Due to its well-defined genome, the fruitfly Drosophila melanogaster has become a very important model organism in olfactory research. Despite all the research invested, few natural odour ligands have been identified. By using a combined gas chromatographic—single receptor neurone recording technique (GC—SC), we set out to identify active odour molecules in head space-collected volatiles from preferred food sources, i.e. different overripe or rotting fruit. In total, we performed 101 GC—SC experiments on 85 contacted sensilla. Using GC—mass spectrometry, we identified 24 active compounds. Synthetic samples of these compounds were used to establish dose—response curves for several of the receptor neurone types encountered. The response patterns of individual neurones were repeatable, and neurones were found to reside in stereotyped pairs. In total,we identified eight distinct sensillum types based on response profiles of 12 olfactory receptor neurone types. In most recordings, a single GC peak would produce a strong response, whereas a few other, often chemically related,compounds would produce weaker responses. The GC—SC recordings revealed that the olfactory receptor neurones investigated were often selective and could be divided into distinct functional types with discrete characteristics. Dose—response investigations revealed very low response thresholds to the tested compounds. Six of the novel ligands were also tested for their behavioural effect in a T-maze set up. Of these, five elicited attraction and one elicited repulsion.
Research in insect olfaction has produced valuable insights into neural processes involved in sensory coding and processing. The fruitfly Drosophila melanogaster has emerged as a highly suitable model organism for olfactory research. It has an anatomically relatively simple and morphologically well-characterized olfactory system(Stocker, 2001). Additionally,as the complete genomic sequence is available(Adams et al., 2000; Rubin et al., 2000), there are extensive possibilities for genetic manipulations.
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, ligand—OR 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. GC—electrophysiology, in particular GC linked with recordings from single ORNs, so called GC—single cell (GC—SC), has proven itself a potent method for identification of odour ligands(Wibe et al., 1997; Stensmyr et al., 2001). The GC—SC 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 GC—SC 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 dose—response functions as well as behavioural responses to several of the identified odorants.
Materials and methods
Recordings from receptor neurones
Throughout this study, we used the wild-type Oregon R stock of Drosophila melanogaster L. For the electrophysiological recordings, a fly was mounted in a truncated pipette tip with the antennae protruding from the narrow end. The pipette tip was fixed with wax on a microscope slide, and the antennae gently placed on a cover slip and stabilized with a glass micropipette (method modified from Clyne et al., 1997). To establish contact with the ORNs, we used tungsten microelectrodes, electrolytically sharpened in KNO2 solution(Hubel, 1957). We positioned the recording electrode and the indifferent electrode (inserted into the head capsule) using a preparation microscope and a recording unit with combined joystick micromanipulators and amplifier (Syntech INR-02, Hilversum, The Netherlands). Action potentials from contacted ORNs were visualized on an oscilloscope (Tekscope, Tektronix, Beaverton, USA) linked to a loudspeaker for audio monitoring. Recordings of action potentials were stored on a computer and analysed with the software Auto Spike v. 4.0 (Syntech, Hilversum, The Netherlands). The activity of co-located ORNs in single sensilla was based on differences in spike amplitude. The ORN with the largest spike amplitude was termed A; the second largest B and so forth. As spike amplitudes sometimes changed during extensive firing, we had to complement the static template used by the software for spike sorting with manual sorting, where attention was also paid to the shape of the spikes. Contacts where problems arose in assigning identity to ORNs were excluded from further analysis. The position of the recording electrode was noted on template drawings of the Drosophila antennae (based on Shanbhag et al., 1999). Magnification (up to 220×) was too small to accurately pinpoint the morphological types of sensilla contacted but was, however, sufficient to accurately note the general area recorded from.
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 μgμl-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 mm×15 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.
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 m×0.25 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 GC—SC 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.
We also performed dose—response 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.
Active compounds in the extracts were identified through coupled GC—mass spectroscopy (GC—MS). 1-2 μl extract was injected onto a Hewlett Packard 5890 GC Plus equipped with a 30 m×0.25 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.
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 10μg 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.
Drosophila ORNs respond selectively to odours in the large set of stimuli screened
We recorded neural activity of ORNs from single olfactory sensilla on the third antennal segment (Fig. 1). In total, we performed 101 GC—SC experiments on 85 contacted sensilla from both male (46 sensilla from 17 individuals) and female(39 sensilla from 15 individuals) Drosophila. An example of a simultaneous GC—SC recording is shown in Fig. 2. No difference in response profile between the sexes was observed, and thus the data were pooled. Responses to one or several of the screened fruit odorants were observed in 69 recordings. Of these, 12 were excluded from analysis due to bad contact or contact being lost during the experiment.
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 GC—MS, 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 GC—SC 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).
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 dose—response 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 dose—response 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).
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 Drosophilaattractants as ethyl acetate.
The fruitfly D. melanogaster antenna houses highly sensitive ORNs physiologically tuned to detect odours emitted by potential food sources. By using a novel technique, combining a chemical and a biological detector, a number of novel odour ligands were identified. These ligands were subsequently shown to elicit attraction or repulsion in the fly. The ORNs can inform the animal that food is present in the vicinity and they can also signal the quality of the food.
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 GC—SC 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 dose—response 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 dose—response 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).
We would like to express our gratitude to Dr Christer Löfstedt for support and help on chemistry as well as to Drs John Carlson and Marien de Bruyne for help on Drosophila electrophysiology. This work was supported by grants from the Swedish Research Council. E.G.'s and A.B.'s visit in Sweden was sponsored by a European Union SOCRATES exchange grant.