Novel natural ligands for Drosophila olfactory receptor neurones
1 Division of Chemical Ecology, Department of Crop Science, Swedish
University of Agricultural Sciences, PO Box 44, SE-23053 Alnarp,
Sweden
2 Department of Experimental Biology, University of Cagliari, S.S. 554 Km,
4500 Monserrato, Italy
* Author for correspondence (e-mail: bill.hansson{at}vv.slu.se)
Accepted 13 November 2002
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Summary |
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Key words: Drosophila, olfaction, insect, antenna, food odour, receptor neurone, ligand, odour coding
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Introduction |
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The recent identification of the first insect odorant receptors (OR) in
Drosophila (Clyne et al.,
1999; Gao and Chess,
1999
; Vosshall et al.,
1999
) has opened up new possibilities for research regarding
olfactory coding in insects. The
60 Drosophila OR genes (DORs;
Vosshall et al., 2001
) encode
a family of seven transmembrane G-protein-coupled receptors, whose function is
to recognize specific odorant molecules
(Störtkuhl and Kettler,
2001
; Wetzel et al.,
2001
). The DORs are expressed in dendrites of olfactory receptor
neurones (ORNs) housed in sensilla located on the antennae and the maxillary
palps, the two olfactory organs of Drosophila.
Although Drosophila is currently a favoured model for olfactory
research, information regarding odour ligands is scarce. With the cloning of
the DOR family, this shortcoming is highlighted, as much related future work
will rely on the availability of key stimuli of the system. Drawing
conclusions concerning, for example, ligandOR interactions is difficult
if the relevance of the ligands at hand is questionable. More information
regarding biologically significant odour ligands is therefore sorely needed.
Investigations into odour detection in Drosophila ORNs are
surprisingly few considering the amount of work done on other aspects of the
system. The thorough studies by de Bruyne et al.
(1999,
2001
) report several odorants
eliciting responses from antennal and maxillary palp ORNs. Other studies
report odorants of various potency as ligands
(Siddiqi, 1991
;
Clyne et al., 1997
). These
studies have all relied on synthetic stimulus sets. A problem when screening
with synthetic odorants is that only a fraction of all possibly important
stimuli can be tested. The number of potentially biologically relevant
odorants for a polyphagous species like Drosophila is in the range of
thousands. Thus, in the studies so far conducted it is likely that many key
ligands have been overlooked.
Linking electrophysiology with gas chromatography (GC)
(Arn et al., 1975;
Wadhams, 1982
) can solve this
shortcoming. GCelectrophysiology, in particular GC linked with
recordings from single ORNs, so called GCsingle cell (GCSC), has
proven itself a potent method for identification of odour ligands
(Wibe et al., 1997
;
Stensmyr et al., 2001
). The
GCSC technique enables screening of single odours present in extracts
of favourable food resources that are likely to contain the odorants that the
ORs have evolved to detect. Here, we report physiological responses from
antennal ORNs using a GCSC stimulation technique. We screened a large
number of potential ligands from favoured food resources and characterized the
olfactory tuning of 12 ORN types housed in eight physiologically distinct
types of sensilla. In addition, we present doseresponse functions as
well as behavioural responses to several of the identified odorants.
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Materials and methods |
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Odour stimuli
We chose six different fruit types for extraction of volatile components.
The fruits chosen, on the basis of them being attractive to
Drosophila, were banana, litchi, mango, papaya, passionfruit and
pineapple. In addition, we also prepared a yeast extract. From these sources,
we prepared nine extracts, three from banana (from various stages of ripeness)
and one from each of the others. Prior to volatile extraction, the fruit was
left to ripen and the yeast was mixed with sucrose. The volatile contents were
collected using a closed loop stripping setup, as described by Stensmyr et al.
(2001). We also used 22
synthetic compounds: acetoin, butyl acetate, butyl butyrate, cyclohexanol,
ethyl butyrate, ethyl hexanoate, ethyl 3-hydroxyhexanoate, furfural, hexyl
acetate, hexyl n-butyrate, isoamyl acetate, isoamyl alcohol, isovaleric acid,
methyl hexanoate, phenylacetonitrile (Aldrich, purity >98%),
phenylacetaldehyde (Aldrich, purity >90%), ethylidene acetone (Aldrich,
purity >70%), acetyl furan, 2,3-butanediol, ethyl 3-hydroxybutyrate,
1-hexanol (Fluka, purity >97%) and phenylethyl alcohol (Sigma, purity
>98%). Neat compounds were diluted in redestilled hexane or
CH2Cl2 in decadic steps from a concentration of 10 µg
µl-1 down to 100 pg µl-1. From the extracts and
from the synthetic compounds, 10 µl was pipetted onto small pieces of
filter paper (approximately 10 mmx15 mm; Munksjöpapp, Grycksbo,
Sweden) placed in Pasteur pipettes. Blank cartridges, containing only filter
paper plus solvent, were also prepared. We frequently refilled or renewed the
test cartridges.
Odour stimulation
Once contact with ORNs was established, the test cartridges containing
odours were screened (presented in random order) for physiological activity,
i.e. if the odours elicited a change in the action potential firing frequency.
A glass tube, with its outlet approximately 10 mm from the antenna, delivered
a constant flow of charcoal-filtered and humidified air at a velocity of 0.5 m
s-1 over the preparation. Stimulation was performed by inserting
the tip of the test cartridge into a hole in the glass tube, approximately 15
cm before the outlet. The test cartridge was connected to a stimulus
controller (Syntech CS-02, Hilversum, The Netherlands) that generated air
puffs (2.5 ml in 0.5 s) through the cartridge into the constant air stream in
the glass tube. ORNs responding to cartridge-delivered fruit extract odours
were further examined by stimulation of the active extract(s) via GC.
A sample of approximately 2-3 µl of the extract to be tested was injected
onto the GC column for separation. At the end of the GC column, a cross split
was installed, leading half of the effluent to the flame ionization detector
(FID) and half out of the GC oven into the glass tube carrying the constant
airflow to the antenna, enabling simultaneous recordings of activity from ORNs
and FID. The GC separations were performed on a 30 mx0.25 mm i.d. polar
capillary column (HP-innowax) fitted in a Hewlett Packard 5890 GC Plus.
Carrier gas was hydrogen at 0.5 m s-1 linear velocity. Detector
temperature was set at 250°C and injector temperature at 225°C. Oven
temperature was maintained at 40°C for 2 min, then programmed to rise to
230°C at either 20°C min-1 or 10°C
min-1.
During the GCSC recordings, an increased firing rate of action potentials of at least twice the spontaneous activity was interpreted as a response (no inhibitions were observed; see Results). We quantified the response strength as the maximum spike frequency (spikes s-1; counted over 200 ms intervals) during the period of increased neurone activity following stimulation.
Doseresponse relationships
We also performed doseresponse trials with synthetic compounds in
order to establish the response thresholds for those odorants eliciting the
strongest response. These were presented in increasing decadic dosages from
100 pg to 10 µg from Pasteur pipettes using the stimulus controller
described above. The response strength was calculated by subtracting the
number of spikes 1 s after stimulation with the number of spikes in the
preceding 1 s period. The net response was then subtracted by the net response
to blank in order to avoid any non-specific responses.
Chemical analysis
Active compounds in the extracts were identified through coupled
GCmass spectroscopy (GCMS). 1-2 µl extract was injected onto
a Hewlett Packard 5890 GC Plus equipped with a 30 mx0.25 mm ID polar
capillary column (HP-innowax). Helium was used as carrier gas, set at 0.44 m
s-1. A gradient of 10°C min-1 (alternatively 5°C
min-1) from 40°C to 230°C with a starting temperature hold
of 6 min was utilized. Separations then passed through an HP5972 mass
spectrometer in scan mode with an electron ion source and quadropole mass
filter. Active FID peaks were identified by their mass spectra. These spectra
were compared with reference spectra from a NIST/EPA/NIH 75K electronic
database
(http://www.nist.gov/srd/nist1a.htm).
The mass spectra identification was confirmed via co-injection of the
synthetic compound with the extract itself.
Behavioural analysis
To screen the behavioural effect of identified odorants we used a T-maze
setup (Tully and Quinn, 1985;
Helfand and Carlson, 1989
).
Tested odorants were diluted in paraffin oil (except acetoin, which was
diluted in water) in decadic steps ranging from 10 pg µl-1 to 10
µg µl-1. 10 µl of the test odorant was applied to a 1
cm2 filter paper; as a control, we used identical filter papers
with 10 µl of the solvent. 20-30 flies of the same sex, 3-8 days old, were
introduced into the setup and exposed to an airstream (1 l min-1)
passing through the system. The flies were allowed 30 s to choose between the
odour side and the control side, thereafter the flies were gathered and
counted. Flies were only tested once and all tests were performed in the dark
to exclude visual effects. The response index (RI) was calculated as
the number of flies choosing odour (O) subtracted by the number of
flies in the control (C), divided by the sum of all choosing flies
(O+C); i.e.
RI=(O-C)/(O+C). An RI of
1.0 equals full attraction, whereas an RI of -1.0 equals full
avoidance. Indifference to the odour is indicated by an RI of zero.
Flies not making any choice were excluded from further analysis.
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Results |
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Individual ORNs responded in a very selective manner to the screened odorants. Typically, an ORN would only respond to a few of the GC-separated extract components, and all responses observed were excitatory. The maximum number of responses from an individual ORN to any of the extracts was from a banana sample, which contained seven active components. In most recordings, a single peak would produce a strong response, whereas a few other compounds would produce weaker responses (Fig. 2). As we used extracts, we were not able to correct for dosages. The components were present in the ratio that they occur in nature. However, the compounds eliciting response were not necessarily those that occurred in the highest amounts. On the contrary, many of the active compounds were only present in very low amounts, some of them barely visible on the FID trace.
Using GCMS, we identified the active compounds. In total, we observed responses to 35 FID peaks in the fruit extracts. We found 31 of these peaks to correspond to 23 different compounds, of which four occurred in more than one extract. The remaining four peaks either occurred in too low amounts or did not produce clear mass spectra. These components remain unidentified. The identified compounds are presented in Table 1).
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Eight physiological types of sensilla were identified
Our GCSC recordings revealed that the ORNs could be divided into
functional types with discrete characteristics
(Fig. 3). The response patterns
from individual neurones were repeatable, and neurones were found to reside in
stereotyped constellations. In total, we identified eight physiologically
distinct sensillum types based on the response profiles of their ORNs
(Fig. 4). The topographical
distribution on the antennae of these sensilla is shown in
Fig. 5. In addition, we also
screened the most potent stimuli for each ORN type as synthetics in known
concentration ranges to verify the neurones' response characteristics and
detection thresholds (Fig. 6;
see below).
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From the proximo-medial region of the antennae, which houses the large s.
basiconica (Shanbhag et al.,
1999), we identified three types of sensilla, which we denoted S1
(sensillum type 1), S2 and S3 (see Fig.
4). The S1 type, which housed four ORNs, was the most frequently
encountered (N=32). Of these four ORNs, the A ORN did not respond to
any fruit compounds. The C ORN responded strongly and selectively to
CO2, whereas the B ORN responded most strongly to acetoin; weaker
responses were elicited by three other compounds. The D ORN was found to fire
moderate responses to furfural. The S2 type (N=13) housed two
neurones, of which only the B ORN responded to the screened odorants. The
primary ligand was ethyl 3-hydroxybutyrate, although these neurones also
responded, less strongly to five other compounds. Three of these remain
unidentified. One of the identified compounds, ethyl butyrate, shared
structural properties with the primary ligand, while the third most-potent
stimulus was cyclohexanol, a radically different molecule. From the third
sensillum type in this region, the S3 type (N=24), we only observed
increased activity from the A neurone, which responded most strongly to ethyl
hexanoate and, in decreasing strength, to six other components of structural
proximity. These compounds were all similarly sized (6-8-carbon length)
straight-chained esters, some only differing slightly from each other. In
addition, a compound that remains unidentified also elicited moderate
responses. The distribution of these three sensillum types within this
antennal region showed slight differences between them
(Fig. 5). We found all three
types along the medial section. However, the S1 and S2 types were also found
on the posterior face, in proximity to the sacculus opening.
The other identified sensillum types were not found clustered in specific
regions of the antenna but had a more scattered distribution. The S6
(N=1) and S7 (N=3) types both responded selectively to
single compounds: sec-amyl acetate and isoamyl alcohol, respectively. The S6
sensillum was found lateral to the sacculus opening, a region that exclusively
houses s. coeloconica (Shanbhag et al.,
1999). The S8 sensillum (N=4) was distinctive in that it
housed three neurones. The B ORN responded to three similar phenolic compounds
of which phenylacetonitrile triggered the strongest response. The presence of
three ORNs in this sensillum suggests that it was an s. coeloconicum
(Shanbhag et al., 1999
). The
S4 (N=4) and S5 (N=6) types had similar, partly overlapping,
response spectra. The most efficient ligand, triggering strong responses for
the S5A ORNs, was 1-hexanol, which also elicited weak responses from the S4A
ORNs. The S4A ORNs did, however, respond slightly more strongly to butyl
butyrate. The B ORN of both sensillum types responded to isoamyl acetate.
However, S5B responded more strongly to this compound. In addition, the S5B
ORNs responded to three additional compounds that elicited no activity from
the S4B ORNs whatsoever. In addition, we found seven sensilla for which the
key stimuli remain unidentified.
Testing the identified key ligands as synthetics, we obtained recordings from both male (51 sensilla from seven individuals) and female (45 sensilla from five individuals) flies. No difference in response patterns between sexes was observed, thus data were pooled. We relocated and obtained doseresponse relationships for five of the identified ORN types from five different sensillum types. The S1B and S2B ORNs were both highly sensitive, capable of detecting their key ligands at 100 pg doses (Fig. 6B,C), whereas the S5A and S8B ORNs were slightly less sensitive to their respective ligands, requiring 1 ng doses to elicit response (Fig. 6D,E). For the S3A ORN, we tested the four most potent stimuli as synthetics. Ethyl hexanoate and methyl hexanoate produced highly similar response curves, both compounds already eliciting responses at 100 pg doses, whereas ethyl butyrate and butyl butyrate required a 100-fold and a 10 000-fold increase, respectively, in dose to produce any response (Fig. 6A). We also confirmed the S4 and S7 classes; however, we were not able to obtain reliable doseresponse curves for any of their neurones.
Behavioural effect of the identified ligands
We screened the behavioural response to a set of the identified odorants
over a range of concentrations in a T -maze setup
(Fig. 7). From the identified
ligands, we chose to test acetoin, butyl butyrate, ethyl hexanoate, ethyl
3-hydroxybutyrate, 1-hexanol and phenylacetonitrile, all potential key ligands
for six of the identified ORN classes. Although not identified in our study,
ethyl acetate was also included. T-mazes vary individually, as well as the
conditions under which they are operated (e.g. the method of applying the
stimuli and the stimulus amount and temperature), thus we included ethyl
acetate as a reference for the RI functions of the novel ligands, as
the behavioural effect of this odorant in T-mazes is well known
(Ayyub et al., 1990;
Alcorta, 1991
;
Acebes and Ferrús,
2001
).
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In total, we examined the behavioural response of 7120 flies in a total of 392 experiments. We did not observe any difference between the sexes, thus the data were pooled. All of the tested odorants were attractive, except 1-hexanol, which had an increasingly negative RI throughout the whole concentration range tested. The attractive odorants were only attractive over certain concentration ranges, and the overall response pattern followed the same trend with indifference to low concentrations, attraction to intermediate concentrations and repulsion to high concentrations. Butyl butyrate, ethyl 3-hydroxybutyrate and phenylacetonitrile had a very narrow concentration range in which the RI was positive, whereas the other attractive compounds were attractive over a wider range. The peak RI values for the attractive compounds are in the same range as we found for ethyl acetate and suggest that these novel ligands are equally potent Drosophila attractants as ethyl acetate.
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Discussion |
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In several of the model systems used in olfactory research today, very few biologically relevant key odour stimuli are known. Investigations often rely on synthetic odour compounds chosen randomly from the laboratory stock. An advantage for researchers investigating insects has traditionally been that behaviourally relevant odour ligands have indeed been identified and used. Unfortunately, this has not been the case for one of the main insect model systems, the fruitfly D. melanogaster. In the present investigation, we set out to identify natural odour ligands for this species. By collecting odours from potential food sources and using the insect itself as a detector, we found ligands that were singled out by ORNs among a very large selection of molecules extracted. In extracts typically containing hundreds of components, responses were generally recorded to a few compounds, and never to more than eight. The fly thus seems to single out key compounds in the extracts. Subsequent screening with the novel ligands as synthetic compounds showed that the neurones were capable of detecting key ligands at very low concentrations. Furthermore, the compounds were behaviourally active.
The odours identified reflect the food preference of Drosophila, namely rotten fruit. We found, for example, ORNs detecting microbial volatiles (e.g. acetoin and isoamyl alcohol) and ORNs detecting typical fruit volatiles (e.g. ethyl hexanoate and isoamyl acetate). Accordingly, of the six novel ligands tested for behavioural effect, five were attractive. The only repellent odour, 1-hexanol, is a so-called green leaf volatile, i.e. characteristic of green plant tissue and unripe fruit. Being an indicator of unsuitable food sources, this may explain why fruitflies avoid 1-hexanol. Considering both the highly sensitive receptor neurones, physiologically tuned to detect the compounds identified here, and the behavioural responses recorded, we conclude that the odours identified are indeed key sensory stimuli used to identify and locate suitable food sources of the fruitfly.
In the dataset presented here for Drosophila, different degrees of specificity are observed. A general problem in investigations of olfactory functions is the fact that you can never test the complete odour set. However huge the stimulus set used, a natural comment will be: "how can you be sure that you have not missed the key stimulus?" The potential stimulus spectrum for an ORN is unlimited so the answer to the question must be that a logically selected stimulus spectrum was used. Using the GCSC method we can approach the `real' world as we are actually testing hundreds of odours at their naturally occurring proportions. In exchange, we have to sacrifice the absolute control of concentration as all compounds will occur in the amounts found in the extracted stimulus medium, in this case fruit. To remedy the lack of threshold information and doseresponse curves, we performed stationary investigations of a large number of ORNs to establish thresholds and dose dependencies. From these it was clear that the ORNs investigated are indeed highly sensitive to the ligands identified.
How do our results correlate with previous investigations of
Drosophila olfaction? de Bruyne et al.
(2001) characterized the
physiological specificity of ORNs housed in antennal basiconic sensilla using
synthetic stimuli. Based on the response profiles of the ORNs, they reported
seven distinct types of sensilla. The three types of sensilla we found in the
proximo-medial region display similarities in distribution as well as in ORN
tuning with the large s. basiconica described by de Bruyne et al. However,
using natural stimuli, we report other, albeit similar, key ligands. For
example, we found acetoin was a primary ligand for the S1B neurone, whereas
the corresponding ORN in the de Bruyne et al. study responded to
2,3-butanedione, a structurally similar compound. Acetoin is a good key-ligand
candidate for this ORN type. It is effective at low concentrations
(Fig. 6) and makes ecological
sense as a ligand. The primary stimulus we identified for the S2B ORN, ethyl
3-hydroxybutyrate, is also a good key-ligand candidate. It produced strong
responses and was effective at very low concentrations
(Fig. 6B), whereas hexanol and
ethyl butyrate only elicited moderate responses from what probably constitutes
the corresponding ORN type in the de Bruyne et al. study.
How do insects code odours? Pheromone detection is widely accepted to rely
on highly selective and sensitive ORNs. In recent investigations it has also
been shown that plant odour-detecting ORNs can match pheromone ORNs with
respect to selectivity and sensitivity (e.g. Dickens, 1990; Anderson et al.,
1995; Hansson et al., 1999;
Stensmyr et al., 2001
). Both
these types of neurones will also respond to compounds of structural
similarity but generally at a higher concentration than that required to
elicit the same magnitude of response by the key ligand(s). Thus, pheromones
and general odours in insects are likely to be coded along similar principles,
i.e. the ORNs are primarily tuned to one, or maybe a handful, of chemically
similar key ligands that elicit responses at very low concentrations. However,
the ORs present on the ORNs are not only capable of binding these key ligands,
as the ORN will also respond to other compounds if these are presented in high
enough dosages and if they are of structural proximity. No ORN has so far been
identified that will respond to a single compound only, irrespective of
concentration, but the response threshold will differ for the compounds
depending on their interactions with the receptor proteins of the ORN. In moth
pheromone-detecting ORNs, the decrease in activity when moving from the key
ligand to a structurally similar one has been shown to be directly
proportional to the conformational energy needed to fold the `suboptimal'
ligand into a conformation most closely mimicking the key molecule
(Gustafsson et al., 1997
).
How does Drosophila code odours? We challenged the ORNs with a very large number of odorants (probably in the range of 1000) extracted from natural fly resources, and out of all these odorants, only 27 compounds, a fraction of all screened, elicited a response. Five ORN types responded only to single compounds. ORNs that did respond to several compounds were primarily stimulated by compounds that shared structural properties. The S3A, S4B, S5B and S8B ORN classes were each triggered by molecules sharing a functional group as well as being similar in the overall hydrocarbon structure. Of the ORNs that responded to multiple odorants, in the majority of cases (see ORNs S1B, S2B, S3A, S4B and S5B) one of the ligands produced a significantly stronger response than the alternate ligands. Naturally, the response magnitude as well as the ligand spectrum of the ORNs is affected by the stimulus amount applied. However, in many cases the ligand eliciting the strongest response in an extract did not occur in the highest amounts. The response curve of the S3A ORN obtained through screening with synthetic compounds over a wide concentration range shows that these ORNs are primarily configured for two very similar esters, and that alternate ligands require much higher doses in order to elicit response. Generally, the doseresponse relationships show a very low detection level for several of the ligands and indicate a high degree of sensitivity.
The fruitfly thus seems to code odour molecules in a fashion that is
presently emerging for several different insect types. At low concentrations,
detection is performed with an arguably high selectivity. When concentrations
are increased the specificity is decreased. The specificity of the system
might thus vary with distance to the source. When considering the range of
concentrations that a fruitfly will move through, such a system can be
envisaged as highly appropriate. Flies need to detect food sources at a
distance, i.e. they need to detect some key odours with very high sensitivity.
As the fly moves towards the food source it will experience a concentration
gradient from very low concentrations to more or less saturation close to or
on the substrate. Most likely key ligands are detected first, at a distance,
and as the fly moves closer and closer the threshold for several other
molecules is reached and the `distance-specialist' becomes a
`close-range-generalist'. An initial attraction by selective ORNs is gradually
transformed into an across-fibre coded, detailed odour image. Discussions of
specificity will always be a matter of concentration. As mentioned above, not
even pheromone receptors display an absolute specificity
(Peterlin et al., 2002).
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Acknowledgments |
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