docAn Integrated System-Wide Maize Atlas: from Transcriptome to
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An Integrated System-Wide Maize Atlas: from Transcriptome to
1/19/2016 What shapes phenotype? An Integrated System-Wide Maize Atlas: from Transcriptome to Proteome Networks Heirloom tomatoes (color, shape, taste…) Justin Walley Iowa State University Butterfly spot patterns Plant Pathology & Microbiology Dog breeds (pigmentation, morphology…) Advancing our understanding of biological systems thru transcriptomics Root Spatiotemporal map (Brady et al., Science 2007) Mouse & human maps (Su et al., PNAS 2004) Maize gene expression atlas Pericarp/ Aleurone •27 DAP Endosperm •8 DAP •10 DAP •12 DAP •27 DAP Embryo •20 DAP •38 DAP • Germinating Anther Tassel •1 mm •2 mm Vegetative Meristem Leaf •Symmetrical DZ •Stomatal DZ •Growth Zone •Juvenile •Mature Silk Spikelet Ear Primordia •1 mm •2-4 mm •6-8 mm Internode •6-7 •7-8 Primary Root •Whole Root •Stele •Cortex •Maturation Zone •Elongation Zone mRNA and Protein Quantification Pollen •Mature •Germinated Seminal Root Chloroplast Mitochondria •Seedling Glyoxysome •Bottom Leaf •Middle Leaf •Top Leaf Complete maize atlas data summary •Proteome: 33 tissues and developmental stages •Sub-cellular organelles •Transcriptome: 23 tissues and developmental stages Gene A Gene B Total Identified • Counting based relative quantification • The number of mRNA fragments (RNAseq) or spectra (protein) that map to a given gene is proportional to the amount of the gene-product • mRNA quantified using Cufflinks • Protein quantified using Spectral Counting Transcriptome (RNA-seq) 62,547 Non-Modified Proteome 18,646 Phosphorylation Sites 31,595 Most comprehensive integrated dataset for any organism Liu et al., Analytical Chem 2004 www.maizegdb.org Walley et al., Unpublished 1 1/19/2016 Low abundance mRNA rarely produce protein Assessing the relationship of mRNA to protein DNA Transcriptional Regulation mRNA AAA Post-transcriptional Regulation Translational Regulation Post-translational Regulation Protein Weakly positive correlations between mRNA and protein 0.7 Pearson Correlation Proteins are more likely to originate from genes annotated as protein coding Detected mRNA Detected Protein 5% 44% 56% Filtered Set 0.6 0.5 0.4 0.3 0.2 0.1 0 95% Working Set • Calculated using the 14,030 genes who’s mRNA and protein were both quantified mRNA, protein, and phosphoprotein exhibit different accumulation patterns Abundance Phosphoprotein Vegetative Meristem Internode 6-7 Internode 7-8 Leaf Zone1 Leaf Zone 2 Leaf Zone 3 Mature Leaf Primary Root Root Meristem Root Cortex Root Elong Zone Secondary Root Mature Pollen Female Spikelet Ear Prim 2-4mm Ear Prim 6-8mm Silk Endosperm 12 DAP En Crown 27 DAP Per/Aleu 27 DAP Embryo 20 DAP Embryo 38 DAP Germ Kern 2 DAI Non-modified Protein Vegetative Meristem Internode 6-7 Internode 7-8 Leaf Zone1 Leaf Zone 2 Leaf Zone 3 Mature Leaf Primary Root Root Meristem Root Cortex Root Elong Zone Secondary Root Mature Pollen Female Spikelet Ear Prim 2-4mm Ear Prim 6-8mm Silk Endosperm 12 DAP En Crown 27 DAP Per/Aleu 27 DAP Embryo 20 DAP Embryo 38 DAP Germ Kern 2 DAI Vegetative Meristem Internode 6-7 Internode 7-8 Leaf Zone1 Leaf Zone 2 Leaf Zone 3 Mature Leaf Primary Root Root Meristem Root Cortex Root Elong Zone Secondary Root Mature Pollen Female Spikelet Ear Prim 2-4mm Ear Prim 6-8mm Silk Endosperm 12 DAP En Crown 27 DAP Per/Aleu 27 DAP Embryo 20 DAP Embryo 38 DAP Germ Kern 2 DAI mRNA Clustered How do mRNA and protein coexpression networks compare? A B C 1 • 2 3 4 5 6 Sample 7 8 Gene coexpression networks are used to determine how genes are related to one another – Closely related genes often function in similar biological processes • mRNA or proteins with correlated expression patterns are considered coexpressed and are connected in a coexpression network Horvath and Dong, PLoS Comp Bio 2008 2 1/19/2016 Coexpression network analyses mRNA vs mRNA Topology of mRNA and protein coexpression networks is different Protein vs Protein Hypothetical • Observed Suggests that different conclusions on the functional relatedness of genes will reached • Calculated using the 10,979 genes who’s mRNA and protein were both quantified in at least 5 tissues # of edges per node (mRNA) Most hubs are not shared between mRNA and protein co-expression networks Gene Regulatory Network (GRN) reconstruction • Used GENIE3 to build the GRNs # of edges per node (protein) Huynh-Thu et al. PLoS One 2010 GRN predicts known Kn1 targets • GENIE3 predicts true TF targets determined using ChIP-seq Topology of mRNA and protein derived GRNs is different • Little overlap in the predicted TF targets when using mRNA versus protein abundance as the predictor Bolduc et al. GenesDev 2012 3 1/19/2016 Integrated network predicts more true Kn1 targets Summary & Perspectives • Proteomic profiling provides a genome-wide view of cellular composition and signaling that cannot be inferred from mRNA observations alone • Limited overlap between mRNA and protein derived coexpression networks • GRNs reconstructed using mRNA, protein, or phosphorylation are largely complementary • http://maizeproteome.ucsd.edu/ • http://www.maizegdb.org/ Topology of mRNA and protein coexpression networks is different Acknowledgments Steve Briggs Zhouxin Shen Ryan Sartor Kevin Wu Josh Osborn NRSA Postdoctoral Fellowship Collaborators Joe Ecker (Salk Institute) Bob Schmitz (Univ Georgia) Vineet Bafna (UCSD) Natalie Castellana San Diego Center for Systems Biology Collaborators Laurie Smith (UCSD) Michelle Facette Virginia Walbot (Stanford) John Fowler (Oregon State Univ) Frank Hocholdinger (Univ Bonn) Eric Schmelz (USDA) Alisa Huffaker (USDA) Plant Genome Research Program Topology of mRNA and protein derived GRNs is different Some abundant proteins lack mRNA Protein Abundance Rank (Thousands) Endosperm 12 DAP Embryo 20 DAP 8 10 6 7.5 4 5 2 2.5 0 0 0 6 12 18 24 0 6 12 18 mRNA Abundance Rank mRNA Abundance Rank (Thousands) (Thousands) Protein > mRNA Protein = mRNA mRNA > Protein 24 • Little overlap in the predicted TF targets when using mRNA versus protein abundance as the predictor Microarray: Sekhon Plant J 2011 Walley et al., PNAS 2013 4 1/19/2016 Hypothesis 1: Transcript levels cycle diurnally while the protein is stable Model Observed 20 Protein Observed 16 DAP Protein mRNA Abundance # of Transcripts Abundance Model Cycling Non-Cycling 16 12 mRNA Hypothesis 2: Transcription and translation occur earlier in development 8 18 DAP 2 1 0 4 12 3 4 1 Time (h) 16 18 Time (DAP) 0 20 er yo sp br do Em En 20 DAP m • In Arabidopsis, of ~1700 diurnally cycling mRNA only 2 produced proteins that cycled Circadian Microarray: Khan et al. 2010 Baerenfaller et al., MSB 2012; Walley et al., PNAS 2013 Hypothesis 3: Protein moves from another tissue Model Microarray: Sekhon Plant J 2011 Walley et al., PNAS 2013 Eukaryotic Gene Expression Observed # of Transcripts Endosperm = En mRNA P Embryo = Em 45 40 35 30 25 20 15 10 5 0 20 DAP DNA Transcriptional Regulation mRNA AAA Post-transcriptional Regulation Translational Regulation P Post-translational Regulation Protein Whole Seed = WS Microarray: Sekhon Plant J 2011 Walley et al., PNAS 2013 Predicting protein kinase-substrate relationships Kinase activation is independent of abundance Activated Kinase Kinases are activated by phosphorylation of their activation loop domain Non-modified Kinase 67 AI D 2 Em P m A P er D G 38 AP DA P D 7 A E m 20 u 2 D l e 27 Emr/A n w P e C ro AP D E n 1 2 AP D E n 10 P A I A En D 2 D 8 En Em P m A P er D G 8 AP DA P 3 D 7 A E m 20 u 2 D l e 27 Emr/A n w P e C ro AP D E n 1 2 AP D E n 1 0 AP En 8 D En Relative Abundance 0 0.2 0.4 0.6 0.8 1 Walley et al., PNAS 2013 5 1/19/2016 Abundance Predict kinase-substrate relationships on the basis of coexpression Protein kinase-substrate network reconstruction Activated kinase A Phosphopeptide 1 Phosphopeptide 2 • Network of 9 activated kinases and 762 substrates 1 2 • Significant overlap between predicted ZmMPK6 substrates and known Arabidopsis MPK6 substrates 3 1 2 3 4 5 6 Sample 7 4 8 6 5 • Correlate the expression pattern for each activated kinase to the expression pattern of all phosphopeptides 7 9 – Phosphopeptide 1 is a predicted substrate of activated kinase A 8 Yellow dots = Activated Kinase Red dots = Predicted Substrate Walley et al., PNAS 2013 Conserved amino acids are predicted to be phosphorylated by the same kinase OPH2 OPH1 • bZIP TFs OPH1 & 2 are predicted to be phosphorylated by a GSK kinase • Can mutation of this GSK enhance the nutritional quality of maize? * OPH2 OPH1 OPH2 OPH1 a, g zein GSK OPH2 OPH1 Spatiotemporal patterns of developmental and biochemical processes Clustering of non-modified proteins (≥5 normalized spectral counts; 4439 proteins) Germ Em 2 DAI Em 38 DAP Em 20 DAP Per/Aleu 27 DAP En Crown 27 DAP En 12 DAP En 10 DAP En 8 DAP sis he nt Sy & ch e e ar as as n St GP ote atio A Pr rad e in eg te D ys h C tarc S & ds oi an op pr e yl at en on Ph asm J ll a lW el ed t C la gu re A AB m is ol ab et M d pi Li s si he nt Sy n ei ot & s Pr si he nt Sy n n tio ei a ot ad Pr egr D OPH1 O2 OPH2 OPH1 Walley et al., PNAS 2013 Low P OPH2 OPH1 X OPH2 OPH1 O2 – Suggests that this defense hormone plays a role in protecting the seed a, g zein OPH2 OPH1 OPH2 OPH1 High • Lipid biosynthesis is enriched in the early embryo • Starch metabolism is enriched in the crown endosperm • Jasmonate biosynthesis is enriched in the pericarp OPH1 Walley et al., PNAS 2013 Walley et al., PNAS 2013 Identified sites of phosphorylation that may regulate starch biosynthesis Cytosol Starch Sucrose SP SH1 UDP P P P P P Fructose P P AE Phosphorylation regulates mitochondrial carrier SBEI familySP proteins P ZPU1 SUS1 P SU1 P ISO2 SUS2 ISO3 P SBE2a AE P P P Phosphatase SBE1 P UGP1 P P SSIV UGPa DU1 SSIIIb SSIIb SSIIc P P UGPb SU2 UTP AE SP UDP-glucose SS1 Glucose 1-P PPi ATP WX1 ADP-glucose BT2 BT1 AGPa AGPSLZM AGPb Glucose 6-P AGPa AGPSLZM AGPb P P P Relative Abundance High P Plastid AP D AP 27 D u 7 le n 2 r/A w Pe Cro AP D En 1 2 A P D I En 1 0 P D A A En 8 D m 2 E En r m A P e D G 38 A P D Em 2 0 PGMb P P P PGIa FRK2 Low PGMa P P PHI1 FRK1 AGPc Glucose 1-P GPT Em ADP PGIb P AGPSEMZM ATP AGPc P SH2 P PPi FRKb GBSII BT2 P P P P AGPSEMZM P SBEI ADP-glucose SH2 P ATP FRKa Kinase P P ADP P Walley et al., PNAS 2013 6
