Chemical Senses Advance Access originally published online on March 30, 2007
Chemical Senses 2007 32(5):433-443; doi:10.1093/chemse/bjm009
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Component Information Is Preserved in Glomerular Responses to Binary Odor Mixtures in the Moth Spodoptera littoralis
1 Department of Safety Pharmacology, AstraZeneca R&D Södertälje, SE-151 85 Södertälje, Sweden 2 Department of Crop Science, Division of Chemical Ecology, Swedish University of Agricultural Sciences, PO Box 44, SE-230 53 Alnarp, Sweden 3 Centre for Bioengineering, University of Leicester, Leicester LE1 7RH, UK 4 Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Hans-Knoell-Strasse 8, D-07745 Jena, Germany
Correspondence to be sent to: Kwok Ying Chong, Centre for Bioengineering, University of Leicester, Leicester LE1 7RH, UK. e-mail: kyc11{at}leicester.ac.uk or Mikael A. Carlsson, AstraZeneca R&D Södertälje, SE-151 85 Södertälje, Sweden. e-mail: mikael.a.carlsson{at}astrazeneca.com
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Abstract |
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Natural odors are often complex mixtures of different compounds. These mixtures can be perceived to have qualities that are different from their components. Moreover, components can be difficult to distinguish within a blend, even if those components are identifiable when presented individually. Thus, odor components can interact along the olfactory pathway in a nonlinear fashion such that the mixture is not perceived simply as the sum of its components. Here we investigated odor-evoked changes in Ca2+ concentration to binary blends of plant-related substances in individually identified glomeruli in the moth Spodoptera littoralis. We used a wide range of blend ratios and a range of concentrations below the level at which glomerular responses become saturated. We found no statistically significant cases where the mixture response was greater than both component responses at the same total concentration (synergistic interactions) and no statistically significant cases where the mixture response was less than either component presented individually (suppressive interactions). Therefore, we conclude that, for the plant mixtures studied, information of their components is preserved in the neural representations encoded at the first stage of olfactory processing in this moth species.
Key words: glomeruli, mixture interaction, moth, olfaction, optical imaging
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Introduction |
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TopAbstractIntroductionMaterials and methodsResultsDiscussionAcknowledgementsReferences |
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Natural odor stimuli rarely occur as single compounds but are, in fact, very often complex mixtures of different molecular components. Chemically mediated behavior of both aquatic and terrestrial animals is often driven by such complex mixtures (Laing 1989
), which comprise both compound and mixture information that is behaviorally relevant. In addition, these odors are often present in a background "soup" of irrelevant and interfering compounds. Despite the chemical complexity and low signal to noise ratio, the olfactory apparatus can filter out relevant information, for example, about food acceptability, presence of predators, and sexual status of the opposite sex. Yet, little is known about how mixtures are encoded within the olfactory pathway to support this type of processing.
Mixtures of odors are often perceived as having unique synthetic qualitative properties, and it is generally difficult to distinguish the individual components of the blend (Moskowitz and Barbe 1977; Laing and Francis 1989; Laing and Livermore 1992). Hence, the neural representation of individual components within the context of a mixture is believed to interact nontrivially along the olfactory pathway in a manner that may hinder the deconvolution of component information from mixture responses. When the neural response to a blend is not the simple sum of the responses to its constituents, then a nonlinear mixture interaction has occurred. Such nonlinear interactions can contribute to the synthetic perception of a blend, which includes suppression, where the blend response is weaker than that of one or more of the components at the same concentration, and synergy, where the blend response is higher than that of the most strongly responding component at the same total concentration (Duchamp-Viret et al. 2003). Neural representations of odor components within a mixture may start to interact nontrivially even at the olfactory receptor neuron (ORN) level, with individual ORN responses to odor mixtures that exhibit suppression or synergy: in invertebrates, such as insects (Akers and Getz 1993; Carlsson and Hansson 2002; Ochieng et al. 2002) and crustacean (Steullet and Derby 1997), and in vertebrates, such as fish (Kang and Caprio 1991, 1997) and mammals (Duchamp-Viret et al. 2003; Oka et al. 2004). More commonly, mixture interactions have been observed in second-order neurons in vertebrates as well as invertebrates (De Jong and Visser 1988; Christensen et al. 1991; Tabor et al. 2004).
Nonlinear coding of mixtures at the periphery would seem to make identifying the individual components of a blend from the mixture representation a difficult task, and yet information relating to the components can persist in the perception of odors, as has been observed in rats (Linster and Smith 1999) and honeybees (Hosler and Smith 2000). This suggests a so-called nonconfigural, elemental aspect to odor perception in addition to the synthetic paradigm. This together implies that neural representations of complex odor mixtures comprise both configural and elemental properties. It is, however, unclear where and how mixture interactions take place in the olfactory pathway and its neural representation.
The insect antennal lobe (AL) and the mammalian olfactory bulb consist of a species-specific number of glomeruli, each of which represents the input from all ORNs housing a specific receptor (Mombaerts et al. 1996; Vosshall et al. 2000). A combinatorial across-neuron pattern among the ORNs (Malnic et al. 1999) is thus represented as glomerular activity patterns. This has been confirmed in a number of optical imaging studies in vertebrates (Friedrich and Korsching 1997; Rubin and Katz 1999; Uchida et al. 2000; Meister and Bonhoeffer 2001) as well as insects (Joerges et al. 1997; Galizia et al. 1999; Carlsson et al. 2002). It has been suggested that glomerular activity patterns constitute a spatial olfactory code (Galizia et al. 1999). Such a code or representation is dependent not only on the chemical structure of the odor molecule but also on the concentration (Carlsson and Hansson 2003; Sachse and Galizia 2003). A handful of imaging studies have tested odor blends and the results diverge. Joerges et al. (1997) reported strong suppressive interactions between components using optical imaging in the honeybee. Other studies have reported responses to mixtures that represent the linear sum of responses to the components (Belluscio and Katz 2001). Recently, Tabor et al. (2004) demonstrated that mixture interactions were weak or negligible in the ORN presynapses in the zebrafish glomeruli, whereas both suppressive and synergistic interactions were observed in olfactory bulb output neurons.
Our model animal, the noctuid moth Spodoptera littoralis, is a broad generalist species found on at least 84 different host-plant species (Brown and Dewhurst 1975). Anderson et al. (1993) demonstrated that S. littoralis is strongly deterred by a complex mixture (but not to submixtures) of odorants induced by larval feeding. This indicates that synergistic blend interactions occur along the olfactory pathway in this species. In the present study, we exposed the animal to 3 plant-related compounds (and their binary mixtures) common to many of the moth's host plants. Odorants were chosen as they have previously been shown to activate either overlapping or nonoverlapping subsets of ORNs and glomeruli depending on the binary combination (Anderson et al. 1995; Jönsson and Anderson 1999; Carlsson et al. 2002; Carlsson and Hansson 2003). Thus, mixture phenomena due to either agonistic or antagonistic interactions could potentially occur. We studied responses in individually identified glomeruli by means of Ca2+ imaging. This method basically reports input activity in the glomeruli (Galizia et al. 1998). In an attempt to mimic natural conditions in which an animal may realistically find itself, we used plant odors in a narrow range of biologically relevant concentrations (Carlsson and Hansson 2003) and mixtures at several different ratios. We investigated if the responses to the blends showed suppressive or synergistic effects. Responses to the mixtures were, however, predictable from the responses to the individual constituents. We did not observe any statistically significant cases of suppression or synergy, leading us to conclude that component information for some common plant–plant odors is preserved in the neural representation at the first stage of olfactory processing for this moth species.
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Materials and methods |
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Animals and staining
We recorded neural activity optically in the AL of male S. littoralis by imaging Ca2+ dynamics. A detailed description of the preparation of the animals and the experimental setup can be found elsewhere (Carlsson et al. 2002; Carlsson and Hansson 2003), but the following gives a brief summary of the experimental procedures. Experiments were performed on 1–5 days post emergent male moths. The head capsules were cut open between the compound eyes, and extraneous material removed to expose the ALs. A calcium-sensitive dye (CaGR-2-AM, Molecular Probes, Eugene, OR) was bath applied to the uncovered brain. The dye was dissolved in 20% Pluronic F-127 in dimethyl sulfoxide (Molecular Probes) and diluted in moth saline (Christensen and Hildebrand 1987) to a final concentration of 
30 µM. After incubation (60 min in 10–12°C) and rinsing in moth saline, recordings were done in vivo.
We used TILL Photonics air-cooled imaging system (Gräfelfing, Germany) with a 12-bit slow-scan CCD camera. Filter settings were dichroic: 500 nm, emission low pass 515 nm, and the preparation was excited at 475 nm. Sequences of 40 frames and a sampling rate of 4 Hz (200 ms exposure time) were recorded through an upright microscope (Olympus, Hamburg, Germany) with a 20x (NA 0.50; Olympus) water immersion objective. On-chip binning (2 x 2) was performed, which resulted in a pixel size corresponding to 1 x 1 µm. Execution of imaging protocols and initial analyses of data were made using the software Till-vision (TILL Photonics).
Odor stimuli
Each animal was tested with a set of binary blend stimuli consisting of mixtures of 2 particular compounds. Three host-plant compounds—geraniol, linalool, and phenylacetaldehyde (PAA) were used. Previous electrophysiological and optophysiological studies have shown that these odorants evoke strong responses in ORNs and glomeruli (Anderson et al. 1995; Carlsson et al. 2002; Carlsson and Hansson 2003). In addition, both larvae and adults could be trained to respond behaviorally to these compounds (Carlsson et al. 1999; Fan and Hansson 2001). Odorants were dissolved in paraffin oil, which does not evoke a detectable response in the AL (Carlsson and Hansson 2003). Ten microliters of the solvent containing the respective odorant were applied on a filter paper (5 x 15 mm). For the control stimulus, paraffin oil solvent was applied alone onto filter paper. Two filter papers were inserted in a Pasteur pipette attached to a plastic pipette tip at the proximal end (total volume 4.5 ml), each containing an odorant or pure paraffin oil. This means that blends were constructed by mixture of the headspace. The pipettes were sealed with Parafilm (American National Can Co., Chicago, IL) and stored in a freezer until the start of an experiment. Odorants were delivered in a randomized order. We allowed at least 60 s between stimulations to reduce potential adaptation effects. Altogether, 10 different stimulus loads were used of each compound (2.5–80 µg) and 12 different binary mixtures. In addition, the third compound that was not included in the binary mixtures was tested at 4 doses in order to physiologically identify the corresponding glomerulus. Thus, including a control stimulus, in each experiment, we used 37 different stimuli. We have in a previous study shown that the concentrations lie between threshold of optophysiological detection and saturation (Carlsson and Hansson 2003).
A moistened and charcoal-filtered continuous air stream (30 ml s–1) was ventilating the antenna ipsilateral to the recorded AL through a glass tube (7 mm inside diameter). The glass tube ended 10 mm from the antenna. An empty Pasteur pipette was inserted through a small hole in the glass tube, blowing an air stream of 5 ml s–1. Another air stream (ca. 5 ml s–1) was blown through the odor-laden pipette by a computer-triggered puffer device (Syntech, Hilversum, The Netherlands) during 1 s (starting at frame 12) into the continuous stream of air. During stimulation, the air stream was switched from the empty pipette to the odor-laden one in order to minimize the influence of added air volume.
Data processing
To correct for bleaching, a bleaching baseline function of time, Fb(t), was taken from a nonresponding region of the AL. The bleach-corrected response, r(ti), was taken to be the relative change in fluorescence 
F(ti)/Fb(ti), where F(ti) = F(ti) – Fb(ti) for each time step ti at each frame i = 1, ..., 40. In addition, over stimulus trials, the response magnitude often decreased. This caused a strong time dependency that added extra variability in the data. The stimuli were presented in a random order, and so the weakening was not stimulus dependent. Thus, this weakening over stimulations could be fitted (in a least-squares sense) with an exponential decay of the form ae–n/
, where the amplitude a and the decay constant are free parameters, and n is the chronological index of the stimulation. This artifact was then normalized from the data series by dividing the magnitude of each response by this exponential fit evaluated at n. This normalization also had the effect of adjusting to 1 the mean of maximum response magnitudes over all the trials.
Finally, to attain an overall response to a stimulus, the response was integrated over time. In practice, this meant the sum 
where 
t = 0.25 s. Because stimulus was applied at the 12th frame, this summation starts from the frame 12.
Automated location of glomeruli
Glomeruli were located by an automation of the techniques that have been employed previously (Carlsson et al. 2002; Carlsson and Hansson 2003). First, a threshold of 50% of maximum response magnitude was used to determine which pixels were of active glomeruli. Glomeruli are known to be convergent sites for ORNs expressing the same type of receptor (Gao et al. 2000; Vosshall et al. 2000; Couto et al. 2005; Fishilevich and Vosshall 2005). Because the activity we observe is dominated by presynaptic activity of these receptor neurons, each glomerulus can be considered as a single functional unit with highly correlated activity within each glomerulus. First, we define a response profile space as follows. To each pixel, denoted by the location, (i, j), of that pixel, we assign the response profile vector 
where Rij(Sk) is the mean response of the 5 x 5 pixel region around pixel (i, j) to the stimulus k = 1, ..., 37. Hereafter, this will be called the pixel response. Pixels that have correlated response profiles are located in neighboring regions of this profile space. Thus, because a glomerulus is a single functional unit, so pixels from a mutual glomerulus can be expected to cluster together. Moreover, pixels from different glomeruli, which respond differently to this stimulus set, will have differing pixel responses and so will be separated in response space. Thus, the glomeruli form distinct clusters. We applied cluster analysis to these pixel responses in order to identify functionally equivalent pixel regions. We used Ward's linkage method for calculating the separation between clusters in response space. This approach is akin to that of analysis of variance (ANOVA). First, for a given cluster G of pixel responses, we define the centroid to be 
where NG is the number of points in G. We define the sum of squares of the deviation from the centroid to be 
. The distance DW between 2 clusters, G and H, is calculated as the increase in the sum of squares if these 2 clusters were combined into one cluster: DW(G,H) = 
2(G
H) – [2(G)+2(H)]. The algorithm is started with all the pixel responses as separate points. At each iterative step, the Ward method combines those 2 points or clusters into a single cluster that causes the smallest increase in the sum of squares of deviances. The iterations stop when a predetermined number of clusters has been reached. For agglomerative clustering methods, the number of clusters must first be specified a priori. This number has been obtained iteratively to minimize the number of clusters, yet still portrays the main features.
Criteria for mixture interactions
We used a definition for suppression, where the blend response is weaker than that of either of the responding components (same concentration singly or in mixture), and synergy, where the blend response is stronger than that of the most strongly responding component at the same total loading as the blend. The following criteria were used:
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For statistical comparisons, we used a 1-way ANOVA or a Student's t-test.
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Results |
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TopAbstractIntroductionMaterials and methodsResultsDiscussionAcknowledgementsReferences |
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In each animal, glomeruli were located from localized and coherent regions of activation using an adaptation of an existing method (Carlsson et al. 2002
; Carlsson and Hansson 2003). This new method allowed the automatic detection of glomeruli without the need to manually decide the location of glomeruli by viewing activity maps to all stimuli. From these glomeruli, 3 were identified in each animal based on the respective physiological response. The 3 compounds we used (geraniol, linalool, and PAA) each activated a single glomerulus most strongly. In addition, several other glomeruli displayed weaker activity. We refer to the glomerulus that gave the greatest response magnitude for a compound as the "best" glomerulus for that component. The "PAA-best" glomerulus, with strongest activity for PAA, was located in the medial part of the AL, whereas the other 2 glomeruli were located more laterally (see also Carlsson et al. 2002; Carlsson and Hansson 2003). The linalool-best glomerulus was located ventral of the "geraniol-best" glomerulus. The latter glomerulus actually responded similarly to geraniol and linalool, but during geraniol stimulation this glomerulus was the one with the greatest response magnitude. During linalool stimulation, this glomerulus gave only the second strongest response of all the glomeruli. These 3 glomeruli could be unambiguously identified in all animals and allowed us to pool data across individuals. Figure 1 shows a typical example of false color–coded responses to geraniol, linalool, and the binary mixtures of these in a single animal. In all images, identical intensity scales were used for ease of comparison.
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Only animals where a complete or near-complete stimulus panel (see Odor stimuli) that could be tested were further analyzed. Out of 28 animals with recorded responses, 13 animals were used in the analysis.
Dose–response curves for individual components and their binary mixtures were constructed based on the total stimulus load (Figure 2). The geraniol-best glomerulus was strongly activated by both geraniol and linalool and only weakly by PAA at the highest concentrations. The "linalool-best" glomerulus, on the other hand, was only strongly activated by linalool, whereas geraniol and PAA evoke detectable responses only at much higher concentrations. Finally, the PAA-best glomerulus was only activated by the PAA within the concentration range used. Therefore, we show only dose–response curves for blends with at least one potent compound. The dose–response curves for the blends all lay between the curves for the individual components, except for blends of geraniol and linalool in the geraniol-best glomerulus, which elicit equal responses to either of the components or to the blend. For a clearer view, the responses are shown in a bar chart (Figure 3). In each section, the bars show responses to stimuli with the same total load. The differences between the mean responses within each section were tested with an ANOVA. In the geraniol-best glomerulus, responses to stimuli containing the same total load of geraniol, linalool, or a blend of these did not differ (P = 0.226–0.997). All other combinations were, however, highly significantly different (P = 0.014–0.000).
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We tested for synergistic interactions according to the criterion as stated in Materials and methods. We compared the blend response with single component responses where the single component is at the same concentration load as the total concentration load of the blend. This ensured that we did not identify a synergistic effect when an increase in response was merely due to an increase in concentration load as 2 components were added together. We statistically tested all pairs (strongest responding compound vs. mixture) containing identical total loads (Figure 4). No responses to the blends were significantly stronger than the responses to the corresponding single components (84 comparisons, all P > 0.05, Student's t-test). That is, no synergistic effects were observed. Finally, we tested the occurrence of suppressive interactions (Figure 5). In this test, we compared the blend responses with the component responses such that the components were at the same concentration load as that from which the blend was comprised. In this way, any decrease in the response to the individual components compared with the response to the blend cannot be the result of lowering the concentration load of either component. Every combination where the component had the same concentration singly or in a mixture was compared. Out of 168 comparisons, we find 62 meet the suppression criterion, and only in 4 cases did we find a mixture that evoked a significantly weaker (P < 0.05, t-test) response than the component alone. The observed suppressions were not dependent on mixture, concentration, or ratio (see Figure 5). Furthermore, when a Bonferroni correction for multiple tests is applied, a P < 0.05 threshold level for the whole suppression experiment requires individual tests to give a P value less than 0.05/62
8 x 10–4. This only takes into account the cases where the criterion was met and a Student's t-test was performed, yet none of the individual tests were significant to this level.
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Discussion |
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Mixture interactions in moths have been frequently observed, both in behavioral studies (Arn et al. 1980
; Löfstedt et al. 1982; Wu et al. 1995) and in electrophysiological recording from second-order neurons of the AL (Christensen and Hildebrand 1987; Christensen et al. 1989, 1991, 1995; Anton and Hansson 1995; Wu et al. 1996; Anton et al. 1997; Hartlieb et al. 1997). It is, however, unclear whether these interactions originate in peripheral receptor cells, by computational processes in the AL or by feedback from higher brain regions. Here we studied responses in single glomeruli representing the convergent input from hundreds of ORNs likely housing identical receptor proteins (Gao et al. 2000; Vosshall et al. 2000). Potential blend interactions detected by Ca2+ imaging of the glomeruli could result from nonlinear activation at the receptor site, by feedback activity in the ORN terminals, or a combination of both. Two conservative approaches were used to identify possible mixture interactions. Suppression was defined as a response to a blend that was significantly weaker than the strongest responding component alone applied at the same concentration as in the mixture. Synergy, on the other hand, was defined as a blend response that is stronger than that of the most strongly responding component at the same total loading as the blend. Using this approach, we could not identify any synergistic or suppressive interactions, irrespective of concentration or ratio. That is, responses to binary blends of plant-related compounds in single glomeruli could be predicted based on the responses to the individual components. It cannot, however, be excluded that less pronounced interactions may have been overlooked with this approach.
The nearly complete lack of suppressive interactions (and complete lack, after the Bonferroni correction) in our study as opposed to the study by Joerges et al. (1997), using the same imaging technique, can be explained by the differences in stimulus loads used. The doses in our study were chosen to be well below saturation level (Carlsson and Hansson 2003) to mimic naturally occurring concentrations. Joerges et al. (1997), on the other hand, used nondiluted substances, which in our model would elicit responses above saturation level (Carlsson and Hansson 2003). In a previous experiment, we showed that suppression in pheromone-sensitive ORNs in the moth Agrotis segetum only occurs at very high concentrations of mixtures (Carlsson and Hansson 2002). It is not unlikely that competition for a receptor site is significant when a large abundance of molecules is present. Even though a compound does not activate the receptor itself, it may act as a competitive antagonist and block or inhibit the action of a responsive compound. Suppressive interactions in ORNs found in other experiments could be explained by the fact that one of the blend components excited the neuron, whereas the second component inhibited it. Such a dual function of ORNs has been observed in the pheromone-detecting subsystem in moths (Kaissling et al. 1989; Hansson et al. 1990).
Generally, ORNs are broadly tuned to a wide range of odor stimuli and have overlapping receptive fields. Thus, we might expect that chemically dissimilar odorants that stimulate nonoverlapping subsets of receptors should display elemental properties, whereas similar odorants stimulating highly overlapping subsets of receptors would induce more interaction between the odorants, giving rise to configural properties in the representation. This is supported by behavioral studies that demonstrate the effects of odor component similarity on the ability of rats to distinguish blends from their components (Laing et al. 1989; Kay et al. 2003; Wiltrout et al. 2003). However, our results indicate that blends of either similar (geraniol and linalool) or dissimilar (geraniol and PAA; linalool and PAA) odorants are linearly represented at the first stage of olfactory processing.
Strong mixture interactions have been observed in the pheromone-processing subsystem in moths. Some output neurons, projection neurons (PNs), from the male-specific cluster of glomeruli, the macroglomerular complex (MGC), have been reported to have blend-specific properties and respond exclusively to a species-specific blend and not to its individual components (Christensen and Hildebrand 1987; Christensen et al. 1989, 1991, 1995; Anton and Hansson 1995; Wu et al. 1996; Anton et al. 1997; Hartlieb et al. 1997). Such interactions have, however, not been reported from studies of ORNs (Akers and O'Connell 1988, 1991; Almaas and Mustaparta 1990; Berg and Mustaparta 1995; Carlsson and Hansson 2003). Thus, blend-specific responses in the pheromone subsystem are likely elicited by interglomerular computation within the MGC. Responses to mixtures of plant-related compounds in insects have been less well studied and the results diverge. De Jong and Visser (1988) showed that extracellular responses in certain ORNs in the Colorado potato beetle were suppressed when stimulated with binary mixtures containing general green leaf volatiles. Likewise, suppressive interactions were observed in single cockroach ORNs (Getz and Akers 1997). However, Akers and Getz (1993) found that responses to binary mixtures of aromatics and octyls in the honeybee were often stronger than would be predicted.
In honeybees, a blocking paradigm was used to show that conditioning could alter the perception of blends (Hosler and Smith 2000). In their experiment, preconditioning to one odorant diminished the strength of association between a reward and another odorant when animals were conditioned to associate the reward with the blend of both odorants. In order to associate the unconditioned stimulus with components of a blend rather than only the blend in full, the components must be identifiable from the blend. This shows that mixture interactions can be altered by prior experience, so that components are recognized within a mixture. Therefore, it may be possible that an animal can exploit both configural and elemental coding paradigms depending on what it has learned from its environment. Associative learning of odors alters the synapses that drive PNs of the AL (Yu et al. 2004). With such plasticity at this level, it may be possible for animals to alter the role of these neurons between configural and elemental coding. For such a system, it would be necessary to preserve elemental information up to this stage of processing, as indicated by our data.
The infrequent and often weak (at moderate concentrations) mixture interactions observed at the ORN level may be necessary in a plastic system. That is, information should be reliably transferred to second-order neurons where collateral processes or efferent feedback may alter responses to specifically important mixtures after previous experiences. The more frequently observed blend interactions in pheromone-sensitive ORNs could be explained by the fact that the pheromone subsystem in moths is far less plastic (Hartlieb et al. 1999).
In summary, we did not find any significant mixture interactions between common plant compounds at biologically realistic concentrations at the AL input level. That is, the second component of a mixture, regardless if it is excitatory or neutral, does not alter the response to the mixture in an unpredictable manner. However, we find it likely that mixture interactions occur downstream of the ORNs. These interactions may be different in naive and experienced animals. In order to preserve the encoding of quality up to the second-order neurons, blend interactions in ORN should be weak or negligible.
We plan to study responses to the same odor panel used in the present experiment in both PN selective imaging (Carlsson et al. 2005) and in differential conditioning in order to elucidate if mixture interactions occur downstream of ORNs and how this would influence perception.
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The authors are grateful to the European Commission for funding the research described in this paper under the project "A Fleet of Artificial Chemosensing Moths for Distributed Environmental Monitoring (AMOTH)" funded under the Information Society Technologies Future and Emerging Technologies Programme (awarded to T.C.P. and B.S.H. grant reference IST-2001-33066, project website http://www.amoth.org/) and to the Swedish Research Council (Vetenskapsrådet) for its support to B.S.H.
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Footnotes |
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* Authors contributed equally.
Authors share joint seniority.
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References |
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Akers RP, Getz WM. Response of olfactory receptor neurons in honeybees to odorants and their binary mixtures. J Comp Physiol A (1993) 173(2):169–185.
Akers RP, O'Connell RJ. The contribution of olfactory receptor neurons to the perception of pheromone component ratios in male redbanded leafroller moths. J Comp Physiol A (1988) 163:641–650.[CrossRef][Medline]
Akers RP, O'Connell RJ. Response specificity of male olfactory receptor neurons for the major and minor components of a female pheromone blend. Physiol Entomol (1991) 16(1):1–17.
Almaas TJ, Mustaparta H. Pheromone reception in tobacco budworm moth, Heliothis virescens. J Chem Ecol (1990) 16(4):1331–1346.[CrossRef][ISI]
Anderson P, Hansson BS, Lofqvist J. Plant-odor-specific receptor neurons on the antennae of female and male Spodoptera littoralis. Physiol Entomol (1995) 20:189–198.
Anderson P, Hilker M, Hansson BS, Bombosch S, Klein B, Schildknecht H. Oviposition deterring components in larval frass of Spodoptera littoralis (Boisd.) (Lepidoptera: Noctuidae): a behavioural and electrophysiological evaluation. J Insect Physiol (1993) 39:129–137.[CrossRef][ISI]
Anton S, Hansson BS. Sex pheromone and plant-associated odour processing in antennal lobe interneurons of male Spodoptera littoralis (Lepidoptera: Noctuidae). J Comp Physiol A (1995) 176:773–789.
Anton S, Löfstedt C, Hansson BS. Central nervous processing of sex pheromones in two strains of the European corn borer, Ostrinia nubilalis (Lepidoptera: Pyralidae). J Exp Biol (1997) 200:1073–1087.[Abstract]
Arn H, Städler S, Rauscher S, Buser HR, Mustaparta H, Esbjerg P. Multicomponent sex pheromone in Agrotis segetum: preliminary analysis and field evaluation. Z Naturforschung (1980) 35c:986–989.
Belluscio L, Katz LC. Symmetry, stereotypy and topography of odorant representation in mouse olfactory bulbs. J Neurosci (2001) 21:2113–2122.
Berg BG, Mustaparta H. The significance of major pheromone components and interspecific signals as expressed by receptor neurons in the oriental tobacco budworm moth, Helicoverpa assulta. J Comp Physiol A (1995) 177:683–694.
Brown ES, Dewhurst CF. The genus Spodoptera (Lepidoptera, Noctuidae) in Africa and the Near East. Bull Ent Res (1975) 65:221–262.[ISI]
Carlsson MA, Anderson P, Hartlieb E, Hansson BS. Experience-dependent modification of orientational response to olfactory cues in larvae of Spodoptera littoralis. J Chem Ecol (1999) 25:2445–2454.[CrossRef][ISI]
Carlsson MA, Galizia CG, Hansson BS. Spatial representation of odours in the antennal lobe of the moth Spodoptera littoralis (Lepidoptera: Noctuidae). Chem Senses (2002) 27:231–244.
Carlsson MA, Hansson BS. Responses in highly selective sensory neurons to blends of pheromone components in the moth Agrotis segetum. J Insect Physiol (2002) 48:443–451.[CrossRef][ISI][Medline]
Carlsson MA, Hansson BS. Dose-response characteristics of glomerular activity in the moth antennal lobe. Chem Senses (2003) 28:269–278.
Carlsson MA, Knüsel P, Verschure PFMJ, Hansson BS. Spatio-temporal Ca2+ dynamics of moth olfactory projection neurons. Eur J Neurosci (2005) 22:647–657.[CrossRef][ISI][Medline]
Christensen TA, Hildebrand JG. Male-specific, sex pheromone-selective projection neurons in the antennal lobes of the moth Manduca sexta. J Comp Physiol A (1987) 160:553–569.[CrossRef][Medline]
Christensen TA, Hildebrand JG, Tumlinson JH, Doolitle RE. Sex pheromone blend of Manduca sexta: responses of central olfactory interneurons to antennal stimulation in male moths. Arch Insect Biochem Physiol (1989) 10(4):281–291.[CrossRef][ISI]
Christensen TA, Mustaparta H, Hildebrand JG. Chemical communication in heliothine moths. II. Central processing of intraspecific and interspecific olfactory messages in the male corn earworm moth, Helicoverpa zea. J Comp Physiol A (1991) 169:259–274.
Christensen TA, Mustaparta H, Hildebrand JG. Chemical communication in heliothine moths. VI. Parallel pathways for information processing in the macroglomerular complex of the male tobacco budworm moth Heliothis virescens. J Comp Physiol A (1995) 177:545–557.
Couto A, Alenius M, Dickson BJ. Molecular, anatomical, and functional organization of the Drosophila olfactory system. Curr Biol (2005) 15:1535–1547.[CrossRef][ISI][Medline]
De Jong R, Visser JH. Integration of olfactory information in the Colorado potato beetle. Brain Res (1988) 447:10–17.[CrossRef][ISI][Medline]
Duchamp-Viret P, Duchamp A, Chaput MA. Single olfactory sensory neurons simultaneously integrate the components of an odour mixture. Eur J Neurosci (2003) 18:2690–2696.[CrossRef][ISI][Medline]
Fan R-J, Hansson BS. Olfactory conditioning in the moth Spodoptera littoralis. Physiol Behav (2001) 72:159–165.[CrossRef][Medline]
Fishilevich E, Vosshall LB. Genetic and functional subdivision of the Drosophila antennal lobe. Curr Biol (2005) 15:1548–1553.[CrossRef][ISI][Medline]
Friedrich RW, Korsching SI. Combinatorial and chemotopic odorant coding in the zebrafish olfactory bulb visualized by optical imaging. Neuron (1997) 18:737–752.[CrossRef][ISI][Medline]
Galizia CG, Nagler K, Holldobler B, Menzel R. odour coding is bilaterally symmetrical in the antennal lobes of honeybees Apis Mellifera. Eur J Neurosci (1998) 10:2964–2974.[CrossRef][ISI][Medline]
Galizia CG, Sachse S, Rappert A, Menel R. The glomerular code for odor representation is species specific in the honeybee Apis Mellifera. Nat Neurosci (1999) 2:473–478.[CrossRef][ISI][Medline]
Gao Q, Yuan BB, Chess A. Convergent projections of Drosophila olfactory neurons to specific glomeruli in the antennal lobe. Nat Neurosci (2000) 3:780–785.[CrossRef][ISI][Medline]
Getz WM, Akers RP. Response of American cockroach (Periplaneta americana) olfactory receptors to selected alcohol odorants and their binary combinations. J Comp Physiol A (1997) 180:701–709.[CrossRef]
Hansson BS, Szöcs G, Schmidt F, Francke W, Löfstedt C, Tóth M. Electrophysiological and chemical analysis of sex pheromone communication system of the mottled umber, Erannis defoliaria (Lepidoptera: Geometridae). J Chem Ecol (1990) 16(6):1887–1897.[CrossRef][ISI]
Hartlieb E, Anton S, Hansson BS. Dose-dependent response characteristics of AL neurons in the male moth Agrotis segetum (Lepidoptera: Noctuidae). J Comp Physiol A (1997) 181:469–476.[CrossRef]
Hartlieb E, Hansson BS, Anderson P. Sex or food? appetetive learning of sex odors in a male moth. Naturwissenschaften (1999) 86(8):396–399.[CrossRef][ISI]
Hosler JS, Smith BH. Blocking and the detection of odor components in blends. J Exp Biol (2000) 203:2797–2806.[Abstract]
Joerges J, Küttner A, Galiza G, Menzel R. Representations of odours and odour mixtures visualized in the honeybee brain. Nature (1997) 387:285–288.[CrossRef]
Jönsson M, Anderson P. Electrophysiological response to herbivore-induced host plant volatiles in the moth Spodoptera littoralis. Physiol Entomol (1999) 24:377–385.[CrossRef]
Kaissling K-E, Meng LZ, Bestmann H-J. Responses of bombykol receptor cells to (Z,E)-4,6-hexadecadiene and linalool. J Comp Physiol A (1989) 165:147–154.[CrossRef]
Kang J, Caprio J. Electro-olfactogram and multiunit olfactory receptor responses to complex mixtures of amino acids in the Channel catfish, Ictalurus punctaatus. J Gen Physiol (1991) 98:699–721.
Kang J, Caprio J. In vivo responses of single olfactory receptor neurons of the channel catfish to binary mixtures of amino acids. J Neurophysiol (1997) 77:1–8.
Kay LM, Lowry CA, Jacobs HA. Receptor contributions to configural and elemental odor mixture perception. Behav Neurosci (2003) 117:1108–1114.[CrossRef][ISI][Medline]
Laing DG, Cain WS, McBride RL, Ache BW, eds. Perception of complex smells and tastes. (1989) Sydney, Australia: Academic Press.
Laing DG, Francis GW. The capacity of humans to identify odors in mixtures. Physiol Behav (1989) 46:809–814.[CrossRef][Medline]
Laing DG, Livermore BA. A perceptual analysis of complex chemical signals in humans. In: Chemical signals in vertebrates—Doty RL, Müller-Schwartz D, eds. (1992) New York: Plenum Press. 587–593.
Laing DG, Panhuber H, Slotnick BM. Odor masking in the rat. Physiol Behav (1989) 45:689–694.[CrossRef][Medline]
Linster C, Smith BH. Generalization between binary odor mixtures and their components in the rat. Physiol Behav (1999) 66:701–707.[CrossRef][Medline]
Löfstedt C, Van Der Pers JNC, Löfqvist J, Lanne BS, Appelgren M, Bergström G, Thelin B. Sex pheromone components of the turnip moth, Agrotis segetum: chemical identification, electrophysiological evaluation, and behavioural activity. J Chem Ecol (1982) 8(10):1305–1322.[CrossRef][ISI]
Malnic B, Hirono J, Sato T, Buck LB. Combinatorial receptor codes for odors. Cell (1999) 96:713–723.[CrossRef][ISI][Medline]
Meister M, Bonhoeffer T. Tuning and topography in an odor map on the rat olfactory bulb. J Neurosci (2001) 21:1351–1360.
Mombaerts P, Wang F, Dulac C, Chao SK, Nemes A, Mendelsohn M, Edmondson J, Axel R. Visualizing an olfactory sensory map. Cell (1996) 87:675–686.[CrossRef][ISI][Medline]
Moskowitz HR, Barbe CD. Profiling of odor components and their mixtures. Sens Processes (1977) 1:212–226.[ISI][Medline]
Ochieng SA, Park KC, Baker TC. Host plant volatiles synergize responses of sex pheromone-specific olfactory receptor neurons in male Helicoverpa zea. J Comp Physiol A (2002) 188:325–333.[CrossRef][ISI][Medline]
Oka Y, Omura M, Kataoka H, Touhara K. Olfactory receptor antagonism between odorants. EMBO J (2004) 23:120–126.[CrossRef][ISI][Medline]
Rubin BD, Katz LC. Optical imaging of odorant representations in the mammalian olfactory bulb. Neuron (1999) 23:499–511.[CrossRef][ISI][Medline]
Sachse S, Galizia CG. The coding of odour-intensity in the honeybee antennal lobe: local computation optimizes odour representation. Eur J Neurosci (2003) 18(8):2119–2132.[CrossRef][ISI][Medline]
Steullet P, Derby CD. Coding of blend ratios of binary mixtures by olfactory neurons in the florida spiny lobster, Panulirus Argus. J Comp Physiol A (1997) 180:123–135.[CrossRef][Medline]
Tabor R, Yaksi E, Weislogel JM, Friedrich RW. Processing of odor mixtures in the zebrafish olfactory bulb. J Neurosci (2004) 24:6611–6620.
Uchida N, Takahashi YK, Tanifuji M, Mori K. Odor maps in the mammalian olfactory bulb: domain organization and odorant structural features. Nat Neurosci (2000) 3:1035–1043.[CrossRef][ISI][Medline]
Vosshall LB, Wong AM, Axel R. An olfactory sensory map in the fly brain. Cell (2000) 102:147–159.[CrossRef][ISI][Medline]
Wiltrout C, Dogra S, Linster C. Configurational and nonconfigurational interactions between odorants in binary mixtures. Behav Neurosci (2003) 117:236–245.[CrossRef][ISI][Medline]
Wu W-Q, Anton S, Löfstedt C, Hansson BS. Discrimination among pheromone component blends by interneurons in the male ALs of two populations of the turnip moth, Agrotis segetum. Proc Natl Acad Sci USA (1996) 93:8022–8027.
Wu W-Q, Hansson BS, Löfstedt C. Electrophysiological and behavioural evidence for a fourth sex pheromone component in the turnip moth, Agrotis segetum. Physiol Entomol (1995) 20:81–92.
Yu DH, Ponomarev A, Davis RL. Altered representation of the spatial code for odors after olfactory classical conditioning: memory trace formation by synaptic recruitment. Neuron (2004) 42:437–449.[CrossRef][ISI][Medline]
Accepted 9 February 2007
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