Dynamic characterization of olfactory receptors in the fruit fly

Transcription

Dynamic characterization of olfactory
receptors in the fruit fly
Drosophila melanogaster
!
$###%
(&'&
Dynamic characterization of olfactory receptors
in the fruit fly
Dissertation for obtaining
the degree doctor of natural sciences
(Dr. rer. nat.)
Presented to
the department of biology Universität Kassel
by Julia Schuckel
Kassel 2010
Accepted as dissertation by Fachbereich 18
Universität Kassel
First Referee: Prof. Dr. Monika Stengl
Second Referee: Prof. Andrew French PhD (Dalhousie University, Halifax)
Day of the oral examination:
2010
Contents
ERKLÄRUNG: EIGENE BEITRÄGE UND VERÖFFENTLICHTE TEILE DER ARBEIT .......................ii
SUMMARY ............................................................................................................................................1
ZUSAMMENFASSUNG I ...................................................................................................................... 3
AUSFÜHRLICHE ZUSAMMENFASSUNG II ........................................................................................5
Kapitel 1…………………………………………………………………………………………………………..8
Kapitel 2…………………………………………………………………………………………………………..9
Kapitel 3……………………………………………………………………………………….…………………11
Fazit 1-3 .……………………………………………………………………………………………………… 13
References…………………………………………………………………………………………………….. 14
INTRODUCTION ...............................................................................................................................17
The antennal olfactory system………………………………………………………………………….17
Sensillum structure…………………………………………………………………………………………. 19
Olfactory receptors…………………………………………………………………………………………. 19
Olfactory transduction………………………………………………………………………………….…. 22
Odor plumes………………………………………………………………………………………………….. 24
Odor stimulation…………………………………………………………………………………………….. 25
Analysis methodology……………………………………………………………………………………… 26
Aims of the thesis…………………………………………………………………………………………… 28
Literature………………………………………………………………………………………………………. 29
ABBREVIATIONS ..............................................................................................................................33
Chapter 1: Dynamic properties of Drosophila olfactory electroantennograms INTRODUCTION ...............................................................................................................................37
MATERIALS AND METHODS ...........................................................................................................38
Stimulation……………………………………………………………………………………………………..38
Stimulus and response measurements……………………………………………………………… 39
Experimental control and data processing………………………………………………………….39
Experimental protocols…………………………………………………………………………….…….. 39
RESULTS ...........................................................................................................................................39
DISCUSSION .....................................................................................................................................41
Different fruit odors produced characteristically different dynamic responses………..41
The basis of the time delay…………………………………………………………………..………….42
Why do Drosophila need such rapid responses to fruit odors………………………….……42
REFERENCES ....................................................................................................................................43
Chapter 2: A digital sequence method of dynamic olfactory characterization
INTRODUCTION ...............................................................................................................................47
Stimulator design…………………………………………………………………………………………….48
Stimulus measurements…………………………………………………………………………………..48
Electrophysiological measurements …………………………………………………………………. 48
RESULTS ...........................................................................................................................................49
Frequency response and coherence functions of the
stimulating system…………………………………………………………………………………………. 49
Reliability of stimulation parameters at the sensory organ…………………………………..49
Effects of flow velocity on stimulation bandwidth and odorant concentration…….…. 50
Experimental demonstration……………………………………………………………………………. 50
DISCUSSION .....................................................................................................................................51
Methods of dynamic olfactory stimulation………………………………………………………….51
Construction and operation………………………………………………………………………………51
Frequency range and receptor adaptation………………………………………………………….51
Other system characteristics…………………………………………………………………………….51
Conclusions……………………………………………………………………………………………………. 52
REFERENCES ....................................................................................................................................52
Chapter 3: Two Interacting Olfactory Transduction Mechanisms Have linked
Polarities and Dynamics in Drosophila melanogaster Antennal Basiconic Sensilla
Neurons
INTRODUCTION ...............................................................................................................................55
Animal preparation and electrophysiology………………………………………………………….56
Olfactory stimulation………………………………………………………………………………………. 56
RESULTS ...........................................................................................................................................57
Types of sensilla recorded………………………………………………………………………………..58
Excitatory and inhibitory responses…………………………………………………………………..59
Responses to mixed excitatory and inhibitory odorants……………………………………….60
Dose-response estimation………………………………………………………………………………..60
Responses of smaller units……………………………………………………………………………….61
DISCUSSION .....................................................................................................................................61
Physiological basis of the dynamic parameters…………………………………………………..61
Differences to previous data…………………………………………………………………………….63
Mechanisms of excitatory and inhibitory responses in single neurons…………..…….. 63
REFERENCES ....................................................................................................................................63
Erklärung: Eigene Beiträge und veröffentlichte Teile der Arbeit
Kapitel 1: Dynamische Eigenschaften von olfaktorischen Elektroantennogrammen an Drosophila
(Dynamic properties of Drosophila olfactory electroantennograms)
·
·
·
·
Durchführung aller Ableitungen mit Pheromonstimulation und einem Teil der Ableitungen
mit Duftstimulation bei 28 Fliegen.
Auswertung und Analyse aller Experimente
Verfassen des Manuskripts in Zusammenarbeit mit Prof. Andrew French, PhD und Prof.
Päivi Torkkeli, PhD
Dieses Kapitel wurde in der vorliegenden Form im Journal of Comparative Physiology A
veröffentlicht.
(Dynamic properties of Drosophila olfactory electroantennograms Schuckel J, Meisner S,
Torkkeli PH, and French AS. J Comp Physiol A 194:483–489, 2008)
Kapitel 2: Eine digitale Sequenz -Methode zur dynamischen olfaktorischen Charakterisierung
(A digital sequence method of dynamic olfactory characterization)
·
·
·
·
·
·
Planung und Umbau des Ableitstandes
Entwicklung des Stimulationsystems in Zusammenarbeit mit Prof. Andrew French, PhD
Durchführung aller Experimente
Verfassen des Manuskripts in Zusammenarbeit mit Herrn Prof. Andrew French, PhD
Dieses Kapitel wurde in der vorliegenden Form im Journal of Neuroscience Methods
veröffentlicht.
(A digital sequence method of dynamic olfactory characterization. Schuckel J and French
AS J Neurosci Methods 171: 98–103, 2008)
Kapitel 3: Zwei miteinander interagierende Transduktionsmechanismen in basikonischen
Sensillen von Drosophila melanogaster zeigen einen Zusammenhang zwischen Polarität und
Dynamik
(Two Interacting Olfactory Transduction Mechanisms Have Linked Polarities and Dynamics
in Drosophila melanogaster Antennal Basiconic Sensilla Neurons)
·
·
·
·
Durchführung aller Experimente
Verfassen des Manuskripts in Zusammenarbeit mit Prof. Andrew French, PhD
und Prof. Päivi Torkkeli, PhD
Dieses Kapitel wurde in der vorliegenden Form im Journal of Neurophysiology
veröffentlicht.
(Schuckel J, Torkkeli PH, and French AS. Two Interacting Olfactory Transduction
Mechanisms Have Linked Polarities and Dynamics in Drosophila melanogaster Antennal
Basiconic Sensilla Neurons. J Neurophysiol 102: 214–223, 2009)
Summary
Summary
Temporal changes in odor concentration are vitally important to many animals orienting and
navigating in their environment. How are such temporal changes detected? Answering these
questions requires accurate methods of delivering dynamically changing temporal-spatial
odor stimuli and quantitatively characterizing the resulting neural responses.
Within the
scope of the present work an accurate stimulation and analysis system was developed to
examine the dynamics of physiological properties of Drosophila melanogaster olfactory
receptor organs. Subsequently a new method for delivering odor stimuli was tested and
used to present the first dynamic characterization of olfactory receptors at the level of single
neurons.
Initially, recordings of the whole antenna were conducted while stimulating with different
odors. The odor delivery system used was a servo controlled system allowing the dynamic
characterization of the whole fly antenna, including its sensilla and receptor neurons.
Based on the obtained electroantennogram data a new odor delivery method called digital
sequence method was developed.
In addition the degree of accuracy was enhanced,
initially using electroantennograms, and later performing recordings of odorant receptor cells
at the single sensilla level. In this work I could show for the first time that different odors
evoked different responses within one neuron depending on the chemical structure of the
odor. Two distinguishable responses were observed. The two types of responses differed in
their polarity, i.e. they were inhibitory or excitatory, and in their dynamics. Excitatory
responses were observed when stimulating with the aliphatic odors ethyl acetat, ethyl
butyrate and hexanol. Inhibitory responses were observed stimulating with the aromatic
substances phenylethyl acetate and methyl salicylate. Further experiments with a mixture of
inhibitory and excitatory odor could not be differentiated from responses of the inhibitory
odor alone. In addition different models for drosophila olfactory transduction in fruit odors
were proposed.
The present work offers new insights into the dynamic properties of olfactory transduction in
Drosophila melanogaster and describes time dependent parameters underlying these
properties.
1
Zusammenfassung I
Zusammenfassung
Für viele Tiere sind zeitliche Änderungen der Duftkonzentration von essentieller Bedeutung
bei der Orientierung in ihrer Umwelt. Luft-Turbulenzen beeinflussen die Verteilung von
Düften in Raum und Zeit. Aufgrund der Luftbewegungen entstehen komplexe Strukturen,
sogenannte Duftfilamente, in denen vielfältige Konzentrationsänderungen und Frequenzen
eines Duftes vorkommen können. Diese spezifische raum-zeitliche Struktur der Duftfilamente
ist von entscheidender Bedeutung. Wie werden diese Änderungen wahrgenommen? Um
diese
Fragen
beantworten
Stimulationsapparat
sowie
zu
ein
können,
wurde
in
Analyseverfahren
der
vorliegenden
entwickelt,
um
Arbeit
ein
olfaktorische
Rezeptororgane der Taufliege Drosophila melanogaster auf ihre dynamischen Eigenschaften
hin zu untersuchen. Zu Beginn der Arbeit wurden Ableitungen der ganzen Antenne,
Elektroantennogramme, durchgeführt. Die Stimulation mit verschiedenen Düften erfolgte
mittels eines servo-kontrollierten Stimulationssystems. Basierend auf den Ergebnissen der
Elektroantennogramme wurde die Methode in zweierlei Hinsicht weiterentwickelt. Zum einen
wurde die neu entwickelte digitale Sequenz-Methode zur Stimulation eingesetzt und zum
anderen wurde die Untersuchungsebene hin zu Einzelsensillenableitungen verfeinert. Im
Rahmen der Arbeit wurde diese neue Methode getestet und angewendet um, zum ersten
Mal,
dynamische
Charakterisierungen
von
olfaktorischen
Rezeptororganen
auf
Neuronenebene zu zeigen. Es konnte gezeigt werden, dass Düfte abhängig von ihrer
chemischen Struktur innerhalb eines Neurons unterschiedliche Antworten erzeugen. Zwei
verschiedene Antworttypen konnten beobachtet werden. Diese unterschieden sich in ihrer
Polarität,
exzitatorische Antworten konnten bei aliphatischen Düften und inhibitorische
Antworten bei aromatischen Düften beobachtet werden. Des weiteren waren Unterschiede
in
ihrer
Dynamik,
den
Eigenschaften
bei
verschiedenen
Änderungsraten
der
Duftkonzentration, zu erkennen. Experimente mit einer gleichanteiligen Mischung aus
aromatischem und aliphatischem Duft zeigten, dass die Antworten auf das Duftgemisch
nicht von denen auf die aromatischen Düfte allein zu unterscheiden waren. Anschliessend
wurden verschiedene mögliche Modelle für die Transduktion vorgestellt, welche diese
Ergebnisse erklären könnten.
Die vorliegende Arbeit präsentiert neue Einsichten in die dynamischen Eigenschaften der
olfaktorischen Transduktion bei Drosophila melanogaster und beschreibt zeitabhängige
Parameter, die diesen Eigenschaften zugrundeliegen.
3
Zusammenfassung II
Zusammenfassung:
Die Welt besteht aus einer unzählige Fülle physikalischer und chemischer Strukturen und
Substanzen. Ihre Sinnessysteme sind für Tiere Grundvoraussetzung, um ein stimmiges
Abbild ihrer Umwelt zu abzubilden. Jedes für Tiere durch ihre Sinne wahrnehmbare
Phänomen kann potentiell zur Orientierung und Navigation dienen. Der olfaktorische Sinn
spielt bei vielen Verhaltensweisen eine wichtige Rolle, unter anderem bei der Lokalisation
von Nahrungsquellen oder bei der innerartlichen Kommunikation durch Pheromone. Zum
Beispiel wird die Taufliege Drosophila melanogaster besonders stark von dem Duft von
Essigsäure oder fermentierendem Obst angelockt. Luft-Turbulenzen beeinflussen die
Verteilung von Düften in Raum und Zeit. Aufgrund der Luftbewegungen entstehen komplexe
Strukturen,
sogenannte
Konzentrationsänderungen
Duftfilamente
und
oder
Frequenzen
Duftwolken,
eines
Duftes
in
denen
vorkommen
vielfältige
können.
Die
spezifische raum-zeitliche Struktur dieser Duftfilamente ist entscheidend für das Verhalten
verschiedener Insektenarten. Beispiele sind die Orientierung von Gelbfiebermücken anhand
der Duftwolkenstruktur von CO2 beim Auffinden von Säugetieren (Geier et al. 1999), oder
die Flugorientierung von Triatoma infestans, einer blutsaugenden Wanzenart, in Richtung
von (ein- und aus-) atmenden Menschen (Barrozo und Lazzari 2006).
Die Flugroute von männlichen Schwärmern, die der Pheromonspur von weiblichen
Schwärmern folgen, ist ebenfalls dadurch bestimmt, wie die Struktur der Duftwolke bzw.
Pheromonwolke charakterisiert ist (Justus et al. 2005; Mafra-Neto und Cardé 1995; Willis
und Baker 1984, Murlis et al. 1992). Um erfolgreich navigieren zu können, ist offensichtlich
entscheidend, wann, wo und wie Gerüche wahrgenommen und verarbeitet werden. Bei
Untersuchungen an Schwärmern wurden Gehirnareale gefunden, welche eine wesentliche
Rolle bei der Informationsverarbeitung von Duftwolken spielen (Vickers et al. 2001). Über
die Transduktion der Informationen aus Duftwolken auf der Ebene der olfaktorischen
Rezeptorneurone, die ganz am Anfang des Verarbeitungsprozesses dieser dynamischen
Duftinformationen stehen, ist jedoch wenig bekannt. Deshalb ist es äußerst wichtig
herauszufinden, wie diese Konzentrationsänderungen detektiert werden und welche
Komponenten der olfaktorischen Transduktion zeitabhängig sind. Primäres Ziel dieser Arbeit
war es zunächst, dynamische Antworteigenschaften der Antenne und später auch einzelner
Sensillen der Taufliege D. melanogaster auf sich schnell verändernde Duftkonzentrationen
hin zu charakterisieren. Um diese Fragestellung untersuchen zu können, ist es zum einen
wichtig, dass die verwendete Methode es ermöglicht, olfaktorische Stimuli zu präsentieren,
5
Zusammenfassung II
deren Konzentration sich zeitlich dynamisch verändert, und zum anderen, dass die
resultierenden neuronalen Antworten quantitativ ausgewertet werden können.
Elektrophysiologische Ableitungen:
Bei
den
Ableitungen
von
der
Antenne
(Elektro-
antennogramme: EAGs) wurden Glaselektroden verwendet. Die Öffnung einer mit Elektrolyt
gefüllten Glaskapillare wurde über die Antennenspitze gestülpt. Bei den extrazellulären
Einzelableitungen der Sensillen wurden elektrochemisch angespitzte Wolframelektroden
verwendet, welche durch die Kutikula in das Haar eingestochen wurden. Die EAGs bzw.
Einzelsensillenableitungen
wurden
in
der
späteren
Auswertung
als
Ausgangssignal
verwendet.
Stimulusgabe und –messung: Zeitlich veränderliche Duftstimuli wurden zunächst mit Hilfe
eines servo-kontrollierten Windtunnels und später mit einem Windtunnel erzeugt, dessen
wesentlicher Bestandteil ein Ventil war, durch das ein Luft-Duft-Gemisch strömte und
welches sich Software-gesteuert digital öffnen und schließen ließ. Die verschiedenen
Trägergaskonzentrationen
Photoionisationsdetektor
(relative
(PID),
Duftkonzentrationen)
welcher
wurden
Gaskonzentrationen
mit
mit
einem
einem
zeitlichen
Auflösungsvermögen im Millisekundenbereich messen konnte, möglichst nah an der Antenne
gemessen. Die Besonderheit des PIDs im Vergleich zu anderen Gasmessgeräten ist die
Eigenschaft, auch besonders schnelle Gaskonzentrationsänderungen (0 – 330 Hz) noch
auflösen und detektieren zu können. Innerhalb des PIDs wird das Gas hohen Intensitäten
von ultraviolettem Licht ausgesetzt. Das Licht ionisiert die Moleküle des Gases.
Darauffolgend verteilen sich die Ionen an positiven und negativen Elektroden. Die so
entstandene
Ionenverteilung
erzeugt
einen
Strom,
welcher
proportional
der
Gaskonzentration ist.
Frequenzanalyse: Die Frequenzanalyse ist eine Technik zur Charakterisierung dynamischer
Eigenschaften eines unbekannten Systems. Voraussetzung ist, dass die Eingangs- und
Ausgangssignale des Systems als Funktionen der Zeit repräsentiert werden können. Die am
häufigsten verwendete und hier eingesetzte Art der Frequenzanalyse basiert auf der
Annahme, dass das zu analysierende Input/Output-System linear und stationär ist. Linear
bedeutet,
dass
die
Verdoppelung
des
Eingangssignals
eine
Verdoppelung
des
Ausgangssignals zur Folge hat. Stationär bedeutet, dass die Eigenschaften des Systems sich
nicht mit der Zeit verändern. Die Frequenzanalyse basiert auf der Fourier-Theorie, die
besagt, dass fast alle einfachen Funktionen der Zeit durch die Zusammensetzung von
mehreren Sinus- und Cosinusfunktionen der Zeit dargestellt werden können. Eine
Zusammenfassung II
Eigenschaft von linearen Systemen ist, dass eine Sinuskurve im Eingangssignal immer auch
eine Sinuskurve derselben Frequenz im Ausgangssignal erzeugt. Sowohl die Amplituden der
Sinuskurve im Ausgangssignal als auch die Maxima und Minima der Kurve des
Ausgangssignals ändern sich gewöhnlich im Vergleich zum Eingangssignal. Diese
Veränderung wird auch als Phasenverschiebung bezeichnet.
Um Frequenzantworten eines unbekannten Systems messen zu können, werden sinusoidale
Eingangssignale verschiedener Frequenzen erzeugt und die entstandenen Ausgangssignale
gemessen. Daraus wird die Änderung von Amplitude und Zeitverschiebung, des Eingangsgegenüber dem Ausgangssignal, in Abhängigkeit von der Frequenz berechnet. Die
Stimulation eines unbekannten Systems mit einem Rauschsignal, also der Summe aller
denkbaren Frequenzen eines bestimmten Frequenzbereiches, ermöglicht die Messung der
kompletten Frequenzantwort in einem einzigen Experiment. Dazu werden sowohl das
Rauschsignal (Eingangssignal) als auch das Ausgangssignal mit Hilfe der schnellen FourierTransformation in deren einzelne Frequenzanteile zerlegt.
Um Frequenzantwortanalysen zu generieren, wurden die gemessenen Konzentrationsunterschiede des PIDs als Eingangssignal (Input) und die neuronalen Antworten der
Antenne
oder
Sensillen
als
Ausgangssignal
(Output)
betrachtet.
Die
gemessenen
Frequenzantworten sowohl der gesamten Antenne als auch einzelner Rezeptorneurone
ließen sich gut mit Hilfe von Filterfunktionen beschreiben. Die Parameter der Filterfunktionen
(Amplitude, Zeitkonstanten und Zeitverzögerung) waren jeweils charakteristisch für die
getesteten Düfte. Dabei ist die Amplitude (α) ein Maß für die Antwortstärke, die
Zeitkonstanten (τx)
beschreiben
die
Grenzfrequenzen
der
Filterfunktion G(f).
Die
Zeitverzögerung (∆t) ist ein Maß für die Phasenverschiebung zwischen Eingangs- und
Ausgangssignal.
Aus den Frequenzantworten wurde außerdem die Informationskapazität (R) berechnet. R
gibt die maximale Datenrate an, die über einen Informationskanal (in diesem Fall die
Antenne oder die Rezeptorneurone) übertragen werden kann. R wurde aus der
Kohärenzfunktion bestimmt, welche die lineare Korrelation zwischen Eingangs- und
Ausgangssignal angibt.
Die Arbeit gliedert sich in drei Kapitel:
7
Zusammenfassung II
Kapitel 1:
Dynamische Eigenschaften von olfaktorischen Elektroantennogrammen an Drosophila
(Dynamic properties of Drosophila olfactory electroantennograms)
Die dynamische Charakterisierung von Photo- und Mechanorezeptoren konnte in der
Vergangenheit dazu beitragen, Aufschluss über funktionelle Komponenten und die
quantitative Repräsentierung sensorischer Informationen in diesen Sinnessystemen zu
geben (Juusola und French 1997, Juusola et al. 2003). Um das olfaktorische System von
Drosophila melanogaster besser untersuchen zu können, wurde ein servo-kontrolliertes
laminares Luftstrom-System verwendet und mit einem Photoinonisationsdetektor zum
Messen von Gaskonzentrationen kombiniert, um Elektroantennogramme zu charakterisieren.
Die Antenne von Fliegen beider Geschlechter wurde mit den vier verschiedenen Fruchtdüften
Butylbutyrat, Isoamylacetat, Hexylacetat und Phenylethylalkohol (Stensmyr et al.2003) bei
insgesamt 42 Tieren und dem Aggregationspheromon (Z)- 11-octadecenylacetat (Hedlund et
al. 1996) bei 24 männlichen Tieren stimuliert. In den gemessenen Elektroantennogrammen
konnten keine signifikanten Unterschiede in den Frequenzantworten auf die verschiedenen
Fruchtdüfte zwischen den Geschlechtern gefunden werden.
Frequenzantwortfunktionen wurden über eine Bandbreite von 0 bis 100 Hz gemessen und
konnten gut mit Tiefpassfilterfunktionen ersten Grades beschrieben werden. Für alle
verwendeten Düfte wurden jeweils verschiedene Parameter berechnet. Die verschiedenen
berechneten
Zeitkonstanten
(Zeitverzögerung (∆t)
und
die
Zeitkonstante (τ)),
Amplituden (α) und Informationskapazitäten R, waren für jeden Duft individuell und die
Werte variierten bis zum Zehnfachen voneinander. Die mittlere Zeitkonstante (τ) für die vier
Fruchtdüfte reichte von 4,40 ms bei Isoamylacetat bis zu 35,5 ms bei Phenylethylalkohol.
Die Grenyfrequenzen liegen also zwischen 36,3 Hz (Isoamylacetat)
und
4,5 Hz
(Phenylethylalkohol). Der Wert für die Amplitude (α) war ebenfalls für die verschiedenen
Düfte unterschiedlich und lag zwischen 3,64 pA/ppm für Isoamylacetat und 1,47 pA/ppm für
Phenylethylalkohol. Die Zeitverzögerung (∆t: Phasenverschiebung zwischen Eingangs- und
Ausgangssignal) war für alle Fruchtdüfte niedrig und war bei den verschiedenen Düften
nicht signifikant verschieden. Das negative Vorzeichen des Wertes der Zeitverzögerung (∆t)
bei den Fruchtdüften konnte durch die Zeit erklärt werden, die beim Einsaugen des Gases
durch die PID-Nadel vergeht. Die Antworten auf das Pheromon hatten wesentlich niedrigere
Amplituden (α) und Informationskapazitäten (R) als die, die bei den restlichen getesteten
Zusammenfassung II
Düften vorkamen. Bei beiden Parametern Amplitude (α) und Informationskapazität (R)
waren die gemessenen Werte beim Pheromon im Vergleich zu den niedrigsten gemessenen
Parameterwerten für die Fruchtdüfte (Phenylethylalkohol) um ~60% kleiner. Die Werte der
Zeitkonstanten (τ) lagen für das Pheromon im gleichen Bereich wie die der Fruchtdüfte. Der
Zeitverzögerungswert (∆t) war jedoch im Gegensatz zu dem bei den Fruchtdüften
gemessenen Werten positiv. Eine mögliche Erklärung für die längere Zeitverzögerung beim
Pheromon könnte die Größe der Moleküle sein, die beim Pheromon im Vergleich zu den
getesteten Fruchtdüften wesentlich kleiner war. Die deutlichen Unterschiede zwischen den
Werten der Zeitkonstanten der verschiedenen Düfte inklusive des Pheromons deuten darauf
hin,
dass
vermutlich
verschiedene
Chemotransduktionsmechanismen
nebeneinander,
abhängig jeweils vom getesteten Duft, in der Antenne existieren. Die Ergebnisse warfen die
Frage auf, welche zeitabhängigen Schritte in der Transduktion die verschiedenen Parameter
beschreiben könnten. Mögliche Kandidaten waren Diffusion durch die Kutikulaporen,
Bindung an Duftbindemoleküle oder second-messenger-Kaskaden. Die Ergebnisse legten
nahe, weitere Experimente an einzelnen Sensillen durchzuführen.
Kapitel 2:
Eine digitale Sequenz-Methode zur dynamischen olfaktorischen Charakterisierung
(A digital sequence method of dynamic olfactory characterization)
Um olfaktorische Rezeptoren dynamisch zu untersuchen, gab es bis zum Zeitpunkt der
Publikation unterschiedliche methodische Ansätze. Zum einen wurde die Art der
Duftstimulation weiterentwickelt, hierzu führten Bau et al. (2005) Untersuchungen mit einem
Windtunnel durch, in den sie einen gepulsten Duftstimulus entließen. Ein anderer Ansatz
war, einen Pheromonreiz in einen turbulenten Luftstrom strömen zu lassen, um
unterschiedliche randomisierte Duftkonzentrationen zu generieren (Justus et al. 2005). Zum
anderen wurden Systeme entwickelt, um die Detektion des gegebenen Stimulus besser
messen zu können. Photoionisationsdetektoren (PIDs) ermöglichten die Messung von sich in
hoher Geschwindigkeit verändernden Gaskonzentrationen von Gasen mit niedrigem
Ionisationspotential, welche mit verschiedenen Düften vermischt werden konnten (Justus et
al., 2005; Vetter et al., 2006). Später wurde eine Methode entwickelt, die eine verbesserte
9
Zusammenfassung II
Kontrolle des Stimulus und eine genauere Messung der Gaskonzentration mit Hilfe des PIDs
bot. Bei dieser Methode wurde der Duftstimulus mit einem servo-gesteuerten System
generiert, welches Düfte kontinuierlich und variabel präsentieren konnte (French und
Meisner, 2007). In der vorliegenden Arbeit wurde eine neue, alternative Methode entwickelt,
die eine Weiterentwicklung der oben beschriebenen servo-gesteuerten Methode ist. Dabei
wurde ein Duft-Tracergas- Gemisch mit Hilfe eines Magnetspulenventils in einen
Luftstromkanal entlassen. Es wurde ein Ventil verwendet, welches sich durch pseudorandomisierte binäre Signale (M-Sequenzen) steuern ließ, die von einer Software generiert
wurden.
Um die dynamischen Eigenschaften des entwickelten Stimulationssystems zu überprüfen
wurden Frequenzantwortanalysen vorgenommen, bei denen als Eingangssignal die softwaregenerierte binäre Sequenz (das bedeutet, entweder geöffnetes oder geschlossenes Ventil)
und als Ausgangssignal die vom PID gemessene Gaskonzentration dienten. Es zeigte sich,
dass die Frequenzantwortfunktion G(jω) sich gut mit einem zuvor verwendeten Modell von
French und Meisner (2007) für laminaren Luftstrom entlang einer Röhre erklären ließ:
2
G(jω)=α exp(jω∆t) exp (-ω/ωc) 1/(1+jωτ)
Bei diesem war Omega (ω) die Kreisfrequenz, j= √-1, Alpha (α) die Amplitude der
Frequenzantwort, und Delta t (∆t) die Zeitverzögerung zwischen Öffnung des Ventils und
Ankunft des Tracergases am PID. Omegac (ωc) war die Grenzfrequenz einer Gauss´schen
Funktion, welche die Diffusion des Tracergases beschreibt. Tau (τ) war die Zeitkonstante
einer Tiefpassfilterfunktion für die Reibung des Gases an der Röhrenwand.
Um die positionsabhängige Zuverlässigkeit der Stimulation am sensorischen Organ
gewährleisten zu können, wurde überprüft, ob es Effekte bei unterschiedlichen Horizontalund Vertikalpositionen des PIDs vor der im Durchmesser 5 mm großen Mündung des
Luftkanals gab. Innerhalb der 5 mm waren die Effekte der Positionsänderung des PID in 1
mm Schritten um den Mittelpunkt herum zu vernachlässigen. Die Parameter ∆t und ωc
waren gänzlich unabhängig von der Position, die Amplitude (α) war im Zentrum maximal
und nahm von innen nach außen hin bis zu einem Minimum von 55% ab. Die
Zeitkonstante (τ) nahm von innen (~ 2,5 ms) nach außen (~6 ms) hin zu.
Um die experimentelle Anwendbarkeit des Stimulationssystems zu demonstrieren, wurden
Elektroantennogramme an der Antenne von D. melanogaster durchgeführt. Es wurden
Zusammenfassung II
Frequenzantwort- und Kohärenzfunktionen gemessen, bei denen mit den Fruchtdüften
Butylbutyrat und Hexylacetat stimuliert wurde. Das Eingangssignal war die vom PID
gemessene Gaskonzentration, während das Ausgangssignal das gemessene neuronale Signal
der Antenne war. Die gemessenen Frequenzantwortfunktionen ließen sich gut mit einer
Tiefpassfilterfunktion ersten Grades erklären.
Vorteile der digitalen Methode im Vergleich zu der servo-kontrollierten Stimulationsmethode
waren vor allem eine grössere Stimulusbandbreite, aber auch die unkompliziertere
Baukonstruktion des Systems und die Tatsache, dass durch den binären Charakter der
Stimulation duftfreie Phasen entstanden, eine Eigenschaft, die besonders bei der Stimulation
von schnell adaptierenden Rezeptoren wichtig ist.
Kapitel 3:
Zwei miteinander interagierende Transduktionsmechanismen in basikonischen
Sensillen von Drosophila melanogaster zeigen einen Zusammenhang zwischen
Polarität und Dynamik
(Two Interacting Olfactory Transduction Mechanisms Have Linked Polarities and
Dynamics in Drosophila melanogaster Antennal Basiconic Sensilla Neurons)
Wie der olfaktorische Transduktionsmechanismus in Drosophila melanogaster im Detail
funktioniert, konnte bisher nicht eindeutig geklärt werden. Es existieren verschiedene
Modelle zur Transduktion, welche jeweils auf physiologischen und genetischen Experimenten
basieren (Wicher et al. 2008, Sato 2008, Nakagawa und Vosshall 2009). Die dynamische
Charakterisierung von olfaktorischen Rezeptorneuronen eröffnet eine weitere Möglichkeit,
funktionelle Komponenten der Transduktion zu untersuchen.
In dieser Arbeit wurden Frequenzantwortfunktionen zwischen Fruchtdüften und den
neuronalen Antworten olfaktorischer Rezeptorneuone auf diese Düfte berechnet. Als
Eingangssignal (Input) diente hierbei die Fruchtduftkonzentration und als Ausgangssignal
(Output) Aktionspotentiale von einzelnen basikonischen Sensillen der Antenne von
Drosophila. Es wurde eine Methode benutzt, die zufällig variierende Duftkonzentrationen mit
schneller kontinuierlicher Messung der Konzentration, möglichst nah an der Antenne, mit
Hilfe eines PIDs ermöglichte (digitale Sequenz-Methode; Schuckel und French 2008). Um zu
überprüfen, ob sich die verschiedenen Duftkonzentrationen während der Experimente in
einem Bereich befanden, in dem sie keine Sättigung hervorrufen, wurden Dosis-AntwortBerechnungen erstellt. Bei der grafischen Darstellung von Antwortamplituden (α) und
11
Zusammenfassung II
Duftkonzentrationen war zu erkennen, dass die verwendeten Duftkonzentrationen ungefähr
in linearen Regionen der Dosis-Antwort-Beziehung lagen.
Das wichtigste Ergebnis dieser Arbeit war, dass zwei klar voneinander unterscheidbare
Antwortmuster
existieren:
Exzitatorische
Bandpassfilterantworten
bei
Ethylacetat,
Ethylbutyrat und Hexanol, und inhibitorische Tiefpassfilterantworten bei Methylsalicylat und
Phenylethylacetat. Das Maximum der Bandpassfrequenzantworten lag bei einer Frequenz
von 1-10 Hz.
Alle gemessenen Frequenzantworten ließen sich gut mit linearen Filterfunktionen erklären.
Die gefitteten Parameter (Amplitude (α), Zeitkonstanten (τx) und Zeitverzögerung (∆t))
stimmten jeweils bei den zwei verschiedenen Antworttypen miteinander überein. Das
Vorkommen von zwei verschiedenen Antworttypen warf die Frage auf, ob es möglicherweise
verschiedene Transduktionswege innerhalb desselben olfaktorischen Neurons geben könne,
und ferner, wie diese verschiedenen Antworten moduliert werden. Um dies weiter zu
untersuchen, wurden Experimente durchgeführt, in denen mit einer Mischung aus zwei
Düften
stimuliert
wurde.
Die
Mischung
bestand
jeweils
zu
gleichen
Teilen
aus
inhibitorischem und exitatorischem Duft. Bei Stimulation der Neurone mit der Duftmischung
wurden Antworten gemessen, die charakteristisch für die Antworten auf inhibitorische Düfte
waren. Eine mögliche Erklärung ist, dass die Interaktion während der Stimulation bewirkte,
dass inhibitorische Düfte die exzitatorischen Düfte unterdrücken.
Des
weiteren
wurde
für
jede
Ableitung
die
Informationskapazität (R)
aus
der
Kohärenzfunktion berechnet. Obwohl die Kohärenz relativ hoch war, ein Hinweis auf ein
gutes Signal-Rausch-Verhältnis, war die Informationskapazität (R), verglichen mit Werten
aus der visuellen oder der Mechanotransduktion, wesentlich kleiner. Die kleineren Werte der
Informationskapazität könnten eine Folge der im Vergleich zu den anderen Systemen
limitierten Bandbreite des olfaktorischen Stimulus sein.
Des weiteren wurden auf der Basis der Ergebnisse der Arbeit verschiedene mögliche
Transduktionsmodelle vorgeschlagen. Ein Modell war die Mehrfachbindung von exzitatorisch
und inhibitorisch wirkenden Duftmolekülen an einen Rezeptor mit jeweils gegensätzlichem
Effekt auf die Transduktionsionenkanäle, ein weiteres Modell die Möglichkeit mehrerer
Rezeptoren für die unterschiedlichen Düfte, die je nach Rezeptor gegensätzliche Effekte auf
die Inonenkanäle ausüben. Des weiteren könnten auch Kaliumkanäle, die an der Basis des
Rezeptorneurons liegen und nicht mit dem Sensillenlymphraum in Verbindung stehen, das
Öffnen und Schließen der Ionenkanäle beeinflussen. Weiterhin könnten auch verschiedene
Schließungsgrade der Transduktionskanäle existieren.
Zusammenfassung II
Fazit aus Kapitel 1-3
In den Arbeiten im Rahmen meiner Dissertation wurde eine präzise Stimulations- und
Auswertungsmethode
entwickelt,
um
physiologische
Rezeptororgane der Taufliege Drosophila melanogaster
Eigenschaften
olfaktorischer
dynamisch zu untersuchen.
Anschließend wurde diese Untersuchungsmethode auf ihre Anwendbarkeit geprüft und
verwendet, um die erste dynamische Charakterisierung von olfaktorischen Rezeptoren auf
Einzelneuronenebene zu präsentieren. Die Grundlage der Arbeit waren die Ableitungen der
ganzen Antenne mit verschiedenen Düften mit Hilfe eines servo-gesteuerten Systems, die
eine Beschreibung der dynamischen Antworten der ganzen Antenne, einschließlich aller ihrer
Sensillen und olfaktorischen Rezeptorneurone, lieferte. Darauf aufbauend wurde zum einen
die digitale Sequenz-Methode zur Duftstimulation entwickelt. Zum anderen wurde die
Untersuchungsebene von Ableitungen der ganzen Antenne hin zu Einzelsensillenableitungen
verfeinert, was eine detailliertere Charakterisierung auf neuronaler Ebene ermöglichte. Bei
den EAGs wurden deutlich voneinander unterscheidbare Zeitkonstanten zwischen allen
getesteten Düften inklusive des Pheromons gemessen. Besonders gross war der Unterschied
der Zeitverzögerungs (∆t). ∆t war bei den verwendetet Fruchtdüften negativ und beim
Pheromon positiv, dies bedeutet dass zwischen Eingangs- und Ausgangssignal beim
Pheromon mehr Zeit vergangen war. Dies könnte zum einen physikalische Gründe haben,
wie die Größe der Molekühle und daraus resultierende unterschiedliche Geschwindigkeiten
im Luftstrom. Zum anderen könnten aber auch unterschiedliche Transduktionmechanismen
zu den unterschiedlichen Zeitparametern führen. Bei den Sensillenableitungen wurde
ausschließlich mit Fruchtdüften stimuliert. Bei den Sensillenableitungen konnte ich zum
ersten
Mal
zeigen,
dass
es
zwei
verschiedene
Duftantworten
innerhalb
eines
Rezeptorneurons auf unterschiedliche Fruchtdüfte mit verschiedener chemischer Struktur
gab. Die beiden Antworttypen auf die Fruchtdüfte unterschieden sich sowohl in ihrer
Polarität, waren also inhibitorischer oder exzitatorischer Natur, als auch in ihrer Dynamik,
den Eigenschaften bei verschiedenen Änderungsraten der Duftkonzentration. Exzitatorische
Antworten wurden bei Stimulation mit den aliphatischen Düften Ethylacetat, Ethylbutyrat
und Hexanol beobachtet. Inhibitorische Antworten konnten bei Stimulation mit den
aromatischen Düften Phenylethylacetat und Methylsalicylat gemessen werden. Weitere
Experimente mit einer Duftmischung aus inhibitorischem und exzitatorischem Duft zeigten,
dass die Duftantworten nicht von denen der inhibitorischen allein unterscheidbar waren.
Außerdem wurden auf der Basis der vorliegenden Daten hypothetische Modelle für
13
Zusammenfassung II
verschiedene
mögliche
Wege
der
olfaktorischen
Transduktion
bei
Fruchtdüften
vorgeschlagen.
Eine über den Rahmen dieser Arbeit hinausgehende Frage ist, welcher Transduktionsweg
(metabotrop
oder
ionotrop)
oder
welche
Kombination
von
verschiedenen
Transduktionswegen bei Drosophila vorkommen? Es wird vermutet, daß ORs mit OR83b
Heterodimere bilden. Aber wirken diese heterodimeren Komplexe als Liganden gesteuerte
Ionenkanäle oder werden sie G-protein aktiviert? Ionenkanäle haben eine besonders
schnelle
Reaktionszeit
und
second
messenger
Transduktionswege
sind
besonders
empfindlich für geringe Konzentrationen von Duft. Beide Eigenschaften sind bei der
Verfolgung einer Duftspur vermutlich von Wichtigkeit. Aufbauend auf meinen bisherigen
Arbeiteiten wäre es interessant dynamische Charakterisierungen von OSNs durchzuführen,
die „loss of function” . Mutationen der G proteine oder second messenger aufweisen. Die
zeitlichen Parameter könnten im Vergleich zum Wildtyp Auskunft über die Kinetik der
Transduktion geben. Bei einem rein ionotropen Mechanismus würde kein Unterschied
sichtbar werden, bei einer ionotropen/metabotropen Mischform würde in den niedrigeren
Frequenzen eine Veränderung der zeitlichen Parameter auftreten, aber in den höheren
Frequenzen nicht.
Die vorliegende Arbeit bietet neue Erkenntnisse zu den dynamischen Eigenschaften der
olfaktorischen Transduktion bei Drosophila melanogaster und beschreibt zeitliche Parameter,
die ihr zugrunde liegen.
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temporal structure of the stimulus. J Comp Physiol A 2006; 192(8): 827-31.
Bau J, Justus KA, Carde RT. Antennal resolution of pulsed pheromone plumes in three moth
species. J Insect Physiol 2002; 48(4): 433-442.
Bau J, Justus KA, Loudon C, Carde RT. Electroantennographic resolution of pulsed pheromone
plumes in two species of moths with bipectinate antennae. Chem Senses 2005; 30(9): 77180.
French AS, Meisner S. A new method for wide frequency range dynamic olfactory stimulation and
characterization. Chem Senses 2007; 32(7): 681-8.
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Hedlund K, Bartelt RJ, Dicke M, Vet LEM. Aggregation pheromones of Drosophila immigrans, D.
Zusammenfassung II
phalerata, andD. subobscura. Journal of chemical ecology 1996; 22(10): 1835-1844.
Justus KA, Carde RT, French AS. Dynamic properties of antennal responses to pheromone in two
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Neuron 1997; 18(6): 959-68.
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the
light response in Drosophila photoreceptors. J Neurophysiol 2003; 90(3): 2014-21.
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flight of Cadra cautella males. Physiol Entomol 1995; 20: 117-133.
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15
Introduction
Introduction
Introduction
The world comprises an abundance of
odorants in space and time, creating a
physical, and chemical substances, and
range of chemical concentrations and
structures with a similarly wide range of
structures such as filaments and plumes.
properties. For animals, their sensory
Animals detect varying concentrations and
systems are the key to creating a valid
durations of odors while encountering or
conception of the external world. Any
following
property of the external world that an
Successful navigation depends crucially on
animal can perceive and possibly measure
when, where, and how these chemical
may also be used as a directional guiding
cues are encountered and processed.
cue to orient the animal and navigate in
its environment. The number of possible
chemical cues that animals can perceive is
unknown. In addition to various inorganic
odor sources, almost every organism
emits a range of odors. The sense of smell
allows animals to use these chemical cues
to interact with their environment in order
to locate food sources, avoid harmful
such
turbulent
structures.
The fruit fly, Drosophila melanogaster,
provides a useful and established model
animal that uses odor as a major sensory
input to its behavior. The primary aim of
this thesis was to investigate the dynamic
responses of the Drosophila antenna and
its olfactory receptors to rapidly changing
odor concentrations in time.
environments, find mating partners, and
perform numerous other behaviors.
Chemical cues may be pheromones that
play different roles in the interactions
between members of the same species, or
they are more general odors like food
odors. For example, fruit flies are strongly
attracted to the smell of vinegar or
fermenting fruit and male moths are
highly sensitive to the pheromone signal
emitted by female moths. Movements of
the air influence the distribution of
The antennal olfactory system
Fruit flies smell with their antennae and
maxillary pulps. I focused my work on the
antenna. Each antenna comprises three
segments,
two
basal
segments,
the
scapus and pedicellus, and a third distal
segment, the flagellum. The antenna
surface is covered with specialized sensory
hairs, known as sensilla (Fig 1). All about
410 olfactory sensilla of the antenna are
17
Introduction
located on
sensillum
the third segment. Each
contains
olfactory
sensory
al. 1999). Trichoid sensilla are sensitive to
pheromones,
coeloconic
sensilla
are
neurons (OSNs). In turn a given OSN has
sensitive to food odors, water vapor,
defined
molecules
ammonia, and a breakdown product of
(ORs) in its cell membrane. Olfactory
amino acids: putrescine. Basiconic sensilla
sensilla fall into three major classes that
are sensitive to food odors and CO2. The
differ in size, morphology, and their
different types of sensilla are innervated
distribution patterns on the antenna:
by characteristic sets of olfactory receptor
trichoid, coeloconic and basiconic sensilla
neurons and hence a characteristic set of
(Venkatesh and Singh 1984; Shanbhag et
ORs (Fig. 1).
olfactory
receptor
al. 1999). These distribution patterns are
highly conserved. The three major types
of
sensilla
are
further
divided
into
subtypes: three different subtypes of
trichoid sensilla (antennal trichoid 1-3),
three
different
large
basiconic sensilla
(antennal basiconic 1-3), seven thin and
small
basiconic sensilla
(antennal
basiconic 4-10), and the four types of
coeloconic sensilla (antennal coeloconic 14). Within a given sensillum the OSNs are
simply differentiated as neuron A and B,
and continuative C, D, if they contain
more
than
two
OSNs.
In
an
electrophysiological recording the different
OSNs can be distinguished according to
Figure 1: OSN classes in large basiconic
sensilla. Or genes expressed in each OSN class
are indicated. All are Or genes except Gr10a
and
Gr21a. ab: antennal basiconic; Gr:
gustatory receptor;
OSN: Olfactory sensory
neuron; Or: Olfactory receptor. (Couto et al.
2005, modified).
their different spike heights. The neuron
with
the
larger
spike
amplitudes
is
referred to as neuron A, the neuron with
the next smaller amplitudes as neuron B
and so forth (deBruyne et al. 2001).
A given stimulus activates a distinct
subset of OSNs that converge on defined
glomeruli in the antennal lobes of the
brain (Vosshall et al. 2000; Gao et
In the fruit fly the different classes of
al. 2000), which are the primary odor
hairs are sensitive to different types of
processing centers of the insect brain,
substances (Stocker 1994; Shanbhag et
comparable
to
the
olfactory
bulb
in
Introduction
vertebrates (Hildebrand 1995; Hildebrand
and
Shepherd
1997).
Second
order
neurons which innervate the glomeruli
project to higher brain centers, such as
the lateral horn or the mushroom body
calyx (Stocker 1994).
Sensillum structure
Most of the olfactory sensilla house two
OSNs, some contain only one, but others
up to four (de Bruyne et al. 1999, 2001).
The bipolar OSNs extend their dendrites
into the sensillum lymph of the sensory
hair where they interact with odors
(Fig. 2). At the other pole the neurons
Figure 2. Cartoon of a basiconic sensillum
containing two olfactory neurons (OSN) and
their supporting cells:
the
thecogen (Th),
trichogen (Tr), and tormogen cells (To). (E)
epidermal cell, (ly) lymph space (Park 2002,
modified).
project axons to the olfactory glomeruli.
The identity, intensity and the temporal
Olfactory receptors
profile of an olfactory stimulus are all
represented in the activities of those
The D. melanogaster odorant receptor
OSNs. There are approximately about
family is encoded by 60 genes that
1200 OSNs in each antenna (Stocker
produce
2001). In insects OSNs are surrounded by
alternatively spliced (Clyne et al. 1999;
thecogen, tormogen, and trichogen cells
Vosshall et al. 1999; Couto et al. 2005).
(Sanes et al. 1976; Keil 1989; Shanbhag
Individual ORs are expressed in different
et al. 2000), sometimes called supporting
subsets of OSNs (Tab. 1). Each OR
or auxiliary cells, which secrete sensillum
exhibits an individual receptive range
lymph and keep each sensillum electrically
when stimulated with a wide range of
insulated from its neighbor (Vosshall et al.
odors. These ranges vary from narrowly
2007). Odorants are thought to enter the
tuned, where the receptor is responsive to
sensillum lymph of a hair via pores in the
just a few odors, to broadly tuned where
cuticle (Keil 1982; Steinbrecht et al. 1997;
the receptor responds to many different
Rutzler et al. 2005).
odors.
62
ORs,
two
genes
are
19
Introduction
Table 1:
Molecular
and
functional
organisation of D. melanogaster antennal
receptors. OR: olfactory receptors; OSN:
olfactory sensory neuron; +: excitatory; (-):
inhibitory; *:no data available (Laissue and
Vosshall 2008, modified).
OR
Or
Or
Gr
Or
Gr
Or
Or
Or
OSN
7a
9a
10a
10a
21a
22a
22b
33b
ab
ab
ab
ab
ab
ab
ab
ab
ab
Or 42b
ab
Or 43b
ab
Or 47a
ab
Or 49a
ab
Or 49b
ab
Or 56a
ab
Or 59b
ab
Or 67a
ab
Or 67b
ab
Or 67c
ab
Or 69aA ab
Or 69aB ab
Or 82a
ab
Or 85a
ab
4A
8
1D
1D
1C
3A
3A
5B
2B
1
8A
5B
10
6B
4B
2A
10
9
7
9
9
5A
2B
odor evoking responses
(of 110 tested)
+ (-) strongest
ligand
19 (30) E2-hexenal
21 (0) 2-pentanol
* * *
9 (27) ethyl benzoate
* * carbon dioxide
29 (0) methyl hexanoate
* * *
0 (6) no strong ligand
*
14
11
*
3
*
6
31
*
8
*
*
1
4
*
(0)
(0)
*
(19)
*
(0)
(6)
*
(9)
*
*
(5)
(31)
Or 85b
ab 3B
22 (1)
Or
Or
Or
Or
ab 10
ab 1
ab 7A
ab6Bx
0 (4)
* *
21 (8)
* *
85f
92a
98a
98b
*
ethyl butyrate
propyl acetate
*
2-methylphenol
*
methyl acetate
phenylethyl alcohol
*
ethyl lactate
*
*
geranyl acetate
ethyl3-hydroxybutyrate
6-methyl-5hepten-2-one
no strong ligand
*
ethyl benzoate
*
determines the receptive range of the
OSN (Sato and Touhara 2009).
In contrast to the “one neuron, one
receptor” rule that is valid in the mouse
olfactory system (Serizawa et al. 2003)
the rule is not tenable in the fruit fly (Sato
et al. 2008). One of the ORs, Or83b, is
expressed in all OSNs that are housed in
basiconic and trichoid sensilla (Vosshall et
al. 1999; Vosshall and Stocker 2007). In
addition Or83b is also expressed in the
OSNs of coeloconic sensilla that express
OR35a (Yao et al. 2005). In some cases
two or even three ORs are co- expressed
alongside
with
Or83b
(Vosshall
and
Stocker 2007).
Both vertebrate and fly olfactory receptor
genes
encode
receptor
proteins
with
seven transmembrane spanning domains.
Vertebrate ORs are members of the
G-protein-coupled receptor super family.
For the two fruit fly ORs (OR83b and
OR22a) it was shown, and for the rest of
the ORs assumed, that their topology is
inverted compared to this family (Lundin
et al. 2007; Smart et al. 2008) with their
N-termini located in the cytoplasm and
their
C-termini
located
extracellularly
(Benton 2006a, 2006b, 2009a).
In
addition,
the
sensitivity
to
each
The
ORs
function
as
heteromeric
individual odor can vary over a wide
complexes of an odor binding OR and the
concentration
2003,
co receptor OR83b that possibly form an
The
odor gated ion channel. ORs that detect
combination of co-expressed ORs, which
(lipid-derived) pheromones, additionally to
is always the same for a given OSN,
OR83b, require the function of a sensory
Hallem
and
range
(Robertson
Carlson
2006).
Introduction
neuron membrane protein (SNMP). SNMPs
Using
are related to the CD36 receptor family,
stimulation, the responses of different ORs
lipid-binding cell surface receptors that act
to a panel of ~100 diverse odors were
in immune recognition in other organisms
tested (Hallem and Carlson 2006). Almost
including mammals (Benton et al. 2007,
all of these odors (97%) elicited either an
2009a, 2009b).
excitatory or an inhibitory response from
With the exception of the OR35a/OR83b–
at least one of the tested receptors. The
expressing neurons, OSNs in coeloconic
existence
sensilla
do
not
express
OR83b
or
the
single
of
pulse
two
method
response
for
modes,
excitation and inhibition, among many
members of the OR or gustatory receptor
receptors
(GR) gene families. Benton et al. (2009c)
available to the receptor. Inhibition may
characterized
of
also function in suppressing noise in
(alongside
certain channels that signal the presence
an
chemoreceptive
additional
receptors
group
expands
they called ionotropic receptors (IRs). IRs
importance.
are ionotropic glutamate receptor (iGluR)
trichoid
related receptors and are expressed in
pheromones
sensory neurons in antennal coeloconic
and Carlson 2007) but inhibited by fruit
sensilla.
electrical
odors (Hallem et al. 2004). Both excitatory
properties of insect olfactory receptors,
and inhibitory responses have also been
previous studies concentrated on stimulus
observed
presentations that were not representative
cockroach some sensilla containing two
of the rapid odor fluctuations encountered
neurons responded with both excitation
in natural environments, mostly because
and inhibition to lemon oil (Tichy et al.
of the complicated nature of the stimulus.
2005). Also in Manduca sexta a few
Dynamic
insect
olfactory sensilla responded both ways to
has
primarily
a range of odors (Shields and Hildebrand
maximum
frequency
olfactory
characterization
receptors
concentrated
discrimination
on
using
of
pulsatile
For
sensilla
in
of
space
of
characterize
odorants
coding
olfactory and gustatory receptors) which
To
specific
the
Drosophila
example
were
biological
excited
by
(van der Goes van Naters
other
insects.
In
the
2001).
stimuli
Receptors must be able to detect stimuli
(Barrozo and Kaissling 2002; Bau et al.
that persist in time while at the same time
2005), thus much of what is known is
retaining the ability to respond to further
derived from studies that used steady
changes. Adaptation is known as the
exposure to single odors.
reversible decrease in sensitivity of a
21
Introduction
sensory cell after prolonged or repeated
temporal kinetic patterns differ for odor
stimulation (Stengl et al. 1992). When an
stimuli of different quality and quantity
odor stimulus is presented for a longer
across the receptor repertoire. In some
duration of time, there is a decline in the
receptors phasic responses terminated
sensory response, despite the presence of
quickly
the odor, an effect that was called
continued well beyond the end of the odor
desensitization. Zufall and Leinders-Zufall
stimulus. In five tested receptors the
(2000) showed evidence that there are
duration of the odor pulse were encoded.
three different forms of odor adaptation
More
present
Short-term
prolonged responses (Hallem and Carlson
and
2006).
in
adaptation,
single
OSNs.
desensitization,
long
in
others
prolonged
Possible
tonic
pulses
responses
evoked
more
mechanisms
for
adapting
adaptation in Drosophila olfactory neurons
responses that detect changes of an odor
could include time-dependent gating of
stimulus are called phasic responses.
odorant receptors, intracellular signaling,
Where a receptor fires APs throughout or
or adaptation of action potential encoding.
lasting
adaptation.
Quickly
continues firing even after the duration of
Olfactory transduction
the odor stimulus, it is called tonic
response
(Randall
1997).
Cockroach
olfactory receptors respond with lower
Olfactory transduction is the creation of
sensitivity to slow changes compared to
an electrical signal in the membrane of a
fast changes of odor concentration (Tichy
sensory cell in response to the arrival of
et al. 2005). Little is known about the
an odorant molecule. The nomenclature
adaptation properties in D. melanogaster
varies
OSNs. DeBruyne et al. (2001) observed
transduction may refer to an initial graded
both fast off responses and slow off
change in membrane conductance or to
responses in two OSNs housed in the
the complete process leading to action
same sensillum stimulated with the same
potentials in the axon. Nevertheless, the
odor.
two
crucial initial step in olfactory transduction
may
is always binding of the odor to an
They
different
speculated
response
that
the
modes
in
the
literature,
so
that
simultaneously present a phasic and tonic
olfactory receptor (OR).
representation of selected features of the
In
odor environment. Studies with a larger
Caenorhabditis elegans, binding of an
number of different odors revealed that
appropriate ligand to a G-protein-coupled
vertebrates
and
the
nematode
Introduction
receptor changes the conformation of the
2008).
receptor
of
different G proteins or G protein subunits
second
were shown (Boto et al. 2010; Chatterjee
messengers (Ha and Smith 2008). In
et al. 2009; Laue et al 1997; Schmidt et
insects
al.1998; Talluri et al. 1995; Tanoue et al.
and
G-proteins
leads
and
the
to
activation
generation
mechanisms
of
of
signal
transduction are under debate. There are
In
addition
the
presence
of
2008)
similarities
It is thought that OR83b combines with
between the fruit fly’s ORs and other G-
other ORs to function as heteromeric
protein-coupled receptors.
complexes, but it remains unclear whether
However, mutations affecting different
these heteromers operate independently
second messenger pathways (cAMP, IP3)
as direct ligand gated ion channels, or if
lead to a change in olfactory perception
they are also G-protein activated (Fig. 3).
only
moderate
sequence
(Wicher et al. 2008, 2009; Kain et al.
Figure 3: Models of olfactory transduction. (a) Direct ligand‐gated model (Sato et al. 2008). Direct ligand gated nonselective cation channels are activated rapidly by stimulation with very high concentrations of odor in the absence of G‐protein signaling. (b) G‐protein‐coupled model (Wicher et al. 2008). In this model ORx is a G‐
protein‐coupled receptor and OR83b is a cyclic nucleotide gated ion channel. Odor activation leads to a faster ionotropic pathway and a slow pathway that involves G‐protein‐coupling of ORx, leading to the production of intracellular cAMP, which activates OR83b. Abbreviations: AC, adenylate cyclase; Gs, stimulatory G‐protein; cAMP, cyclic adenosine monophosphate; OR, olfactory receptor (Nakagawa 2009, modified)
Introduction
A direct ion channel gated (ionotropic)
system (Touhara and Vosshall 2009).
pathway has the advantage of a very fast
Chemical molecules become airborne as
reaction,
while
dependent
provides
a
a
second
messenger
they are released from their solid or liquid
(metabotropic)
pathway
structures. In a stable atmosphere without
higher
sensitivity
to
the
turbulence an odor concentration gradient
system.
forms around the source and could lead
Another important feature of odorant
animals to the odor source by simply
receptors are a set of proteins in the
following the gradient. In the natural
receptor lymph that are essential for
environment structurally complex odor
odorant-receptor binding. Relatively little
plumes are created as the wind takes
is known about the detailed functional
volatile odor molecules away from their
properties
sources (Murlis et al. 1992). These time
of
these
odorant
binding
proteins in OSNs that detect fruit odors.
dependent
They
insects in finding odor sources.
are
probably
essential
for
transferring
odorants
across
the
properties
are
crucial
for
hydrophobic to hydrophilic interface and
have been hypothesized to play important
roles
in
olfactory
neuron
dynamics
(Kaissling 2009).
In OSNs that detect pheromone (11-cis
vaccenyl acetate) the neurons are not
directly activated by the volatile ligand,
but
instead
are
activated
by
the
pheromone- pheromone binding protein
complex. A mutation in the lush gene that
Figure 4: Paired laser -induced fluorescence
of an odor jet. Image is false colored so that
concentration increases from blue (lowest) to
red (highest) (Weissburg 2000, modified).
leads to a lack of the odorant binding
protein LUSH the flies are behaviorally and
electrically
unresponsive
to
the
pheromone (Xu et al. 2005).
Inside an odor plume, filaments of low
and high concentrations, and all ranges in
between, intersperse with areas in the
plume where odor is totally absent (Vetter
et al. 2006) (Fig. 4).
Odor plumes
In addition to these biophysical properties
An odor is a chemical compound that
described
animals can perceive with their olfactory
influences the spatiotemporal sequences
above,
the
insect
itself
Introduction
of odors that are detected by the OSNs. In
physiological
moth it was found that the wing beat
aspect of the odor plume and only
frequencies are also playing a role in odor
characterize the responses as functions of
detection. Each down stroke of the wings
odor
accelerates the airflow over the antennae
commonly used approach to stimulating
and
olfactory receptors is to deliver short odor
therefore
the
sensilla
as
well
(Tripathy et al. 2010).
experiments
identity
and
ignore
this
concentration.
A
pulses and observe the responses during
Hence, in order to find odor sources
animals need to be able follow these
spatiotemporal sequences of low and high
odor concentration.
the
stimulus
and
for
a
short
time
afterwards. Because of the difference
between this type of stimulus and a
natural odor stimulus, which may include
rich spatiotemporal information, it is very
likely that such experiments do not reveal
the whole dynamic repertoire of OSN
Odor stimulation
responses.
Some insects are known to rely on the
The
time dependent properties of odor plumes
concentration has meant that even those
to find the odor source. For example, male
studies that attempt to include dynamic
moths
trace
information have usually relied on brief
leading to a female will only follow a
pulsatile stimuli. Time duration of those
plume that contains rapid concentration
stimuli ranged from 100ms to 5 sec
changes (Justus et al. 2005; Vickers et al.
(Hallem and Carlson 2006). However,
2001; Vickers 2006). Other examples are
recent
blood feeding insects that follow CO2
measurements
plumes to find mammalian hosts. The
resultant concentration changes at the
structure of an odor plume influences the
animal may be very different from those
up wind flight behavior of mosquitoes,
that were anticipated (Vetter et al. 2006).
following
a
pheromone
and some haematophagous insects only
followed an odor plume when the CO2
pulsations were similar to those of the
human breathing range (Geier et al. 1999;
difficulty
of
advances
have
measuring
in
gas
shown
odor
phase
that
the
In this work, recent advances in gas
phase instrumentation (photo ionization
detector: PID) were used to stimulate
Barrozo and Lazzari 2006).
olfactory receptive organs with rapidly
Creating such
changing, but as accurately as possible
complex spatiotemporal
stimuli is so sufficiently difficult that many
measured,
odor
concentrations
to
25
Introduction
investigate odor responses to stimuli with
an unknown input-output system (see
strongly
yellow highlight in Fig. 5).
time
dependent
properties.
Stimuli measured by the PID ranged
between 0 and 330 Hz.
Analysis methodology
A technique called linear system analysis
(LSA) provides an established general
method
of
properties
measuring
of
the
dynamic
input–output
systems
(Bendat and Piersol 1980). LSA has been
used in previous studies to investigate
dynamic properties of different types of
receptors e.g. mechano-, photo- and
chemoreceptors
(Juusola
and
French
1997; Justus et al. 2005). In this work
LSA
was used to examine the dynamic
responses
of
antenna
and
olfactory
sensilla,
respectively.
Figure 5: Visualization of steps necessary to
calculate frequency response and view them in
a bode plot.
When a linear system receives a sinusoidal
input it produces a sinusoidal output of
the same frequency, but possibly differing
in amplitude (Gain) and time shift (Phase).
Any
linear
completely
dynamic
system
characterized
can
by
be
the
measurement of these amplitude and
phase changes, known as its frequency
response function. Frequency response
analysis
(FRA)
is
a
technique
for
characterizing the dynamic properties of
The exact physical nature of the system is
not important, only that its input and
output signals can be represented as
single-valued functions of time. Examples
would be the height of water in a bathtub
(the output signal) versus the rate of
water flowing into the bathtub (the input
signal), or the electrical charge on a
capacitor (output signal) as a function of
net current flowing into and out of it in an
electronic circuit (the input signal). In a
Introduction
manner that depends not only on the
unknown system can simply be done by
input (which can also vary with time) but
subjecting it to a series of sinusoidal
on the properties of the physical system
inputs at different frequencies, measuring
(e.g. shape of the bathtub) and the
the resultant outputs and calculating the
memory of the system (e.g. how fast
change in amplitude and time shift versus
water was flowing into the bathtub at all
frequency.
times in the past). The simplest and most
system with “white noise” (a random
common
response
unpredictable signal containing sinusoids
analysis assumes that the system is linear
of all possible frequencies within a certain
and
frequency
type
stationary.
of
frequency
Linearity
means
that
Stimulating
band)
an
allows
unknown
the
entire
doubling the input signal will double the
frequency response to be measured from
output signal. Stationary means that the
a
properties of the system (e.g. shape of
sinusoidal frequencies in the input and
the bath) do not change with time.
output signals are then separated by the
Almost any single valued function of time
(such as the height of water in the bath
versus time) can be represented by the
single
experiment.
The
different
Fourier transform and the changes from
input to output compared as if they had
each been used separately.
sum of a set of sinusoidal functions of
An example for a low-pass frequency
time (the Fourier theory). A sinusoid in
response would be a bath, where rapid
the input signal will always produce a
sinusoidal
sinusoid of the same frequency in the
flowing into the bath (e.g. 100 cycles per
output, although the amplitude of the
second) would make very small changes
output sinusoid and the times of its peaks
in the height of the bath water versus
and troughs (the phase) relative to the
time. Slow fluctuations (e.g. 0.001 cycles
input will usually be different. Since any
per second) would produce large changes
signal can be considered to be a sum of
in the height versus time. The bath
different sinusoids, and any sinusoidal
frequency
input produces a sinusoidal output, it
output
follows
each
frequency response is a representation of
in
the system's response to a sinusoidal
amplitude and phase will allow to predict
input at different frequencies. The output
the output signal for any input signal.
of the system to the input is a sinusoid of
Measuring the frequency response of an
the same frequency but possibly with a
sinusoidal
that
measuring
frequency
is
how
affected
as
fluctuations
response
the
of
giving
frequency
the
a
water
smaller
rises.
The
27
Introduction
different magnitude and phase. One way
over a range of frequencies and then
to view the frequency response of a
becomes
system is via a Bode plot (Fig. 5).
frequencies. A band pass filter response
In the present work the unknown system
was the fly. How does the antenna or
single sensilla respond to odor stimulation
at different frequencies?
less
sensitive
at
higher
indicates that the sensitivity of the system
peaks at certain frequencies and is lower
at lower and higher frequencies. The filter
function time constants indicate at which
frequencies the changes in the rising or
To investigate this question random noise
falling of the response occurs. The lower
was used as the input signal and the
part of the Bode plot shows the phase
recorded biological signal (EAGs or action
shift (time shift) between input (stimulus)
potentials, respectively) as the output
and the output (action potential) signal.
signal.
To
potentials
avoid
were
aliasing
digitally
the
action
filtered
by
Aims of the thesis
convolution with a sinc function (sin(x)/x)
(French and Holden 1971). To limit the
bandwidth, the signal was down sampled
to 100 Hz. The sampled data (tracer gas
concentration
as
input
and
action
potentials as output) were transferred
from the time domain into the frequency
domain via the Fast Fourier transform
(FFT) (Cooley and Tukey 1965) into
segments
of
sample
pairs.
Response
functions between the PID voltage and
action potentials were calculated by direct
spectral estimation and plotted as Bode
plots of phase and log gain versus log
It is clear that temporal changes in odor
concentration are vitally important to
many animals, but this raises many
questions.
How
are
such
temporal
changes detected? How does temporal
sensitivity vary amongst the large number
of odors that an animal may detect?
Which
stages of the
cascade
are
odor detection
time-dependent?
Which
stages limit the temporal changes that can
be detected? How are dynamic signals
coded into action potentials and processed
in higher centers?
frequency (Bendat and Piersol 1980).
The upper part of the Bode plot shows the
Answering
these
relation of the output to the input signal.
accurate
methods
It shows how strong the response is at
dynamically changing spatiotemporal odor
different frequencies. A response that is
stimuli and quantitatively characterizing
fitted by a low
the resultant neural responses. My thesis
pass filter
function
indicates that its power remains the same
describes
how
an
questions
of
accurate
requires
delivering
dynamic
Introduction
odorant stimulation and analysis system
was developed and tested in the fruit fly.
Then the system was used and to provide
an
initial
characterization
of
primary
odorant receptor cells of D. melanogaster
at the single neuron level. These are
early, but crucial, steps in answering some
of the important questions about kinetics
in insect odor detection posed above.
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Introduction
Negative feedback regulation ensures
the one neuron-one receptor rule in
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Abbreviations
Symbols and abbreviations
Symbols:
amplitude of frequency response
α
ω
radial frequency
τ
time constant of filter function
R
information capacity
∆t
time delay between input and output
Abbreviations:
ab
antennal basiconic
ac
antennal coeloconic
AC
adenylate cyclase;
at
antennal trichoid
cAMP
cyclic adenosine monophosphate
E
epidermal cell
EAG
electroantennogram
FFT
Fast Fourier transform
FRA
Frequency response analysis
Gr
gustatory receptor gene
Gr
gustatory receptor gene
iGluR
ionotropic glutamate receptor
IRs
ionotropic receptors
LSA
linear system analysis
LUSH
odor binding protein called lush
ly
lymph space
OR
olfactory receptor
OR
olfactory receptor gene
ORN
olfactory receptor neuron (synonym for OSN)
OSN
olfactory sensory neuron (synonym for ORN)
PID
photoionization detector
ppm
parts per million
SNMP
sensory neuron membrane protein
Th
thecogen cell
To
tormogen cell
Tr
trichogen cell
33
Chapter I
Dynamic properties of Drosophila olfactory
electroantennograms
Chapter I
J Comp Physiol A (2008) 194:483–489
DOI 10.1007/s00359-008-0322-6
Chapter I
ORIGINAL PAPER
Dynamic properties of Drosophila olfactory electroantennograms
Julia Schuckel Æ Shannon Meisner Æ
Päivi H. Torkkeli Æ Andrew S. French
Received: 3 December 2007 / Revised: 5 February 2008 / Accepted: 20 February 2008 / Published online: 5 March 2008
Ó Springer-Verlag 2008
Abstract Time-dependent properties of chemical signals
are probably crucially important to many animals, but little
is known about the dynamics of chemoreceptors. Behavioral evidence of dynamic sensitivity includes the control
of moth flight by pheromone plume structure, and the
ability of some blood-sucking insects to detect varying
concentrations of carbon dioxide, possibly matched to host
breathing rates. Measurement of chemoreceptor dynamics
has been limited by the technical challenge of producing
controlled, accurate modulation of olfactory and gustatory
chemical concentrations over suitably wide ranges of
amplitude and frequency. We used a new servo-controlled
laminar flow system, combined with photoionization
detection of surrogate tracer gas, to characterize electroantennograms (EAG) of Drosophila antennae during
stimulation with fruit odorants or aggregation pheromone
in air. Frequency response functions and coherence functions measured over a bandwidth of 0–100 Hz were well
characterized by first-order low-pass linear filter functions.
Filter time constant varied over almost a tenfold range, and
was characteristic for each odorant, indicating that several
dynamically different chemotransduction mechanisms are
present. Pheromone response was delayed relative to fruit
odors. Amplitude of response, and consequently signal-tonoise ratio, also varied consistently with different compounds. Accurate dynamic characterization promises to
provide important new information about chemotransduction and odorant-stimulated behavior.
J. Schuckel S. Meisner P. H. Torkkeli A. S. French (&)
Department of Physiology and Biophysics,
Dalhousie University, B3H 1X5 Halifax, NS, Canada
e-mail: [email protected]
Keywords Antenna Chemosensory Noise Odor Frequency response
Abbreviations
PID
Photoionization detector
EAG Electroantennogram
ppm Parts per million
OR
Odorant receptor
Introduction
Time-dependent properties of chemoreception are crucially
important for many functions, but poorly understood. For
example, the flight path of a male moth is determined by
the temporal-spatial structure of the female pheromone
plume (Justus et al. 2002; Mafra-Neto and Cardé 1995;
Willis and Baker 1984), and plume temporal structure
signals distance to the source (Justus et al. 2002; Murlis
et al. 1992). Neural substrates for decoding this information have been proposed in moth CNS (Vickers et al.
2001), but little is known about the dynamic responses of
antennal sensory neurons that initially detect the odorant.
Dynamic plume sensitivity also controls mosquito orientation to CO2 from mammalian hosts (Geier et al. 1999),
and Triatoma infestans, a hematophagous insect, is selectively attracted to CO2 pulsations in the human breathing
range (Barrozo and Lazzari 2006).
Dynamic characterization has helped to identify functional components, and quantitative representation of
information, in mechanoreceptors and photoreceptors
(Juusola and French 1997; Juusola et al. 2003), but this
approach has been very limited in chemoreceptors (Justus
et al. 2005). The major experimental limitation to studying
37
Chapter I
chemosensory dynamics is in controlling the concentration
of chemical delivered to the sensory structure. A common
method is to release short pulses of chemical into a stream
of air or other fluid passing over the sensory organ
(Barrozo and Kaissling 2002; Bau et al. 2002), but this
limits stimulation to relatively low frequencies (typically
below about 30 Hz), and concentration at the sensory
receptor is difficult to estimate because of diffusion during
transit from stimulator to preparation. In contrast,
mechanical or light stimulation can be delivered at frequencies several orders of magnitude higher, their physical
intensity at the sensory receptor can be measured accurately, and they can be delivered with a wide bandwidth of
frequencies, allowing techniques such as direct spectral
analysis (Bendat and Piersol 1980), nonlinear systems
analysis (Marmarelis and Marmarelis 1978) and naturalistic stimulation (Karmeier et al. 2006).
Wide bandwidth dynamic odorant stimulation was
achieved by turbulent flow in a wind tunnel (Justus et al.
2005) combined with photoionization measurement of a
surrogate tracer gas. This approach allowed both linear and
nonlinear systems analysis of moth pheromone receptors at
higher frequencies than achieved previously. However,
turbulent stimulation does not allow accurate control of the
amplitude and frequency ranges of the stimulus. Additionally, odorant concentration versus distance is
unpredictable in turbulent flow, raising the possibility that
concentration measured even a short distance from the
sensory receptor may not accurately estimate the actual
stimulus.
Drosophila antennal olfactory neurons vary in the rate
of decay of their responses to step odorant presentations,
and this variation is related to the type of odorant receptor
involved (Hallem et al. 2004). Defined wide bandwidth
olfactory stimulation of Drosophila has recently been
achieved using servo-controlled release of odorant and
surrogate gas into a small laminar flow wind tunnel (French
and Meisner 2007). Here, we used this system to record the
first full frequency response measurements of Drosophila
electroantennograms (EAG) to a series of fruit odors and to
aggregation pheromone.
Materials and methods
Stimulation
Olfactory stimulation was performed by a laminar air flow
system (Fig. 1) whose operational parameters have been
described before (French and Meisner 2007). Primary airflow was produced by a small fan (Proten DFC601512M,
Cooler Guys, Kirkland, WA) at one end of a 90 mm square
plexiglas box. The fan was driven by a 9 V DC power
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484
supply. Air flowed through a honeycomb (30 mm long 9 5
mm pitch) to give laminar flow and then entered a circular
plexiglas flow tube (22 mm internal diameter, 110 mm
long). The fly was positioned at the far end of the flow tube,
within 2–3 mm of the exit and 2–3 mm of the tube center
line.
Secondary flow from a cylinder of compressed air
containing 1,000 ppm propylene tracer gas (BOC, Halifax,
NS, Canada) was regulated to 20 kPa initial pressure. It
flowed through an odorant cartridge made from the shaft
of a 5 ml transfer pipet (Fisher Scientific, Ottawa, ON,
Canada), containing a rectangular piece of filter paper
Fig. 1 Drosophila antennal recording and stimulation system. Laminar air flow, created by a 60 9 25 mm computer fan pushing air
through a 5 mm pitch honeycomb, flowed through a circular plexiglas
tube 115 mm long and 22 mm internal diameter (flow tube). Odorant
chemicals were introduced by air flowing over a rectangular piece of
filter paper (45 9 15 mm) in a cartridge made from a 5 ml transfer
pipet. The air source contained 1,000 ppm propylene as a tracer gas.
Air with mixed odorant and propylene tracer flowed through the tip of
a Pasteur pipet in the center of the flow tube, with the tip variably
occluded by the flat surface of a 5 mm silicone elastomer bead, moved
by a servo-controlled electromagnetic pusher. The fly was positioned
in the center of the circular tube and 3 mm from its mouth. Tracer
propylene was detected by a photoionization detector (PID), with the
mouth of the aspirating needle (0.76 mm internal diameter) located
about 1 mm above the fly
Chapter I
485
(45 9 15 mm), into a Pasteur pipet whose tip was located
in the center of the flow tube, and 10 mm from its origin.
The tip of the pipet was variably occluded by a bead of
silicone elastomer (Mastercraft tub and tile silicone sealant,
Canadian Tire Corporation, Toronto, ON Canada), driven
by a servo-controlled electromechanical stimulator. Odorant chemicals and mineral oil were purchased from Sigma
(Oakville, ON, Canada). Odorants were mixed with mineral oil at 20% v/v and pheromone at 50%. Volumes of 50
ppm were loaded into separate cartridges for each mixture.
Stimulus and response measurements
Tracer gas concentration was measured by a miniature
photoionization detector (mini-PID, Model 200A, Aurora
Scientific Inc, Aurora, ON, Canada), whose needle inlet
probe tip was located directly above and within 1 mm of the
fly antenna. The PID had a frequency response of 0–330 Hz
and a concentration range of 0.05–500 ppm propylene. The
typical measurement range used here was 1–20 ppm. The
PID needle probe was 57 mm long with an internal diameter
of 0.76 mm, giving a volume of 0.0259 ml. PID pump speed
was 660 ml/min giving a time delay of 2.35 ms.
Primary air flow velocity was estimated from the flow
tube dimensions and the delay between movements at the
pipet tip and the resulting PID signal, after subtracting the
PID needle delay, to be 2.37 m/s, giving a volume flow of
900 ml/s. Secondary airflow relative to primary airflow was
estimated from the mean concentration of propylene at the
PID to be 1:100, giving a secondary air flow rate of 9 ml/s.
Flies (Drosophila melanogaster, Oregon R #2376,
Bloomington Drosophila stock center, Bloomington, IN)
were maintained in the laboratory. Flies of both sexes were
used within two days of hatching. Procedures for recording
EAG were similar to those described previously (Alcorta
1991). Animals were held in the cut end of a 100 ll plastic
pipet tip. A reference glass microelectrode (*1 lm tip
diameter) was inserted into one eye, while a larger glass
microelectrode (*20 lm tip diameter) was pushed against
the distal end of one antenna. Both electrodes were filled
with Drosophila saline (Hazel et al. 2003). EAG were
recorded as electrical current with a List EPC-7 patch clamp
amplifier (ALA Scientific Instruments, Westbury, NY).
Current recordings are proportional to voltage recordings
but give lower noise levels (Minor and Kaissling 2003; Yao
et al. 2005). All experiments were performed at room
temperature (20 ± 2°C) and humidity *40%.
Pseudorandom Gaussian white noise was generated by the
computer via a 33-bit maximum length binary sequence
algorithm (Golomb 1967) driving a 12-bit digital-to-analog
convertor into the position servo control. The PID voltage
and EAG current were digitized via a 16-bit analog-todigital convertor and sampled at 5 ms intervals. Sampled
time domain data were transferred to the frequency domain
using the fast Fourier transform (Cooley and Tukey 1965)
in segments of 512 sample pairs. Frequency response
functions (amplitude and phase) between PID voltage
(input) and EAG current (output) were calculated by direct
spectral estimation and plotted as Bode plots of phase and
log amplitude versus log frequency. Coherence functions
(Bendat and Piersol 1980) were calculated from the same
data. Frequency response functions were fitted by a
coherence-weighted minimum square error process to a
first-order low-pass filter function (Justus et al. 2005):
Gðjf Þ ¼ a expðj2pf DtÞ 1=ð1 þ j2pf stÞ
ð1Þ
where G(jf) is the complex gain of the frequency response,
a is a constant amplitude, Dt is a pure time
delay,
s is the
pffiffiffiffiffiffi
ffi
time constant of the linear filter and j2 ¼
1: Coherence
functions, c2(f) were used to estimate the information
capacity of the antennal response (Shannon and Weaver
1949):
Z
R ¼ log2 1=1 c2 ðf Þ df :
ð2Þ
Experimental protocols
Experiments used natural fruit odorants that stimulate
Drosophila antennal chemoreceptors: butyl butyrate,
isoamyl acetate, phenylethyl alcohol, and hexyl acetate
(Stensmyr et al. 2003), and the Drosophila aggregation
pheromone, (Z)-11-octadecenyl acetate (Hedlund et al.
1996). In the fruit odorant experiments each fly, of either
sex, was stimulated with all four odorants, in turn, using a
random number sequence to create a different order of
stimulation for each animal. Total stimulation time for each
odor was 100 s. Between different odors, flies received 100
s of air flow without odor. In pheromone experiments, male
flies were stimulated for 100 s. The experimental apparatus
was flushed with clean air for periods of at least 10 min
between experiments. Ambient air at the apparatus was
removed by a direct connection to the building exhaust
system. Statistical analysis was performed by Prophet 6.0
software (AbTech Corporation, Charlottesville, VA).
Experimental control and data processing
Results
All experiments were controlled by custom-written software via a personal computer and a data acquisition
board (NI6035E, National Instruments, Austin, TX).
Fruit odorant experiments were conducted on a total of 42
different animals; pheromone experiments on 24 animals.
39
Chapter I
Odorant cartridge concentration (see ‘‘Materials and
methods’’) was chosen to approximate the final concentrations at the animal used previously in Drosophila
experiments (Yao et al. 2005), taking into account the ratio
of primary to secondary air flows and the filter paper
dimensions. Preliminary experiments showed that EAG
signals dropped significantly at lower concentrations but
tended to saturate with increased concentrations. A typical
frequency response function is shown for stimulation by
hexyl acetate (Fig. 2). The fitted parameters of Eq. 1 were
used to draw the solid lines through the amplitude and
phase data. True amplitude units of the frequency response
cannot be given because the ratio of odorant molecules to
tracer gas was unknown. They are shown as antennal
Fig. 2 Frequency response function between tracer gas concentration
(input) and electroantennogram current (output) for hexyl acetate
stimulated Drosophila. Amplitude values represent the ratio of
electroantennogram current from the patch clamp amplifier to tracer
gas concentration. Amplitude and phase data were fitted by Eq. 1
(solid lines) with the following parameters: a = 4.44 pA/ppm, s = 7.06
ms, Dt = -1.78 ms. Fitted amplitude and time constant values are
indicated by arrows; first-order filter asymptotic slope is also shown.
Note that the phase relationship lags (decreasing phase value) at low
frequencies due to the filter function, but the negative delay caused by
the PID needle causes it to lead (increasing phase values) at
frequencies above *40 Hz. Inset shows 2 s of original recordings of
PID output of tracer gas concentration (upper) and the resulting
Drosophila electroantennogram current (lower) during pseudorandom
stimulation
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486
current versus tracer gas concentration (pA/ppm propylene). The amplifier reported positive current flowing
outwards from the electrode, or into the antenna, and
positive current increased with olfactory stimulation.
Olfactory frequency response functions were reliable, and
well-fitted by Eq. 1, with high coherence function values
(near unity) over a wide bandwidth.
Initial experiments suggested a reduction in sensitivity,
or adaptation, during the first few seconds of stimulation.
To quantify this effect, recorded data were separated into
consecutive sets of 2,048 input–output data pairs (10.24 s)
and frequency response functions calculated for each set.
Plotting the fitted data versus time showed that adaptation
was mild, and complete within 50 s of initial stimulation
(Fig. 3). Therefore, all further analysis was performed
using data collected between 50 and 100 s of initial
stimulation, including the data shown in Fig. 2. All further
data analysis was applied to experiments where complete
responses were obtained for the full 100 s to all four
odorants or pheromone. Four parameters, a, s, Dt (Eq. 1)
and R (Eq. 2) were calculated for each compound.
The relatively constant response observed during 50 s
(Fig. 3) also indicates that the concentration of the odorants
at the animal was approximately constant during the
Fig. 3 Electroantennogram responses adapted slightly for about 30 s
after the onset of odorant stimulation. Sampled data was divided into
five consecutive sets of 2,048 input–output pairs, giving mean time
values of 5.12, 15.36, 25.6, 35.84, and 46.08 s. Frequency response
functions were estimated for each set of data and fitted with Eq. 1, as
in Fig. 2. Data (mean ± standard error) are shown for normalized
amplitude (a/a0, where a0 was the amplitude at 5.12 s and for time
constant, s, for the four odorants used. Numbers of experiments are
indicated next to the labels
Chapter I
487
Fig. 4 Fitted experimental parameters for experiments on 52 flies.
Four fruit odorants were used in random order to stimulate each of 28
flies. Pheromone was used to stimulate a different set of 24 flies.
Amplitude, a, time constant, s, and delay, Dt, were obtained from
frequency response functions (Fig. 2) fitted by Eq. 1. Information
capacity, R, was obtained from corresponding coherence functions via
Eq. 2. Data are shown as mean values ± standard error
experiments. This could be due to the high ratio (*100) of
primary to secondary air flow (see ‘‘Materials and methods’’), so that evaporation was limited by the relatively
slow secondary air flow. In preliminary experiments,
response amplitude remained approximately constant for
periods of at least 500 s (data not shown).
To test for gender differences in fruit odorant sensitivity,
statistical analysis was applied to eight experiments on
female flies and nine experiments on male flies. Mann–
Whitney rank sum tests (2-sided) failed to show any significant differences between male and female flies for any
of the sixteen groups tested (4 odorants by 4 parameters).
Therefore, all further fruit odorant tests used combined data
from both sexes. Pheromone experiments were all conducted on male flies.
The four fitted parameters (Fig. 4) were measured from
28 flies that all received the four different fruit odorants in
random order. Parameter values were compared by Friedman’s all pairwise test, using the null hypothesis that all
estimates of each parameter were from the same population. The hypothesis was rejected for all measurements of
time constant, s (P = 0.0001) and information capacity,
R, (P = 0.0001). For the amplitude parameter, a, the
hypothesis was rejected for the group (P = 0.0001), but not
between the effects of isoamyl acetate and hexyl acetate
(P[0.05). For the delay parameter, Dt, the hypothesis was
not rejected (P = 0.0523) although there was a significant
difference between the effects of butyl butyrate and hexyl
acetate (P \ 0.05).
In summary, the four fruit odorants each gave significantly different dynamic responses, with mean time
constant varying from 4.40 (isoamyl acetate) to 35.5 ms
(phenylethyl alcohol). Amplitude also varied with odorant
from 3.64 (isoamyl acetate) to 1.47 pA/ppm (phenylethyl
alcohol) and was matched by variation in information
capacity. Time delay was small and negative with no
consistent evidence that it varied with different odorants.
Pheromone responses had much lower amplitude than
fruit odor responses (Fig. 4), which made the experiments
more difficult and strongly reduced the estimated information capacity. Pheromone experiments were conducted
separately on 24 male flies. Amplitudes, a, and information
capacities, R, were about one third of the lowest fruit
odorant parameters (phenylethyl alcohol). Time constant
values, s, were within the range of the fruit odorants (mean
21.1 ms) but the delay parameter, Dt, was now positive
(mean 4.8 ms) instead of negative.
Discussion
Different fruit odors produced characteristically
different dynamic responses
The four odorants gave different time constants, amplitudes
and information capacities (indicating different signalto-noise ratios). What is the basis for these differences?
Frequency response functions were well fitted by a firstorder low-pass filter function (Eq. 1) that also fitted moth
EAG responses to pheromones (Justus et al. 2005). For
moths it was argued that the filter occurs between odorant
arrival at the antennal surface and opening of ion channels
to produce receptor current in the chemosensory neuron.
A similar argument can be made for Drosophila, because
some time constant measurements were much longer than
expected for cell membranes, and adaptation during action
potential encoding tends to cause high-pass behavior
(Carlsson and Hansson 2002). Time-dependent steps could
include odorant diffusion through cuticular pores, binding
to extracellular proteins, binding to membrane odorant
receptor molecules, or intracellular second messenger
cascades (Benton 2007). The relatively long time constant
41
Chapter I
for phenylethyl alcohol suggests that molecular size and/or
aromaticity may be limiting factors.
Previous work linked initial firing rates and adaptation
rates to olfactory receptor molecules (ORs), rather than
neuron type, when receptor molecules were expressed in
different Drosophila neurons (Hallem et al. 2004), but the
ORs corresponding to the four odorants used here are not
yet well defined. Stensmyr et al. (2003) identified eight
major functional types of Drosophila antennal sensilla,
each containing 1–4 action potential producing neurons,
although morphological characterization was not possible.
Using their nomenclature, the four odorants used here
would be expected to excite neurons S3-A (butyl butyrate
and isoamyl acetate), S4-A (butyl butyrate and isoamyl
acetate), S4-B (isoamyl acetate), S5-B (isoamyl acetate and
hexyl acetate) and S8-B (phenylethyl alcohol), suggesting
that at least three ORs were stimulated here.
Hallem et al. (2004) mapped 13 Drosophila ORs to
different receptor neurons in antennal basiconic sensilla.
Their map indicates that isoamyl acetate would stimulate
the OR10a and OR22a receptors in ab1D and ab3A neurons, as well as the OR19a receptor in an unknown neuron.
The other three odorants were not tested. None of the four
odorants were tested in coeloconic sensilla recordings (Yao
et al. 2005). There is evidence of sexual differences in the
density of some odorant receptors in Drosophila (Dobritsa
et al. 2003) but no differences in EAG responses to fruit
odors were observed here.
Therefore, the available evidence indicates that several
different ORs were stimulated by the four odorants, and
that some of the odorants stimulated more than one type of
OR. However, there was no evidence for multiple filter
time constants in the frequency response functions.
Therefore, if dynamic behavior is determined by ORs, the
data suggest that different ORs responding to a single
odorant have similar time constants. Linkage of dynamic
properties to an OR does not necessarily mean that the OR
function itself determines the time constant, which could
also be due to associations between ORs and different
groups of odorant binding proteins or second messenger
pathways. The finding that dynamic behavior follows ORs
into different neurons (Hallem et al. 2004) makes it less
likely that penetration through the cuticle or diffusion to
the receptor neuron is controlling dynamics.
Adaptation of olfactory response was small for all
odorants and did not seem to affect the time constant
(Fig. 2). This agrees with single unit recordings of both
basiconic (Dobritsa et al. 2003) and coeloconic (Yao et al.
2005) sensilla. Single unit recordings to step olfactory
stimuli showed some adaptation during the initial second,
which would probably not have been detected by the frequency response measurements if it occurred with random
stimulation.
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The basis of the time delay
The negative delay in fruit odor responses (Fig. 4) can be
explained by the experimental arrangement. Flow through
the PID sampling needle should give a measurement delay
of about -2.5 ms, so observed values of -1 to -2 ms
imply a maximum EAG delay of less than 1 ms for fruit
odors and *7 ms for pheromone. The origin of EAG
current is not clearly established. Electrical models have
been proposed that include several current sources
(Kapitskii and Gribakin 1992), and any contribution of
action potentials to EAG has been challenged by experiments using chemical treatment to block action potentials
(Lucas and Renou 1992).
Although the basis of the delay in olfactory responses
cannot yet be identified, it is interesting that significant
delay was seen in both moth (Justus et al. 2005) and
Drosophila pheromone responses, but not in Drosophila
fruit odor responses. This could be related to molecular
size because the pheromone used here was much larger
than any of the fruit odors.
It is difficult to interpret the dependence of frequency
response amplitude, a, or information capacity, R, on the
different odorants. EAG amplitude could be affected by
receptor current amplitude, number of active neurons and
positions of the active neurons relative to the recording
electrode. Information capacity is directly dependent on
signal-to-noise ratio in a linear system. There is no significant evidence for nonlinearity in the Drosophila EAG
recordings, and R clearly varied with a (Fig. 4), suggesting
that all recordings included a similar background noise
level.
Why do Drosophila need such rapid responses to fruit
odors?
The time constant of *4.5 ms for isoamyl acetate
transduction was three times faster than moth pheromone
transduction, and would allow detection of odorant
changes up to *50 Hz. Pheromone responses were much
more delayed than those of fruit odors. Are these differences behaviorally important? Why is there such a wide
range of time constants for different odorants? Which
stages of chemotransduction are rate limiting? The present study has raised several interesting questions about
olfactory dynamics, and must be followed by detailed
analysis of single olfactory neuron responses. However, it
has already provided a simple, quantitative dynamic
model that can be used to test hypotheses about chemotransduction. Accurately characterizing the dynamic
properties of these neurons should provide important
new information about the physiology and ethology of
chemosensation.
489
Acknowledgments Supported by the Canadian Institutes of Health
Research and the Dalhousie Medical Research Foundation. The
experiments complied with the ‘‘Principles of animal care’’, publication No. 86-23, revised 1985 of the National Institute of Health, and
were approved by the Dalhousie University Committee on Animal
Care.
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Drosophila melanogaster and its use for identifying olfactory
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receptor cells in female silkmoths Bombyx mori L. J Insect
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sensory neurons to blends of pheromone components in the moth
Agrotis segetum. J Insect Physiol 48:443–451
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(2003) Amino acids modulate ion transport and fluid secretion by
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coding by mechanoreceptor neurons. Neuron 18:959–968
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contribute early nonlinear amplification to the light response in
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43
Chapter II
A digital sequence method of dynamic
olfactory characterization
Chapter II
Journal of Neuroscience Methods 171 (2008) 98–103
Chapter II
Journal of Neuroscience Methods
journal homepage: www.elsevier.com/locate/jneumeth
A digital sequence method of dynamic olfactory characterization
Julia Schuckel, Andrew S. French ∗
Department of Physiology and Biophysics, Dalhousie University, Halifax, Nova Scotia, Canada B3H 1X5
a r t i c l e
i n f o
Article history:
Received 11 January 2008
Received in revised form 23 February 2008
Accepted 25 February 2008
Keywords:
Olfaction
Drosophila
Antenna
Sensory receptor
Frequency response
Photoionization
a b s t r a c t
Measurements of system dynamics, such as input–output frequency response estimation, have been
widely used in neuroscience. Dynamic characterization of sensory systems has been particularly useful
because both the amplitude and time-dependent properties of sensory input signals can often be accurately controlled. However, chemoreceptors have proved less amenable to these approaches because it is
often difficult to accurately modulate or measure chemical concentration at a sensory receptor. New methods of dynamic olfactory stimulation have recently been introduced that combine controlled mechanical
release of odorant with detection by photoionization of surrogate tracer gas mixed with the odorant. We
have developed a modified version of this approach based on rapid binary switching of odorant flow using
pseudo-random binary signals (maximum-length sequences, or M-sequences) generated by a software
shift register. This system offers several advantages over previous methods, including higher frequency
range stimulation, experimental simplicity and the possibility of computational efficiencies. We show that
there is predictable dynamic odorant concentration at the sensory receptor and we explore the stimulation parameters as functions of total air flow rate and spatial location. A typical application of the system
is shown by measuring the frequency response function of a Drosophila electroantennogram.
© 2008 Elsevier B.V. All rights reserved.
1. Introduction
Systems analysis provides an established general method
of measuring the dynamic properties of input–output systems
(Bendat and Piersol, 1980). Both linear and nonlinear systems analysis have been used to characterize a wide variety of physiological
systems (Marmarelis and Marmarelis, 1978), and have become particularly important in neuroscience (French et al., 1972; French and
Marmarelis, 1999; Karniel and Inbar, 1999). In the case of linear systems, a complete dynamic characterization, such as the frequency
response function or impulse response function, can be used to
predict the response of the system to any other stimulus.
Systems analysis often employs a random, or approximately
random, signal that is sometimes called “white noise” or “pseudorandom noise”. White noise stimulation can be used to characterize
linear, and many nonlinear, biological systems (Marmarelis and
Marmarelis, 1978). Practical generation of pseudorandom noise
is commonly achieved by deriving an analog signal from an
M-sequence shift register (Golomb, 1981), where the term
“M-sequence” is derived from “maximum length sequence”. Msequences are generated by applying a feedback loop to a binary
shift register so that repeated shift operations cause the binary
number stored in the shift register to cycle through every possi-
∗ Corresponding author. Tel.: +1 902 494 1302; fax: +1 902 494 2050.
E-mail address: [email protected] (A.S. French).
0165-0270/$ – see front matter © 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.jneumeth.2008.02.013
ble value, giving the maximum length sequence of binary numbers.
M-sequence shift registers can be implemented from physical components or as computer software. Although not truly random, the
order of binary numbers appearing in an M-sequence is sufficiently
complex to create a pseudorandom signal, for example, by driving
the inputs in parallel to a digital-to-analog converter.
Alternatively, the binary digits appearing at one end of the Msequence shift register can be used as the input to an unknown
system. Because these values are either one or zero, it is possible to
perform very efficient computation on the resulting output signals
from the system by techniques such as the Walsh–Hadamard transform (French and Butz, 1974). This approach has been utilized for
rapid characterization of visual function, where multiple receptors
and lateral interactions between receptors produce large numbers
of parallel output signals from a single input (Sutter, 2001).
Systems analysis of chemoreceptors has proved more difficult
than other sensory modalities because of the nature of the stimulus.
The basic requirements for linear and nonlinear systems analysis of
sensory receptors are the abilities to modulate the signal in a controlled manner over a wide range of frequencies, and to accurately
measure both the input stimulus signal and the output neural signal with appropriate time resolution. Dynamic measurements of
some insect olfactory receptors have been achieved using pulsed
release of chemicals in wind tunnels (Bau et al., 2005). Another
approach used randomly varying pheromone concentration produced by turbulent flow in a wind tunnel (Justus et al., 2005).
There has also been progress in detecting rapidly changing airborne
47
Chapter II
J. Schuckel, A.S. French / Journal of Neuroscience Methods 171 (2008) 98–103
chemical concentration by photoionization of tracer gas with low
ionization potential added to the chemical stimulant (Justus et al.,
2005; Vetter et al., 2006). Continuously variable release (CVR) of
odorant into a wind tunnel by a servo-controlled system, combined
with photoionization detection of tracer gas, was able to provide
a range of controlled olfactory stimuli, including pseudorandom
signals suitable for systems analysis (French and Meisner, 2007).
Here, we describe an alternative method of pseudorandom
olfactory stimulation in which odorant and tracer gas are released
into a flow tube by a solenoid valve switched by the single binary
digits emerging from a an M-sequence. This method offers several
important advantages over the CVR method, including wider stimulus bandwidth and simpler construction. We demonstrate use of
the system to measure a Drosophila electroantennogram frequency
response function.
2. Materials and methods
2.1. Stimulator design
The stimulating system consisted of a Plexiglas box (100 mm
long by 70 mm square) divided into two compartments (Fig. 1). A
40 mm × 10 mm fan (EVERCOOL EC4010M12CA, Cooler Guys, Kirk-
99
land, WA) raised the air pressure in the first compartment (pressure
chamber), which then flowed along a plexiglas tube to its open exit
projecting from the second compartment. The fan (nominally 12 V
dc) was driven by a variable voltage dc power supply. The flow rate
could be varied by changing the driving voltage from 6 to 12 V. The
fly was positioned at the far end of the tube, within 2–3 mm of the
exit and 2–3 mm of the tube center line.
Secondary air flow came from a cylinder of compressed air containing 1000 ppm propylene tracer gas (BOC, Halifax, NS, Canada),
regulated to 20 kPa initial pressure. It flowed through an odorant cartridge made from the shaft of a 5 ml transfer pipet (Fisher
Scientific, Ottawa, ON, Canada), containing a rectangular piece
of filter paper (45 mm × 15 mm), through a two-way (open or
closed) solenoid valve (Cole-Parmer 01340-02, Montreal, QC) into
a 16 gauge hypodermic needle with its tip located in the center of
the flow tube and 42 mm from the exit. The solenoid valve used
PTFE materials in contact with the gas, and was driven by a 24 V dc
power supply via a photovoltaic relay (Fig. 1) to be either fully open
or fully closed. The valve had a specified switching time of 5 ms, but
this was not the limiting factor in determining the overall dynamic
performance.
Odorant cartridge design was similar to that described previously (French and Meisner, 2007), but the concentrations of
odorants were reduced to account for the lower ratio of primary to
secondary air flow (Fig. 4). Actual odorant concentration depended
on evaporation rate from the filter paper, which was unknown and
probably different for each odorant. Odorant chemicals and mineral
oil were purchased from Sigma (Oakville, ON, Canada) and mixed at
20% (v/v) before final dilution. 10 ␮l volumes of each mixture were
loaded into separate cartridges. Fresh cartridges were prepared for
each experiment.
2.2. Stimulus measurements
Since the solenoid valve was either open or closed, this signal
was treated as a binary value of either zero or one. Tracer gas concentration was measured by a miniature photoionization detector
(mini-PID, Model 200A, Aurora Scientific Inc., Aurora, ON, Canada).
The PID samples the gas through an inlet needle probe. The tip of
the probe was located directly above and within 1 mm of the fly
antenna. The PID has a frequency response of 0–330 Hz and a concentration range of 0.05–500 ppm propylene. The PID inlet needle is
57-mm long with an internal diameter of 0.76 mm, giving a volume
of 0.0259 ml. We used a gas sampling rate of 1150 ml/min, giving a
time delay of 1.35 ms. Additional delay must occur as the gas passes
through the ionizing chamber, so we assumed a total delay between
the gas reaching the PID sample tube and the PID output signal of
2 ms.
2.3. Electrophysiological measurements
Fig. 1. Experimental apparatus. Upper: flow system was constructed from plexiglas. A variable speed fan compressed the air in the pressure chamber, driving the
primary air flow through the plexiglas flow tube (60-mm long, 6.4 mm internal
diameter). Secondary air flow containing odorant chemical mixed with tracer gas
passed through the solenoid valve into a 16 gauge stainless steel hypodermic needle
with its opening at the center of the flow tube and located 42 mm from the tube
exit. The experimental animal and photoionization detector (PID) tip were approximately 3 mm from the exit of the flow tube. Middle: electronic circuit to control
the solenoid valve used a photovoltaic relay (PVG612) with its light emitting diode
driven by a transistor (2N5192) in the emitter follower configuration. Lower: cartoon
of the binary shift register implemented in software to generate the M-sequence of
valve on–off signals. Each bit shifted to the left on a regular clock sequence, with the
new value of the first bit derived from the exclusive OR of the 13th and 33rd bits.
Flies, Drosophila melanogaster, Oregon R #2376 (Bloomington
Drosophila stock center, Bloomington, IN) were raised and maintained in the laboratory using a standard diet (Lewis, 1960) at a
temperature of 22 ± 2 ◦ C under a 13 h light/11 h dark cycle. Flies
of either sex were used within 2 days of hatching. Procedures for
recording electroantennograms were similar to those described
previously (Alcorta, 1991). Animals were held in the cut end of
a 100 ␮l plastic pipet tip. A reference glass microelectrode electrode (∼1 ␮m tip diameter) was inserted into one eye, while a
larger glass microelectrode (∼20 ␮m tip diameter) was pushed
against the distal tip of one antenna. Both electrodes were filled
with Drosophila saline (Hazel et al., 2003). Electroantennograms
were recorded as electrical current with a List EPC-7 patch clamp
amplifier (ALA Scientific Instruments, Westbury, NY). All experiments were performed at room temperature (20 ± 2 ◦ C) and in a
100
Chapter II
controlled humidity chamber (<40%). The animal preparation was
mounted on an air driven anti-vibration table. The stimulating system was mounted separately, and mechanically isolated from the
preparation.
2.4. Experimental control and data processing
All experiments were controlled by custom-written software
via a personal computer and a data acquisition board (NI6035E,
National Instruments, Austin, TX). Binary M-sequences were generated by the computer using a 34-bit binary shift register with
feedback from the logical exclusive OR of the 13th and 33rd bits
(Fig. 1). The PID voltage and electroantennogram current were
digitized via a 16-bit analog-to-digital converter and sampled at
5 ms intervals. Sampled time domain data (20,000 input–output
pairs) were transferred to the frequency domain using the fast
Fourier transform (Cooley and Tukey, 1965) in segments of 512
sample pairs. Frequency response functions between the binary
M-sequence (input) and PID voltage (output), or between PID
voltage (input) and electroantennogram current (output) were calculated by direct spectral estimation and plotted as Bode plots of
phase and log gain versus log frequency. Frequency response functions were fitted by a coherence-weighted minimum square error
process. Note that the segment length combined with sample interval (512 and 5 ms here) fixes the lowest frequency measurement
(1000/(512 × 5) ms = 0.39 Hz here), whereas sample interval alone
fixes the highest frequency measurement (100 Hz here). Longer
segments can be used if lower frequencies are of interest, but this
usually requires longer experiments.
The coherence function is a normalized measure of linear correlation between the input and output signals (Bendat and Piersol,
1980). A value of unity indicates that all of the output signal at
a given frequency can be produced by a linear transformation of
the input at that frequency. Values below unity mean either that
some uncorrelated signal (noise) was added between the input and
output, or that the system behaved at least partially nonlinearly.
Coherence functions were calculated from the same data as the
frequency response functions. Coherence was used to estimate the
information capacity, R, of the stimulating system (Shannon and
Weaver, 1949):
R=
log2
1
1 − 2 (ω)
dω
(1)
3. Results
3.1. Frequency response and coherence functions of the
stimulating system
Dynamic performance of the stimulation system was evaluated
from frequency response and coherence function measurements
between the M-sequence input and the tracer gas concentration
at the PID (Fig. 2). Frequency response functions, G(jω), were well
fitted by a relationship used previously to model laminar air flow
along a tube (French and Meisner, 2007):
2
1
(2)
(1 + jω)
√
where ω is radial frequency, j = − 1, ˛ is a constant amplitude
factor, t is the time delay between valve opening and tracer gas
reaching the PID, ωc is the half amplitude frequency of a Gaussian
function that approximates three-dimensional diffusion of the gas
as it flows, and is the time constant of a first-order low-pass filter
function that approximates frictional drag between gas flow and
the tube walls. This equation was fitted to the amplitude and phase
data in Fig. 2 as solid lines.
G(jω) = ˛ exp(jω t) exp −
ω
ωc
Fig. 2. Frequency response function between solenoid valve position and propylene
tracer gas concentration measured by the PID, presented as a Bode plot of log amplitude and log phase versus log frequency. Solid lines show Eq. (1) fitted to the data
with values: Fc = 2ωc = 35.41 Hz, = 2.66 ms, t = 38.72 ms. The amplitude parameter, ˛, was normalized to unity (0 dB) at low frequency. Flow volume and velocity,
from t, were: 39.46 ml/s and 1.23 m/s, respectively. Mean propylene concentration
was 104.3 ppm, giving a ratio of primary to secondary air flow of 9.59. The coherence
function for the same data is shown below on the same frequency axis. Information
capacity, from Eq. (2), was: R = 70.0 bits/s. These data were based on 50 s of recorded
data at 10 V fan voltage using 5 ms sampling interval and an M-sequence switching
interval of 20 ms. Inset is shown a section of the original recordings of valve switching (open—up, closed—down), and propylene concentration measured by the PID.
Note the delay between valve switching and propylene concentration change, as
well as periods of zero propylene concentration (arrow).
Part of the raw data used to create the frequency response function is shown inset Fig. 2. Note the delay between valve opening
or closing and tracer gas concentration rising or falling. Also, note
that longer valve closing caused the tracer gas concentration to
approach zero (arrow). Since the input signal was a binary number, the amplitude parameter, ˛, which represents output divided
by input at zero frequency, does not have meaningful dimensions.
For illustration, it was normalized to unity (0 dB) in Figs. 2 and 3.
The coherence function, 2 (ω), was close to unity over a wide
frequency range (Fig. 2), indicating a linear, noise-free relationship
between valve position and tracer gas concentration.
3.2. Reliability of stimulation parameters at the sensory organ
The difficulty of measuring odorant, or tracer gas, concentration at the actual sensory receptor makes it crucial that the dynamic
properties of odorant stimulation be predictable and reliable within
a reasonable distance around the animal. To test this we measured
frequency response functions and coherence functions similar to
those of Fig. 2 at different positions around the center line of
the flow tube (Fig. 3). Following Poiseuille’s law, laminar flow of
gas through a tube produces a radial distribution of gas velocities around the center line, with the highest velocity in the center
(upper panel, Fig. 3). Tracer gas concentration during M-sequence
opening and closing of the solenoid valve was measured by the PID
at the center of the flow tube exit, and at distances of 1 and 2 mm
around the center in the horizontal and vertical directions. Fitted
parameters of Eq. (2) were plotted versus position (Fig. 3, lower
graphs).
49
Chapter II
101
Fan speed affects primary air flow, rather than secondary air flow,
so we also measured the ratio of primary to secondary air flow
to indicate the required concentration of odorant at different fan
speeds.
Frequency at 1% power increased linearly with fan speed to a
maximum of 46 Hz. Information capacity more than doubled over
the same range, reflecting the increasing frequency range, and indicating lack of turbulence. Ratio of primary to secondary airflow
increased linearly, as expected. We also tested the effect of different M-sequence switching rates on stimulator performance (Fig. 4).
More rapid transitions between open and closed valve positions
would be expected to increase the available stimulation frequency,
but the valve switching time of 5 ms placed an upper limit on the
range. There was no significant difference between any measured
parameters for switching rates of 5 and 10 ms, while 20 ms produced a small reduction in frequency range.
3.4. Experimental demonstration
Frequency response and coherence functions were measured
for a Drosophila electroantennogram during M-sequence stimulation with hexyl acetate (Fig. 5). Input was propylene concentration
measured by the PID, while output was antennal current. The frequency response function was fitted by the first-order low-pass
filter function:
Fig. 3. Effects of lateral position in the air flow on dynamic odorant stimulation
parameters, as estimated from tracer gas concentration. Upper and middle figures
show side and end views of the flow tube approximately to scales shown. Frequency
response functions similar to Fig. 2 were obtained at 1 mm spaced positions around
the center line of the air flow (dots in middle figure). Identical M-sequence signals
were used for each recording to remove any variation due to finite signal duration.
Each experiment was repeated four times and fitted by Eq. (1). Mean values of the
fitted parameters are plotted as functions of horizontal and vertical positions (X and
Y) in the lower graphs. Standard deviation bars are within the plotted symbols. Inset
traces show sections of three separate original PID recordings using the same input
sequence to the valve but measured at three of the nine positions.
G(jω) = ˇ exp(jω u)
1
(1 + jω)
(3)
where ˇ is the response amplitude at zero frequency, u is a time
delay and is the filter time constant (French and Meisner, 2007;
Justus et al., 2005). Note the high value of the coherence function.
Part of the original recording (inset) shows that the electroantennogram closely followed the trace gas concentration during the
M-sequence stimulation.
Parameters t and ωc did not vary significantly with position.
(Note that ωc has been converted to temporal frequency, Fc = 2ωc
in Hz, for clarity in Fig. 3.) Amplitude, ˛, was maximal at the center
line and decreased to a minimum of 55% of the center value at
2 mm. Time constant, , also varied with distance, increasing from
a minimum of ∼2.5 ms at the center to a maximum of ∼6 ms at
2 mm from the center.
3.3. Effects of flow velocity on stimulation bandwidth and
odorant concentration
Selection of flow velocity in a dynamic olfactory stimulator
requires a compromise between several factors. Increased velocity
reduces the time available for diffusion, which increases the maximum frequency of concentration changes that can be delivered to
the animal. However, increased velocity also increases the possibility of turbulence, reduces the time for diffusion to a homogenous
concentration, and can interfere with experimental recording from
delicate preparations. Changes in velocity also affect the thickness
of the boundary layer of air at the surface of the animal and thus
the effective concentration of odorant.
To assess the available stimulation frequency we measured the
power spectrum of the PID signal during M-sequence switching at
a range of fan speeds and recorded the frequency at which the spectrum fell to 1% of its maximum value (Fig. 4). Information capacity,
R, provided another measure of available frequency range because it
is obtained from integration over frequency (Eq. (1)). Information
capacity also provided a check of laminar flow, since turbulence
would cause incoherent concentration fluctuations and reduce R.
Fig. 4. Effect of fan speed and M-sequence timing on dynamic odorant stimulation
parameters estimated from tracer gas concentration. Fan voltages were 6, 8, 10 and
12 V, as indicated. Frequency response functions similar to Fig. 2 were obtained at
each voltage and fitted by Eq. (1). Air flow rate and velocity were estimated from the
fitted values of t and tube dimensions (Figs. 1 and 3). Information capacity was
calculated from coherence (Eq. (2)). The power spectrum of the tracer gas signal
was inspected manually to locate the frequency at which it dropped to 1% of its
maximum value. Ratio of primary to secondary air flow was calculated from the
mean concentration of tracer gas throughout each recording, based on a secondary
flow concentration of 1000 ppm propylene. Three recordings at 12 V were made with
M-sequence switching times of 20, 15 and 10 ms (marked A, B, and C, respectively).
Other recordings used 20 ms.
102
Chapter II
an elastomeric bead (French and Meisner, 2007). Assembly of the
system is relatively complex and requires analog servo-control electronics, whereas solenoid valves are commercially available with
a wide range of properties, and can be driven by digital signals.
Physical size is also an issue because the upper frequency limit
is partly controlled by the distance between odorant gas release
and the animal. We found it difficult to fit a mechanical actuator
and position detector close to the exit of the flow tube, whereas
the solenoid valve outlet port could easily be located in this position. This problem might be overcome with lever systems, but
at the cost of added size and complexity. More importantly, the
CVR method required frequent adjustment of the elastomeric bead
position to ensure a suitable movement amplitude range, whereas
the solenoid valve required no adjustment or other intervention.
Another significant operating advantage was that the solenoid
valve closed completely when the signal is removed, so it was
not necessary to disconnect tracer gas supply between experiments.
The binary driving signal makes it easy to produce identical
repeated stimulation signals, as used in Fig. 3. This can be an important feature for testing the reliability of a sensory receptor, and
provides the basis for methods of estimating signal to noise ratio
and entropy that have been used to measure information transmission through neural systems (Juusola and French, 1997).
4.3. Frequency range and receptor adaptation
Fig. 5. Experimental test of M-sequence stimulation system by electroantennogram
recording from Drosophila stimulated with hexyl acetate odorant. Fan voltage was
10 V and the signals were sampled at 5 ms, with an M-sequence switching interval of
20 ms. Frequency response function is shown from 8704 data points (43.52 s), fitted
by Eq. (3) with parameters: ˇ = 0.195 pA/ppm, u = 0.87 ms, = 23.74 ms. Inset shows
part of the original data. Amplitude units are shown as pA/ppm propylene, assuming
that actual concentration of odorant at the animal was unknown, but proportional to
tracer gas concentration. Note the high value of the coherence function over a broad
frequency range. Similar results were obtained from experiments on 10 different
flies, using both butyl butyrate and hexyl acetate as odorants.
4. Discussion
4.1. Methods of dynamic olfactory stimulation
Use of surrogate tracer gas to track odorant concentration
changes has provided a major improvement in our ability to measure rapidly changing input signals to olfactory receptors (French
and Meisner, 2007; Justus et al., 2002, 2005; Vetter et al., 2006).
It has also shown that traditional methods relying on pulsed
release at a distance from the animal are unreliable and depend on
experimental design (Vetter et al., 2006). Generation of controlled
olfactory stimulation with suitable dynamic properties has proved
more challenging. Turbulent flow is relatively simple to produce
and can generate a wide range of stimulus frequencies (Justus et al.,
2005), but it is uncontrolled, unrepeatable and spatially complex, so
that measurements made even close to the animal may not reflect
the concentration at the sensory structure. Continuously variable
release (CVR) into a flow tube provides a controlled dynamic signal
(French and Meisner, 2007), but the M-sequence method offers several additional advantages for linear or nonlinear systems analysis,
as described below.
4.2. Construction and operation
The CVR method uses an electromagnetic actuator and a position detector to variably occlude the aperture of a glass pipet by
The M-sequence method produced a slightly higher frequency
range of stimulation than CVR. The limiting factor is solenoid
valve switching time, so the frequency range could be extended
if faster valves were available. We chose the valve with the most
rapid response of those available from standard laboratory supply companies, but faster valves may exist. Note that the 1% power
spectrum criterion we used (Fig. 4) was an arbitrary measurement. Frequency components were present above this value and
were still adequate for system identification, but this will generally depend on the preparation and particularly the system noise
level. Also note that the frequency range increased with flow
velocity, but the velocities used here were much lower than the
value of 2.4 m/s used in the CVR system (French and Meisner,
2007).
An important property of the M-sequence method was that
tracer gas and odorant concentrations fell to zero about 70 ms after
the valve closed (Fig. 2), so adapting sensory receptors could be
tested by stimulus patterns that allow recovery from adaptation.
This is a limitation of the CVR method because it requires a continuous mean gas flow modulated around the mean. While both
methods could be modified to include periods of zero stimulation,
it is much easier to accomplish using a solenoid valve, and occurs
naturally in M-sequences.
4.4. Other system characteristics
Spatial distribution of odorant concentration is an important
issue for dynamic stimulation (Vetter et al., 2006). The present
system was designed for small animals, such as Drosophila, so
the tested spatial range of about 5 mm diameter around the center line was more than adequate. Within this range the major
effects were a reduction in amplitude, ˛, and an increase in time
constant, , with distance from the center line. Neither would prevent the off-center signal from being useful for dynamic system
characterization, although the frequency range would be slightly
reduced. Clearly, the most important thing is to have the PID
probe located as close as possible to the sensory receptor that
is being examined, to faithfully record the local gas concentration. However, the PID actively removes air, which should be
51
Chapter II
considered in estimating flow rates arriving at and leaving the
receptor.
The observed variation of ˛ and with position from the center line (Fig. 3) support the interpretation of the parameters in
Eq. (2) (French and Meisner, 2007). Diffusion of tracer gas from
the release site in the center of the tube will give maximum concentration in the center line, while the temporal filtering effect
of frictional drag will increase closer to the tube wall. Adherence
and later release of odorant or tracer gas molecules to the tube
wall may also occur in this, or any other tube-based stimulation
system.
The characterizations and experimental test used here did
not require the binary properties of the M-sequence to increase
computational efficiency. However, this feature could be useful
for experiments requiring multiple parallel computations (Sutter,
2001), such as recording from an array of electrodes placed
across an olfactory bulb, or for nonlinear analysis based on higher
order kernels (French and Marmarelis, 1999). The concentration
of odorant delivered to the sensory receptor is not itself an Msequence, but has a simple linear relationship to the sequence (Eq.
(2)), which could be deconvolved from any system characteriza
tion.
Stimulation techniques that flow a carrier gas over a volatile
odorant can be affected by a reduction in odorant concentration as it
evaporates from the substrate. Varying the carrier flow rate, as done
here and in the CVR method, can also cause odorant concentration
variation by changing the time available for evaporation. This issue
was not addressed directly here, but methods of reducing these
effects have been developed (e.g. Grant et al., 1995) and could be
incorporated into the newer stimulation techniques.
4.5. Conclusions
M-sequence olfactory stimulation offers significant advantages
over the CVR approach for several reasons. It is simpler to construct,
operate and control, while offering comparable or better frequency
range of stimulation. The ability to provide odorant-free periods
within a rapidly changing stimulus signal should be particularly
useful for the large number of olfactory receptors that adapt to a
steady concentration.
Acknowledgments
This work was supported by grants from the Canadian Institutes
of Health Research. Shannon Meisner provided expert technical
assistance and animal care.
103
References
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and its use for identifying olfactory capture and transduction mutants. J Neurophysiol 1991;65:702–14.
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systems. IEEE Trans Comput 1974;23:225–32.
French AS, Marmarelis VZ. Nonlinear analysis of neuronal systems. In: Windhorst
U, Johansson H, editors. Modern techniques in neuroscience research. Berlin:
Springer; 1999. p. 627–40.
French AS, Meisner S. A new method for wide frequency range dynamic olfactory
stimulation and characterization. Chem Senses 2007;32:681–8.
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pp. 1–251.
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receptor neurons in mosquito maxillary palp sensilla to carbon dioxide. J Comp
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wind tunnel using a photoionization detector and a tracer gas. Environ Fluid
Mech 2002;2:115–42.
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pheromone in two moth species. J Neurophysiol 2005;93:2233–9.
Juusola M, French AS. The efficiency of sensory information coding by mechanoreceptor neurons. Neuron 1997;18:959–68.
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approach. New York, NY: Plenum Press; 1978. pp. 1–487.
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Chicago, London: University of Illinois Press; 1949. pp. 1–117.
Sutter EE. Imaging visual function with the multifocal—sequence technique. Vision
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Chapter III
Two Interacting
Olfactory Transduction Mechanisms
Have Linked Polarities and Dynamics
in Drosophila melanogaster
Antennal Basiconic Sensilla Neurons
Chapter III
Chapter III
Chapter III
Chapter III
Chapter III
Chapter III
Chapter III
Chapter III
61
Chapter III
Chapter III
63
Chapter III
Acknowledgments
First of all I would like to thank Andrew French and Päivi Torrkeli for offering me the
opportunity to join their lab and work on this very interesting and yielding topic. They would
always take the time to offer support when it was needed. And let me work independently
otherwise. I would like to thank you for giving me a very nice start in Halifax both at home
and in the lab. Thanks for having a good sense of humor which was very appreciated. We
will always have a funny story to tell about bikes.
Many thanks to Monika Stengl for not only giving me the chance to go abroad and still stay
rooted in Marburg/Kassel, but also encourage me strongly to do so. Thank you for the
inspiration and all the support over the years.
Many thanks to Christine Nowack and Mireille Schäfer the other members of the committee,
for being kind enough and agree to be part of my dissertation .
Thank you also to Keram Pfeiffer, Ulli Höger, Izabel Panek and Shannon Meisner for creating
a nice atmosphere in the lab. Special thanks to Ulli who would always, always help to figure
things out and many times would offer hot glue as the most obvious of all solutions. Also
special thanks to Shannon, who started the olfaction project in the lab and taught me how to
do EAGs and was always supportive in many other ways.
Thank you to the lab in Marbug/Kassel especially Nico Funk, Christian Flecke and Achim
Werkenthin and Wolf Hütteroth for keeping connected and exchanging about science and a
lot of science unrelated things.
Thank you to the Wednesday Tupper-Henry house crew and the “dancers” for all the fun
we had.
Thank you to my roomies Steph Glover, Ross Soward, Jonah Bernstein, and the neighbors
for making Sarah Street a great place to be.
Thank you to Anita Hopes, Bettina Westerheider, Miriam Schulz, Doro Barth, Tamara
Münkemüller, Matthias Schleuning, Alex Bolen, Katie McKay, Angela Carlsen, Chriz Eickhorst,
Jessie Litven, Sofie Gragdmann, Dawn Kellett, Konstanze Stübner, Annette Kollar, in random
order for being the way you are and for the support and caring you gave me in various ways
I can’t appreciate it enough and feel very lucky to know you.
Very special thanks to Keram Pfeiffer, who supported and helped me in countless ways and
without whom this work would look very differently. It all meant a lot to me.
Thank you to my father who always knew “it” and who was the greatest supporter of
curiosity since I was a child. And thank you to Ingrid who supported me in many ways for
half of my life now.
Erklärung
Ich versichere, dass ich meine Dissertation
Dynamic characterization of olfactory receptors in the fruit fly Drosophila melanogaster
(Dynamische Charakterisierung olfaktorischer Rezeptoren der Tauflige Drosophila melanogaster)
selbstständig, ohne unerlaubte Hilfe angefertigt und mich dabei keiner anderen als der von
mir ausdrücklich bezeichneten Quellen und Hilfen bedient habe.
Die Dissertation wurde in der jetzigen oder ähnlichen Form noch bei keiner anderen
Hochschule eingereicht und hat noch keinen sonstigen Prüfungszwecken gedient.
Kassel,
(Julia Schuckel)

Dynamic characterization of olfactory receptors in the fruit fly

Transcription

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