and others. We also believe MFEs to be related to the “neuronal avalanches” studied by Plenz et al. (2011) and Beggs and Plenz (2003).

By contrast, MFEs are quite distinct from many other forms of synchrony that have been studied, such as synchrony borne from (i) correlated feedforward inputs, (ii) global fluctuations in firing-rate, (iii) strong sources of synaptic depression, or (iv) synaptic delays. The MFEs we see in our model are not easily characterized analytically as an additional feedforward Poisson spiking process (see Brunel, 2000), or as fluctuations of a balanced state (see van Vreeswijk and Sompolinsky, 1998).

While we don't yet have a full characterization of MFEs
ourselves, we have studied them in a less complex network
(Rangan and Young, 2013b). Even this simpler case required
serious effort to analyze mathematically (Rangan and Young,
2013a; Zhang et al., 2014a,b; Zhang and Rangan, 2015). Thus,
at present we eschew any sort of detailed analysis, instead
describing in words the dynamic picture we see in our
model:

MFEs Manifest Within the Control State

In terms of the model's spontaneous activity, many of the PN
firing-events are isolated (i.e., not part of any MFE), but some PN
spikes occur synchronously (see **Figure 15A**). When the model is
driven by an odor pulse the activity levels of both the PNs and
LN5 rise; again the PN activity comprises both MFEs and isolated
spikes, although with both occurring at a much higher frequency
than in the spontaneous state. Shortly after any odor pulse the
PN activity dies down, and the PNs are driven predominantly by
persistent inhibitory currents. These inhibitory currents combine
slow synaptic inhibition with intrinsic SK-currents, giving rise to
an AHP-phase. During this AHP-phase, our model PNs are no
longer in a high-gain state, and exhibit very little firing at all (i.e.,
very few isolated spikes or MFEs).

MFEs Underlie the PTX-on Phenomena

When PTX is applied, the GABA-A coupling strengths are
reduced; the LN2 presynaptic inhibitory current drops; the
LN2 population moves closer to the spiking threshold; the
LN2 firing rate increases significantly; and the net inhibitory
presynaptic currents to the PNs increases somewhat. The net
effect of all this on spontaneous activity is rather simple: the
spontaneous PN firing-rate is somewhat lower in the PTX-
on state than in the control state. The excess inhibitory
currents lower both the probability of isolated spikes and
MFEs.

With regard to stimulated activity, however, the story is more
intricate. As we discussed in **Figure 14**, predominantly excitatory
networks (i.e., networks without strong disinhibition, similar to
**Figure 14A**) tend to have more stereotyped MFEs than networks
that are capable of strong disinhibition (i.e., networks with
strongly competing LN1s and LN2s, similar to **Figure 14C**). A
corollary to this claim is: networks with stronger disinhibition
tend to be more variable than networks without. We believe
that this mechanism underlies the PTX-on reduction in PN
consistency.

To elaborate: recall that the PTX-on state causes the LN2
population to fire more vigorously (i.e., to be “higher gain”)
than in background. As a consequence, the competition between
the LN1s, LN2s and PNs is more acutely felt when PTX is on.
This increased competition means that—for certain choices of
parameters—the cascades that occur within any given MFE are
even more variable than in the control state. This extra variability
gives rise to the PTX-induced reduction in PN consistency
across isolated stimulus pulses. As corroborated by numerical
experiments, this reduction in PN consistency is accentuated as
the overall level of disinhibition rises (see Supplementary Figure
S6F). Note that this phenomenon is not captured via a mean field
reduction, and thus will not manifest in most standard firing-rate
models.

MFEs Underlie the BIC-on Phenomena

Recall that the BIC-on state in our network involves both a
reduction of GABA-A coupling strengths as well as a reduction in
the intrinsic SK-currents within the PNs. The effect of the former
alone would be identical to the PTX-on state. However, the
removal of the SK-currents changes the story quite significantly.
When the PNs no longer have SK-currents, the conclusion of
each MFE no longer heralds the onset of intrinsically generated
AHP-currents. As a result, the PNs participating in any one MFE
are free to participate in another shortly afterwards, as long as
they are not suppressed by inter-glomerular inhibition coming
from elsewhere in the AL.

Thus, under BIC it is possible for the network to generate
"runaway synchronization," where any one glomerulus produces
a stochastic sequence of MFEs with a characteristic period
determined by the feedforward input currents (typically in the
50 ms range). An example of such behavior is illustrated in
**Figure 15B**. During such an MFE-sequence many of the other
glomeruli will be suppressed by this active glomerulus (due to
strong inter-glomerular inhibition, enhanced by the disinhibitory
effects of BIC). This runaway synchronization typically continues
until the active glomerulus "falters," and by chance fails to
generate an MFE. At this point another glomerulus—one that
was initially suppressed—has a chance to grab the reins and
begin its own runaway sequence of MFEs. Such a coup—if it
occurs—typically takes place rather abruptly, as the successor
only needs a short window of opportunity to nucleate an MFE
and take over.

In this manner the spontaneous activity in the BIC-on state
can produce—for any given glomerulus—epochs of periodic
firing (i.e., when the glomerulus is active) alternating with epochs
of quiescence (i.e., when another glomerulus is active). The time-
scale of these epochs is determined by the probability that a
bout of runaway synchronization “falters.” Depending on the
choice of parameters, this falter-probability can be quite small—
corresponding to long epoch timescales in the tens of seconds.
Our intuition underlying this argument is essentially the same
as the discussion in Section 10.6 of Zhang and Rangan (2015),
which also presents an analysis of this phenomenon. As before,
this BIC-induced phenomenon is not captured by a mean-field
reduction.
<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /><figcaption>**FIGURE 15** | Illustration of multiple-firing-events (MFEs) within our model network. (A) Here we show a raster-plot of spontaneous activity within a single model glomerulus from our network. The spikes associated with the different kinds of neurons are indicated via different colored dots. By zooming-in we can see an example of a MFE transpiring over ~5 ms. (B) Here we show a raster-plot of spontaneous activity from the same model-glomerulus under the BIC-on condition, with two MFEs displayed in more detail underneath. This raster-plot is taken during an active-epoch, where this glomerulus is firing at roughly 20 Hz. Two MFEs are shown in detail; the rest indicated with arrowheads. Note that the firing-events within each MFE are far from independent, instead occurring in brief synchronous bursts. In each case the MFE is precipitated by the firing of one or more PNs; these first few spikes trigger a cascade which includes more PNs, as well as LN1s and LN2s.</figcaption></figure>

## Summary

The narration we have provided above captures—as best we can—the essential dynamical features of our computational model. As described, the architectural features of our network give rise to a dynamical regime with many interdependent mechanisms that interact in a complicated way. Whether or not any of these mechanisms applies

to the real AL is an important question, which we turn to now.

## **Section R3: Model Predictions**

It is expected that many of the specific dynamical details of our model will vary depending on our exact choice of parameters. In the previous section we attempted to gloss over these minutiae
and focus only on the salient features of our model: features that persisted across all the parameters which satisfied our benchmarks. Some of these salient features take the form of emergent dynamical mechanisms; mechanisms that we did not explicitly build into our model, but which arose naturally from the interactions of the network. Such emergent mechanisms give rise to predictions regarding the real AL.

Below are two such predictions; both involve emergent dynamical mechanisms which are integral to the function of our model and robust to our choice of parameters.

1. Our model predicts that PNs in the AL participate in MFEs. While many PN spikes are isolated, many also occur as a result of other spikes. This latter phenomenon can involve just 2 PNs, or (more rarely) all the PNs in a glomerulus, and often includes LN spikes as well. These MFEs are most common during the initial response to a stimulus; but can also occur during spontaneous activity.

We feel quite comfortable with this prediction for two reasons. Firstly, MFEs emerged ubiquitously across a very wide swath of parameters that was much larger than—and included—the region in parameter-space that was biologically plausible. Put another way, our model never did anything even remotely reasonable without MFEs occurring. Secondly, we believe that MFEs are a key ingredient in the dynamical interactions responsible for both the PTX- and BIC-induced phenomena.

2. Furthermore, our model predicts that the BIC-induced structured activity encompasses not just the MGC, but many other glomeruli as well. Any glomerulus with a foreshortened AHP-phase—as induced by BIC—can begin producing MFE sequences. Moreover, different glomeruli compete to produce these MFE-sequences: such activity within any one glomerulus ensures—through interglomerular inhibition—that other glomeruli are suppressed. Conversely, a quiescent epoch observed in any one glomerulus necessarily implies an active epoch occurring someplace else.

We are also comfortable with this second prediction for two reasons. Firstly, despite all of our parameter-scanning, we were never able to produce BIC-induced structured activity without the underlying mechanism of competing MFE-sequences. This held true even when we searched across parameters that didn't even satisfy our other benchmarks. In other words, our model seemed incapable of producing structured activity on a 10s timescale in any other way. Secondly, we have observed a similar mechanism at work in a model of the primate visual cortex (Rangan and Young, 2013b). In this previous work, competing MFE-sequences of much the same nature give rise to the slowly shifting patterns of activity observed in the anesthetized cortex—a state which, like our BIC-on state, does not exhibit a prolonged AHP and thus also allows for runaway synchronization.

## Section R4: Validating the Model with Further Experiments

We now depart from our computational model and return to physiology to try and find evidence confirming our two predictions.

Our first prediction above—i.e., the existence of MFEs— has been observed indirectly under a variety of experimental conditions. For example, Christensen et al. (2000) found that PN activity within *Manduca* was not independent, but rather correlated to varying degrees depending on the stimulus. Later work by Lei et al. (2002) found that PNs in the MGC often fired synchronously (i.e., within 5 ms of one another), with the preponderance of synchronous spikes dependent on the recurrent presynaptic inhibition. The nature of this synchrony was further clarified by Christensen et al. (2003), which confirmed that—like the MFEs we see in our model—the synchronous PN firings observed in experiment could not be attributed to coordinated firing-rate fluctuations coming from the LFP. Finally, recent work by Martin et al. (2013) found that, when stimulated, at least 15% of the PN spikes produced within the *Manduca* MGC were participants in a synchronous event spanning <2 ms and involving at least one other PN.

While these experiments have yet to directly confirm (i) the chain of causality linking one spike to the next and (ii) the participation of LNs, taken altogether they strongly suggest that the MFEs we see in our model might be occurring in the AL. Because we did not use these experiments as benchmarks to constrain the dynamics of our model, we can consider them as a kind of validation of the dynamical picture we discovered. Conversely, we could also view the emergent MFEs from our model as further support for many of the conclusions that have been drawn from this experimental work (see e.g., Martin et al., 2011).

Our second prediction above—i.e., that BIC-induced spontaneous patterns involve many glomeruli—has not yet been confirmed. While structured spontaneous activity has been observed within the MGC (see, e.g., our benchmark Lei et al., 2009), such activity outside the MGC has not yet been reported. We take this step here, measuring from glomeruli outside the *Manduca* MGC under BIC application. These new experiments were able to verify aspects of our second prediction:

### BIC Induces Structured Spontaneous Activity Encompassing Glomeruli Outside the MGC

We moved our recording electrode to the medial portion of AL, and recorded from a plant-odorants (hibiscus oil) responsive neuron. This neuron displayed spontaneous bursting patterns, implying that it was a PN (Lei et al., 2011). We found that, under BIC application, this neuron displayed epochs of fast-periodic-spiking lasting 5–10 s long interspersed with quiescent epochs lasting about 20 s long. As exhibited in Figure 12, the time-scale, distribution of, and transitions between these epochs seem commensurate with those reported within the MGC. These drug-induced changes could be reversed by saline wash.

We view this experimental result as an indication that our reasoning is on the right track. Nevertheless, we have yet to directly confirm that (i) the fast-spiking epochs are due to MFE-sequences, and (ii) that the glomeruli compete antagonistically with one another, with only one glomerulus active at a time. Naturally, we hope to carry out such experiments in the future, further illuminating the mechanisms at work within the AL. For
now, however, we take a step back and try and interpret the
results we have so far.

DISCUSSION

This paper describes somewhat of a journey; a trajectory
beginning with physiological experiments, passing through
the realm of modeling and simulation, and ending back
again with more experiments. We started out by measuring
the PN dynamics and AHP-phase within the MGC under
a variety of pharmacological agents. The drastic differences
between PTX- and BIC-application lead us to conjecture
that the AHP-phase involved both (a) disinhibition mediated
by heterogeneous LN connectivity, as well as (b) intrinsic
SK-currents produced by the PNs themselves. To determine
whether or not these two mechanisms could, simultaneously, give
rise to all the phenomena we observed, we built and benchmarked
a computational model. This model highlighted the occurrence of
MFEs—a dynamical mechanism that we did not directly observe
in experiment, but which emerged naturally from the high-gain
state of our biologically plausible model. Using our model as
a stepping-stone, we then returned to experiment; confirming
at least partially some of the predictions we had regarding the
existence of MFEs and their dynamical consequences.

It is certainly possible that our hypotheses are not correct, or
that we have omitted an important feature; PTX and BIC could
affect a variety of specific targets in different ways. For example,
rather than involving SK-channels, BIC could primarily affect the
synapses of the LN2s while PTX primarily affected the synapses
of the LN1s. Nevertheless, we found that our hypotheses were
sufficient to explain the phenomena under study, and we feel
comfortable concluding that:

(a) There exists strong fast recurrent excitatory and inhibitory connectivity throughout the *Manduca* AL—both between and within glomeruli. Moreover, this connectivity is heterogeneous—i.e., different neurons can have very different distributions of pre- and post-synaptic connections. Such heterogeneous connectivity implies that the network is capable of disinhibiting the PNs under the right circumstances.

(b) While the strong recurrent inhibition described in (a) definitely contributes to the strong AHP-phase of the PNs, the AHP-phase also depends on intrinsic inhibitory currents produced by the PNs themselves.

(c) The strong fast excitation mentioned above facilitates MFEs—causally linked spikes spanning subsets of neurons within the AL. Rapid sequences of MFEs are typically held in check by the presence of the AHP-phase, which prevents PNs from participating in many successive MFEs.

These conclusions are bolstered, to one degree or another, by
various lines of experimental evidence, much of which we have
referenced along the way. For example, regarding Conclusion-A,
Christensen et al. (1998a) found that PN activity in the *Manduca*
AL was modulated by disinhibitory microcircuits involving two
GABAergic LNs. Regarding Conclusion-B, we have the results of

Lei et al. (2009), which clearly show that BIC-application shortens
the AHP-phase. Finally, regarding Conclusion-C, we have an
abundance of circumstantial evidence (Christensen et al., 2000,
2003; Lei et al., 2002; Martin et al., 2013), as well as our own BIC-
induced results of **Figure 12**, all of which point toward synchrony
within the *Manduca* AL.

Nevertheless, not all experimental observations line up with
our conclusions. For completeness we briefly discuss some
conflicting evidence.

Is There Disinhibition in the Silkworm
Moth?

A recent study (Fujiwara et al., 2014) probed the inhibitory
circuits in the AL of another moth species—the silkworm moth
*Bombyx mori*—and concluded that odor stimulation produced
both recurrent excitatory and inhibitory currents, with the latter
emerging after some time delay and supressing the excitatory
phase of subsequent odor-evoked responses. This point itself
is not inconsistent with our conclusion-A but is based on a
different measurement. They then further examined how PTX-
application affected the PN dynamics. Unlike our experiments
discussed in Section R1, Fujiwara et al. measured the odor-evoked
PN spike counts, instead of measuring the AHP. They reported
that PTX-application did increase spike counts, in contrast to
our observation that PTX-application lengthened the AHP-phase
(Figure 6). From their observations they concluded that PTX-
application elevates excitatory currents to the PNs; a conclusion
that seems diametrically opposite to our work in Section R1,
where we concluded that PTX-application actually enhances the
inhibitory currents impinging on the PNs. This discrepancy may
be due to the species difference or experimental conditions, but
is also likely related to the specifics of the measurement used.
We remark that, while our conclusions regarding the role of PTX
may differ, both we and Fujiwara et al. agree that PTX does not
affect the mean odor-evoked firing-frequency (Figure 10). While
we don't yet have resolution to this conundrum, earlier work by
Waldrop et al., (1987; Christensen et al., 1998a) points out that
direct excitatory, inhibitory and disinhibitory connections may
all affect the PNs. Antagonizing GABA-A receptors (i.e., PTX)
may produce a syndrome of effects that requires us to consider
the entire network.

Does the AHP-Phase Combine Recurrent
and Intrinsic Currents in the Swordgrass
Moth?

Based on our Conclusion-B, we would predict that the duration
of the AHP phase should be positively correlated with increased
odor concentration; an increased concentration causes higher
LN and PN firing, the former giving rise to more recurrent
inhibition, the latter to a stronger intrinsic SK-current. This is
indeed the case (Figure 7), and moreover this dose-response
relation is disrupted by PTX-application. It's unclear how the
duration of the AHP phase could encode odor concentrations,
but our data at least suggest that the GABA-A receptor-mediated
inhibition is related to the dynamic range of PN's responsiveness.
However, in another moth species—the black swordgrass moth
*Agrotis ipsilon*—the duration of the AHP (termed Inhibitory Phase) is not correlated with odor concentrations (Jarriault et al., 2009). While we cannot yet explain this discrepancy, we noticed that their data do reveal that the magnitude—rather than the duration—of the AHP is concentration-dependent (Figure 7 in Jarriault et al., 2009). Thus, in summary, while we are reasonably confident of the dynamic picture we have painted for the *Manduca*, the mechanisms may reveal themselves differently in other insect species.

## SUMMARY

If indeed our picture is accurate and the *Manduca* AL does exhibit the mechanisms we propose, we are lead naturally to the grander question: what purpose could they serve? We hypothesize that perhaps the *Manduca* has evolved to excel at certain difficult sensory tasks, such as finding a mate on the wing through a highly dynamic scent plume. One necessary computation for such behavior would be to reliably detect and respond to a faint odor-filament spun across a turbulent breeze.

As a moth flies, it encounters such filaments intermittently, with each brief exposure to pheromone lasting no more than a few 10 s of millisecond, and with subsequent glimpses of the odor separated by several 100 s of millisecond. In this kind of scenario it makes some amount of sense for the AL to be in a very high-gain state; with even the slightest hint of pheromone eliciting a vigorous response. Of course, given the brevity of the stimulus, a firing-rate code seems inefficient, and a temporal code (such as the temporal-binding of odor-specific synchronous subsets) might be much more elegant and efficient (Martin et al., 2011). The high-gain state we predict in this paper is consistent with both of these requirements; producing vigorous synchronous bursts of PN activity in response to brief stimulus pulses.

Carrying this narrative forwards, one can imagine the moth having just encountered one such odor-filament, its MGC responding furiously. At this point the moth's AL is blind to the world; after all, a typical consequence of maintaining such a high-gain state is that—after the initial response—it is very easy for recurrent excitatory connectivity to perpetuate that response, regardless of any new stimulus. It is at this point that

the AHP-phase steps in; the recurrent inhibition and intrinsic currents curtailing such a runaway response, and allowing the MGC to “reset” after 100–500 ms. This characteristic AHP-time is not too different from the typical time it might take before the moth bumps into the next odor-filament. At that point the moth’s AL will be in a high-gain state once more; fresh and ready to respond vigorously a second time.

In conclusion, our integrated theoretical and empirical approach supports the notion that both recurrent network interactions and intrinsic currents contribute to the dynamical properties of the projection neurons in the antennal lobe. These properties render a high-gain state, which may be an adaptive feature for the animal's olfactory behaviors.

## AUTHOR CONTRIBUTIONS

HL, designed and performed experiments, analyzed data, drafted manuscript; YY, performed experiments and analyzed data; SZ, provided overall support for YY and guided the project partially; AR, built the mathematical model and analyzed its performance, revised manuscript.

## FUNDING

This work is supported by the National Science Foundation (DMS-2100009 to HL and AR), as well as the National Institute of Health (R01-DC-02751 to John G. Hildebrand), and the National Science and Technology Support Program of China (2015BAD08B01 to YY).

## ACKNOWLEDGMENTS

The authors wish to thank Dr. Hildebrand for his generous support; Mrs. Teresa Gregory for lab maintenance.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fphys.2016.00080

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Lei, Yu, Zhu and Rangan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
# Unexpected plant odor responses in a moth pheromone system

Angéla Rouyar<fnref n="1" />, Nina Deisig<fnref n="1" />, Fabienne Dupuy<fnref n="2" />, Denis Limousin<fnref n="1" />†,
Marie-Anne Wycke<fnref n="1" />, Michel Renou<fnref n="1" /> and Sylvia Anton<fnref n="2" />*

## OPEN ACCESS

**Edited by:**
Anders Garm,
University of Copenhagen, Denmark

**Reviewed by:**
Paul Szyszka,
Universität Konstanz, Germany
Neil Kirk Hillier,
Acadia University, Canada

**\*Correspondence:**
Sylvia Anton,
Neuroéthologie-RCIM,
INRA-Université d'Angers, UPRES EA
2647 USC INRA 1330, 42, rue
Georges Morel, 49071 Beaucouzé
cedex, France
sylvia.anton@angers.inra.fr

†Present Address:
Denis Limousin,
Faculté des Sciences et Techniques,
Institut de Recherche sur la Biologie
de l'Insecte (Centre National de la
Recherche Scientifique UMR 7261),
Université François Rabelais, Tours,
France

**Specialty section:**
This article was submitted to
Invertebrate Physiology,
a section of the journal
Frontiers in Physiology

**Received:** 26 February 2015

**Paper pending published:**
25 March 2015

**Accepted:** 26 April 2015

**Published:** 12 May 2015

**Citation:**
Rouyar A, Deisig N, Dupuy F, Limousin D, Wycke M-A, Renou M and Anton S (2015) Unexpected plant odor responses in a moth pheromone system. *Front. Physiol.* 6:148. doi: 10.3389/fphys.2015.00148

Male moths rely on olfactory cues to find females for reproduction. Males also use volatile plant compounds (VPCs) to find food sources and might use host-plant odor cues to identify the habitat of calling females. Both the sex pheromone released by conspecific females and VPCs trigger well-described oriented flight behavior toward the odor source. Whereas detection and central processing of pheromones and VPCs have been thought for a long time to be highly separated from each other, recent studies have shown that interactions of both types of odors occur already early at the periphery of the olfactory pathway. Here we show that detection and early processing of VPCs and pheromone can overlap between the two sub-systems. Using complementary approaches, i.e., single-sensillum recording of olfactory receptor neurons, *in vivo* calcium imaging in the antennal lobe, intracellular recordings of neurons in the macromglomerular complex (MGC) and flight tracking in a wind tunnel, we show that some plant odorants alone, such as heptanal, activate the pheromone-specific pathway in male *Agrotis ipsilon* at peripheral and central levels. To our knowledge, this is the first report of a plant odorant with no chemical similarity to the molecular structure of the pheromone, acting as a partial agonist of a moth sex pheromone.

**Keywords:** insect olfaction, sex pheromone, volatile plant compounds, interaction, olfactory receptor neuron, antennal lobe, central neuron

## Introduction

Most insects use olfactory cues to communicate and find resources necessary for survival and reproduction. Olfactory-guided behavior, as well as the detection and central processing of sex pheromone and general odor cues have been particularly well studied in moths, in which the olfactory system shows a prominent sexual dimorphism related to sex pheromone communication. Female moths release a species-specific sex pheromone blend, which triggers a well-described oriented flight behavior along the pheromone plume in males, leading them toward the pheromone source (Cardé and Willis, 2008). Both sexes use also flower odors to find nectar sources, and females use plant volatiles in their search for oviposition sites on host plants. Male moths might also use host-plant volatiles to approach the habitat from which females are likely to be calling (Light et al., 1993; Coracini et al., 2004).

Insects detect odorants with olfactory receptor neurons (ORNs), housed within cuticular sensilla on their antennae. In male moths, species-specific pheromones and volatile plant compounds (VPCs) are usually detected and processed by two distinct olfactory pathways (Masson and Mustaparta, 1990) and separation between pheromone and plant signals occurs already at the

<footnote n="1">¹Institut d'Ecologie et des Sciences de l'Environnement de Paris, INRA, Université Pierre et Marie Curie, Versailles, France,</footnote>

<footnote n="2">²Neuroéthologie-RCIM, INRA-Université d'Angers, Beaucouzé, France</footnote>
peripheral level. Information about the pheromone blend is transferred from the antennae via the axons of pheromone-specific olfactory receptor neurons (Phe-ORNs) to the primary olfactory center, the antennal lobe (AL) where it is processed in a male-specific area, the macroglomerular complex (MGC). Information about plant odors is transferred via a different class of olfactory receptor neurons (VPC-ORNs) and processed in sexually isomorphic areas of the AL, the ordinary glomeruli (OG) (Hansson and Anton, 2000). Because natural insect behavior results generally from the integration of multiple information sources, determining to which extent the two sub-systems are completely separated has recently become a very important case study in sensory ecology. More and more information is accumulating, indicating that simultaneous stimulation with pheromone and plant odors leads to interactions at all levels from detection up to behavioral output. The most frequently observed effect of mixture interaction in Phe-ORNs is suppression of pheromone responses when a VPC is added (Den Otter et al., 1978; Kaissling et al., 1989; Pophof and Van Der Goes Van Naters, 2002; Party et al., 2009; Rouyar et al., 2011). In the antennal lobe, plant volatiles either enhance pheromone responses (Namiki et al., 2008; Trona et al., 2010), or have a suppressive effect (Chaffiol et al., 2012; Deisig et al., 2012). Behavioral tests in the wind tunnel or in the field show often synergistic effects of plant odors added to the pheromone, more males being attracted to the mixture. In *Spodoptera exigua*, for example, phenyl-acetaldehyde, (Z)-3-hexenyl acetate or linalool increased captures of males in pheromone traps (Deng et al., 2004), and in wind tunnel experiments (Z)-3-hexen-1-ol, (+)-terpinen-4-ol, (E)-β-caryophyllene and methyl salicylate released with sub-optimal pheromone doses caused a synergistic effect in *Eupoecilia ambiguella* (Schmidt-Büsser et al., 2009). However, in spite of the observed interactions, so far the pheromone and plant odor inputs to the nervous system have been postulated to be highly separated up to their integration in the moth ALs. This consensual view of a high specificity of the pheromone sub-system arises from the repeated observation of a narrow chemical tuning of the pheromone receptor neurons to pheromone-like structures (Masson and Mustaparta, 1990). This high pheromone selectivity has been confirmed by heterologous expression of sex-pheromone receptors from several moth species, confirming they selectively bind the pheromone components over their close structural isomers (Nakagawa et al., 2005; Wanner et al., 2010; Liu et al., 2013). It is thus generally admitted that these olfactory receptors, narrowly tuned to pheromone components, act as molecular filters, preventing the activation of the pheromone pathway by general odorants. However, most studies have focused on pheromone-related compounds, so the capacity of general odorants to be bound to pheromone receptors should be more specifically addressed. As a matter of fact, if in *Helicoverpa zea* or *Spodoptera littoralis*, plant volatiles alone did not elicit responses from the Phe-ORNs (Ochieng et al., 2002; Party et al., 2009), high doses of plant compounds have been observed to activate Phe-ORNs in *Agrotis segetum* (Hansson et al., 1989). We revisit here the question of the sensitivity of the moth pheromone sub-system to plant odorants.

In the present study, using electrophysiological recordings and *in vivo* calcium imaging we show how plant volatiles in the noctuid moth *Agrotis ipsilon* activate not only the plant odor-specific pathway but also Phe-ORNs and the sex pheromone-specific MGC. The VPC heptanal used primarily in our study, with its seven-carbon chain length and an aldehyde function is emitted by various flowers such as linden flowers (*Tilia* sp.) that are attractive to *A. ipsilon* when searching for food (Wynne et al., 1991; Zhu et al., 1993), and is structurally different from the three acetates that constitute the sex pheromone blend of *A. ipsilon*. In the wind tunnel, male *A. ipsilon* were previously shown to be attracted by a linden flower extract (Deisig et al., 2012). We compare here in detail the upwind flight behavior toward heptanal and the pheromone blend. To determine if effects found for heptanal are specific, we also tested Phe-ORN responses to different other plant volatiles.

# Materials and Methods

## Insects

Larvae of *A. ipsilon* were reared in the laboratory on an artificial diet in individual plastic containers at 23°C and 60% relative humidity until pupation. Sexes were separated at pupal stage, and females and males were kept in separate rooms under a reversed 16 h:8 h light:dark photoperiod under similar temperature and humidity conditions. Newly emerged adults were collected every day and provided *ad libitum* with a 20% sucrose solution. The day of emergence was considered day zero of adult life. Four or five day old sexually mature virgin males were used for electrophysiological, optical imaging and wind tunnel experiments. All experiments were performed during the scotophase, when male moths are sexually active. Some complementary experiments were run on males of *S. littoralis*, which were reared under the same conditions as *A. ipsilon*.

## Chemicals

### Sex Pheromones

We used a highly attractive synthetic sex pheromone blend of *A. ipsilon* based on the three components identified previously in natural extracts of the female gland (Picimbon et al., 1997; Gemeno and Haynes, 1998): (Z)-7-dodecen-1-yl acetate (Z7-12:OAc), (Z)-9-tetradecen-1-yl acetate (Z9-14:OAc) and (Z)-11-hexadecen-1-yl acetate (Z11-16:OAc), mixed at a ratio of 4:1:4. This blend was further proven to be the most attractive to males in field tests (Causse et al., 1988) and it has been shown to elicit the same behavior as natural extracts in a wind tunnel (Barrozo et al., 2010b; Vitecek et al., 2013). We preferred in this paper to use the pheromone as a whole to investigate heptanal interactions with the complete stimulus at all integration levels. The three compounds were purchased from Sigma Aldrich (Saint-Quentin Fallavier, France) and diluted in hexane (>98% purity, CAS 110-54-3, Carlo-Erba, Val-de-Reuil, France). Amounts of 10 ng and/or 100 ng of the sex pheromone blend were used in the electrophysiological and calcium imaging experiments; these doses had previously been described as behaviorally and electrophysiologically active (Gadenne et al., 2001; Barrozo et al., 2010a; Chaffiol et al., 2012; Deisig et al., 2012). For ORN
recordings from pheromone sensilla in S. littoralis, the major sex pheromone component, (Z)-9 (E)-11 tetradecadienyl acetate (Z9,E11-14:Ac) was used (Ljungberg et al., 1993).

## Volatile Plant Compounds

Heptanal (98% purity, CAS 66-25-1, confirmed by GC analysis, revealing no traces of pheromone compounds) and VPCs belonging to different chemical families (aldehydes, acetates, terpenes as well as one aromatic compound) were used for some experiments: (Z)-3-hexenyl acetate (Z3-6Ac) (98% purity, CAS 3681-71-8), hexanal (>99% purity, CAS 66-25-1), octanal (98% purity, CAS 124-30-0), linalool (97% purity, CAS 78-70-6), 2-phenylethanol (99% purity, CAS 60-12-8), and α-pinene (97% purity, CAS 7758-70-8). Mineral oil (CAS 8042-47-5) was used to prepare volume-to-volume dilutions at 0.1 and 1%. All compounds were purchased from Sigma Aldrich (Sigma Aldrich, Saint-Quentin Fallavier, France).

## Olfactory Stimulation

Odorants were delivered as described previously (Rouyar et al., 2011). Briefly, charcoal-filtered air was re-humidified and divided in eight equal flows (220 ml/min) directed each to a three-way miniature valve. From there the flow could be directed to one 4 ml glass vial containing the stimulus source by activating the appropriate valve. The connections to the vials were made using PTFE tubing (1.32 mm ID) and hypodermic needles (18 G size). For practical reasons, due to their differences in volatility and polarity it was not possible to use the same type of stimulus sources for pheromone and heptanal or other volatile compounds. For VPCs, the vial contained 1 ml of solution in mineral oil at the appropriate concentration vol/vol. For the pheromone, the vial contained a section of PTFE tubing (1.6 mm ID; L = 20 mm) directly connected to a hypodermic needle and containing 10 or 100 ng of the sex pheromone. Stimulus- and clean air-carrying tubes were maintained together in a 10 cm long metal tubing constituting the stimulation pencil. A plastic cone of a P1000 pipette was placed at the output of the stimulation pencil to serve as a mixing chamber. It was placed approximately 5 mm in front of one of the moths' antennae and focused on antenn sensilla, when we recorded ORNs. In order to stimulate the whole antenna, the cone was placed 20 mm in front of the head in optical imaging experiments, or 5 mm in front of the antenna when we recorded MGC neurons intracellularly. Programming of the electric valves was performed using a Valve Bank (AutoMate Scientific, Berkeley, USA) synchronized with the PC acquisition software.

## Measures of Aerial Concentrations of VPCs

To trace the olfactory stimulus at the output of the delivery system we used a fast response miniature photo ionization detector (Justus et al., 2002) (PID, from Aurora Scientific Inc, Aurora, Canada). Pheromone components could not be traced by this technique due to the high ionization potential of the pheromone molecules, which is above the energy of the PID lamp (10.6 eV). In turn, as all VPCs except phenyl ethanol and octanal produced measurable PID signals in the relevant concentration range we could estimate their concentrations in

ppmv at the output of the stimulator used in electrophysiological experiments.

In a first step, we calibrated the response of the PID to a source of known increasing concentrations of the VPCs. To generate these concentrations, we used an automatic syringe driver (Harvard Apparatus, model 55-2222) equipped with a 250 µl gastight microsyringe (Hamilton) to inject the pure compound at known rate into a controlled flow of charcoal filtered air (60 l/h, controlled by a flowmeter Meterate tube). The probe of the PID was inserted into the flow and the PID gain was settled at x10. The amplitude of the PID signal was measured after each increase of the delivery rate. Knowing the air flow rate and the chemical injection rate, it is possible to calculate the theoretical concentration in the final air (Chaffiol, personal communication) according to Equation 1:

$$C = (F_{chem} * (\mu/M) * Vmolar)/F_{air}$$

Where:

C = final concentration of the compound in ppbv

Fchem = flow rate of the chemical (µl/h)

Fair = (m³/h)

μ = density of the compound (g/cm³)

M = molecular weight of the compound (g/mol)

Vmolar = molar volume for ideal gasses at 25°C (25.10³ ml).

The speed of the syringe driver was adjusted to the suitable rate, and the concentration was allowed to stabilize for 1 min after which the output signal of the PID was measured three times. Subsequently the speed of the syringe pump was increased to reach the next rate step. Measures were done for at least 10 different rates, presented in increasing orders, until the saturation response of the PID (10 mV) was reached. The rates were converted (Equation 1) into ppmv and the experimental data were fitted to a polynomial regression according to the procedure recommended by the PID constructor for calibration (Equation 2):

$$(S_{PID} = aC^2 + bC)$$

where S<sub>PID</sub> is the amplitude of the PID response in volts, and C the concentration in ppmv.

For measurements, the probe of the PID was introduced into the olfactory stimulator to quantify the concentrations of the compounds in the odorized air flows. Compounds were delivered at three dilution levels (0.5, 1, and 10% vol/vol in mineral oil) in the same conditions used for our electrophysiological experiments, and measures were repeated five times. The data in mV were converted into ppmv using equation and values for 0.1% extrapolated from the resulting curve. The concentration measures are summarized in Table 1 where data for 0.1% were extrapolated.

## Electrophysiology

### Single Sensillum Recording of ORNs

Males were briefly anesthetized with CO₂ and restrained in a Styrofoam holder. One antenna was immobilized with adhesive tape. Single sensillum recordings were performed with electrolytically sharpened tungsten wires. The reference electrode was inserted into the antenna, 1-3 segments from the segment carrying recorded sensilla, and the recording electrode was
<table><caption>TABLE 1 | Estimation of the concentrations in ppm at the output of the stimulator used in the electrophysiological experiments for five different volatile plant compounds released from sources containing 1 ml of mineral oil with 0.1, 1, and 10% of the respective compound as calculated from measurements with a photo ionization detector.</caption><thead><tr><th>Compound</th><th>0.1% v/v</th><th>1% v/v</th><th>10% v/v</th></tr></thead><tbody><tr><td>Heptanal</td><td>3.9</td><td>14.4</td><td>119.1</td></tr><tr><td>α-pinene</td><td>0.6</td><td>0.7</td><td>8.0</td></tr><tr><td>Linalool</td><td>0.7</td><td>3.1</td><td>14.4</td></tr><tr><td>Hexanal</td><td>16.5</td><td>19.4</td><td>95.6</td></tr><tr><td>Z3-6Ac</td><td>0.7</td><td>0.9</td><td>17.2</td></tr></tbody></table>

inserted into the base of a sensillum. We recorded two types of sensilla: long trichoid hairs based on antennal branches known to house Phe-ORNs and short trichoid hairs situated on the antennal stem known to house VPC-ORNs in a closely related species, A. *segetum* (Hansson et al., 1989). Recording and reference electrodes were connected to a Neurolog preamplifier (Digitimer, Hertfordshire, UK). The signal was filtered (0.2–10 kHz) and amplified 1000 times. The electrophysiological activity was sampled at 10 kHz and 12 bit resolution with a Data Translation DT3001 analog to digital card. Signals were monitored on the computer screen using Awave software (Marion-Poll, 1995). For analysis, spike sorting and extraction of spike occurrence times from the recordings were also done using Awave software. In some recordings from long trichoid hairs housing Phe-ORNs, the activities of two neurons with different spike amplitudes were analyzed, but only one neuron showed changes in firing rate in response to the sex pheromone. Also earlier recordings from Phe-ORNs showed that several neurons could be present in a given sensillum in A. *ipsilon*, but in all cases only one of them responded to a pheromone compound (Renou et al., 1996; Jarriault et al., 2010).

## Intracellular Recordings of MGC Neurons

Males were slipped inside a 1 ml plastic pipette cone cut at the top. Only the head exceeded the plastic cone and was fixed with dental wax to prevent any movement. As described earlier (Gadenne and Anton, 2000), the head capsule was opened and tracheal sacs and muscles were removed from the front of the head to expose the brain. The neurolemma was removed from the surface of the antennal lobe to facilitate microelectrode penetration. Standard intracellular recording techniques were used (Christensen and Hildebrand, 1987). The preparation was superfused with Tucson Ringer (Christensen and Hildebrand, 1987). The microelectrode was randomly placed into the MGC. Electrode resistances were about 20–100 MΩ. The reference electrode was placed in contact with the brain. Signals were amplified with an AxoClamp-2B amplifier (Molecular Devices, Sunnyvale, California, USA). Neural activity was recorded, digitized, and spike occurrence times extracted using P-clamp software (Molecular Devices, Sunnyvale, California, USA).

## Experimental Protocol

### Phe-ORN and MGC Neuron Responses to Heptanal

We tested the response of Phe-ORNs and MGC neurons to heptanal by stimulating the antenna with a 200 ms heptanal puff

at a dose of 0.1 and 1%. Phe-ORNs were recorded during 1 min and the odorant stimulation started at 30 s, lasting for 200 ms. For MGC neurons, odorant stimulation started 5 s after recording onset and inter-stimulus-intervals lasted for 10 s. Ten second interstimulus intervals are sufficient to allow AL neurons to reach the pre-stimulus spontaneous activity level and have been used in earlier studies of AL neurons in A. *ipsilon* (e.g., Barrozo et al., 2010a). We tested the pheromone at 100 ng and as controls, pure mineral oil and hexane, each for 200 ms.

### Phe-ORN Responses to other VPCs

As we obtained unexpected responses of Phe-ORNs to heptanal, we also tested the effects of other VPCs on these ORNs: Z3-6Ac, hexanal, octanal, linalool, 2-phenylethanol and α-pinene. To check if we were recording from Phe-ORNs, we first stimulated the antenna with a 100 ng pheromone puff. Then puffs of the other compounds at 1% were randomly presented. As controls, the solvents hexane and mineral oil were tested.

### VPC-ORN Responses to Pheromone

To check if VPC-ORNs also respond to the pheromone, we stimulated short trichoid sensilla situated on the stem of the antenna with 100 ng pheromone during 200 ms. To test if we had indeed contact with VPC-ORNs, we presented puffs of 0.1% of the VPCs heptanal, Z3-6Ac, hexanal, octanal, linalool, 2-phenylethanol, and α-pinene. As controls, we tested the solvents hexane and mineral oil.

### Species Specificity of Phe-ORN Responses to VPCs

To test if the effect induced in Phe-ORNs by VPCs is specific to A. *ipsilon*, we recorded long trichoid sensilla in male S. *littoralis*, which have been shown to house one Phe-ORN tuned to the major sex pheromone compound Z9,E11-14:Ac. We stimulated sensilla with heptanal and linalool at 1% during 200 ms. To test if we were recording from Phe-ORNs, we first stimulated the antenna with 100 ng pheromone during 200 ms. As controls we presented the two solvents hexane and mineral oil.

### Calcium Imaging

#### Insect Preparation

Males were mounted individually in Plexiglas chambers and the head was fixed to prevent movements. The head capsule was opened and glands and trachea were removed. Ten microliter of dye solution (50 mg Calcium Green 2-AM dissolved in 50 ml Pluronic F-127, 20% in dimethylsulfoxide, Molecular Probes, Eugene, OR, USA) were bath-applied on the brain for a minimum of 1 h. The brain was then washed with saline solution (Tucson Ringer) containing 150 mmol/l NaCl, 3 mmol/l CaCl₂, 3 mmol/l KCl, 10 mmol/l N-Tris-methyl-2-aminoethanesulfonic acid buffer, and 25 mmol/l sucrose (pH 6.9).

#### Data Acquisition

Recordings were done using a T.I.L.L. Photonics imaging system (Martinsried, Germany) coupled to an epifluorescent microscope (Olympus BX-51WI, Olympus, Hamburg, Germany) equipped with a 10x water immersion objective. Images were taken using a 1004 × 1002 pixel 14-bit monochrome CCD camera (Andor iXON) cooled to −70°C. Each measurement consisted of 80
frames at a rate of 5 frames/s (integration time for each frame: 10–15 ms). The excitation light was applied using a monochromator (T.I.L.L. Polychrom V). The microscope was equipped with a GFP-BP filter set composed of a 490 nm dichroic beamsplitter and a 525/550 nm emission filter.

## Data Analyses

Because identification of individual glomeruli by anatomical staining of the AL after calcium imaging experiments is not possible in *A. ipsilon*, we defined regions of interests (ROI), possibly referring to individual glomeruli for OGs. Homologous ROIs could be identified by superposing activity maps using Adobe Photoshop (CS3). Raw data were analyzed using custom-made software written in IDL (Research Systems Inc., Colorado, USA) and Visual Basic (Microsoft Excel). Each recording corresponded to a three-dimensional matrix with two spatial dimensions (x and y size in pixels of the ROI) and a temporal dimension (length of the recording, 80 frames). Signals were subjected to three treatments: (i) For reduction of photon (shot) noise, raw data were filtered in both the spatial and temporal dimensions using a median filter with a size of 3 pixels. (ii) Relative fluorescence changes (ΔF/F) were calculated as (F-F₀)/F₀, taking as reference background F₀ the average of five frames (frames 5 to 10) before odor stimulation. (iii) To correct for bleaching and for possible irregularities of lamp illumination in the temporal dimension, we subtracted from each pixel in each frame the median value of all the pixels of that frame. The maximum signal was obtained about 3 s after odor onset (around frame 30) and the minimum about 12 s after odor onset (around frame 60). We present activity maps with the best possible spatial definition of odor-induced signals from frames 30 to 60 where each pixel represents the mean of its values at frames 29–31 minus the mean of its values at frames 59–61.

For quantitative analysis of the data, we focused on the fast (positive) signal component evoked by odor stimulations, which is related to an intracellular calcium increase from the extracellular medium, thought to reflect mostly pre-synaptic neuronal activity from ORNs (Galizia et al., 1998; Sachse and Galizia, 2003). For each identified activity spot, the time course of relative fluorescence changes was calculated by averaging 25 pixels (5 × 5) at the center of each activity spot and well within its borders. The amplitude of odor-induced responses was calculated as the mean of three frames at the signal's maximum (frames 29–31) minus the mean of three frames before the stimulus (frames 7–9). This value was then used in all computations.

## Experimental Protocol

Each animal was subjected to three series of olfactory stimulations with interstimulus intervals (ISIs) of 100 s. Odor stimulation started 3 s after recording onset and lasted for 200 ms. One AL was recorded in each insect. All animals were tested with a dose of 1% heptanal, 1% linalool as well as 100 ng of the pheromone. As control, we tested pure mineral oil and hexane.

## Wind Tunnel Experiments

The behavior of male moths responding to pheromone or heptanal was observed in a wind tunnel made of 19 mm thick

Plexiglas, with a flight section of 190 cm length × 75 cm width × 75 cm height (VT Plastics, Genevilliers, France). Plexiglas doors on the front side of the tunnel allowed access to the test section. The down- and upwind ends were enclosed with screen made of white synthetic fabric to prevent the insects from escaping but let the air pass through. An exhaust fan at the downwind end of the tunnel sucked the air into the tunnel at a speed of 0.3 ms⁻¹ and evacuated contaminated air to the outside of the building. The room housing the tunnel was maintained in darkness with a single red bulb to provide low intensity light for visual observations. Side infrared illumination for video tracking was provided by an array of eight 5 W IR lamps, of 54 LEDs each, emitting at 850 nm. A vertical screen bearing a randomly arranged pattern of 10 cm diameter black circles was positioned 30 cm behind the rear wall of the tunnel to provide visual cues to the moths outside the camera field.

Moth flight tracks were recorded and analyzed using Trackit 3D 2.0 (SciTracks, Pfaffhausen, Switzerland). Two cameras (Basler Pilot, piA640-210 m with Tamron ½" 4-12F/1.2 lenses) were positioned above the tunnel at 60 cm from each other to cover the whole flight section with overlapping fields. The images from the two cameras were analyzed in real time and the x, y, and z coordinates of each moth's position were extracted every 10 ms. Tracks were saved on the computer in form of “.csv” files that were further processed using scripts developed in R Core Team (2013).

Experiments were performed at 23°C, 40 ± 10% relative humidity, during the second half of the scotophase (i.e., 4–7 h after lights turned off) which corresponds to the peak activity of male *A. ipsilon*. Five-day old virgin males were tested. A single male was introduced inside a cage on a 36 cm high holder in the middle of the tunnel width and 160 cm downwind from the odor source. After allowing the moth to adapt to the airflow, we applied the odor stimulation and monitored the male's behavior for 3 min. We compared the responses of males to either the pheromone at 100 ng or heptanal at 0.1 or 1% dilutions. Control experiments (no odor) were performed with a clean filter paper as source. Each individual was tested only once. Olfactory stimuli were delivered using the same model of stimulator as in our electrophysiological experiments enabling to deliver odorants by switching solenoid-driven Lee micro valves via a Valve-Bank controller, with separate channels for each odorant. Hypodermic 18 G needles fixed in the middle of the upwind side of the tunnel were used as odor nozzles delivering odorized air flows at the upwind end of the tunnel. The solution of sex pheromone in hexane was deposited on a filter paper introduced in a 4 ml glass vial after solvent evaporation. Heptanal was diluted in 1 ml of mineral oil.

Four behavioral items were scored: activation (walking activity and/or wing fanning on the take-off platform), take off (taking off from the platform) partial flight during the test (flight half way between the release site and the odor source) and source flight (flight ending within 20 cm of the source) before the end of the test (180 s). All males stimulated with the pheromone blend showed activation and performed a take-off in less than 90 s after the beginning of the test, so 90 s was taken as the time limit for scoring these two items for all subsequent experiments.
To compare the orientation of males toward the wind direction in presence of the different odor sources the .csv files produced by Trackit were used to calculate distribution plots of the positions of the male along the tunnel width. The tracks were first smoothed using a local polynomial regression fitting [function "loess"] from R package "stats," (R Core Team, 2013)]. We then extracted the section of the smoothed tracks from the departing point (platform), up to the point where the moth reached its maximum value on the length axis. Finally we calculated the cumulated distribution along the width axis of the males, within the whole length after take off (the first 10 cm from the platform were excluded) and plotted it for each treatment (pheromone, 1% heptanal, 0.1% heptanal, and control).

## Statistical Analyses

For electrophysiological experiments, spike occurrence times were analyzed using custom-written R scripts (R Core Team, 2013). Firing rates were calculated using the local slope of the cumulative function of spike times (Blejec, 2005). The slope was calculated over a moving spike window between the n-2 and n+2 spikes (5 spikes). Thus, each spike was attributed a firing rate and its occurrence time. The maximum firing rate during the 1st second from stimulus start was measured for each recording. The mean ± standard error of the maximum firing rates was calculated for each stimulation. Data were compared using a Student t-test for paired data followed by tests to check for data set normality (Shapiro test) and variance homogeneity (Fisher-Snedecor test), concerning data from ORN recordings, or were compared using a Wilcoxon test for paired data for data from MGC neuron recordings.

The experimental decline of the averaged responses was fitted with an exponential asymptotic decay function by determining the non-linear least-squares estimates of parameter of an exponential model (function nls of R). Curves of firing rates were standardized relatively to the maximum firing rate. The asymptotic decay functions were estimated from the time of the maximum firing rate to 1s after the stimulation times (Equation 3):

$$FR = a + b_* e^{(-c * time)}$$

where FR is the maximum firing rate, a is the offset, b the initial firing rate, and c the rate coefficient of the curve. The time values for 90% decay (td90) were calculated from Equation 3.

We estimated the response latency for each recording using custom-written R scripts. First, we calculated a threshold for excitation response as the 95th percentile of spike firing rates before stimulation onset (spontaneous activity). Second, we looked for the first spike crossing this threshold within the expected response time window corresponding to 1s after stimulation start. We defined this spike occurrence time as response latency. We compared median latencies between two treatments using a chi-square test.

Calcium responses induced by different odors in different glomeruli were compared using Statistica (Version'99, www.statsoft.com). We performed One- or Two-Way ANOVAs for repeated measures with the two factors odor and glomerulus. When interactions among factors were significant, simple effects

were analyzed by means of a One-Way ANOVA with or without the RM factor, and then followed by a Tukey's test for post-hoc comparisons if necessary.

For wind tunnel experiments, a Fisher's exact test was used to compare scores of response of male moths to heptanal and the pheromone.

## Results

### Heptanal Activates VPC-ORNs but also Phe-ORNs

The antennae of *A. ipsilon* are bipectinate with ORNs tuned to pheromone (Phe-ORNs) mainly housed in the trichoid sensilla situated on the branches while ORNs tuned to volatile plant compounds (VPC-ORNs) are predominantly housed within sensilla localized on the antenna stem (Renou et al., 1996). We thus recorded from olfactory sensilla sampled either from the antennal branches or antennal stem and attributed functional types to ORNs according to the most active stimulus. An extensive screening of noctuid pheromone components has evidenced a majority (32 out 52) of Phe-ORNs responding exclusively to Z7-12:Ac, some neurons responding mainly to Z5-10:Ac but also to Z8 and Z9-12:Ac, and only one neuron responding to Z9-14:Ac, but no neuron responding to Z11-16:Ac (Renou et al., 1996). These functional types of Phe-ORNs were never encountered in a same sensillum. On the antennal branches, 92% of all Phe-ORNs encountered responded to Z7-12:Ac (Renou et al., 1996). Thus, we expected the latter neuron type to largely dominate our results. Our single-sensillum recordings showed that Phe-ORNs on the branches responded to the pheromone in a phasic-tonic mode (Figure 1A) and already at a dose of 1 ng (Figure 1B, red curve). Interestingly, these Phe-ORNs responded also to 1% heptanal (Figure 1B, solid green curve). This dilution corresponds to a total amount of 8.1 mg heptanal at the source and an aerial concentration of 14 ppm. The response amplitude to heptanal increased with increasing doses at the source but did not reach saturation at the highest dose tested (Figure 1B, solid green curve). The heptanal dose-response curve was clearly shifted toward higher concentrations, compared to the dose-response curve for the pheromone, indicating a lower potency for heptanal to activate Phe-ORNs.

On the other hand, the ORNs housed in olfactory hairs located on the antennal stem did not respond to the pheromone as expected from VPC-ORNs, but responded to heptanal with higher firing rates and a lower threshold compared to Phe-ORNs. These VPC-ORNs started to respond already at a dose of 0.1% corresponding to 0.81 mg (3.9 ppmv) heptanal at the source (Figure 1B, dashed green curve). In the following experiments, 10 and 100 ng or 0.1 and 1% will designate low and medium stimulus strengths for pheromone and heptanal, respectively.

We then compared the response dynamics of Phe-ORNs to the pheromone and 1% heptanal (Figure 2). All 51 Phe-ORNs examined responded to the pheromone by a simple phasic-tonic excitatory response (Figures 2A,B, left) with 0.331 s median latency (Figure 2C, left). Among these 51 Phe-ORNs,
<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

**FIGURE 1** | *Agrotis ipsilon* Phe-ORNs respond to heptanal. (A) Typical examples of the responses of Phe-ORNs to the pheromone (Phe) and three doses of heptanal (Hep). Scale: vertical bar = 1 mV; horizontal bar = 1 s. The vertical gray bars indicate the stimulus (200 ms). (B) Dose-response curves of Phe-ORNs to a 200 ms puff of the pheromone or of Phe-ORNs and VPC-ORNs to heptanal. Mean of the maximum firing rates during the 1st second from stimulus start (± SEM). Doses of heptanal are in v/V % of dilution in mineral oil with their equivalence in mg (lower horizontal axis); doses of pheromone are in ng deposited on filter paper (upper horizontal axis).

Phe-ORNs responded already to the lowest pheromone dose tested and the maximum firing rate increased with increasing doses (solid red line, *n* = 16).

Phe-ORNs started to respond to heptanal at a dose of 1% (solid green curve, *n* = 12) while VPC-ORNs responded already at a dose of 0.1% (dashed green curve, *n* = 14). Controls (black dot) refer to pooled data of stimulation with pure hexane and pure mineral oil.

40 responded also to 0.1% heptanal, even though with lower firing frequencies (Figures 2A,B, right). The latency of the response to heptanal (median = 0.3 s Figure 2C right) was not significantly different from the response to pheromone (Student's t-test, *p* = 0.22). The firing responses to heptanal were generally phasic-tonic. However, the response patterns were more variable compared to those to pheromone (Figure 2A). In seven Phe-ORNs the responses to heptanal showed prolonged after-response firing activity, while in several others, responses presented a post-stimulus period of silence. Nevertheless, the decay of the response to pheromone or heptanal showed globally equivalent kinetics (Figure 2D), the firing rate decreased by 90% after 0.250 s with pheromone vs. 0.229 s in response to heptanal. However, the experimental data for heptanal were less well fitted to the theoretical exponential decay function than with the pheromone (Figure 2D), due to the post stimulus firing activity above the level expected from the simple exponential decay model in some neurons.

## Different Other Volatile Plant Compounds Activate Phe-ORNs

Most of the ORNs housed in the sensilla sampled on the branches also increased their firing in response to some of the VPCs tested at 1%, although the maximum firing rate in response to VPCs was generally lower compared to pheromone. Heptanal and the

<figure><img src="image_45.png" /></figure>

**FIGURE 2** | Response dynamics of Phe-ORNs to the pheromone or heptanal are very similar. The dynamics of the response of ORNs sampled on antennal branches to a 200 ms pulse of 100 ng pheromone (left column) or 1% heptanal (right column) are compared. (A) Raster plots of the firing activity of 51 individual neurons. The vertical gray bars show the stimulus time. (B) Frequency plots of the maximum firing rates for the same sample of neurons (time bin = 50 ms). Means of the 51 recordings. Error bars in pink represent standard deviation. (C) Kaplan-Meier curves of the response latencies; *p* is the proportion of neurons that responded to the olfactory stimulus at a given time. (D) Exponential decrease model for response end. The red dots represent the estimated values for 90% decrease.

six additional VPCs elicited generally a single excitatory phase in Phe-ORNs (Figure 3A). Hexanal, however, triggered also an excitatory-inhibitory response in some Phe-ORNs (Figure 3A). Out of 46 tested ORNs situated on the branches of the antennae and showing clear responses to the pheromone, 40 responded also to Z3-6Ac, 27 to hexanal, 30 to linalool, 26 to octanal, 11 to 2-phenylethanol and only 2 to α-pinene (Figure 3B). However, the Phe-ORNs could not be sorted into functional sub-types according to their response profiles to the seven VPCs (Figure 3B). The intensity or frequency of the firing response of Phe-ORNs to VPCs was apparently not correlated to their
<figure><img src="image_46.png" /></figure>

**FIGURE 3** | Phe-ORNs respond to different volatile plant compounds. (A) Typical examples of responses of Phe-ORNs to different volatile plant compounds. Single sensillum recordings from different sensilla show that Phe-ORN responses are similar between odors except for hexanal which induced two different response patterns. Scale: vertical bar = 1 mV; horizontal bar = 1 s. The vertical gray bar indicates the stimulus (200 ms). (B) Response-profiles of

individual Phe-ORNs to seven VPCs. Each line presents the responses of a single ORN; each column shows the responses of different neurons to one odorant. The diameter of circles is proportional to the intensity of the response measured as the absolute maximum firing rate reached within 1 s after stimulus onset. Phe, pheromone; Hep, heptanal; Z3Ac, (Z)-3-Hexenyl acetate; Hxal, hexanal; Lin, linalool; Oct, octanal; PhenOH, 2-phenylethanol; Pin, α-pinene.

chemical structure, nor to their volatility. For instance, aldehydes were not globally more active than the other VPCs; α-pinene was practically inactive although its vapor pressure is quite close to that of heptanal (500 and 300 Pa, respectively). For five Phe-ORNs, maximum firing rates were slightly higher for certain VPCs than for the pheromone itself.

## The Pheromone Does not Activate ORNs on the Antennal Stem

Another set of single sensillum recordings was performed from the short sensilla trichodea localized on the antennal stem. The results revealed that only one of the sampled presumed VPC-ORNs (n = 26), which responded to at least one of the VPCs (examples of recordings in **Figure 4A**), responded also strongly to 100 ng of the pheromone (**Figure 4B**). This confirms a clear, but not exclusive spatial segregation of general odorant and pheromone detection in the antennae, most of the ORNs contained in the stem area being tuned to general odorants, while Phe-ORNs are mostly housed in branch hairs. The level of firing activity during responses was generally lower and stem-ORNs showed more specificity in their responses to the different VPCs compared to Phe-ORNs which each responded to several VPCs (**Figures 3B, 4B**).

## Heptanal Does not Activate Phe-ORNs of S. littoralis

To verify if VPC responses in Phe-ORNs are species-, or pheromone structure-dependent, we also recorded from 10 sensilla housing Phe-ORNs in another noctuid moth species, *S. littoralis*. Phe-ORNs responded to 100 ng of the major sex

pheromone compound Z9,E11-14:Ac with a maximum firing rate of 228.1 spikes/s ± 30.6 (mean of 10 replicates ± SEM) while the firing activity in response to 1% heptanal (31.8 ± 16.3) was not significantly different from that to the control (11.3 ± 5.04; paired Student's t-test *p* = 0.148) (**Figure 5**).

## Heptanal Evokes Calcium Responses in the MGC

*In vivo* calcium imaging was performed to obtain a global pattern of the odor-evoked input to the antennal lobe. Global brain staining with a calcium-sensitive dye reveals odor-induced activity of all neuronal populations, however, due to their quantitative predominance, activity recorded originates mainly from ORN responses. Thus, such global responses are complementary to individual neuronal responses obtained with SSR or intracellular recordings. Stimulating the antennae of male *A. ipsilon* revealed calcium responses induced by the plant odors heptanal and linalool in the area of ordinary glomeruli. The pheromone elicited responses in the MGC. The solvents hexane and mineral oil did not elicit any response (**Figure 6A**). Odor-evoked signals were typical stereotyped biphasic signals usually obtained with bath application of the dye Calcium Green 2-AM, with first a fast fluorescence increase followed by a slow fluorescence decrease below baseline (**Figure 6B**; Galizia et al., 1997; Stetter et al., 2001; Sandoz et al., 2003). The two VPCs (heptanal and linalool) induced calcium responses in ROIs within the area of OGs (**Figures 6B,D**). Response intensity to 1% heptanal in ROIs 6, 7, and 8 was significantly stronger compared to response intensities induced by 1% linalool (**Figure 6D**, Post-hoc Tukey's test, ROI 6: *p* = 0.05; ROI 7: *p* = 0.02; ROI 8: *p* = 0.005), while
<figure><img src="image_47.png" /></figure>

**FIGURE 4** | VPCs activate general odorant ORNs. (A) Typical examples of responses of Stem-ORNs from different sensilla to the set of VPCs. Single sensillum recordings show the presence of several spike sizes in most sensilla and generally excitatory responses in one of the ORNs of each sensillum. Scale: vertical bar = 1 mV; horizontal bar = 1 s. The vertical gray bar indicates the stimulus (200 ms). (B) Response-profiles of ORNs sampled on the antennal stem to seven VPCs and the pheromone. Only one of the

ORNs responded to the sex pheromone. Each line presents the responses of a single ORN (n = 26); each column shows the responses of the different neurons to one odorant. The diameter of circles is proportional to the intensity of the response quantified as the absolute maximum firing rate reached during 1 s following stimulus onset. Phe, pheromone; Hep, heptanal; Z3Ac, (Z)-3-Hexenyl acetate; Hxal, hexanal; Lin, linalool; Oct, octanal; PhenOH, 2-phenylethanol; Pin, α-pinene; MO, mineral oil control.

<figure><img src="image_48.png" /></figure>

**FIGURE 5** | Typical recordings of the firing activity of *Spodoptera littoralis* Phe-ORNs stimulated with pheromone or VPCs. ORNs identified as pheromone receptor neurons from their position within long trichoid sensilla of the male antenna and their strong response to 100 ng Z9,E11-14:Ac (upper recording) did neither respond to 0.1% heptanal (middle trace) nor to 1% heptanal (lower trace). Scale: vertical bar = 1 mV; horizontal bar = 1 s. The vertical gray bar indicates the stimulus (200 ms).

linalool did not induce significantly stronger responses in any of the observed ROIs compared to heptanal (Figures 6B,D). The pheromone did not induce significant responses in the area of ordinary glomeruli (Figure 6D). In agreement with data obtained from our single sensillum recordings, not only stimulations with

100 ng sex pheromone, but also with 1% heptanal and 1% linalool evoked calcium responses in the pheromone-specific MGC of the AL (Figures 6B,C). Statistical analysis of the activity of 3 ROIs within the MGC area revealed that overall response intensity was not different between the 3 ROIs. Pooled data of the 3 ROIs within the MGC were not significantly different between heptanal-, linalool-, and pheromone-induced calcium signals [One-Way ANOVA, $F_{(2, 15)} = 2.53, p = 0.11$, Figure 6C, n = 6].

## Heptanal Activates MGC Neurons in the Antennal Lobe

We recorded intracellularly from 35 MGC neurons with clear responses to the pheromone. Twenty-five of the recorded neurons showed an excitatory response followed by an inhibitory phase to 100 ng pheromone (type A neurons, Figure 7A) and 10 neurons showed a purely excitatory response (type B neurons, Figure 7B). Although more than half of the neurons responded to 1% heptanal with the same pattern as to the pheromone (Figure 7, and neurons 1 to 5 in Figure 8), responses to heptanal were more variable than to the pheromone (Figure 8). For both concentrations of heptanal, purely inhibitory responses or an initial inhibitory phase before an excitatory response appeared in addition to the excitatory responses to the pheromone (Figure 8). When stimulated with 0.1% heptanal, more than half of the neurons responded with pure inhibition (e.g., neurons 3, 5, 6, 7 in Figure 8) or not at all (e.g., neurons 2, 8, 9 in Figure 8). Also the evolution of response patterns from 0.1 to 1% heptanal was highly variable (Figure 8).
<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /></figure>

<figure><img src="image_51.png" /></figure>

**FIGURE 6** | Heptanal and pheromone evoke calcium responses in the AL. (A) Odor induced *in vivo*-calcium imaging signals obtained in a male stimulated with heptanal, linalool, pheromone, pure hexane, and pure mineral oil. All maps are scaled to the same minimum/maximum. Eight ROIs were defined to cover the areas of OG (ROI1 to ROI8). Due to the large size of the MGC, three ROIs were defined within the MGC (MGC1 to MGC3). (B) Time course of

<figure><img src="image_52.png" /></figure>

odor-evoked calcium activity in the MGC and OG4 in the isomorphic glomeruli indicated in (A). (C) Mean (± SEM) calcium response intensity in the MGC and (D) in OG1 to OG8 induced by heptanal (n = 6), linalool (n = 6), and the pheromone (n = 6), hexane (n = 4) and mineral oil (n = 4). The gray bar in (B) indicates the stimulus (200 ms). Hep, Heptanal; Lin, linalool; Phe, pheromone; MO, mineral oil; Hex, hexane; OG, ordinary glomeruli.

To compare responses between the three stimuli quantitatively, we pooled all neurons displaying an excitatory phase and compared maximum firing rates and latencies statistically (Figure 9). Maximum firing rates in MGC neurons were significantly higher in response to the pheromone than to 0.1% heptanal (Wilcoxon signed rank test for paired data, V = 625, p = 5.821⁻¹⁰) and to 1% heptanal (V = 535, p = 1.51⁻⁵). Maximum firing rates in response to 1% heptanal were significantly higher than responses to control (V = 561, P = 1.454⁻⁵) but not to 0.1% heptanal (V = 295, p = 0.972). Response latencies were also statistically different between the three stimuli (χ² = 65.2 on 2 degrees of freedom, p = 7.11⁻¹⁵). But latencies in response to the pheromone were not only shorter, but also less variable than in response to heptanal (Figure 9). The

Kaplan-Meier estimator curves for latency illustrate the larger spreading of response latencies to heptanal, especially to the lower concentration and the large proportion of non-responding neurons (Figure 9).

## Heptanal Does not Trigger Complete Upwind Flight

Male moths were generally very active in the wind tunnel, even in the absence of an olfactory stimulus as shown by the high percentage of activation (78%) and take off (56%) in control tests (Table 2). However, none of these active moths reached the upwind end of the tunnel in control experiments. A significantly higher percentage of males were taking off (96.0%, χ² = 19.73 p < 0.001) and performed a sustained flight (92.0%, χ² = 19.42
<figure><img src="image_53.png" /><figcaption>**FIGURE 7** | MGC neurons respond to the pheromone and heptanal. Examples of recordings of a biphasic (A) and a monophasic (B) neuron, responding similarly to the pheromone and heptanal. Note the responses in</figcaption></figure>

A to solvent controls, probably of mechanosensory origin (Jarriault et al., 2009). The gray bars indicate the stimulus (200 ms). Vertical scale A: 10 mV, B: 20 mV; horizontal scale 1 s.

<table><caption>**TABLE 2** | Flight responses of virgin male *A. ipsilon* to heptanal and the pheromone in a wind tunnel.</caption><thead><tr><th>Stimulus</th><th>Number of males</th><th>Activation</th><th>Taking off</th><th>Partial flight</th><th>Source flight</th></tr></thead><tbody><tr><td>Pheromone</td><td>50</td><td>100</td><td>96.0</td><td>92.0</td><td>34.0</td></tr><tr><td>0.1% heptanal</td><td>26</td><td>84.6</td><td>76.9</td><td>61.5</td><td>0.0</td></tr><tr><td>1% heptanal</td><td>24</td><td>91.7</td><td>79.2</td><td>75.0</td><td>0.0</td></tr><tr><td>Control</td><td>50</td><td>78.0</td><td>56.0</td><td>50.0</td><td>0.0</td></tr></tbody></table>

Data are the percentages of four behavioral items carried out within 90 s.

$p < 0.001$), reaching the half-length of the tunnel before the end of the test (34%, $\chi^2 = 48.52$ $p < 0.001$) in presence of the pheromone compared to the control stimulation. Males took off significantly earlier in response to the pheromone (median time for take off = 21.5 s), compared to heptanal 0.1 and 1% or control (59.0, 53.0, and 56.0 s, respectively; $\chi^2 = 48.7$ on three degrees of freedom, $p = 1.51^{-10}$). Male *A. ipsilon* arrived close to the source only in presence of the pheromone. There was no statistical difference between heptanal at 0.1% and control stimulation for all items. In turn significantly more males took off (79.2%; $\chi^2 = 19.73$, $p < 0.001$) and performed a partial flight (75.0%; $\chi^2 = 19.42$, $p < 0.001$) when stimulated with heptanal at 1%, compared to controls (Table 2).

In presence of the pheromone, the distribution map of moths in the wind tunnel revealed a strong density of male presence in the longitudinal axis downwind to the pheromone source (Figure 10). In turn, in presence of both concentrations of heptanal, males did not fly upwind. Control tracks showed a preference of male moths for the front side revealing a possible un-controlled heterogeneity in the room environment but no preference for the longitudinal median axis.

## Discussion

### Phe-ORNs are Sensitive to Volatile Plant Compounds

Using complementary approaches, we show that heptanal and other plant odorants activate the pheromone-specific pathway in male *A. ipsilon*. Still, heptanal was less efficient than the sex pheromone—dose response curves were shifted toward higher concentrations—thus behaving as a partial agonist at the detection level. Although we used a three-component pheromone blend for stimulation, we suppose that we primarily investigated the detection of plant odorants within Phe-ORNs tuned to the major pheromone component Z7-12:Ac, because these ORNs represent more than 90% of all pheromone-detecting neurons on the antennal branches, which we recorded from Renou et al. (1996). Insect olfactory receptors (ORs) involved in the detection of compounds having high biological relevance to the insect's biology show generally narrow tuning ranges (Wang et al., 2010) confering high selectivity to ORNs that express them. Among ORNs, Phe-ORNs are thus considered as specialists, narrowly tuned to individual pheromone constituents and not responding to general odorants (Masson and Mustaparta, 1990). However, some exceptions to this rule are known: receptor neurons tuned to the main pheromone component, codlemone, in the antennae of male *Cydia pomonella* respond also to pear ester, ethyl (E,Z)-2,4-decadienoate (De Cristofaro et al., 2004). In this particular example, the structural resemblance between codlemone and pear ester, could account for the capacity of this compound to excite Phe-ORNs. The OR for codlemone has not yet been found but curiously, CpomOR3, later identified as a specific receptor for pear ester (Bengtsson et al., 2014) and having a close relationship with moth pheromone receptors, did not bind components of the codling moth pheromone blend. In turn, responses of Phe-ORNs to short chain alcohols or
<figure><img src="image_54.png" /><figcaption>**FIGURE 8** | Heptanal evokes more variable responses in MGC neurons than the pheromone. Superimposed raster and frequency plots of responses of MGC neurons illustrating the variability of responses to</figcaption></figure>

heptanal between neurons and between pheromone and the two concentrations of heptanal within neurons. Scale: vertical bar = 100 spikes, horizontal bar = 1 s. Red bar indicates the stimulus (200 ms).

monoterpenes have been mentioned in *A. segetum* and *S. exigua* whose pheromones are long-chain acetates (Hansson et al., 1989; Dickens et al., 1993). Besides these few examples of their direct activation by VPCs, Phe-ORNs' specificity can also be challenged by mixture interactions with pheromone as reported in various moth species (Rouyar et al., 2011 and references therein). However, to our knowledge, the present work is the first detailed report of a plant odorant with no chemical similarity to the molecular structure of the pheromone acting on its own as a partial agonist of a moth pheromone. Besides heptanal

several other plant volatile compounds were found to activate the firing of Phe-ORNs in *A. ipsilon*. In turn, most Phe-ORNs did not respond to α-pinene. To determine whether the aldehyde function was important for activity, we tested two other short chain aldehydes, hexanal and octanal. Both compounds did not elicit higher responses compared to the other VPCs, showing that there is no simple correlation between chemical structure and the capacity to excite Phe-ORNs. The finding that Phe-ORNs respond to higher concentrations of VPCs compared to pheromone is well in agreement with the observation that
<figure><img src="image_55.png" /></figure>

**FIGURE 9** | Quantitative analysis of MGC neuron responses to the pheromone and heptanal. Boxplot of mean maximum firing rates (Box = median with first and third quartiles, whiskers = second and fourth quartiles, circles = outliers) and Kaplan-Meier estimator curves for the latencies of MGC neuron (n = 35) responses. MGC neurons respond with lower maximum firing rates and longer latencies to 0.1 and 1% heptanal compared to the pheromone. Control refers to pooled data of stimulation with pure hexane and pure mineral oil. Hep, heptanal; Phe, pheromone.

OR specificity is generally dose dependent, an increase in the concentration of the odorants broadening the response spectra (Hallem and Carlson, 2006). Although we cannot exclude that *A. ipsilon* Phe-ORNs might express multiple OR types, we hypothesize that pheromone-binding ORs are activated by high doses of heptanal in this species in analogy to what has been found for interactions between pheromones and plant odors at the molecular level: Modifications of pheromone responses by certain plant volatiles have been shown to be dependent on a pheromone-specific OR in *Heliothis virescens* (Pregitzer et al., 2012). It should be noted that in the field VPC concentrations needed to activate Phe-ORN might be reached only very close to their plant sources.

Contrasting with this ability of heptanal, and to lesser extent linalool, to activate pheromone receptors in *A. ipsilon*, both compounds did not activate the Phe-ORNs tuned to the main pheromone component in *S. littoralis*. On the contrary, linalool has been shown to be an antagonist of pheromone detection in the latter species (Party et al., 2009). Such differences between moth species support the hypothesis that activation of heptanal might not be an unspecific pharmacological effect. Differences between the two moth species might result from binding affinity in the pheromone ORs for heptanal, correlated to the different chemical structure of their pheromone ligands. Alternatively, this cross-sensitivity of *A. ipsilon* Phe-ORNs, but not those of *S. littoralis*, to pheromone and heptanal could reflect a specific adaptation of male *A. ipsilon* due to the ecological advantage for them to detect plant odorants attractive to sexually mature females (Landolt and Phillips, 1997).

## MGC Neurons are Sensitive to Volatile Plant Compounds

Calcium imaging at the AL level showed strong calcium responses to heptanal, and to a lesser degree to linalool in the MGC, showing that the activation of Phe-ORNs by VPCs produces a strong input in the pheromone-specialized areas of the olfactory centers. Interestingly, the size of the activated area in the MGC and the number of activated OGs was larger at the high concentration (1%), compared to the low concentration (0.1%) suggesting a broadening of responses at high stimulus concentration, probably due to additional responses from ORNs which are less tuned to the ligand.

Almost all MGC neurons responding to 100 ng of the pheromone were also activated by 1% heptanal. However, maximum firing rates were significantly lower with 1% heptanal and the response patterns were much more variable than those to the pheromone and in many cases included an initial inhibition before the excitatory response. These results are in accordance with earlier studies in *A. ipsilon*, where both stimuli elicited responses, although tested only at lower doses (Jarriault et al., 2009; Barrozo et al., 2011; Chaffiol et al., 2012). Activation of MGC neurons by VPCs at behaviorally active doses has been also reported in several other moth species: *S. littoralis* (Anton and Hansson, 1995), *Manduca sexta* (Reisenman et al., 2008), *C. pomonella* (Trona et al., 2010), and *Cydia molesta* (Varela et al., 2011). This across-pathway stimulation has so far essentially been explained to originate from the AL network linking ordinary glomeruli to the MGC by local interneurons (LNs) and thus allowing indirect input of VPC information to the MGC via interglomerular excitation and/or inhibition and allowing interactions between different odorants at the central nervous level (Lei and Vickers, 2008). Our new data in *A. ipsilon* show now that VPC activation within the MGC is probably a combination of direct activation of Phe-ORNs by VPCs directly transmitted to the MGC and indirect activation through the network activity of local interneurons, themselves receiving VPC-ORN input within OGs. In those MGC neurons, which respond in the same way to the pheromone and heptanal, it is likely that a direct activation of Phe-ORNs by VPCs is dominant. The more variable response patterns to heptanal and specifically the initial inhibition in the heptanal responses in the remaining MGC neurons not occurring for the pheromone, indicates that input from VPC neurons might activate the inhibitory LN network of the AL. In the future we will need to test the contribution of the AL network to global MGC input and individual MGC neuron responses to volatile plant components by different experimental approaches, using for example blockers of GABA or histamine, the main local interneuron transmitters (Galizia and Szyszka, 2008).

## Behavioral Significance of Responses to VPCs in the Pheromone Subsystem

Since heptanal activated Phe-ORNs and central neurons in the MGC it was interesting to investigate the behavioral consequences. Moths' upwind flight responses to floral volatiles that signal for nectar sources are well established (Riffell et al., 2013). Male *A. ipsilon* were found to perform upwind flights not only to a linden flower extract in a wind tunnel (Barrozo et al.,
<figure><img src="image_56.png" /></figure>

**FIGURE 10** | Males perform upwind flight to pheromone but not to heptanal in a wind tunnel. Occupancy maps present side views of the summated tracks of male *A. ipsilon* in the wind tunnel. *n* = 37 tracks (100 ng pheromone); *n* = 21 (0.1% heptanal), *n* = 18 (1% heptanal), *n* = 25 (control). The arrow at the right of each diagram indicates the

position of the stimulus source and the direction of the wind. The white dot indicates the point of male departure. Scales are given in meters. To draw the map, the summated numbers of recordings of a male presence within a 1 cm square were calculated from the coordinates of 3D tracks and color-coded.

2010a), but also in response to 100 µg of heptanal disposed on a filter paper (Deisig et al., 2012). Looking only at male flight scores in the wind tunnel does not reveal if an odor is perceived as a sexual or a feeding signal. We undertook here a more precise comparison of male flight behavior in the wind tunnel in presence of pheromone or heptanal with the same stimulation device as in physiological experiments. Male moths did take off in presence of heptanal and performed sustained flight at the high heptanal dose but they did not orient to the source. This absence of oriented flight toward heptanal in virgin males that responded readily to the pheromone strongly suggests that in spite of the activation of the pheromone pathway male moths did not perceive heptanal as a sexual signal. Since heptanal triggered intense activity in the MGC, the question is to determine which part of the olfactory system is responsible to operate discrimination of the pheromone from general odorants when the chemical specificity of Phe-ORNs is challenged. The convergence of a great number of ORNs on a few projection neurons and reciprocal interconnections between projection neurons through LNs do probably not facilitate the discrimination of heptanal from pheromone within the AL. However, a high degree of divergence occurs again

between AL output and mushroom bodies, where a couple of hundred projection neurons make synaptic contact with high numbers of Kenyon cells (2500 in *D. melanogaster*; up to 170 000 in the honey bee; numbers provided in Galizia and Szyszka, 2008). Both intrinsic properties of Kenyon cells, such as active dendritic conductance, high action potential thresholds (Perez-Orive et al., 2002; Demmer and Kloppenburg, 2009) and postsynaptic inhibition (Papadopoulou et al., 2011) contribute to sparse coding within the mushroom bodies. This particular circuitry described in the upper levels of the olfactory system might allow better extraction of pheromone information from the contextual odorants (essentially a background of plant odors), especially when those contextual odorants trigger some activity in the pheromone sub-system. In addition, a spatially much broader representation of heptanal within the antennal lobe compared to the pheromone might contribute to the discrimination between the two signals.

## Ecological Relevance of Heptanal Cross-Activity

Although heptanal is not rare among the volatile organic compounds emitted by plants, its role in insect chemical ecology
is relatively poorly documented. References in the literature indicate that this aldehyde is present in odor blends that have proven to be either attractive or repellent according to the species considered. Its production and release are induced for instance in maize following its infestation by leafhoppers, but the semiochemical activity on the insects has not been assessed (Oluwafemi et al., 2012). Heptanal has been reported to attract ovipositing females of the potato tuber moth, *Phtorimaea operculella* (Ma and Xiao, 2013). In turn, it is a component of synthetic blends designed as repellent for conifer infecting bark beetles (Huber et al., 2001). Blossoms of linden (*Tilia americana*) are intensively visited by adult *A. ipsilon* as a source of nectar (Wynne et al., 1991; Zhu et al., 1993). Blooming linden liberate huge amounts of volatiles among which heptanal is a major constituent so that it might be an environmental cue to *A. ipsilon* males indicating food sources and indirectly also the presence of females. This could explain that developing sensitivity to high concentrations of heptanal might be advantageous in the context of pheromone communication for males, largely compensating the drawback associated to reduced specificity of the pheromone sub-system. *S. littoralis*, on the other hand, is mainly distributed in Africa and the middle east. Adults feed on a large variety of

flowering plants, including e.g., Solanaceae, citrus, and clover. Even though heptanal is emitted in small amounts from a variety of flowering plants visited by *S. littoralis*, such as strawberry (Klatt et al., 2013), it is not known to specifically attract this species. In turn, linden trees, emitting considerable amounts of heptantal and attracting *A. ipsilon* are native throughout the temperate northern hemisphere and not present in the natural habitat of *S. littoralis*. Such ecological differences might explain the differences in sensory physiology between the two moth species.

## Acknowledgments

The authors thank Christophe Hanot for technical assistance, Corinne Chauvet and Cyril Le Corre for insect rearing, and the two referees for very helpful comments on an earlier version of the manuscript. The research was funded by grants from the French national research funding agency (ANR-11-BSV7-0026) and the Plant Health and Environment Department (SPE) of the French National Agricultural Research Institute (INRA). AR was supported by a PhD fellowship from the French Ministry of Science and Education.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Rouyar, Deisig, Dupuy, Limousin, Wycke, Renou and Anton. *This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*
# A Challenge for a Male Noctuid Moth? Discerning the Female Sex Pheromone against the Background of Plant Volatiles

Elisa Badeke, Alexander Haverkamp, Bill S. Hansson and Silke Sachse *

Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Jena, Germany

## OPEN ACCESS

**Edited by:**
Sylvia Anton,
Institut National de la Recherche
Agronomique, France

**Reviewed by:**
Michel Renou,
Institut National de la Recherche
Agronomique, France
Bente Gunnveig Berg,
Norwegian University of Science and
Technology, Norway

*Correspondence:
Silke Sachse
ssachse@ice.mpg.de

**Specialty section:**
This article was submitted to
Invertebrate Physiology,
a section of the journal
Frontiers in Physiology

**Received:** 31 January 2016

**Accepted:** 04 April 2016

**Published:** 25 April 2016

**Citation:**
Badeke E, Haverkamp A, Hansson BS and Sachse S (2016) A Challenge for a Male Noctuid Moth? Discerning the Female Sex Pheromone against the Background of Plant Volatiles. *Front. Physiol.* 7:143.
doi: 10.3389/fphys.2016.00143

Finding a partner is an essential task for members of all species. Like many insects, females of the noctuid moth *Heliothis virescens* release chemical cues consisting of a species-specific pheromone blend to attract conspecific males. While tracking these blends, male moths are also continuously confronted with a wide range of other odor molecules, many of which are plant volatiles. Therefore, we analyzed how background plant odors influence the degree of male moth attraction to pheromones. In order to mimic a natural situation, we tracked pheromone-guided behavior when males were presented with the headspaces of each of two host plants in addition to the female pheromone blend. Since volatile emissions are also dependent on the physiological state of the plant, we compared pheromone attraction in the background of both damaged and intact plants. Surprisingly, our results show that a natural odor bouquet does not influence flight behavior at all, although previous studies had shown a suppressive effect at the sensory level. We also chose different concentrations of single plant-emitted volatiles, which have previously been shown to be neurophysiologically relevant, and compared their influence on pheromone attraction. We observed that pheromone attraction in male moths was significantly impaired in a concentration-dependent manner when single plant volatiles were added. Finally, we quantified the amounts of volatile emission in our experiments using gas chromatography. Notably, when the natural emissions of host plants were compared with those of the tested single plant compounds, we found that host plants do not release volatiles at concentrations that impact pheromone-guided flight behavior of the moth. Hence, our results lead to the conclusion that pheromone-plant interactions in *Heliothis virescens* might be an effect of stimulation with supra-natural plant odor concentrations, whereas under more natural conditions the olfactory system of the male moth appears to be well adapted to follow the female pheromone plume without interference from plant-emitted odors.

**Keywords:** *Heliothis virescens*, pheromone-guided flight behavior, plant volatiles, wind tunnel, GC-MS
## INTRODUCTION

Odors present in the environment provide information that is crucial for insect survival and reproduction. Most insects use these olfactory cues for finding food, identifying suitable oviposition sites and communicating with their mates. Volatiles that are emitted by plants represent major cues with which an insect detects suitable host plants (Visser, 1986; Bruce et al., 2005), while pheromones are used for intraspecific identification and communication. Lepidoptera males, for example, are able to detect conspecific females releasing a species-specific pheromone blend. In the heliothine moth *Heliothis virescens* (Lepidoptera, Noctuidae), it has been shown that females produce a complex blend of up to seven components in their pheromone glands (Roelofs et al., 1974; Tumlinson et al., 1975; Klun et al., 1979; Pope et al., 1982). Wind tunnel and field experiments have shown that the behavioral activity of this pheromone blend depends highly on the ratio of its individual components (Vetter and Baker, 1983; Ramaswamy and Roush, 1986; Vickers et al., 1991). The pheromone blend is detected by specialized olfactory sensory neurons (OSNs) housed in sensilla trichoidea on the male antenna (Almaas and Mustaparta, 1990, 1991; Berg et al., 1995; Vickers et al., 2001). These OSNs send their axons to the antennal lobe (AL), which represents the primary olfactory processing neuropil, consisting of an array of olfactory glomeruli. Sex pheromone information is processed in a male-specific part of the AL (Hansson and Anton, 2000), the macroglomerular complex (MGC), which in male *Heliothis virescens* comprises four glomeruli (Christensen and Hildebrand, 1987; Hansson et al., 1992, 1995; Vickers and Baker, 1996; Berg et al., 1998; Vickers et al., 1998). The remaining, so-called ordinary, glomeruli process the information of all other odorants including plant and fruit volatiles (Galizia et al., 2000; Hillier and Vickers, 2007). This segregation of the olfactory pathway is partially maintained in the higher brain centers, such as the lateral horn (Zhao et al., 2014).

*Heliothis virescens* is a pest species, and feeds on many plants and crops such as cotton, tomato, soybean, tobacco and chickpea (Fitt, 1989; Cunningham and Zalucki, 2014). Several studies have shown that the olfactory system of both males and females is able to detect and process many volatiles emitted by these host plants (Loughrin et al., 1990; Tingle and Mitchell, 1992; Stranden et al., 2003; Rostelien et al., 2005; Hillier et al., 2006; Hillier and Vickers, 2007). Notably, the chemical diversity of volatile compounds found in all the floral scents investigated so far has been estimated to more than 1700 chemicals (Knudsen et al., 2006). Furthermore, the volatile composition of plants can change depending on environment and stress (reviewed by Dicke and Van Loon, 2000; Beyaert and Hilker, 2014). Damaged plants often emit different volatiles as well as different ratios of the volatile composition compared to undamaged plants. Considering this enormous diversity of chemical compounds, finding a sexual partner in such a complex environment is a big challenge for male moths. They have to detect minute amounts of the conspecific female pheromone blend against a constant background of many other odors. Although pheromone compounds are processed in a separate part of the olfactory system, it has been shown in several moth species that plant volatiles can influence pheromone

detection and vice versa (Chaffiol et al., 2014; Deisig et al., 2014). Interestingly, plant compounds can even enhance the detection of pheromone components. For example, in the corn earworm *Helicoverpa zea*, simultaneous application of plant odorants with the major sex pheromone component of the moth increases the firing rate of pheromone-responsive OSNs in males, although those neurons do not respond to stimulation with plant odorants separately (Ochieng et al., 2002). Moreover, in beetles (Nakamuta et al., 1997) and many lepidopteran species (Dickens et al., 1993; Light et al., 1993; Reddy and Guerrero, 2000; Deng et al., 2004; Namiki et al., 2008; Schmidt-Büsser et al., 2009; Gurba and Guerin, 2015) the behavioral response is also increased when plant compounds are combined with the corresponding pheromone components. In contrast, a variety of studies demonstrated that pheromone detection can also be inhibited by interactions with plant odorants (Den Otter et al., 1978; Kaissling and Bestmann, 1989; Pophof and Van Der Goes Van Naters, 2002; Party et al., 2009, 2013; Hillier and Vickers, 2011; Chaffiol et al., 2012; Deisig et al., 2012; Pregitzer et al., 2012; Hatano et al., 2015). Hatano et al. (2015) showed this inhibitory effect even at the behavioral level. These contradictory findings give raise to the question whether the olfactory background is modulating the intraspecific communication of insects. Indeed, in *Heliothis virescens*, certain plant-emitted volatiles reduce the detection of Z11-16:Ald, the major sex pheromone component, at the level of the pheromone receptor HR13 (Pregitzer et al., 2012). Single sensillum recordings of Z11-16:Ald-tuned OSNs concur with this inhibitory effect (Hillier and Vickers, 2011). Moreover, in the same study, a suppressive effect for OSNs being tuned to the minor component Z9-14:Ald could be demonstrated. However, whether these effects at the sensory level are maintained throughout the olfactory system and thus may affect male moth behavior is unknown. We therefore analyzed whether a background of plant volatiles influences pheromone-guided behavior in *Heliothis virescens* using wind tunnel experiments. We analyzed the impact of complete and naturally occurring odor blends as well as of individual plant volatiles at different concentrations. Furthermore, we quantified the volatile emissions of all stimuli using gas chromatography analysis. Surprisingly, we observed pheromone-plant interactions only at high and supra-natural odor concentrations. We therefore conclude that pheromone-plant interactions in *Heliothis virescens* might not occur under natural conditions and that male moths are able to detect their conspecific female against a complex background of plant volatiles.

## MATERIALS AND METHODS

### Insect Rearing

We obtained *Heliothis virescens* from the Department of Entomology in the Max Planck Institute of Chemical Ecology in Jena. Moths originated from Clemson University in Clemson, South Carolina. These were maintained at the institute for several generations, where they were reared as follows: Eggs of *H. virescens* were gained from single pair matings in 0.5 l cups. In order to minimize inbreeding depression, females and
males of different families were chosen. A mesh on top of the mating cups allowed the females to oviposit their eggs. Larvae were subsequently maintained in 10-cm Petri dishes containing artificial pinto bean diet (Burton, 1979). They were separated at second instar. After eclosion, about 15–20 males of the same age were segregated into 30 × 30 × 30 cm rearing cages. A 10% sucrose solution was provided *ad libitum*. Animals were kept at 60% rel. humidity and at 23–25°C under a 16:8 h light-dark cycle. The light level during scotophase was 0.4 lux. 2- to 6-day-old virgin male moths were used for behavioral experiments.

## Plant Material

In order to use the headspaces of whole plants for volatile collection and behavioral experiments, cotton (*Gossypium hirsutum*) and tomato plants (*Solanum lycopersicum*) were grown individually in 1-liter pots in the greenhouse at 23–25°C and 50–70% rel. humidity under a 16:8 h light-dark cycle. After the beginning of their elongation stage and until the experiments were performed, plants were transferred to a climate chamber providing 22–25°C and 60–70% relative humidity. They were watered daily with 100 ml tap water supplemented with 0.12

g\*ml-1 fertilizer. For the experimental approach undamaged or damaged plants were taken. In order to damage plants, four to five third- and fourth-instar larvae of *H. virescens* were allowed to feed on the plant before the behavioral assay was conducted. Larvae were removed from the plants after 24 h.

## Behavioral Approach

### Wind Tunnel

Insects were tested in a 220 × 90 × 90 cm Plexiglas wind tunnel (Figure 1A) under infrared and red light conditions with a white light supply of 0.4 lux. A purified, humidified and tempered airflow of 0.27 m/s was blown through the wind tunnel, providing 23°C and 60–70% relative humidity.

### Stimulus Device

For synthetic odorants the odor plume was created by connecting separately two 50 ml glass bottles via *Teflon* tubing to the stimulus outlet on a stick 55 cm long (Figure 1). The distance to the upwind end of the wind tunnel was 23 cm. Pumps, which sucked the ambient air through a charcoal filter for cleaning, generated a stimulus flow of 0.48–0.50 l/min through the tubing leaving

<figure><img src="image_57.png" /><figcaption>**FIGURE 1** | The wind tunnel system. (A) Schematic representation of the wind tunnel system including the stimulus device. The ceiling and the floor were covered by green dots in order to provide a pattern for the insects to orient on. Arrows indicate the air stream. An air flow is transported via pumps through the stimulus bottles and released by the stimulus outlet. The pheromone-loaded air is pulsed beforehand at 10 Hz by using a cross-valve. phe = pheromone (A'). Magnification of the stimulus outlet (dashed square). The dotted orange line represents the middle nozzle, which emits a pulsed pheromone stimulus, while the blue lines highlight the constant plant odor flow released by the surrounding nozzles. (B) Two representative flights of different males (yellow, red) toward the pheromone blend. (C) The percentage of male *H. virescens* attempting flight behavior, achieving upwind flight and making source contact is similar for constant (N = 25) and pulsed (N = 27) pheromone stimulation (*p* > 0.05, Fisher's exact test). (C',C'') Visualization of the constant and pulsed odor plume using a photoionization detector (PID) at 110 cm distance from the stimulus outlet. Dotted and continuous lines below the curves represent the odor stimulation. Fewer volatiles can be detected in the pulsed (C') odor plume than in the constant plume (C''). PID measurements: U\_pulsed = 1.81 V, U\_constant = 4.77 V.</figcaption></figure>
the bottles. In each of the bottles, a rubber septum loaded with the test odorants was inserted. The bottle, which contained the pheromone blend, was additionally connected to an *Arduino* microprocessor-controlled cross-valve before being released by the middle nozzle (ID 1 mm) of the stimulus outlet (**Figure 1A'**). Thus, pulsed stimulations of 10 Hz could be achieved. It has been shown that pulsed stimulation affects the flight behavior of male moths in the wind tunnel (Vickers and Baker, 1994). We therefore compared pheromone attraction to either a constant pheromone plume or a pulsed pheromone plume using an optimal pulse frequency of 10 Hz (**Figure 1C**). The second stimulus bottle was connected to the circular arranged nozzles (ID 0.5 mm each). For experiments using the headspaces of different plants, a glass cylinder (101) containing a plant was connected to the system instead of to the second stimulus bottle. A Teflon disc on the bottom with a central opening separated green plant material from soil and roots. Compressed, charcoal-filtered air with a flow of 1 l/min was inserted into the cylinder. Only 0.48–0.54 l/min of the cylinder headspace was sucked via a pump into to the wind tunnel.

## Animal Handling

All experiments were performed 2–7 h during scotophase, when pheromone responsiveness is highest (Shorey and Gaston, 1965). At least 1 h before testing, male moths were transferred individually into Ø 7 × 10 cm mesh tubes and placed in a small room near the wind tunnel that had the same conditions. Active moths were chosen for testing. At the beginning of each experiment, a mesh tube containing a moth was inserted into a releasing device in the odor plume at the downwind end of the wind tunnel. The releasing device was controlled via the microprocessor in order to open the cage automatically 2 min after placing the moth in the mesh tube. Flight behavior was subsequently recorded for 5 min. After the first source contact within this time interval, males' behavior was tracked for 2 min.

## 3-D Video Tracking

During the experiment the releasing device, all wind tunnel conditions and the flight paths were computer-controlled from a separate room. In order to observe odor-guided flight behavior, we used a custom-built video tracking system. Four cameras (C615, Logitech, Newark, NJ, USA, 800 × 600 pixels, 0.3 cm² pixel size), which were located at the side and on the top of the wind tunnel, recorded the flight path of each moth. By using a background subtraction algorithm, the position of each moth was calculated at a rate of 10 Hz. A fifth camera, which was attached to the upwind end of the wind tunnel, allowed the recording of males' behavior close to the odor source.

## Determining Optimal Conditions for the Wind Tunnel

In order to monitor pheromone attraction and to study whether it is influenced by background volatiles, we started to find the best conditions for the bioassay. A stimulus device was used to create a point source emitting either a pulsed or a constantly emitted pheromone blend of *Heliothis virescens* together with a surrounding odor plume of a constant solvent release (**Figure 1A'**). When stimulating with the conspecific

pheromone blend, male moths showed clear pheromone-guided upwind flight behavior. This behavior can be characterized by locking on to the pheromone plume followed by upwind flight, zigzagging, casting behavior and, finally, contact with the source (**Figure 1B**). When placed in a constant or a pulsed pheromone plume, all moths started their flight within 5 min. (**Figure 1C**). Hence, the type of stimulation influenced neither the percentage of moths attempting upwind flight nor the number of source contacts. In order to compare the pulsed and constant odor plume structure, we measured the presence of volatiles using a photoionization detector (PID). The results showed that the probability that a moth hits a volatile in a pulsed odor plume is less than the probability that a moth hits one in a constant plume (**Figures 1C', C''**). However, although the odor plume structure was different, pheromone attraction was similar for both odor applications. We chose pulsed pheromone stimulation for all subsequent experiments in our study.

## Odorants

All synthetic odorants tested were commercially available and acquired from Sigma (http://www sigma-aldrich.com), Bedoukian (http://www bedoukian.com) or pherobank (http://www pherobank.com). They were obtained in the highest available purity. β-caryophyllene (CAS 87-44-5, purity > 98.5%), racemic linalool (CAS 78-70-6, purity > 97%) and (Z)-3-hexen-1-ol (CAS 928-96-1, purity > 98%) are well-described plant compounds. They are detectable by male and female *Heliothis virescens* (Paré, 1997; De Moraes et al., 2001; Skiri et al., 2004; Rostelien et al., 2005; Hillier and Vickers, 2007), and they have been used previously in studies investigating plant-pheromone interaction on *H. virescens* (Dickens et al., 1993; Hillier and Vickers, 2011; Pregitzer et al., 2012).

A synthetic pheromone blend, which contained the seven components, (Z)-11-hexadecenal (Z11-16:Ald, CAS 53939-28-9, purity 97-98%), (Z)-9-tetradecenal (Z9-14:Ald, CAS 53939-27-8, purity > 93%), tetradecenal (14:Ald, purity > 98%), hexadecanal (16:Ald, CAS 629-80-1, purity > 93%), (Z)-7-hexadecenal (Z7-16:Ald, CAS 56797-40-1, > 95%), (Z)-9-hexadecenal (Z9-16:Ald, CAS 56219-04-6, purity > 90%) and (Z)-11-hexadecenol (Z11-16:OH, CAS 56683-54-6, purity > 98%), was used (Roelofs et al., 1974; Tumlinson et al., 1975; Klun et al., 1979). We prepared the blend relative to Z11-16:Ald (100%) and added 5% Z9-14:Ald, 5% 14:Ald, 10% 16:Ald, 2% Z7-16:Ald, 2% Z9-16:Ald and 1% Z11-16:OH of the compounds (Pope et al., 1982), in order to test the sexual attraction of *H. virescens* males toward their conspecific pheromone blend. Tetradecenal was synthesized from commercially available tetradecanol (Sigma) by the Research Group Mass Spectrometry/Proteomics in the Max Planck Institute of Chemical Ecology in Jena.

Both synthetic plant compounds and the pheromone blend consisted additionally of 1.25% of the antioxidant 3,5-Di-tert-butyl-4-hydroxytoluene (BHT, CAS 128-37-0, purity ≥ 99%, Sigma). They were subsequently pipetted on individual rubber septa (Thomas Scientific, http://www.thomassci.com/). Before being used, rubber septa were cleaned with hexane (CAS 110-54-3, Sigma), which was furthermore used as a solvent for all odorants. For plant components, concentrations between 30 and
300 µg/µl were used. The pheromone blend was adjusted to Z11-16:Ald with a concentration of 300 µg/µl. We always indicate the final concentration for each rubber septum.

## Volatile Collection, Analysis, and Quantification

In order to quantify the actual amount of volatiles being released by the rubber septum and pumped through the tubing into the wind tunnel, we used polydimethylsiloxane (PDMS) tubes (OD 2.3 mm, Reichelt Chemietechnik, http://www.rct-online.de). By introducing the PDMS tubes for 2 h into the odor flow close to the stimulus outlet, we could collect volatiles during testing. Volatiles being released by plants were collected with the same approach. Samples were stored at -20°C until use. All samples were examined on an Agilent 7890A gas chromatograph (Agilent Technologies, CA) running in splitless mode and being connected to an Agilent 5975C mass spectrometer (electron impact mode, 70 eV, ion source: 230°C, quadrupole: 150°C, mass scan range: 33-350 u). We used a nonpolar column (HP-5 MS UI, 30 m length, 0.25 mm ID, 0.25 µm film thickness, J and W Scientific) under constant helium flow of 1.1 ml/min. The GC oven was programmed to hold 40°C for 3 min, to increase the temperature at 5°C/min to 200°C, then to increase temperature at 20°C/min to 260°C. The maximum temperature was held for 10 min. For identification, mass spectra were compared with Kovats retention time indices to reference compounds or to those published by the National Institute of Standards and Technologies (NIST, version 2.0). Retention times for all compounds were determined by using standards. Quantifications of emission rates were subsequently calculated based on the comparison of the internal standard of 10 ng/µl 1-Bromohexane (CAS 111-25-1, purity 98 %, Sigma) and peak area of single compounds.

## Data Analysis and Statistics

Microsoft Excel, Gnu R, custom-written Matlab scripts (MATLAB version- Mathworks, USA) and Adobe Illustrator were used in order to analyze and plot all data. Statistics were performed with the software Gnu R and GraphPad Instat. We calculated the emission rate of volatiles being released within 1 h for each compound based on the internal standard by using the commercial software GC ChemStation (Agilent Technologies) and Microsoft Excel.

In order to investigate the attractiveness of volatiles in the wind tunnel, we calculated the percentage of moths (1) starting to fly, (2) achieving upwind flight, and (3) contacting the source for each group of odor stimulation. An odor plume was called attractive if moths reached and contacted the odor source. In order to investigate pheromone-plant interaction, we further examined the average number of source contacts per male out of all individual moths within a group for the test period. We quantified the number of contacts for another 2 min after the first contact. Males without contacts were counted as zeros. For statistical analysis, the group tested with the pheromone blend alone was always taken as a control group. The percentage of moth within a test group was compared to the pheromone group by means of Fisher's exact test, with a Bonferroni-Holm

correction. The number of source contacts was evaluated using the Kruskal-Wallis test with Dunn's multiple comparisons test. The pheromone-guided flight behavior of each attracted male was analyzed in more detail by calculating the percentage of relative abundance of flight angles in y- and z-direction and the average upwind speed within an 80 cm distance from the stimulus outlet. Both angles and upwind speed were measured with an interval of 10 Hz. The last 10 cm of the track were excluded due to the fact that it could not be tracked reliably in all moths. Animals which performed zigzagging and casting movements possessed flight angles greater than zero degrees. Angles around zero degrees exhibit straight upwind movement. Upwind speed (cm/s) is the speed of an animal relative to the odor source. Positive values indicate upwind movement, negative values downwind movement, while values around zero indicate cross-wind movement. The Kruskal-Wallis test and Dunn's multiple comparisons test were used for statistics.

# RESULTS

## Host Plant Headspaces Did Not Affect Pheromone Attraction

Since it has been shown that different plant-emitted volatiles affect detection of the major sex pheromone component Z11-16:Ald in male *Heliothis virescens* at the physiological level (Hillier and Vickers, 2011; Pregitzer et al., 2012), we tested whether behavioral performance is similarly affected. In order to provide a naturally occurring odor source, we used the headspaces of two host plants, tomato and cotton, to examine their influence on pheromone-guided flight behavior (Figure 2A, left panel). First, we tested the headspaces of the two host plants alone. We observed that both the tomato headspace as well as the cotton headspace induced only very low degrees of upwind flight and source contact (N = 17-20, upwind 1-3 moths, contact 0-1 moth; data not shown). We next applied the conspecific pheromone blend to each plant headspace simultaneously. The results reveal that a pheromone plume with a background of either tomato (Figure 2A, middle panel) or cotton headspace (Figure 2A, right panel) showed similar attractiveness as compared to a pheromone blend with no plant odor background. The number of source contacts was also not affected (Figure 2C, Table 1). Hence the pheromone-guided flight was not influenced by the presence of a naturally occurring plant odor blend.

It has been shown that larval damage influences the composition and/or the emission rate of plant volatiles (De Moraes et al., 1998). The attraction of female moths to a damaged plant headspace depends on the amount of herbivore-induced plant volatiles (Späthe et al., 2013). In order to examine whether herbivore damage significantly influences pheromone detection, we let four to five larvae feed on both host plants and tested the attractiveness of the induced headspace in our wind tunnel. Only three moths at most moved upwind when placed in a damaged tomato or cotton odor plume, but none of them contacted the source (N = 15-17; data not shown). When a damaged tomato plant headspace was
<figure><img src="image_58.png" /><figcaption>A</figcaption></figure>

<figure><img src="image_59.png" /><figcaption>B</figcaption></figure>

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<figure><img src="image_62.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_63.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_64.png" /></figure>

**FIGURE 2** | Influence of host plant headspaces on pheromone-guided flight behavior. (A) Percentage of moths attempting flight behavior, achieving upwind flight and making source contact, when simultaneously stimulated with the pheromone blend and a tomato (middle panel) or cotton (right panel) plant headspace. Plants were intact or damaged by larvae. The left panel highlights the changes in the odor stimulation device. The headspace of the plants was sucked via a pump through the wind tunnel. The pulsed pheromone stimulation was implemented as described in **Figure 1**. There was no significant difference in pheromone attraction when insects were stimulated simultaneously with undamaged or damaged tomato or cotton headspaces compared to pheromone stimulation alone (p > 0.05, Fisher's exact test, Bonferroni-Holm correction). (B) Percentage of moths attempting flight behavior, achieving upwind flight and making source contact, when simultaneously stimulated with the pheromone blend and the synthetic odorants β-caryophyllene (left panel), (Z)-3-hexenol (middle panel) or linalool (right panel) each in two different concentrations (100 and 300 µg/µl). While β-caryophyllene did not affect pheromone-guided flight behavior, high concentrations of (Z)-3-hexenol decreased the amount of moths contacting the source. A similar tendency was observed for linalool. Asterisks represent significant differences (p < 0.05, Fisher's exact test with Bonferroni-Holm correction). The bracket indicates significant differences without Bonferroni-Holm correction (p = 0.0426). (C) Number of contacts per individual moth for all tested males from (A). No differences in the number of contacts when different plant headspaces were used (p > 0.05, the Kruskal-Wallis test, Dunn's multiple comparisons test). (D) Number of contacts per individual moth for all tested males from (B). Moths had significantly fewer contacts when high dosages of (Z)-3-hexenol or linalool were applied to the septa than when they were not (p < 0.05, the Kruskal-Wallis test, Dunn's multiple comparisons test). car, β-caryophyllene; cot, cotton; lin, linalool; phe/phero, pheromone; tom, tomato; Z3-hex, (Z)-3-hexenol.
<table><caption>TABLE 1 | Effect of intact and damaged tomato and cotton plants on pheromone-guided flight behavior.</caption><thead><tr><th>Stim. 1</th><th>Stim. 2</th><th>Sample size</th><th>Flight [%]</th><th>Upwind [%]</th><th>Source contact [%]</th><th>Upwind speed [cm/s] ± SD</th><th>Number of contacts ± SD</th></tr></thead><tbody><tr><td>–</td><td>Phero</td><td>27</td><td>96.3</td><td>66.7</td><td>51.9</td><td>25.8 ± 29.6</td><td>1.19 ± 1.71</td></tr><tr><td>Tom</td><td>Phero</td><td>30</td><td>93.3</td><td>70</td><td>63.3</td><td>22.7 ± 27.3</td><td>1.37 ± 1.56</td></tr><tr><td>Tom damaged</td><td>Phero</td><td>20</td><td>80</td><td>50</td><td>40</td><td>24 ± 22.7</td><td>0.6 ± 0.99</td></tr><tr><td>–</td><td>Phero</td><td>24</td><td>91.7</td><td>70.8</td><td>50</td><td>24 ± 22.7</td><td>1.75 ± 2.67</td></tr><tr><td>Cot</td><td>Phero</td><td>23</td><td>95.7</td><td>60.9</td><td>47.8</td><td>30.5 ± 23</td><td>1 ± 1.38</td></tr><tr><td>Cot damaged</td><td>Phero</td><td>28</td><td>92.9</td><td>67.9</td><td>42.9</td><td>25.1 ± 33.6</td><td>0.75 ± 1.17</td></tr></tbody></table>

Number of tested individuals and the percentages of male moths, for the experiments shown in **Figures 2A,C**, which started their flight, showed upwind movement and had source contact; also their upwind speed. The last column represents the number of contacts for all tested males. Stimulus (stim.) 1 and 2 together form the odor plume. Odorants of stimulus 1 were emitted continuously, while stimulus 2 (pheromone) was pulsed. A (–) in stimulus 1 represents the use of a solvent instead of an odorant. SD, standard deviation.

no significant differences within a column to the solvent-pheromone stimulation (p > 0.05, Fisher's exact test with Bonferroni-Holm correction; Number of contacts and upwind speed: Kruskal-Wallis with Dunn's multiple comparisons test).

cot, cotton; phero, pheromone; tom, tomato.

presented together with the pheromone blend, we observed that 12% fewer individuals reached the source as compared to the pure pheromone blend (Figure 2A, middle panel, Table 1). However, this decrease was not significantly different from the response to the pheromone blend without background. Likewise, moths flying in a pheromone plume did not contact the source significantly more often (Figure 2C, Table 1). The same applies for the cotton headspace: larval damage in cotton plants affected neither pheromone-guided flight behavior nor the number of odor source contacts (Figure 2A, right panel, Figure 2C, Table 1).

In order to analyze pheromone-guided flight behavior in more detail, we dissected the flight mechanism. We asked how males manoeuer in response to an odor source and if their flight patterns are influenced by different odor plumes. We therefore examined the flight angles of attracted individuals as well as individual's upwind speed (Figure 3). In Figure 3A the relative abundance of flight angles for male moths in a pure pheromone plume and a tomato-pheromone plume are representative examples. Independent of odor stimulation, the most abundant flight angles of male *Heliothis virescens* were around zero degrees, indicating a relatively straight upwind flight. Angles up to ±180° represented additional zigzagging and casting behavior. Analysis of the upwind speed of the attracted insects resulted in values around 27 cm/s regardless of the odors present in the plume (Figure 3B, Table 1). In summary, we observed that neither the number of source contacts nor the flight pattern was affected when a complete plant headspace was applied simultaneously with the pheromone blend.

## Certain Plant-Emitted Volatiles Reduced Pheromone Attraction

Interestingly, we did not observe the significant reduction in pheromone-elicted flight behavior suggested in previous studies. These however reported plant-pheromone interactions in moths using single plant-related compounds instead of complete headspaces. In order to analyze whether single plant volatiles could affect the pheromone response, we tested the three plant-emitted volatiles, β-caryophyllene, (Z)3-hexenol and linalool, each in two different concentrations based on the study by Pregitzer et al. (2012). As a side note, all of these compounds are

up-regulated in larval-damaged plants (Paré, 1997; De Moraes et al., 1998, 2001; Stranden et al., 2003; Morawo and Fadamiro, 2014).

In comparison to pure pheromone stimulation, both concentrations of β-caryophyllene in combination with the pheromone did not reduce the attractiveness of the pheromone (Figures 2B,D, left panels, Table 2); moreover, β-caryophyllene alone did not attract any male moths, independent of its concentration (tested concentrations: 60, 100, 200, 300 µg/µl; N = 16–19; data not shown). Likewise, male moths did not respond to (Z)3-hexenol alone (100, 300 µg/µl; N = 16; data not shown). However, adding 300 µg/µl of (Z)3-hexenol to the pheromone plume significantly reduced the number of individuals (by 33%) and their frequency contacting the source, although equal percentages displayed upwind flight (Figures 2B,D, middle panels, Table 2). Interestingly, lowering the concentration of (Z)3-hexenol (i.e., 100 µg/µl) did not significantly decrease the moths' response to pheromones. We observed a similar dose-dependent effect when insects were stimulated simultaneously with the pheromone blend and the odor linalool. Linalool alone at concentrations of 30, 60, 100, 200, or 300 µg did not attract males at all and resulted in no upwind flights (N = 15–30; data not shown). However, adding the highest concentration of linalool to the pheromone plume resulted in 22% fewer individuals contacting the source compared to the number contacting the source when only the pheromone was used (Figures 2B,D, right panels, Table 2). This effect was also concentration-dependent, since we did not observe any reduction in pheromone-guided flight behavior when we reduced the concentration of linalool.

We observed similar flight angles in a pheromone plume compared to those in a plume consisting of the pheromone blend and β-caryophyllene, (Z)3-hexenol or linalool, as shown for β-caryophyllene and (Z)3-hexenol (Figure 3A, Table 2). The distribution histograms represent the cumulated azimuth and zenith angles of all male moths contacting the stimulus outlet. Since we measured less animals for (Z)3-hexenol, the histogram shows less cumulated angles. However, the distribution of the angles is similar to those of the other stimuli. Most angles were around zero degrees. Furthermore, males moved upwind to the
## Concentration Quantification of Synthetic Odorants vs. Plant-Released Volatiles

Our experiments show that only the application of linalool and (Z)3-hexenol at high concentration reduced the attractiveness of male *Heliothis virescens* to the pheromone, while the headspace of host plants did not show any influence. In order to analyze whether the difference is just a matter of odor concentration, we quantified the actual amount of the synthetic odorants released by the rubber septa (Figures 4A,B). While 3 ng of the major sex pheromone component Z11-16:Ald could be quantified via PDMS tubes, the plant components, β-caryophyllene, (Z)3-hexenol and linalool, were measured in much higher amounts. The amount of β-caryophyllene was 3.5-fold higher than the amount of (Z)3-hexenol, while the linalool release was 5-fold higher than the amount of (Z)3-hexenol. When pipetting three times the concentration on a rubber septum, both plant volatiles resulted in doubled emission rates, while only 1.5-fold of linalool was detected.

Are the synthetic single odor quantities that reduced the attractiveness of pheromones in our wind tunnel studies similar to those released by intact and damaged tomato and cotton plants? To find out, we quantified the release rate of β-caryophyllene, (Z)3-hexenol and linalool in damaged and undamaged host plants (Figure 4C). Larval damage in tomato and cotton plants led to an increase of β-caryophyllene (Figure 4C, left panel), and β-caryophyllene was released in quantities comparable to those of the synthetic odorant. However, β-caryophyllene had no effect on pheromone-guided flight behavior in male moths (Figure 2A). In contrast, (Z)3-hexenol and linalool could not be detected in undamaged plants or were found in only low quantities in damaged plants (Figure 4C, middle and right panels). This discrepancy shows that the concentrations of (Z)3-hexenol and linalool that reduced pheromone attraction (Figure 2A) were much higher than the natural emission of an entire plant. Hence, odorants that influence pheromone-guided behavior in male moths are not emitted in comparable quantities by plants. We therefore conclude that plant-pheromone interactions in *Heliothis virescens* most likely occur only under laboratory conditions, where very high odor concentrations are used.

## DISCUSSION

We show that pheromone-plant odor interactions occur at the behavioral level of male *Heliothis virescens*, similar to those previously observed at the sensory level (Hillier and Vickers, 2011; Pregitzer et al., 2012). However, we also show that these interactions occur only at supra-natural concentrations of certain plant-emitted volatiles. Our findings therefore suggest that, in a natural environment, male moths are able to detect their conspecific female against a complex background of plant volatiles without negative effects on their pheromone-directed flight behavior.

Certain plant-related volatiles interfere with the detection of the major sex pheromone component of *Heliothis virescens* at the pheromone receptor HR13 and thereby reduce the

<figure><img src="image_65.png" /></figure>

**FIGURE 3** | Influence of individual plant volatiles on pheromone-guided flight behavior. (A) Relative abundance of flight angles being performed by male moths within the upwind section of the wind tunnel close to the source, when pheromones alone (orange), a pheromone and the headspace of a damaged tomato plant (dark green), pheromone and β-caryophyllene or (Z)3-hexenol (dark blue, both with 300 µg), were used for stimulation. The distribution histograms cumulate all tracks of moths contacting the stimulus outlet. Flight angles are shown as cumulated azimuth and zenith angles. Most of the time insects facing upwind in the odor plume they perceived performed zigzagging and casting activities. (B) Males moths showed similar upwind speeds ($p > 0.05$, the Kruskal-Wallis test, Dunn's multiple comparisons test), when stimulated with the pheromone alone (orange, $N = 41$), or the pheromone in combination with 100 µg ($N_{car} = 12$, $N_{Z3-hex} = 8$, $N_{lin} = 9$) or 300 µg ($N_{car} = 13$, $N_{Z3-hex} = 3$, $N_{lin} = 14$) of artificial odorants (blue), or with the headspaces of cotton ($N_{intact} = 8$, $N_{dam} = 13$), or tomato plants ($N_{intact} = 10$, $N_{dam} = 7$) (green). blk, blank; car, β-caryophyllene; cot, cotton; lin, linalool; phe/phero, pheromone; tom, tomato; Z3-hex, (Z)3-hexenol.

source with an average 25 cm/s (Figure 3B). In summary, adding certain plant-related compounds at high concentration to the pheromone plume reduced the pheromone-guided response in male *Heliothis virescens* but did not lead to a different flight pattern: neither the flight direction in order to approach the odor source nor the upwind speed was influenced by plant volatiles.
<table><caption>TABLE 2 | Effect of β-caryophyllene, (Z)3-hexenol and linalool on pheromone-guided flight behavior.</caption><thead><tr><th>Stim. 1</th><th>Stim. 2</th><th>Sample size</th><th>Flight [%]</th><th>Upwind [%]</th><th>Source contact [%]</th><th>Upwind speed [cm/s] ± SD</th><th>Number of contacts ± SD</th></tr></thead><tbody><tr><td>–</td><td>Phero</td><td>22</td><td>90.1</td><td>63.6</td><td>50</td><td>22.6 ± 25.5</td><td>1 ± 1.23</td></tr><tr><td>car100</td><td>Phero</td><td>26</td><td>88.5</td><td>80.8</td><td>53.8</td><td>28.9 ± 28</td><td>1.04 ± 1.22</td></tr><tr><td>car300</td><td>Phero</td><td>32</td><td>96.9</td><td>68.8</td><td>43.8</td><td>24.7 ± 33.4</td><td>1.09 ± 1.53</td></tr><tr><td>–</td><td>Phero</td><td>25</td><td>92</td><td>52</td><td>44</td><td>29.1 ± 21.8</td><td>1.16 ± 1.84</td></tr><tr><td>Z3-hex100</td><td>Phero</td><td>27</td><td>74.1</td><td>44.4</td><td>33.3</td><td>31.1 ± 28.2</td><td>1 ± 1.71</td></tr><tr><td>Z3-hex300</td><td>Phero</td><td>28</td><td>75</td><td>50</td><td>10.7*</td><td>30.2 ± 28</td><td>0.27 ± 0.93**</td></tr><tr><td>–</td><td>Phero</td><td>34</td><td>91.2</td><td>67.6</td><td>50</td><td>15.8 ± 30.2</td><td>1.32 ± 1.66</td></tr><tr><td>lin100</td><td>Phero</td><td>32</td><td>100</td><td>56.3</td><td>43.8</td><td>16.1 ± 40.4</td><td>0.94 ± 1.9</td></tr><tr><td>lin300</td><td>Phero</td><td>54</td><td>88.9</td><td>46.3</td><td>27.8 (*)</td><td>23.7 ± 34.5</td><td>0.63 ± 1.51*</td></tr></tbody></table>

Number of tested individuals and the percentages of male moths, for the experiments shown in **Figures 2B,D**, which started their flight, showed upwind movement, and had source contact; also, their upwind speed. The last column includes the number of contacts for all tested males. The stimuli were applied as described in **Table 1**. SD, standard deviation.

*Within a column indicate significant differences to the solvent-pheromone stimulation ("p < 0.05, **p < 0.01, Fisher's exact test with Bonferroni-Holm correction, (*) p < 0.05, Fisher's exact test, p > 0.025 with Bonferroni-Holm correction; Number of contacts and upwind speed: Kruskal-Wallis with Dunn's multiple comparisons test).

car, β-caryophyllene; lin, linalool; phero, pheromone; Z3-hex, (Z)3-hexenol.

response of pheromone-detecting OSNs in the MGC (Pregitzer et al., 2012). Interestingly, this interference varies for different plant compounds: linalool and (Z)3-hexanol strongly suppress the pheromone response, while other compounds, such as β-caryophyllene, do not lead to any reduction. These findings correlate well with our behavioral results from experiments using the wind tunnel: while β-caryophyllene did not influence pheromone-guided flight behavior, high concentrations of (Z)3-hexenol and linalool reduced the attractiveness of the pheromone by at least 22%. Hence our results show that the coding of pheromone-plant interactions at the sensory level corresponds to the altered behavioral responsiveness of male moths. The representation of odor-induced activity in the AL therefore allows a prediction of the behavioral outcome. Notably, a correlation between the representation of odors in the AL and the behavioral performance has already been demonstrated in several species, such as honeybees (Guerrieri et al., 2005), flies (Knaden et al., 2012) and moths (Kuebler et al., 2012).

The behavioral performance of the moth ultimately results from the odor representation in higher brain centers and is determined by the integration of different processing channels within the neuronal network. Interestingly, when the antenna of the male *Heliothis virescens* moth was stimulated with β-caryophyllene and the major sex pheromone component Z11-16:Ald, single sensillum recordings showed an enhanced spiking activity compared to the response evoked by Z11-16:Ald alone (Hillier and Vickers, 2011). In contrast, when the major pheromone component was exchanged for the minor pheromone component, Z9-14:Ald, the pheromone response was suppressed (Hillier and Vickers, 2011). Although β-caryophyllene is influencing the neuronal activity of pheromone-responsive OSNs in the periphery, we did not observe any effect of this plant volatile onto the pheromone-guided flight behavior in our windtunnel experiments. Since β-caryophyllene modulates the major and minor pheromone pathways in opposing directions (Hillier and Vickers, 2011), the detection of the whole pheromone blend, including the two compounds, Z11-16:Ald and Z9-14:Ald, might not be modulated in the end.

Moreover, in the same physiological study (Hillier and Vickers, 2011), both major and minor sex pheromone components, when blended with the plant volatile linalool or (Z)3-hexenol, elicited reduced spiking activity in the corresponding pheromone-responsive OSNs. Likewise, in our wind tunnel assay, when high concentrations of the two plant compounds were added, the attractiveness of the complete pheromone blend was decreased, which resulted in reduced pheromone-guided flight behavior.

The three compounds that we used in our study are not the only volatiles being detected in plant headspaces. It would therefore be interesting to know if and how other plant volatiles, when added to the pheromone blend, influence the pheromone-guided behavior of a moth. This is of particular interest, since it has been observed that some of these green leaf volatiles increase the number of males caught in pheromone traps (Dickens et al., 1993). However, when we tested the whole headspaces of cotton and tomato plants, independently of their physiological condition, we did not find any influence on pheromone-guided flight behavior.

Host plants of *Heliothis virescens* that are damaged by larval feeding release volatiles such as β-caryophyllene, (Z)3-hexenol and linalool (e.g., Paré, 1997; De Moraes et al., 1998; Morawo and Fadamiro, 2014). All of these were used in our study. When we quantified the natural emission of these compounds, we realized that, except for β-caryophyllene, these odorants occur in only very low concentrations in the headspace of intact or damaged cotton and tomato plants. Although volatiles are usually emitted in higher amounts during daytime than in the dark (De Moraes et al., 2001), male moths are active in the scotophase. Therefore, they will encounter low concentrations of plant volatiles. When the results from the wind tunnel and GC-MS experiments were combined, we observed that unnaturally high concentrations of (Z)3-hexenol and linalool reduced the heliothine moths' attraction to pheromones, while a lower dose, which represents the more natural situation, did not affect the attraction.
A

<figure><img src="image_66.png" /></figure>

B

<figure><img src="image_67.png" /></figure>

C

<figure><img src="image_68.png" /></figure>

**FIGURE 4** | Emission rates of volatiles from rubber septa and entire plants. (A) GC-MS example traces showing the relative abundance of the synthetic odorants β-caryophyllene (upper panel), (Z)-3-hexenol (middle panel) or linalool (lower panel). 300 µg/µl of the odorant were loaded on a rubber septum, and the headspace was collected for 2 h with PDMS tubes. Arrows indicate the corresponding odor peak in the headspace. The asterisk represents the peak of a siloxane, which is a constituent of PDMS tubes. (B) Amounts of volatiles, which were released by rubber septa loaded with the pheromone blend (N<sub>300µg</sub> = 2), β-caryophyllene (N<sub>100µg</sub> = 3, N<sub>300µg</sub> = 3), (Z)-3-hexenol (N<sub>100µg</sub> = 2, N<sub>300µg</sub> = 2), or linalool (N<sub>100µg</sub> = 3, N<sub>300µg</sub> = 3). The averaged emission rates are shown as bar plots (±SEM). Bars represent odorants used in a concentration of 100 µg/µl (light blue) or 300 µg/µl (dark blue). (C) Comparison of the odor amount emitted from the rubber septa shown in (B) and the corresponding compounds in the plant headspace of intact (light green) and damaged (dark green) tomato (N<sub>intact</sub> = 2, N<sub>dam</sub> = 4) and cotton plants (N<sub>intact</sub> = 2, N<sub>dam</sub> = 4). Bars represent the averaged emission rates. Similar amounts of β-caryophyllene (left panel) were found in the odor emitted from the rubber septa and in odors released by the plants. (Z)-3-hexenol (middle panel) and linalool (right panel) released from the plants were either not detected or occurred in low amounts that were not comparable to the amounts being released by the rubber septa. car, β-caryophyllene; cot, cotton; lin, linalool; phero, pheromone; tom, tomato; Z3-hex, (Z)-3-hexenol.

Taken together, our study underlines the importance of using
natural concentrations in order to investigate the ecological
relevance of odorants and their influence on animals' behavior.

AUTHOR CONTRIBUTIONS

EB and SS together conceived and designed the study. EB
planned and carried out all experiments. EB and SS analyzed
and interpreted the results, prepared the figures and wrote the

paper. AH helped to analyze the windtunnel data. BSH provided
intellectual and financial support. All authors critically revised
the article.

ACKNOWLEDGMENTS

We are grateful to Daniel Veit for technical support and
Pedro Gouveia for his help with the 3D tracking. The authors
would like to thank Gabriel Walther and Regina Seibt for their
contribution to the insect rearing as well as Jerrit Weißflog for the synthesis of tetradecenal. Furthermore we would like to thank Sonja Bisch-Knaden, Jürgen Krieger, David Heckel, and Sinéad O'Keeffe for comments and Emily Wheeler for editorial

assistance. This study was supported by a grant of the Deutsche Forschungsgemeinschaft (SPP 1392, SA909/3-2) to EB and SS, the Max Planck Society (BSH and SS) and the Federal Ministry of Education and Research (BMBF research grant to SS).

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Badeke, Haverkamp, Hansson and Sachse. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
# Comparative Neuroanatomy of the Lateral Accessory Lobe in the Insect Brain

Shigehiro Namiki * and Ryohei Kanzaki

Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan

The lateral accessory lobe (LAL) mediates signals from the central complex to the thoracic motor centers. The results obtained from different insects suggest that the LAL is highly relevant to the locomotion. Perhaps due to its deep location and lack of clear anatomical boundaries, few studies have focused on this brain region. Systematic data of LAL interneurons are available in the silkmoth. We here review individual neurons constituting the LAL by comparing the silkmoth and other insects. The survey through the connectivity and intrinsic organization suggests potential homology in the organization of the LAL among insects.

**Keywords:** command neuron, premotor center, ventral body, lateral lobe, descending neuron

**OPEN ACCESS**

**Edited by:**
Sylvia Anton,
Institut National de la Recherche
Agronomique, France

**Reviewed by:**
Uwe Homberg,
Philipps-Universität Marburg,
Germany
Steffen Harzsch,
University of Greifswald, Germany

**\*Correspondence:**
Shigehiro Namiki
namiki@rcast.u-tokyo.ac.jp

**Specialty section:**
This article was submitted to
Invertebrate Physiology,
a section of the journal
Frontiers in Physiology

**Received:** 20 February 2016

**Accepted:** 03 June 2016

**Published:** 23 June 2016

**Citation:**
Namiki S and Kanzaki R (2016)
Comparative Neuroanatomy of the
Lateral Accessory Lobe in the Insect
Brain. Front. Physiol. 7:244.
doi: 10.3389/fphys.2016.00244

The lateral accessory lobe (LAL) is a neuropil that is highly associated with the central complex (CX). The LAL is thought to facilitate communication between the CX and the motor centers. For example, it is proposed that the LAL receives input from the CX and selects the activity of descending output (Wolff and Strausfeld, 2015). Perhaps due to the deep location and lack of the clear anatomical boundaries, few studies have focused on the LAL. A variety of response properties have been reported in the neurons innervating the LAL in different species: flight-correlated activity in locusts (Homberg, 1994), walking-correlated activity in crickets and moths (Kanzaki et al., 1994; Zorović and Hedwig, 2013), driving backwards walking in flies (Bidaye et al., 2014). The LAL is present in all insect species reported thus far and they seem to have a common set of subdomains, suggesting the ground pattern of the LAL organization. It is not clear whether the neuronal homology is present at individual neuron level.

This paper examines the comparability of individual neurons among different species by summarizing the experimental data available for the LAL. Systematic analysis of neuronal morphology has been performed heretofore only in the silkmoth *Bombyx mori* (Mishima and Kanzaki, 1999; Iwano et al., 2010; Namiki et al., 2014). The large-scale data of neuronal morphology is available in *Drosophila* (Chiang et al., 2011; Jenett et al., 2012; Milyaev et al., 2012; Costa et al., 2013), which cover the entire brain. We compare the organization of the LAL in the silkmoth with *Drosophila* and other insects.

## ANATOMY

The CX is defined as a group of four midline neuropils: the protocerebral bridge, the fan-shaped body, the ellipsoid body, and the paired noduli (Ito et al., 2014). We essentially follow the terminology proposed by Insect Brain Nomenclature Working Group, though some other studies describe the LAL also as a part of the CX (Boyan and Reichert, 2011; Shih et al., 2015). To improve comparability among arthropod, another definition is also used: the CX as a group
of interconnected neuropils, including the central body, the protocerebral bridge, and the LAL (Richter et al., 2010). Heinze et al. (2013) introduced the term “sun compass neuropils” for the LAL, anterior optic tubercle and the members of the CX because these neuropils are highly interconnected and process compass-related stimuli, such as polarized light (Heinze et al., 2013).

The LAL is located on the lateral side of the CX in insects (Figure 1; Williams, 1975; Ito et al., 2014). The term “ventral body” is also used in the studies of Diptera. The area surrounding the CX, including the LAL, is called the lateral complex (Ito et al., 2014) or the CX accessory regions (Lin et al., 2013). Comparative neuroanatomy for the organization of the lateral complex is in progress. The LAL and the bulb, a small satellite neuropil, are classified as members of the lateral complex in *Drosophila* (Ito et al., 2014). Figure 2 shows the GABA-like immunoreactivity in the LAL and surrounding region in *Bombyx*. There is a small satellite neuropil located dorsal to the LAL, termed the median olive (Iwano et al., 2010), which shows dense immunoreactivity (Figure 2, termed bulb). This area is rarely connected with the LAL. Based on its position and its immunoreactivity, this region might correspond to the lateral triangle in the monarch butterfly (Heinze and Reppert, 2012), the bulb in *Drosophila* (Ito et al., 2014), the median olive (medial bulb) and the lateral triangle (lateral bulb) in the locust (Heinze and Homberg, 2008; Träger et al., 2008; el Jundi et al., 2014), and the lateral complex in the ant (Schmitt et al., 2015). We denote this neuropil as the bulb, according to the brain nomenclature (Ito et al., 2014; Figure 2). The structure similar to the microglomerular complex (Träger et al., 2008; Seelig and Jayaraman, 2013) is present in the bulb (Figure 2).

A small protruding region in the superior-lateral tip of the LAL is present in the fly, called the gall, and is defined as a part of the LAL (Ito et al., 2014). Similarly, a small sub-region called the anterior loblet, is present in the LAL of the monarch butterfly brain (Heinze and Reppert, 2012). The homologous region exists in *Bombyx* because of the presence of a small region with GABA-like immunoreactivity in the LAL (referred to as gall in Figure 2), and neurons with similar morphology to that of the columnar

neurons of the ellipsoid body that project to a similar location as the anterior loblet.

The anterior side of the LAL is relatively well-defined, whereas no clear anatomical boundary for the posterior side in most cases (el Jundi et al., 2009; Iwano et al., 2010; Heinze and Reppert, 2012; Ito et al., 2014; Montgomery and Ott, 2015). Practically, the posterior boundary is often defined by the antennal lobe tracts (Iwano et al., 2010; Heinze and Reppert, 2012), which are well-conserved across species (Galizia and Rössler, 2010; Ito et al., 2014). Immunoreactivity often helps to define the anatomical boundary. Figure 3 shows the serotonin-like immunoreactivity of the LAL and surrounding area (Iwano et al., 2010). The antibody-staining covers the entire LAL in *Bombyx* (Figure 3) and might be useful in demarcating the anatomical border of the LAL (Heinze and Reppert, 2012).

Whereas brain regions are usually defined by their anatomy, Chiang et al. (2011) determined brain regions based on the statistical criterion for the clustering of individual neurons, called “local processing units” (Chiang et al., 2011). Using a large-scale data set of single neuron morphology in *Drosophila*, they have identified 41 local processing units. In most cases, the structured neuropils satisfy the criteria for local processing units. The definition is based on individual neuronal morphology, and hence is more functional than the traditional definition based on anatomical landmarks. Through this process, the authors refined the anatomical boundary of the LAL, and defined the inferior dorsofrontal procerebra (IDFP; Chiang et al., 2011), which is composed of four subdomains, including the hammer body, which occupies the largest volume in the LAL, the round body, and the ventral/dorsal spindle bodies.

## FUNCTION

The function of the LAL is still incompletely understood. We introduce several examples of the experimental data in different insects, which are helpful to consider the function. Especially we focus on a population of descending neurons (DNs) that originate in the brain and project to the thoracic motor centers.

<figure><img src="image_69.png" /><figcaption>**FIGURE 1** | Organization of the silkmoth brain. Anterior and posterior views of the silkmoth brain are shown (A,B). Neuropils were segmented based on immunoreactivity of anti-synatotagmin antibody staining in whole-mount sample. Segmentation of unstructured neuropils are not precise because of the lack of clear anatomical boundaries. Images are modified from Namiki et al. (2014). D, dorsal; V, ventral.</figcaption></figure>