Olfactory combinatorial coding supports risk-reward decision making in C. elegans

Reviewer #1 (Public review):

The authors investigated the response of worms to the odorant 1-octanol (1-oct) using a combination of microfluidics-based behavioral analysis and whole-network calcium imaging. They hypothesized that 1-oct may be encoded through two simultaneous, opposing afferent pathways: a repulsive pathway driven by ASH, and an attractive pathway driven by AWC. And the ultimate chemotactic outcome is likely determined by the balance between these two pathways.

It is not surprising that 1-octanol is encoded as attractive at low concentrations and repulsive at higher concentrations. However, the novel aspect of this study is the discovery of the combinatorial coding of 1-oct in the periphery, where it serves as both an attractant and a repellent. Furthermore, the study uses this dual encoding as a model to explore the neural basis of sensory-driven behaviors at a whole-network scale in this organism. The basic conclusions of this study are well supported by the behavioral and imaging experiments, though there are certain aspects of the manuscript that would benefit from further clarification.

A key issue is that several previous studies have demonstrated a combinatorial and concentration-dependent coding of odorant sensing in the nematode peripheral nervous system. Specifically, ASH and AWC are the primary receptors for repellent and attractive responses, respectively. However, other neurons such as AWB, AWA, and ADL are also involved in the coding process. These neurons likely communicate with different interneurons to contribute to 1-oct-induced outputs. The authors' conclusion that loss of tax-4 reduces attractive responses and that osm-9 mutants reduce repulsive responses is not entirely convincing. TAX-4 is required for both AWC (an attractive neuron) and AWB (a repulsive neuron), and osm-9 is essential for ASH, ADL, and AWA (attraction-associated). Therefore, the observed effects on the attractive and repulsive responses could be more complex. Additionally, the interpretation of results involving the use of IAA to reduce the contribution of AWC at lower concentrations lacks clarity.

The authors did not observe any increased correlation between motor command interneurons and sensory neurons, which is consistent with the absence of a consistent relationship between state transitions and 1-oct application. Furthermore, they did not observe significant entrainment of AIB activity with the 2.2 mM 1-oct application. This might be due to the animals being anesthetized with 1 mM tetramisole hydrochloride, which could affect neural activity and/or feedback from locomotion.

Comments on revisions:

The authors have addressed all my previously raised concerns.

Reviewer #2 (Public review):

Summary:

The authors used whole-network imaging to identify sensory neurons that responded to the repellant 1-octanol. While several olfactory neurons responded to the initial onset of odor pulses, two neurons consistently responded to all the pulses, ASH and AWC. ASH typically activates in response to repellants, and AWC typically activates in response to the removal of attractants. However in this case, AWC activated in response to the removal of 1-octanol, which was unexpected because 1-octanol is a harmful repellant to the worm. The authors further investigated this phenomenon by testing different concentrations of 1-octanol in a chemotaxis assay, and found that at lower (less harmful) concentrations the odor is actually an attractant, but becomes repulsive at higher concentrations. The amplitude of the ASH response appeared to be modulated by concentration, but this was not true for AWC. The authors propose a model where the behavioral response of the worm is the result of integrating these two opposing drives, where repulsion is a result of the increased ASH activity over-riding the positive drive from AWC. The authors further tested this theory by testing mutants that ablated the AWC response (tax-4 or AWC::HisCl) or ASH response (osm-9 or ASH::HisCl). The chemo-silencing (HisCl) and tax-4 experiments were consistent with their hypothesis, while the osm-9 mutation had a limited impact on chemotaxis behavior, highlighting the potential role of osm-9-independent signaling in ASH in response to 1-octanol. While the interneuron(s) that integrate these signals to influence behavior were not identified, the authors did find that increasing concentrations of 1-octanol did increase the likelihood of AVA activity, a neuron which drives reversals (and hence, behavioral repulsion).

Strengths:

This was simple and elegant work that identified specific neurons of interest which generated a hypothesis, which was further tested with mutants that altered neuronal activity. The authors performed both neuronal imaging and behavioral experiments to verify their claims.

Weaknesses:

The authors note that other sensory neurons likely contribute to 1-octanol chemotaxis. Given the NeuroPAL data, it would have been nice to identify these other neurons as well. However, the reviewer is aware that this is tangential to the primary focus of this study.

Reviewer #3 (Public review):

Summary:

This work describes how two chemosensory neurons in C. elegans drive opposite behaviors in response to a volatile cue. Because they have different concentration dependencies, this leads to different behavioral responses (attraction at low concentration and repulsion at high concentration). It has been known that many odorants that are attractive at low concentrations are aversive at high concentrations, and the implicated neurons (at least AWC for attraction and ASH for repulsion) have been well established. None the less, by studying behavior and neural responses in a common context (odor pulses, as opposed to gradients) this provides a clear picture of how these sensory neurons may guide the dose dependent response by separately modulating odor entry and odor exit behaviors.

Strengths:

(1) This work provides good evidence that worms are attracted to low concentrations and repelled by high concentrations of 1-oct. Calcium imaging also makes it clear that dose-dependence of this response is stronger for ASH than AWC.

(2) This work presents calcium imaging and behavior with the same stimulus (sudden pulses in volatile odor concentration), while previous studies often focus on using neuronal responses to pulses to understand navigation of gentle gradients.

Weaknesses:

(1) As a whole it is not clear precisely how important AWC is (compared to other cells) for the attractive response (as the authors correctly acknowledge).

(2) The evidence that AIB minus AVA contains relevant information is weak. It appears the entrainment index in Fig. 6H for AIB-AVA could easily be explained by the negative entrainment between AVA and the stimulus (along with no effect or role for AIB). This is suggested by the similar p-values and similar distribution of random EIs (stretched and mirrored) between the first and last rows of this figure.

(3) The model in Figure 7 would be strengthened if it was demonstrated that IAA is attractive when worms are saturated in a 1/10^4 concentration. Panel 7G (and ref. 39) indicate that 10^-4 IAA activates ASH, which would suggest a different explanation for the change from attraction to repulsion in 7C.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

…other neurons such as AWB, AWA, and ADL are also involved in the coding process. These neurons likely communicate with different interneurons to contribute to 1-octinduced outputs. The authors' conclusion that loss of tax-4 reduces attractive responses and that osm-9 mutants reduce repulsive responses is not entirely convincing. TAX-4 is required for both AWC (an attractive neuron) and AWB (a repulsive neuron), and osm-9 is essential for ASH, ADL, and AWA (attraction-associated). Therefore, the observed effects on the attractive and repulsive responses could be more complex. Additionally, the interpretation of results involving the use of IAA to reduce the contribution of AWC at lower concentrations lacks clarity. A more effective approach might involve using transgenically expressed miniSOG or histamine (HisCl1) to specifically inhibit AWC neurons.

We agree that the sensory inputs into chemotactic behavior are likely more complex, involving other neurons besides ASH and AWC. We now explicitly discuss possibility in the Discussion (lines 449-467).

We have also utilized transgenically expressed HisCl1 in ASH and AWC to address this concern. Crucially, we observe that some of the effects of the broad mutations are reproduced by inactivating ASH and AWC. This finding validates our overall hypothesis that sensory-driven behavior is a balance of simultaneous afferent inputs of opposite valence AND shows that ASH and AWC are involved as expected. We are currently performing a comprehensive analysis of sensory inputs into locomotory decision making, including the neurons mentioned in the Reviewer’s comment.

We also agree that using IAA is not a very clean way to inactivate AWC. The AWC HisCl results referenced above should alleviate this concern. However, the IAA result does put our findings into a broader context of multi-sensory integration which demonstrates the potential usefulness and selective advantages of the dual-input coding architecture that we are hypothesizing.

Furthermore, they did not observe significant entrainment of AIB activity with the 2.2 mM 1-oct application. This might be due to the animals being anesthetized with 1 mM tetramisole hydrochloride, which could affect neural activity and/or feedback from locomotion. 

We now mention these caveats “It is possible that immobilization and anesthetization may be affecting AIB responses to sensory activity and/or proprioceptive feedback from locomotion. However, it is also possible that motor feedback from RIM was obscuring the sensory signal.” Line 357

It is unclear whether subtracting AVA activity from AIB activity provides a valid measure. Similarly, it is unclear how the behavioral data from freely moving worms compares to the whole-network calcium imaging results obtained from immobilized worms.

Ray and Gordus 2025 (Current Biology 35:5534) recently demonstrated that AIB activity can be modeled as the additive convolution of AVA, AWC, and AIA activity, lending validity to our subtractive approach. In their study, AVA was the major contributor, but addition of AWC and AIA signals (i.e. sensory inputs) resulted in a significant greater accuracy. We have now mentioned their work in the manuscript (line 363) “To address this possibility, we subtracted AVA activity, representing the motor state, from the AIB activity (AVA closely mirrors RIM), based on the observation that AIB activity can be modeled as the sum of convolutions of motor activity and sensory activity.” (lines 360-363)

The relationship between network activity in freely moving worms and immobilized worms has been explored by Kato et al 2015 (Cell 163:656-669); we now refer to this work on line 131 “These transitions are related to network state changes which drive spontaneous reversals during foraging in freely moving worms. Immobilization and anesthetization, necessary for confocal imaging, distort certain aspects of these motor command sequences compared to freely moving worms executing the motor commands and receiving proprioceptive feedback. However, the intrinsic motor programs remain intact under these conditions.” (lines 131-136)

Reviewer #2 (Public review):

tax-4, but not osm-9 mutants were used in chemotaxis and imaging assays. It would have been nice to have osm-9 results as well for these assays. The mutants are not specific to AWC and ASH. Cell-specific rescue of these neurons would have strengthened the proposed model.

Osm-9 data are now included in the chemotaxis assays (Fig. 4E).

Cell-specific HisCl data are now included for ASH and AWC (Fig. 4F, G, 5D), confirming our proposed model.

Limited tax-4 data were included in the imaging (Fig. 6), but unfortunately, NeuroPAL imaging in tax-4 has proven to be technically difficult. NeuroPAL images in the tax-4 background appear different, perhaps because of developmental effects on gene expression due to the lack of sensory input (recall that the NeuroPAL color scheme is based on the relative expression levels of 40+ neuronal promoters). Inactivation of individual sensory neurons using HisCl1 or other transgenes may be the simpler approach.

The Results and Discussion have been significantly rewritten to incorporate these new data

We are currently working on a comprehensive study of the sensory inputs into locomotory decision making in the context of chemosensation, which we expect to reveal roles of other neurons besides ASH and AWC and provide a fuller picture of the complexities of this system.

Reviewer #3 (Public review):

(1) It is not clear precisely how important AWC is (compared to other cells) for the attractive response, though the presence of odor-off behavior implicates it. This could be resolved by looking at additional mutants (tax-4 is broad).

We have addressed this concern using transgenically-expressed HisCl1 which has demonstrated a clear role for AWC in overall chemotaxis and locomotory decision making upon encountering the 1-oct/buffer interface in microfluidics devices (Fig. 4F, G, 5D).

(2) Relatedly, dose-dependent chemotaxis data (Figure 4C, D) should be provided for osm-9 animals to get a sense of the degree to which dose-dependence is explained by ASH.

Osm-9 data now included (Fig. 4E)

The Results and Discussion have been significantly rewritten to incorporate these new data

(3) Figure 4A, B should include average traces with errors, as there are several ways the responses can vary across conditions.

Averaged traces with error bars now shown (Fig. 4A, B)

(4) The data in Figure 6G does not appear to have error bars.

Error bars now shown for 6G

Also, it would help to include a more conventional demonstration of AIB responding to stimuli (e.g. averaging stimulus-aligned responses as a percent of the fluorescence value at stimulus onset to perform the desired subtraction).

Fig. 6G top panel shows the stimulus-aligned responses of AIB with no subtraction performed. The 6 sequential stimulations are shown as a single continuous trace, consistent with the experimental protocol utilized. Averaging was performed across the 12 individuals of the sample set. However, we did not calculate the average of responses within a dataset (i.e. first plus second plus third etc.) to avoid obscuring any sensitization/desensitization that might be occurring with multiple stimuli.

Subtracted calcium traces are harder to interpret. As it stands, the evidence that sensory signals are persisting in AIB and not being shunted by proprioceptive feedback in microfluidic devices is not strong.

Addressing the point about proprioceptive feedback in microfluidics devices, the following sentence was added in the Results section: “Immobilization distorts certain aspects of these motor command sequences compared to freely moving worms executing the motor commands and receiving proprioceptive feedback, but the intrinsic motor programs remain intact.” (lines 131-136).

To add context for the AIB-AVA subtraction, Ray and Gordus 2025 (Current Biology 35:5534) recently demonstrated that AIB activity can be modeled as the additive convolution of AVA, AWC, and AIA activity, lending validity to our subtractive approach. In their study, AVA was the major contributor, but addition of AWC and AIA signals (i.e. sensory inputs) resulted in a significant greater accuracy. We have now mentioned their work in the manuscript: “To address this possibility, we subtracted AVA activity, representing the motor state, from the AIB activity (AVA closely mirrors RIM), based on the observation that AIB activity can be modeled as the sum of convolutions of motor activity and sensory activity.” (lines 360-363)

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Figure 1: The number of replicates (n) is missing.

In Fig. 1D, only a single trial is shown as a representative example rather than averages, which would necessitate error bars. The Results and Figure Legend text has been updated to clarify this, and the average CI is now included in the first Results section (lines 111, 976)

Figure 4: The sample size (n = 3-5) is relatively small, which may limit the statistical power.

Sample size was increased to 5 for all data points shown on the new graph (Fig. 4E and noted in the figure legend (line 1019)

Figure 4: The 0.22 mM concentration significantly affects both AWC and ASH. It is also unclear whether this concentration also affects other neurons, such as AWB, ADL, and AWA.

We have not performed exhaustive analysis of other neurons in these datasets. These analyses are difficult and time consuming, so we have opted to present a dataset which supports our hypothesis that multiple afferent pathways of opposite valence act in a balanced way to drive chemotaxis. We are currently performing an in-depth analysis of the sensory inputs into the circuit, which we expect to present in a future study

Reviewer #2 (Recommendations for the authors):

The tax-4 and osm-9 experiments are great, but I recommend clarifying that tax-4 and osm-9 are expressed in other neurons as well. The text gives the impression that these mutants are specific to AWC and ASH, respectively. The authors should note these caveats.

This concern is thoroughly addressed in the descriptions and rationale presented for the use of ASH and AWC HisCl strains.

The authors should also provide the code used to interpret their results.

Code will be provided through Zenodo.org

Reviewer #3 (Recommendations for the authors):

It would help to clarify (early on) the degree to which you are attributing responses to particular cells (e.g. AWC) as opposed to a class of cells with AWC as an example.

This concern is thoroughly addressed in the descriptions and rationale presented for the use of ASH and AWC HisCl strains.

The NeuroPAL imaging and analysis (especially Figures 3D, E) is a bit distracting and appears non-essential. If possible, it would help to combine Figures 2 and 3 with a focus on panels 3ABC to streamline the narrative.

We would prefer to keep the present format so the reader can appreciate the power of the whole-brain approach for analyzing network activity and behavioral outputs in the context of sensory-motor responses. Specifically, our insight that attractive and aversive afferent inputs were activated simultaneously was wholly dependent on this approach. Otherwise, there would have been little to no reason for examining AWC activity at aversive 1-oct concentrations, which was essentially the foundation of the study.

To highlight this point, we have added the following sentence in the Discussion: “This novel insight highlights the value of the whole-brain approach (enabled by the NeuroPAL system) for studying the network dynamics underlying sensory driven behaviors.” Lines 431-433.