novel combination therapies (Anti Cancer Research, 2012). We proposed how epigenetics could be regulating cancer progenitor cell formation and metastasis (Epigenomics 2013, 2014; IJMS 2013, Anti Cancer Research 2014). However, it is not
known how DNMT1 maintains higher methylation level in cancer cells. We employed a
modified Hill equation to determine the cooperativity of binding (n) of DNMT1 to the hemimethylated upstream CpG sites in cancer cells compared to the normal cells. Increase in
“n” value determined the affinity of DNMT1 towards the site of methylation. We observed
that the value of “n” for DNMT1 in cancer cells compared to the normal cells is higher,
even when we consider enhanced expression of DNMT1 in cancer cells. Our results suggest that DNMT1 efficiency is higher in cancer cells to maintain enhanced methylation of
tumor suppressor genes. This is the first demonstration of application of systems biology
approach to epigenetics to explain gene silencing. Our system presents a wide applicability in calculating and determining the genome wide efficacy of methylation and gene
silencing.
Boston, MA
June 17-19, 2014
155
Poster Abstracts
#88 : Comprehensive identification of cooperative microRNA
target regulation through an integrative workflow
Ulf Schmitz1 , Shailendra K Gupta1 , Xin Lai2 , Felix Winter1,2 , Julio Vera2 , Olaf Wolkenhauer1,3
1
Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany
Department of Dermatology, Faculty of Medicine, University of Erlangen-Nuremberg, 91054 Erlangen,
Germany
3
STIAS, Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa
2
MicroRNAs (miRNAs) regulate gene expression at the post-transcriptional level in most
cell-biological processes. Deregulated miRNA expression can lead to or mediate the
emergence and progression of human diseases. There is increasing evidence that pairs
of miRNAs can cooperatively repress translation of mutual target genes. In our own work
we have shown that this phenomenon can realize fine-tuned and adaptive target gene
regulation. However, up to now it was not clear which miRNA pairs may cooperate and
what are their targets.
We here describe a computational workflow to identify and analyze targets of synergistic miRNA regulation. This workflow integrates bioinformatics and systems biology approaches in six steps that iteratively increase the level of confidence on predicted triplexes
composed of two miRNAs and their mutual target mRNA: (i) identification of miRNA binding sites in the 30 untranslated region of gene targets; (ii) identification of putatively cooperating miRNA pairs with binding sites in close proximity; (iii) prediction and analysis
of the local secondary structure of putative RNA triplexes; (iv) prediction of the 3D triplex
structure including Argonaute and the determination of the triplex’ thermodynamic profile
by molecular dynamics simulation; (v) determination of binding affinities among the involved molecules; and (vi) determination of the target repression efficiency by simulations
of a kinetic model.
In a case study, we have analyzed the human genome to identify targets of synergistic miRNA-mediated repression. Our analysis provides evidence that the phenomenon of
cooperative target regulation by miRNA pairs is a common cellular mechanism in human.
A functional enrichment analysis that we performed on the identified targets indicates
relevance of miRNA-cooperativity in cancer (signaling-) pathways.
156
#89 : Diseaseome 2.0: Uncovering disease-disease relationships through the human interactome
Amitabh Sharma1,2 , Joerg Menche3 , Albert-László Barabási1,2,3
1
Channing Division of Network Medicine, Harvard Medical School, Boston, MA-USA
CCNR, Dept. Of Physics, Northeastern University, Boston-MA-USA
3
Department of Theoretical Physics, Budapest University of Technology and Economics, H1111, Budapest,
Hungary
2
Human diseases could be viewed as perturbations of networks. A thorough understanding of the complex topological and dynamical properties of the interactome (Human protein interaction network) is therefore crucial to explain the mechanisms of such perturbed
biological systems. There is extensive evidence in the literature that disease genes are
not placed at random in the interactome. Indeed, they have a higher chance of interacting
with each other than expected by chance indicating that these genes tend to cluster in
the same region of the network. According to the ‘disease module hypothesis’ the cellular components associated with the same disease segregate in the same neighborhood
of the human interactome. Identifying this network neighborhood is not only essential to
understand the molecular mechanisms responsible for the disease, but could help predict
novel drug targets and biomarkers. Yet, given the known incompleteness of the interactome and our limited knowledge of disease-associated genes, the available data may not
yet have sufficient coverage to allow us to map out the modules associated with each disease. Here, we derive the mathematical conditions for the identifiability of disease modules, revealing a list of diseases for which the current interactome is sufficient to uncover
the corresponding disease module. We show that the network-based location of each
disease module determines its pathobiological relationship to other diseases, finding for
example that diseases with overlapping network modules show significant co-expression
patterns, symptom similarity, and comorbidity. In contrast, diseases residing in separated
network neighborhoods are clinically distinct. The developed tools allow us to build an
interactome-based platform to predict the common molecular roots of such clinically unrelated diseases as asthma and celiac disease, or to identify the biological role of low
significance genes identified by genome-wide association studies.
Boston, MA
June 17-19, 2014
157
Poster Abstracts
#90 : Proximity of metabolic and cancer causing genes in
the genome leads to metabolic remodeling in cancers.
Ashwini Kumar Sharma1 , Roland Eils1,2 , Rainer König1,3
1
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg.
Institute of Pharmacy and Molecular Biotechnology, Bioquant Center, University of Heidelberg, Heidelberg.
3
Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University
Hospital, Jena.
2
Cancer metabolism – an emerging hallmark of cancer, is pivotal to cancer progression
and survival. Crucial metabolic genes (MG) have been identified as oncogenes (OG)
and tumor suppressors (TSG) or targets of deregulated signaling. Cancer is a direct
consequence of genomic alterations, somatic copy number alterations (SCNA) that frequently disrupt the genome, occurs across cancer types targeting multiple genes at the
affected loci. However, the underlying challenge is to delineate the driver genes from
the functionally neutral ones. The present work aims to identify how spatial proximity
in the positioning of metabolic and cancer causing genes (CG) in the genome leads to
metabolic remodeling. Our focus was restricted to MGs, where we observe that they are
often co-altered i.e. co-deleted or co-amplified, with key CGs across cancer types, being
proximal and sharing the SCNA susceptible loci. We hypothesize that there could be a
subset of such co-alteration events where the genomic proximity driven alteration of the
MG could further have a functional impact. A hybrid method based on statistical tests, correlation and network analysis was developed to identify such driving co-alterations events.
Briefly, gene pair distances (within a chromosomal arm) is computed for all protein coding genes and their copy number co-alteration frequencies calculated across 16 cancer
types using publically available cancer genomics data generated by the TCGA project.
The method then identifies for each cancer type, those CG-MG pairs whose copy number
co-alteration frequencies are significantly different from other gene pairs within the same
chromosomal arm and separated by similar gene distances. Furthermore the CG-MG
pairs are prioritized by integrating expression data, correlating copy number changes to
gene expression. Finally, we identify functionally important CG-MG pairs by filtering for
those, where neighboring genes of the MG in a metabolic network also show differential
pattern of expression.
158
#91 : Harnessing DNA Damage Repair Pathways in Breast
Cancer Therapy: a Synthetic Lethality Paradigm
Sriganesh Srihari
Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4072, Australia
With an estimated 1.38 million new cases and 458000 deaths worldwide every year,
breast cancer is the most common malignancy among women worldwide. A major contributor to these deaths is a subset of breast tumours that exhibit high relapse and metastasis
rates and do not respond well to traditional therapies. Modulating DNA-damage response
(DDR) pathways has shown immense potential as a specialized therapy to counter aggressive tumours, by inducing high levels of DNA damage in cancer cells thereby forcing
them into apoptosis while minimizing the impact on normal cells. Exploiting synthetic
lethal (SL) interactions has shown some promising results in this regard, most notably in
BRCA1-deficient cells that are sensitive to PARP inhibition. A SL relationship between
two genes exists when cells remain viable when either or both genes are active, but selective killing of cancer cells occurs when both genes are inactivated. However, except
the BRCA1-PARP1 breakthrough few other new SL targets have successfully proceeded
to clinical trials and been adopted in the treatment.Here, we seek to identify novel SL relationships among components of the DDR machinery that can be effectively translated to
treatment of aggressive breast tumours. Through extensive literature searching, we have
curated six DDR pathways to generate a comprehensive and up-to-date map of genes, reactions and mechanisms underlying the DDR machinery. An extensive evaluation of these
pathways against known databases such as KEGG and Reactome shows we had added
at least 101 new genes in 68 new reactions. Furthermore, combining graph-theoretic
modelling integrating protein-interaction and gene-expression datasets with comparative
genomic approaches for extrapolating SL relationships from lower-order eukaryotes such
as yeast, we have identified a subset of gene-targets in these pathways which are now
in the pipeline for siRNA-mediated depletion and validation in the lab.We foresee that
our collaborative computational and experimental efforts focused towards identification of
SL relationships will eventually lead to discovery of specialized drug targets for treating
aggressive breast tumours.
Boston, MA
June 17-19, 2014
159
Poster Abstracts
#92 : Network modeling reveals key features of epithelialto-mesenchymal transition dynamics
Steven N. Steinway1,2 , Jorge Gomez Tejeda Zañudo3 , David J. Feith2 , Thomas P. Loughran,
Jr.2 , Reka Albert3
1
2
3
Penn State Hershey Cancer Institute, The Pennsylvania State University
University of Virginia Cancer Center, University of Virginia
Department of Physics, The Pennsylvania State University
Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue, and establish distant
metastases. A hallmark of EMT is the loss of E-cadherin expression, and one major signal
for the induction of EMT is transforming growth factor beta (TGFβ), which is dysregulated
in up to 40% of hepatocellular carcinoma (HCC). We have constructed an EMT network
of 69 nodes and 134 edges by integrating the signaling pathways involved in developmental EMT and known dysregulations in invasive HCC. We then used discrete dynamic
modeling to understand the dynamics of the EMT network driven by TGFβ. Our network model recapitulates known dysregulations during the induction of EMT and predicts
the activation of the Wnt and Sonic hedgehog (SHH) signaling pathways during this process. We show, across multiple murine (P2E and P2M) and human HCC cell lines (Huh7,
PLC/PRF/5, HLE, and HLF), that the TGFβ signaling axis is a conserved driver of mesenchymal phenotype HCC and confirm that Wnt and SHH signaling are induced by TGFβ
in mesenchymal but not epithelial cell lines. Furthermore, we identify by network analysis nine regulatory feedback motifs that stabilize the EMT process and show that these
motifs involve cross-talk among multiple major pathways. We employ a node perturbation
screen to identify interventions that would abolish epithelial-to-mesenchymal transition.
We demonstrate that the TGFβ driven EMT is robust to suppression of individual nodes
and that 40 out of 2,346 possible combinations of node knockouts suppress EMT. Furthermore, we demonstrate that the knockout combinations act by disrupting feedback loops
that regulate the EMT process. These results establish network modeling as an important tool to identify critical mediators in complex biological processes. We further propose
network modeling as a tool to discover therapeutic targeting strategies within complex
disease pathways, specifically in liver cancer invasion.
160
#93 : Ras mutant-specific response to upstream inhibition
Edward Stites
Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO
EGFR inhibitors are a recommended treatment for patients with colorectal cancer patients. Patients with an activating KRAS mutation are generally resistant to EGFR inhibitors, with the exception of patients that have the activating KRAS G13D mutation.
KRAS G13D shares the same general biochemical defects as the other oncogenic KRAS
mutants, and a mechanism to explain the differential response remains to be determined.
The effects of Ras mutants on upstream (EGFR) inhibition were here studied with a computational model of Ras signaling that has proven useful for studying oncogenic Ras
mutants. Simulations find an increased response to upstream inhibition for Ras G13D
mutants compared to G12D and G12V mutants. As simulations utilize the known biochemical properties of these mutants, the simulations reveal that differential sensitivity to
upstream inhibition is indeed consistent with known Ras biology. Further analysis identifies a single, readily measurable property to be the parameter that indicates response to
EGFR inhibition. Measurement of this property for the >90 KRAS mutants that have to
date been observed in colon cancer may provide a method for evaluating the likelihood
of whether a patient with a KRAS mutation might benefit from treatment with an EGFR
inhibitor. Of note, two KRAS mutants other than G13D have been measured to have this
property. More generally, this work demonstrates how mass-action modeling of oncogene
containing networks can contribute to the advancement of personalized medicine.
Boston, MA
June 17-19, 2014
161
Poster Abstracts
#94 : Delineating G-protein coupled receptor (GPCR)-linked
oscillatory signaling mechanisms through single cell microfluidic analysis and computational modeling
Madhuresh Sumit1,2 , Richard Neubig3 , Jennifer Linderman1,2,4 , Shuichi Takayama1,4,5
1
2
3
4
5
Biophysics Graduate Program, University of Michigan, Ann Arbor, MI 48109
Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109
Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109
Department of Macromolecular Science and Engineering, University of Michigan, Ann Arbor, MI 48109
Although GPCRs are the target of around 40% of all pharmaceuticals, the signaling pathways by which these drugs work are not yet fully understood, raising questions about
their off-target effects and limiting applications. Our work addresses this problem by developing a quantitative understanding of selective modulation of GPCR-linked signaling.
It combines pulsatile microfluidic experiments (providing precise temporal control), and
computational modeling (ensuring that the model developed is verifiable and can be used
to predict the effects of drugs). Our current study focusses on the muscarinic receptor
M3, which is implicated in type 2 diabetes and multiple sclerosis. Using a novel microfluidic platform to deliver low concentration, pulsatile ligand stimulation of GPCRs, we
quantify calcium signaling and NFAT translocation in single HEK293 cells expressing M3
receptors through fluorescence imaging of R-GECO1 (calcium-sensor) and NFAT4-eGFP.
Temporally patterned ligand stimulations with varying concentration (C), duration (D), and
rest period between pulses (R) are delivered to modulate the cell signaling, and in silico model is predicted based on the microfluidic analysis. Our results show, for the first
time, that GPCR-based calcium oscillations (triggered by nano-molar pulses of ligand)
lead to efficient and sustained transcriptional activation as compared to a non-oscillatory
stimulation. We have also quantified the relationship between amplitude and frequency
down-regulation with receptor density, which provides insights into receptor regulation
during dynamic signal processing. These findings will be used to study cells with low receptor densities (mimicking diseased states), and to develop a quantitative understanding
of the effects of various modulating agents (e.g., PAMs, NAMs, and node inhibitors) for
targeted drug discovery.
162
#95 : A new stochastic kinetics approach to quantitative
description of intracellular networks coupled to hidden
cell environment
Jaeyoung Sung1 , Kim Ji-Hyun1 , Lim Yu Rim1 , Park Seong Jun1 , Yang Gil-Suk1 , Song
Sanggeun1 , Yang Sora2 , Lee Nam Ki2
1
2
Department of Chemistry, Chung-Ang University, Seoul, Korea
Department of Physics, POSTECH, Pohang, Korea
We present a new stochastic kinetics approach to a quantitative description of intracellular
networks coupled to hidden cell environment in which the product creation rate coefficient
and the reactant level are unknown stochastic variables that vary from cell to cell and fluctuate over time. A general relationship of the product number fluctuation to the creation
rate fluctuation and the product lifetime is established for a basic unit of intracellular networks. An application of our approach to the central dogma of gene expression provides
a unified quantitative explanation about various gene expression statistics. Our analyses
provide an accurate quantification of the intrinsic and extrinsic noises, which reveal the
general structure of the intrinsic and extrinsic noises in terms of the promoter strength.
This work suggests a new direction in quantitative analyses of the chemical fluctuation
produced by intracellular networks coupled to hidden cell environment about which we
don’t have enough information to construct an explicit model.
Boston, MA
June 17-19, 2014
163
Poster Abstracts
#96 : Global metabolic interaction network of the human
gut microbiota
Jaeyun Sung1 , SungHo Jang2 , Seunghyeon Kim1,3 , Nicholas Chia4 , Yong-Su Jin5,6 , Gyoo
Yeol Jung2 , Pan-Jun Kim1,3
1
Asia Pacific Center for Theoretical Physics, Pohang, Gyeongbuk, South Korea
Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk, South Korea
3
Department of Physics, Pohang University of Science and Technology, Pohang, Gyeongbuk, South Korea
4
Department of Surgery, Mayo Clinic, Rochester, MN, USA
5
Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana,
IL, USA
6
Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
2
Background: Despite recent advances in knowledge of the microbial diversity inside the
human gastrointestinal tract, the global interaction dynamics between the myriad microbial species, and its influence on host health and disease remains poorly understood. To
better understand the emergent properties arising from the interactions between human
gut microbes, we constructed a global network model based on interspecies cross-feeding
relationships.
Methods and Results: We used transport reaction information from public databases (e.g.
KEGG, BioCyc, CAZy, TransportDB), from published genome-scale metabolic models,
and from literature annotations to identify small-molecule metabolic compounds that are
imported and/or exported by microbes found to reside in the human gut. Next, we defined
an interaction between two microbes when one class of species can uptake a metabolic
compound that is secreted by another (i.e. interspecies cross-feeding). Following this
approach, we linked all interacting microbes into a global Microbe-Microbe Network. Using microbiome samples collected from patients across four clinical phenotypes, we superimposed each sample’s microbial abundance information upon our Microbe-Microbe
Network, thereby removing low-quantity species (i.e. nodes), along with their interactions
(i.e. edges). This led to interaction networks specific to individuals, and, in turn, networks
specific to phenotype. Interestingly, we identified network-based topological features, as
well as enrichment of biologically meaningful interspecies interactions, unique to each
clinical phenotype.
Conclusion: We present the first microbial community structure within the human gastrointestinal tract based on interspecies cross-feeding of small-molecule metabolites. Network
analysis of the global microbial symbiosis provides novel insight into the molecular basis
164
of pathophysiology. Furthermore, our results can be utilized for clinical applications in the
form of network-based disease classifiers.
Boston, MA
June 17-19, 2014
165
Poster Abstracts
#97 : Hepatic Response to Refeeding - From Ketone Bodies to Lipids
Christian Tokarski1 , Sebastian Vlaic2 , Reinhard Guthke2 , Stefan Schuster1
1
Department of Bioinformatics, Friedrich Schiller University of Jena, Germany
Leibniz Institute for Natural Product Research and Infection Biology e.V. - Hans-Knöll-Institute (HKI), Jena,
Germany
2
Despite several important functions of the liver including detoxification of drugs, alcohol,
ammonia and other xenobiotics, as well as synthesis of compounds like bile, membrane
lipids and proteins, it is in particularly important in energy storage after meals and in providing energy during periods of starvation. In times of low blood glucose level the brain,
which cannot metabolize fatty acids, is generating energy additionally to glucose from
ketone bodies that are solely produced in the liver. In the liver these are synthesized by
degradation of fatty acids via β-oxidation and exported into the blood. Since a high level
of ketone bodies in blood for prolonged periods leads to damage of the nervous system,
kidney and to a lower blood pH value, it needs to be lowered. Ketone bodies are predominantly utilized in skeletal muscle, heart and brain. The liver itself is only partially able to
utilize them since the two important enzymes limiting the ketolysis, succinyl CoA-oxoacid
transferase (SCOT) and methylacetoacetyl CoA thiolase (MAT), show very low activity in
liver tissue. In order to study the role of the liver in ketone body metabolism, we created
a literature based minimal model of the human fatty acid metabolism. Published gene
expression data from Vollmers et al. was downloaded from GEO (acc. no. GSE13093).
Since this data captures the molecular changes in response to re-feeding mice after a
period of starvation, we seeked to observe differential gene expression among the genes
included in our minimal model. To investigate the relations in the expression of genes we
performed network inference using ExTILAR. Including prior knowledge of diverse types
in the inference we obtained a dynamic gene regulatory network that is able to reproduce
the observed dynamics. Connecting this network with the minimal model of hepatic fatty
acid metabolism, the whole response of the liver to refeeding can be simulated leading to
incorporation of ketone bodies into the liver which seem to be metabolized to form cholesterol or fatty acids via high activity of acetoacetyl-CoA synthetase (AACS) and acetyl-CoA
acetyltransferase 2 (ACAT2).
166
#98 : Reconstruction and in silico analysis of metabolism
in the apicomplexan parasite Toxoplasma gondii
Stepan Tymoshenko1,2 , Rebecca Oppenheim3 , Rasmus Agren4 , Jens Nielsen4 , Dominique
Soldati-Favre3 , Vassily Hatzimanikatis1,2
1
Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, CH1015 Lausanne
2
Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
3
Department of Microbiology and Molecular Medicine, University of Geneva, CMU, 1 Rue Michel Servet,
CH-1211 Geneva
4
Department of Chemical and Biological Engineering, Kemivägen 10, Chalmers University of Technology,
SE412 96 Gothenburg
Toxoplasma gondii is one of the most wide-spread human pathogens worldwide. Chronic
infection with this eukaryotic parasite is asymptomatic and generally not harmful for immunocompetent individuals. Yet in the case of an acquired immunodeficiency or an immunosuppressive therapy reactivation of toxoplasmosis often causes an acute and lifethreatening disease. Current options for treatment of toxoplasmosis are limited and not
well-tolerated as well as inefficent against the encysted, slow growing form. There is a
clearly unmet need for new medication and metabolism is a promising source of drug
targets.
Over the last decade in silico metabolic modelling has been extensively exerted for studying genotype-phenotype relations and prediction of phenotypes upon various perturbations. Metabolic models provide an efficient framework for exploring potential vulnerabilities in metabolism of pathogenic species and thus facilitate discovery of novel drug
targets. ToxoNet1 is a genome-scale metabolic model of T. gondii built in silico using
state-of-the-art automated reconstruction algorithm (RAVEN toolbox). The reconstruction
process and subsequent flux-balance analysis of the model allowed us to get a systematic
overview of the metabolic capabilities of this pathogen. In particular, the model identified
a number of gaps in the current knowledge of Toxoplasma metabolic pathways and clarified its minimal nutritional requirements for asexual replication. Using functionality of
RAVEN toolbox called metabolic tasks we further defined the set of alternative precursors
salvage of which is necessary for the parasite. Within simulated human host cell environment ToxoNet1 predicts a minimal set of 53 enzyme-coding genes to be indispensable for
parasite replication. Some of these predictions represent readily testable hypotheses that
can facilitate identification of novel potential targets and their combination for an effective
intervention against toxoplasmosis.
Boston, MA
June 17-19, 2014
167
Poster Abstracts
#99 : MOCAT: a metagenomics assembly and gene prediction toolkit
Jens Roat Kultima, Peer Bork
European Molecular Biology Laboratory
MOCAT is a highly configurable, modular pipeline for fast, standardized processing of
single or paired-end sequencing data generated by the Illumina platform. The pipeline
uses state-of-the-art programs to quality control, map, and assemble reads from metagenomic samples sequenced at a depth of several billion base pairs, and predict proteincoding genes on assembled metagenomes. Mapping against reference databases allows for read extraction or removal, as well as abundance calculations. By mapping
reads to a database of single-copy phylogenetic marker genes it is possible to calculate
the abundance of metagenomic operational taxonomic units (mOTUs). MOCAT runs on
UNIX machines and integrates seamlessly with the SGE, PBS and LSF queuing systems,
commonly used to process large datasets. Individual processing steps and parameters
were benchmarked and tested on artificial, real, and simulated metagenomes resulting
in an improvement of selected quality metrics. MOCAT can be freely downloaded at
http://www.bork.embl.de/mocat/.
168
#100 : Controllability of protein interaction network identifies human disease genes
Arunachalam Vinayagam1 , Yang-Yu Liu2,3,4 , Bahar Yilmazel5,6 , Ho-Joon Lee7 , Charles
Roesel5,6 , Yanhui Hu1,5 , Young Kwon1 , Amitabh Sharma2,3,4 , Norbert Perrimon1,8 , AlbertLászló Barabási2,3,4
1
Department of Genetics, Harvard Medical School, Boston, MA
Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA
3
Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston,
MA
4
Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA
5
Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, Boston, MA
6
Bioinformatics program, Northeastern University, Boston, MA
7
8
Howard Hughes Medical Institute, 77 Avenue Louis Pasteur, Boston, MA
2
The protein-protein interaction (PPI) network is crucial for cellular information processing and decision making. With suitable inputs, PPI networks drive the cells to diverse
functional states, such as cell proliferation or cell death. Here, we characterized the controllability of a large directed human PPI network comprised of 6,339 proteins and 34,813
interactions, allowing us to classify the proteins as “indispensable”, “neutral” and “dispensable” with respect to network controllability. We find that 21% of the proteins are
indispensable from a control perspective. Interestingly, these proteins are the primary
targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control principles is critical for the transition between healthy and disease
states. Further, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are
“indispensable” from control perspective. As 46 of them have not been previously associated with cancer, it suggests that network controllability will be useful to identify novel
disease genes and potential drug targets.
Boston, MA
June 17-19, 2014
169
Poster Abstracts
#101 : Assessment of the human gut microbiome in response to temporal and colorectal cancer-associated variability
Anita Voigt1,2,3 , Georg Zeller1 , Julien Tap1,4 , Shinichi Sunagawa1 , Jens Roat Kultima1 ,
Paul Costea1 , Petra Schrotz-King5 , Matthias Kloor2,3 , Cornelia M. Ulrich5 , Magnus von
Knebel Doeberitz2,3 , Iradj Sobhani4 , Peer Bork1,3
1
Structural and Computational Biology Unit, European Molecular Biology Laboratory Heidelberg, Germany
Dept. of Applied Tumor Biology, Institute of Pathology, University Hospital Heidelberg, Germany
3
Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, Germany
4
Department of Gastroenterology and LIC-EA4393-EC2M3, APHP and UPEC Université Paris-Est Créteil,
Créteil, France
5
Division of Preventive Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany
2
The gut is a habitat of microbial communities (microbiota) that are very important for
health e.g. by stimulating the immune system and changes of the microbiota have been
linked to diseases such as obesity, type II diabetes and also colorectal cancer (CRC). Before drawing conclusions about disease-associated microbiota changes we assessed the
temporal variability of the gut microbiome in seven healthy subjects using metagenomic
shotgun sequencing. We found that the inter-individual variability of the undisturbed microbiota is high and although it undergoes fluctuations over time, the microbial signatures
are highly individual-specific at species-level. However, perturbations like antibiotics can
lead to a pervasive change of the gut microbial composition. We further investigated
changes in the gut microbiota associated with CRC as several bacterial species (like Fusobacterium nucleatum) have been implicated in the development of CRC. We tested
the potential of the microbiota to separate CRC patients from controls and this microbesbased test was slightly better than the standard screening fecal occult blood test and could
be validated in independent patient and control populations from different countries.
170
#102 : High-Throughput Proteomic and Transcriptomic
Data Integration based on MS/MS and RNA-Seq Data using Prior Pathway Knowledge
Astrid Wachter1 , Thomas Oellerich2 , Jasmin Corso3 , Ekkehard Schütz4 , Annalen Bleckmann1,5 ,
Henning Urlaub3,6 , Tim Beissbarth1
1
Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
Department of Hematology/Oncology, Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany
3
Max Planck Institute for Biophysical Chemistry, Bioanalytical Mass Spectrometry Group, Göttingen, Germany
4
Chronix Biomedical, 5941 Optical Court Suite 203E, San Jose, CA 95138, USA
5
Department of Hematology/Oncology, University Medical Center Göttingen, Göttingen, Germany
6
Bioanalytics, University Medical Center Göttingen, Göttingen, Germany
2
Nowadays information acquired by different high-throughput technologies allows greatly
accelerated research turning data integration into a very current topic. Although a lot of
studies deal with integration of high-throughput data sets, not much emphasis is placed
on incorporating prior biological knowledge into data integration methods yet. We implemented a three-level (protein, transcription factor and transcript/gene level) pathwaybased integration approach which benefits from prior knowledge of public pathway databases.
Biocarta, Reactome, KEGG and Pathway Interaction Database information was retrieved
using the R package rBiopaxParser. This we used to identify pathways of differentially
abundant proteins in the proteomic dataset. We reconstructed a protein network containing a selection of the most relevant pathways, identified affected transcription factors
via TRANSFAC database and performed a downstream determination of corresponding
target genes. The downstream analysis we compared with the upstream analysis which
determined upstream transcription factors and pathways of genes corresponding to differentially expressed transcripts identified in the RNA-Seq data set. Finally, we performed
an overlap analysis with the objective of evaluating the method and prior knowledge incorporation. We used this approach to integrate time-dependent MS/MS (tandem mass
spectrometry) and corresponding RNA-Seq data sets generated by stimulation of human
B cell receptors. With our method an extensive parallel and time-dependent analysis of
signalling events was feasible.
Boston, MA
June 17-19, 2014
171
Poster Abstracts
#103 : Drug efficacy and side effects are strongly associated with its ability to reverse gene expression abnormalities in a mouse model of dyslipidemia
Allon Wagner1 , Noa Cohen1 , Thomas Kelder2 , Elad Liebman3 , David M. Steinberg4 , Marijana Radonjic2 , Eytan Ruppin1,5
1
2
3
4
5
Blavatnik School of Computer Science, Tel-Aviv University, Israel
Microbiology and Systems Biology, TNO, Zeist, The Netherlands
Department of Computer Science, University of Texas at Austin, USA
Department of Statistics and Operations Research, Tel Aviv University, Israel
The Sackler School of Medicine, Tel Aviv University, Israel
High-throughput gene expression data have proven invaluable in studying human disease, and are expected to become a principal element of future personalized medicine.
At present, however, patients’ diagnosis and response to treatment are judged largely
through measurements of disease-relevant physiological markers, mostly in blood and
urine. The association between the molecular and the physiological manifestations of
the disease has nevertheless not been investigated in a systematic, rigorous manner.
Here, we study this fundamental relationship in a LDLR-/- mouse model of dyslipidemia
that was subject to various treatments. We find that: (a) the deviations from the healthy
state to the disease state on the transcriptomic (gene expression) and physiological levels
are strongly correlated. (b) The restoration of individual physiological disease markers to
their baseline values following most drug treatments is strongly correlated with a restoration of the transcriptome back to its normal baseline. (c) Physiological markers that are
known to be associated primarily with a certain tissue are correlated with transcriptomic
changes occurring in this tissue, and, importantly, (d) treatments that induce considerable
‘non-restorative’ transcriptomic alterations are associated with physiological side-effects.
Overall, these results highlight the importance of searching for drugs that are able to
restore the global cellular state back to its healthy norm, rather than rectify particular
disease phenotypes. Such drugs are expected to be more effective and to have lesser
side-effects.
172
#104 : Network-based association of hypoxia-responsive
genes with cardiovascular diseases
Ruisheng Wang, William Oldham, Joseph Loscalzo
Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School
Oxygen is indispensable for cellular viability and function. Low cellular oxygen content
(hypoxia) induces a number of molecular changes at different levels to activate regulatory
pathways for increasing oxygen supply and optimizing metabolism under stress conditions. Hypoxia is involved in the pathobiology of many diseases, such as heart failure,
stroke, and myocardial infarction. Although the complex association between hypoxia and
cardiovascular diseases (CVD) has been recognized for some time, there are few studies
investigating their biological link at a systems-level. In this study, we integrate hypoxia
genes and CVD genes into the human interactome and explore the relationships between
hypoxia and CVD using a systems biology approach. The analyses from macroscale to
microscale all indicate the presence of interesting close relationships between hypoxia
genes and CVD genes. Moreover, we find that hypoxia genes play significant bridging
roles in connecting different CVDs. A few interesting signaling pathways and functional
modules have been identified as common to both hypoxia stress response and CVDs.
These signaling pathways and hypoxia-CVD functional modules provide new insights into
the role of hypoxia in cardiovascular biology and disease.
Boston, MA
June 17-19, 2014
173
Poster Abstracts
#105 : Classifying Cancer Types for Treatability Using PPI
Network Structure
Kathleen Wilkie1 , Michael La Croix2 , Philip Hahnfeldt1
1
2
Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, MA, USA
Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
The existence of a correlation between cancer protein-protein interaction (PPI) network
degree-entropy and cancer 5-year survival probability has been shown. We investigate
the correlation between epidemiological survival data and the molecular details of specific
cancers further through higher levels of degree-connectedness and through the evolution
of cancer network connections compared to a random graph evolution. By comparing
the cancer networks to random graphs of the same size, we create a new metric that
correlates with 5-year survival and may act as a classifier. The correlative relationship
suggests that our method is able to surpass individual variabilities such as cancer site,
disease stage, and structural features of the PPI network, through the use of proxy measurements: 5-year survival data and our entropy-based metrics. Our findings suggest
that the underlying structure of cancer protein-protein interaction networks may be used
to classify cancer types into two groups: those that are more treatable for various reasons
and those that are not.
Furthermore, using spectral analysis techniques we present a new method to identify
potential drug targets. Eigenvector centrality can be used to provide a ranked order to
a subset of the vertices in a graph. To cover the whole network, we consider several of
the largest magnitude eigenvalues and their corresponding eigenvectors found from the
network’s associated adjacency matrix. Highest ranked vertices are considered potential
drug targets and both individual and combination targets are presented. Drug targets are
assumed to act by blocking the entire protein (vertex deletion) and the effects on residual
network structure are discussed.
174
#106 : Network algorithms dicover dysregulated pathways
in RNAi screens
Jennifer Wilson, Sara Gosline, Ernest Fraenkel, Doug Lauffenbruger
Biological Engineering
High-throughput, rna-interference (RNAi) screens are a powerful technique for studying
the significance of gene mutations and deletions in cancer, yet high rates of false positives and false negatives limit the interpretation of these screens. More specifically, a
gene-interference reagent’s performance may vary across replicates, and reagents targeting against the same gene may result in different phenotypic responses.
To account for this uncertainty in a reagent’s performance, we apply a data integration
approach to identify relevant pathways and better interpret screen hits. Our tool, the Simultaneous Analysis of Multiple Networks (SAMNet) uses RNAi screening and mRNA
expression data with a custom interactome and mathematical optimization to find closely
related sub-networks of screening targets. Further, we apply a randomization routine to
look for targets that are specific to the experiment and not solely a result of study bias in
the interactome.
We’ve used SAMNet on an in vivo model of Acute Lymphoblastic Leukemia (ALL) to build
sub-networks that identify differential and common pathways between the in vivo and in
vitro experimental settings. Further, after running the optimization on randomized data,
we enrich for targets that are specific to the ALL model. From these enriched targets,
we can select novel genes for experimental validation and determine relevance of these
genes to the in vivo development of ALL.
Interpreting data in a network context identifies unique pathways not identified in either
dataset alone. Further, this context provides higher confidence to identify true positives
and we postulate that hidden targets from these pathways are candidate false negatives.
Reducing false positives and false negatives improves data interpretation and the design
of future experiments. In a therapeutic-development context, this analysis is useful for understanding mechanisms of disease, identifying biomarkers for diagnosis, understanding
pathways for drug resistance, and hypothesizing potential combinatorial therapeutics.
Boston, MA
June 17-19, 2014
175
Poster Abstracts
#107 : Cell-to-cell variability in NF-κB activation and chromatin at the HIV promoter contribute to noise in the reactivation of latent HIV
Victor Wong1 , Kathryn Miller-Jensen2
1
2
Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT
Department of Biomedical Engineering, Yale University, New Haven, CT
Latent HIV infections are transcriptionally silent and therefore invisible to highly active
antiretroviral therapies (HAART) and the host immune system. Consequently, latency is a
barrier to the complete eradication of HIV. One promising therapeutic strategy is to purge
the latent cellular reservoir by systematically activating latent HIV with latency-reversing
agents (LRAs). However, recent observations demonstrate that induction of latent HIV
provirus is stochastic in patient samples despite maximum T cell activation, suggesting
that this strategy will be ineffective due to a failure to fully activate the latent reservoir.
This approach may be further limited by noise in the kinetics of HIV activation, which may
exceed the duration in which LRAs are effective. A better understanding of the dynamics
and sources of noise in HIV induction is necessary to optimize this strategy. The activation
of key host transcription factors, such as NF-κB, and the reversal of repressive chromatin
at the HIV promoter are critical for the reactivation of latent HIV, but it remains unclear how
these factors contribute to noise in HIV induction. We have used time-lapse microscopy
to the study the kinetics of HIV induction at a single-cell level in two Jurkat T cell line HIV
latency models in response to tumor necrosis factor (TNF), an activator of NF-κB signaling. We find that activation of HIV in both models exhibit substantial noise in the onset of
activation and the kinetics of activation, and that the strength of this noise depends on the
integration site. Co-stimulation with TNF and trichostatin A (TSA), a histone deacetylase
(HDAC) inhibitor, decreases mean activation time and noise in activation in both models,
suggesting that chromatin at the integration site contributes to transcriptional noise. Finally, by quantifying cell-to-cell variability in NF-κB activation using an immunostain, we
find that noise in the NF-κB signaling pathway may contribute to noise in HIV activation.
176
#108 : In Vivo Systems Analysis Identifies Cytokine Drivers
of Neurodegeneration in Alzheimer’s Disease
Levi Wood1,2 , Bradley Hyman2,3 , Douglas Lauffenburger4 , Kevin Haigis1,2
1
2
3
4
Department of Pathology, Massachusetts General Hospital, Boston, MA
Harvard Medical School, Boston, MA
Department of Neurology, Massachusetts General Hospital, Boston, MA
Alzheimer’s disease (AD) and related dementias are estimated to afflict more than 35
million people worldwide. The traditional approach to developing AD therapeutics has
been to intervene in the processing and extracellular accumulation of amyloid beta (Aβ).
Nevertheless, therapies based on the “amyloid hypothesis” have shown minimal efficacy
in patients, suggesting that the activity of Aβ represents only one aspect of the complex
pathogenesis of AD. Since the initial and ongoing physiologic response to Aβ involves microglial and astrocyte immune responses, we hypothesized that glial cytokines may play a
role in promoting AD pathogenesis. Here, we show that a computational modeling analysis correlating cytokine protein expression to quantified pathologic disease state was able
to identify a signature that reliably distinguished postmortem AD brain tissues from nondemented control brains. By comparing expression in different regions of the brain within
individual patients, we were able to identify the cytokines (TNF-α, VEGF, IL-12, and IL-5)
that were most up-regulated in the most degenerative brain region. By applying these
cytokines to primary neuron cultures, we determined that TNF-α and IL-12, both of which
have previously been connected to AD pathogenesis, promoted neuronal death. Surprisingly, we also found that VEGF promoted neuronal death, although only in the presence
of Aβ. This synthetic death phenotype was commensurate with changes in phosphorylation of multiple signaling pathways that govern cell fate and could be abrogated by a
small molecule inhibitor of VEGFR. These data implicate pro-death cytokine signaling in
the pathogenesis of AD and, more broadly, demonstrate that multi-factorial computational
methods can identify novel therapeutics leads for the disease.
Boston, MA
June 17-19, 2014
177
Poster Abstracts
#109 : Exploring the cellular responses to short pulses of
TNF
Robin Lee1,2 , Mohammad Quasaimeh3 , Xianfang Xia1,2,4 , Kate Savery1 , David Juncker3 ,
Suzanne Gaudet1,2,4
1
Department of Cancer Biology and Center for Cancer Systems Biology, Dana-Farber Cancer Institute,
Boston, MA, USA
2
Department of Genetics, Harvard Medical School, Boston, MA, USA
3
Department of Biomedical Engineering, McGill University, Montreal, Canada
4
BBS Program, Harvard Medical School, Boston, MA, USA
How do cells make decisions? We are particularly interested in the cellular responses
to the inflammatory cytokine Tumor Necrosis Factor (TNF). TNF has been implicated in
a variety of diseases. More intriguingly, TNF can promote cell death and cell survival at
the same time. It promotes survival by activating the transcription factor NF-kB while it
promotes cell death by activating the caspase family of intracellular proteases.
In most studies of TNF-induced signaling and cell death, cells are exposed to TNF continuously. However, in vivo, exposure to cytokines such as TNF can be of short duration.
By using a simple microfluidic system and monitoring the real time response of cells
expressing a fluorescently-tagged copy of the NF-kB subunit RelA, we established the
concentration-dependent minimal duration of TNF treatment that induces NF-kB mediated transcription in HeLa cells. We found that while a single 30 second pulse is sufficient
for full activation of NF-kB with 100 ng/ml TNF, a 1 minute pulse is needed for full activation with 10 ng/ml TNF. Strikingly, we observed that the duration of a TNF pulse also has
a great impact on the timing and percentage of cell death. A single TNF pulse of 1 minute
induces as much cell death as continuous treatment for 10 hours, but less death is seen
with a pulse of 1 hour.
178
#110 : A Multiscale-Modeling Approach Towards Understanding the Biological Response to Stress
Nilgun Yilmaz1 , Hans V. Westerhoff2,3 , Andrzej Kierzek4 , Nick Plant4
1
VU Amsterdam, FALW, Molecular Cell Physiology, Amsterdam, The Netherlands.
VU Amsterdam, FALW, Molecular Cell Physiology, Amsterdam, The Netherlands.
3
Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre (MIB), Manchester, UK
4
Centre for Toxicology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey,
GU2 7XH, UK.
2
Biological systems are routinely exposed to external challenge, such as the stress response where each challenge causes a transient spike in blood cortisol. It is clear
that prolonged or repeated stress is associated with the development of pathophysiology; for example, high stress police work is associated positively with the development of
metabolic syndrome.
It is both an important biological question, and challenging modeling scenario, to understand how these metabolic networks function efficiently, balancing both the pharmacodynamic (physiological effect of the challenge on the biological system) and pharmacokinetic
(metabolic response to the challenge, leading to a return to homoeostasis) responses to
stress. This topic has been covered in our previously published small-scale kinetic model
showing an interaction of the stress hormone cortisol with its two cognate receptors, the
high affinity glucocorticoid receptor (GR) and the low affinity pregnane X-receptor (PXR)
[1]. In this publication, we were able to demonstrate that the interaction between these
two receptors, plus the ligand promiscuity of PXR, were central to adapting to increasing frequency stress, potentially reducing the risk of pathologies associated with chronic
stress. However, to fully understand the importance of this interaction network it is important to extend the model to represent whole body responses to stress signalling.
In this work, we are working on development of a multi-scale model that can reproduce the
complex biological responses to stress stimuli. To achieve this, a genome scale metabolic
network (Recon2) was coupled to Petri net representations of the regulatory/gene signalling networks for both the interaction of cortisol with GR and PXR, and the circadian
rhythmicity of cortisol expression. In this presentation, I would like to show you Recon2
is coupled with our small-kinetic network. If the results are available until the conference,
I would like to share some results about the influence of circadian rhythmicity in stress
response, as well.
1. Kolodkin et al. (2013) Nature Communications 4: 1792.
Boston, MA
June 17-19, 2014
179
Poster Abstracts
#111 : Elucidating Mechanisms of Liver Metastasis: an
All-Human Microphysiological Model to Investigate Disease Progression & Therapies
Carissa L. Young1 , Sarah E. Wheeler2,3 , Amanda M. Clark2,3 , Donna B. Stolz2,3 , Alan
Wells2,3 , Linda G. Griffith1 , Douglas A. Lauffenburger1
1
Departments of Pathology, Cell Biology, Pharmaceutical Sciences, Bioengineering, University of Pittsburgh, PA
3
McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA
2
Approximately 90% of cancer-associated mortality is a consequence of distant metastasis, whereby cells from the primary tumor invade or migrate from a primary site to secondary organs. These cancerous cells may either proliferate immediately or lay dormant
(e.g. as pre-malignant micrometastases) for years prior to detection. Clinically, undetected metastases have serious implications for cancer patients; exemplified by ∼33%
of women whom suffer a metastatic relapse within 5 years following removal of breast
cancer. Distant metastases are generally more resistant to treatments than the primary
tumor, hence facilitates the need to develop improved therapeutic approaches rationally
designed based on the molecular pathophysiology within the metastatic microenvironment.
To understand how cellular network deregulation underlies complex disease states, such
as cancer, we implemented systems-based approaches to elucidate human-specific cellular crosstalk between tumor and hepatic tissues. The integration of primary cell sources,
computational and systems biology approaches, as well as emerging biomaterials strategies and microfabrication processes promoted novel experiments and analyses to investigate breast cancer metastasis. We recapitulated the fundamental physiologic functions
and conditions of the liver hepatic niche, including multi-cellular composition, metabolism,
and protein production. Specifically, we determined how changes in the populations of
multiple cell types, in the presence or absence of MDA-MB-231 cancer cells, regulate the
response to inflammatory signals, i.e. cytokines, chemokines, and growth factors while
corroborating the effects of cell function and viability across a broad spectrum of metrics.
Statistical techniques, e.g. PCA and PLS – led to novel insights attributed to cytokine
profiles and signaling networks, providing biological insights of plausible signatures for
early metastatic disease. Collectively, the all-human microphysiological composition of
our experimental system enables the development and validation of predictive models for
cell/tissue behavior relevant in drug discovery.
180
#112 : Detecting and Remembering Gut Dysfunction in
Biomimetic Microfluidic Devices with Living Bacterial Diagnostics
S. Jordan Kerns1,2 , Jonathan W. Kotula1,2 , Katia Karalis2 , Jeffrey C. Way2 , Donald Ingber2 ,
Pamela A. Silver1,2
1
2
Department of Systems Biology, Harvard Medical School
Wyss Institute for Biologically Inspired Engineering
The mammalian gut is a dynamic community of symbiotic microbes that closely interact
with the host and have a profound impact on human health such as inflammatory/autoimmune
disease, obesity, and diabetes. However, our ability to non-destructively interrogate the
gut is limited. We have engineered E.coli and S.typhimurium that sense and record environmental stimuli, and have demonstrated that engineered bacteria with such memory
systems can survive and function in the mammalian gut. The challenge now is to harness
the powerful endogenous sensing and signaling networks of enterobacteria like E.coli and
S.typhimurium to record specific disease biomarkers. However, building biological memory systems capable of acting as clinically relevant diagnostic agents requires iterative
engineering that is impractical with animals and incompatible with cell-based models that
fail to recapitulate key aspects of gut physiology. Biosimilar ‘Gut-on-a-Chip’ microfluidic
devices mimic the mechanical and physiological microenvironments of the mammalian
gut and can be applied as an ex vivo platform to accelerate the development of nondestructive, living bacterial diagnostics of gut enteropathy. Specifically, synthetic bacterial
memory systems activated by upstream endogenous bacterial gene-promoters are shown
here to detect and respond to disease-like states induced in Gut-on-a-Chip devices.
Boston, MA
June 17-19, 2014
181
Poster Abstracts
182
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SBHD 2014 Program Book - Systems Biology of Human Disease 2014

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