Introduction

Animals use past experiences to modify future behavior, and this behavioral plasticity is essential for an animal’s fitness and survival. Previous studies suggest that small sets of neurons, such as those in the mushroom bodies of flies^1,2,3,4 or in the human hippocampus^5,6,7,8,9, can play pivotal roles in integrating information from prior experiences and using it to influence subsequent behavior. Identification of these key neurons represents a crucial step towards understanding both the neural and molecular mechanisms responsible for encoding experiences and modulating behavior.

Despite its small nervous system, the nematode Caenorhabditis elegans displays a robust capacity to modify its behavior based on experience^{10,11,12,13,14}. One example of this is learned pathogen avoidance. Worms are initially attracted to pathogenic bacteria such as Pseudomonas aeruginosa (strain PA14). However, after several hours of exposure, animals associate infection with PA14 specific cues and change their behavior to avoid these bacteria^{15,16,17,18,19,20}. This behavioral transition reduces the chance of infection and thus increases the worm’s odds of survival^18,21,22,23. How does the worm’s nervous system encode this pathogen experience and use this information to change behavior? Previous studies uncovered a range of sensory cues that govern the worm’s behavioral transition, ranging from chemosensory cues^24,25,26,27 and the worm’s innate immune response^16,28 to mechanosensory inputs arising from the bacteria’s biofilm^29,30,31. Olfactory stimuli have also been implicated in learned pathogen avoidance through serotonin modulation^18,16. The underlying neural circuits that encode information about past experiences and their dynamics remain unknown despite this. One strategy to find such neurons would be to carry out a comprehensive screen of the nervous system. However, such a screen is technically challenging due to the lack of neuron subtype-specific promoters and the necessity of systematic timed perturbations of neural activity during pathogen exposure to disrupt the animal’s ability to learn to avoid pathogens in the future.

To discover neurons whose activity patterns governed experience-dependent pathogen avoidance, we performed a comprehensive screen of the nervous system using a compressed sensing based approach³² that overcomes these technical challenges. Through this screen, we discovered that the behavioral transition from attraction to avoidance of PA14 involves transitions in two subbehaviors - exit from the bacterial lawn and lawn re-entry. We found that distinct sets of neurons regulate each subbehavior. In particular, the aversion to re-entry is controlled by two neuronal types, AIY and SIA, which encode pathogen exposure through sustained downregulation of neural activity. Our data further indicated that AIY regulates PA14 specific aversion, and drives stalling at the edge of the pathogenic lawn. Finally, we used the identification of neural substrates of learned pathogen avoidance to explore how long-term changes in neural dynamics might be mediated by neuropeptide signaling between critical neurons that govern transitions in this behavior.

Results section

Prior pathogen exposure alters two behavioral modules to generate learned pathogen avoidance

Infection of the nematode C. elegans by pathogenic bacteria elicits learned pathogen avoidance behavior. Naïve animals dwell and feed on lawns of pathogenic P. aeruginosa (PA14), but after infection, animals avoid P. aeruginosa lawns^15,29. To obtain high-resolution measurements of learned pathogen avoidance behavior, we monitored animals that had been placed on a lawn of pathogenic PA14 for 18 hours. We counted the fraction of animals that remained on the lawn over time (Fig. 1A) at 30min intervals. Within eight hours, half of the animals changed their foraging behavior and left the lawn of pathogenic bacteria. By contrast, nearly all the animals placed on a control lawn of non-pathogenic E. coli (OP50) bacteria remained on the lawn during 18 hours of monitoring (Fig. 1A).

Fig. 1.

The fractional occupancy of worms on a bacterial lawn is affected in principle by two processes - lawn exit and lawn re-entry. We determined how PA14 exposure affected each of these processes. After four hours in the presence of pathogen, the rate constant for the lawn exit (exits per animal per hour) increased dramatically from zero and reached a plateau of roughly 1 exit event per worm-hour after ten hours (Fig. 1B). We observed that worms that left lawns of pathogen after 10-16 hours of exposure repeatedly attempted to return to the lawn but stalled for long periods upon contact with the lawn edge (Fig. 1C). We quantified this re-entry defect by measuring the latency to re-entry, i.e., the delay between first contact with the lawn and re-entry of the animal into the lawn. Animals that had been exposed to pathogen for short periods rapidly re-entered the lawn after an exit with latencies of only a few seconds (Fig. 1D). After five hours of exposure to pathogen, the latency of re-entry began to increase and after twelve hours of exposure the mean latency to re-entry was 36.7±3.5 mins(Fig. 1D). Increases in lawn-leaving rates and latency to re-entry were correlated and coincided with the observed evacuation of the lawn of pathogenic bacteria (Fig. 1D).

To demonstrate that the experience of pathogen exposure was driving the observed changes in behavior, as opposed to some change in the bacterial lawn caused by foraging animals, we exposed animals to pathogenic PA14 for 15 hours, collected those that had left the lawn, and then compared the latency of lawn entry of this cohort to the latencies of a cohort of naive animals that had not experienced pathogen. Naive worms rapidly entered a fresh lawn of pathogen whereas experienced worms displayed increased latency of entry into such fresh lawns (Fig. 1E-G). These results indicated that pathogen exposure changed the internal state of the worm to drive the observed behavioral transition from re-entering the law rapidly to stalled re-entry (Fig. 1H).

A compressed sensing based optogenetic screen to identify neurons that function in learned pathogen avoidance

We next sought to identify neurons that regulate interactions of C. elegans with lawns of pathogenic P. aeruginosa. Conventional approaches to identifying neurons required for a C. elegans behavior involve targeting individual neuron types by microablation^33,34,35 or optogenetics^36,37,38. These approaches are time-consuming and can require the generation of large numbers of transgenic lines to individually interrogate specific neuron types for their role in a given behavior.

We previously showed that an optogenetic screen for neurons that drive a behavior can be performed more efficiently using multiplexed optogenetic manipulations of neurons followed by a compressed sensing analysis to infer individual key neuron types^32,39,40. We performed a compressed sensing based screen using a panel of 29 transgenic C. elegans lines, each expressing the light-gated ion channel Archaerhodopsin-3 (Arch3) under a different promoter (Supplementary Table 1). The panel used for this study, focused on inter neurons, and covered 54 classes of interneuron, 25 classes of sensory neuron, and 8 classes of motor neuron^41,42,43. We optically inhibited neurons during the early stages of pathogen exposure when animals presumably associate pathogen-specific cues with sickness. We then monitored the subsequent dynamics of lawn leaving and re-entry. Animals were transferred to pathogenic lawns, given 1 hour to settle, and then illuminated for two hours with pulses of 525 nm green light (1sec on/off, 5mW/mm²) to inhibit neurons expressing Arch3 in that line (Fig. 2A). The behavior of these animals was then recorded for a total of 18 hours at 3min intervals (Fig. 2A). We performed the same measurements of matched controls that had not been fed the opsin cofactor all-trans retinal and were thus insensitive to photoinhibition (Fig. 2A). To quantify the effect of optogenetic inhibition on behavior, we calculated for each strain a differential retention index - the difference in the area between the temporal lawn occupancy curves of transgenic animals exposed to inhibitory light and a paired no-ATR control (Fig. 2B). Six strains showed significant changes in differential retention index after neural silencing compared to controls (Fig. 2B). Silencing neurons in these strains during the first two hours of pathogen exposure augmented lawn-leaving over the next 18 hours (Fig. S1). These results indicated the existence of a neural mechanism that inhibits the association of pathogenicity with microbe-specific cues during the early stages (first two hours) of pathogen exposure.

Fig. 2.

We next asked whether the accelerated pathogen avoidance observed upon neural silencing resulted from increased lawn exits, increased latency to re-entry, or both. Two Archaerhodopsin lines (Pdop-2::Arch3 and Pmpz-1::Arch3) showed increased exit rates, while four lines (Pflp-4::Arch3, Psams-5::Arch3, Pttx-3::Arch3, and Pnpr-4::Arch3,) did not show changes in exit rates upon inhibition (Fig. 2C, D). Neural inhibition that increased lawn exit rates dramatically increased the number of tracks outside of the lawn (Fig. 2E). We next measured the effects of the early neural silencing on lawn re-entry. We found that four lines (Pdop-2::Arch3, Pnpr-4::Arch3, Psams-5::Arch3 and Pttx-3::Arch3) showed significant increases in latency to re-entry in response to activation of Archaerhodopsin (Fig. 2F,G). For example, the inhibition of the Pnpr-4::Arch3 line over the first two hours of the experiment dramatically decreased lawn re-entry over the entire time course, resulting in worm trajectories stalling at the lawn edge (Fig. 2H). These results indicated that inhibition of neurons during an early phase of learned pathogen avoidance could cause long-term changes in aversion to pathogenic bacteria through modulating distinct behaviors.

A small set of neurons influences the encoding of the memory of pathogen exposure

We next sought to identify specific neurons that influence learned pathogen avoidance. To identify neurons that control specific behaviors (Fig. 3A), one usually thinks of perturbing N neurons of the nervous system one at a time. These measurements can be visualized as an N × N set of equations, which can be easily solved to give the relative contribution of each neuron to the phenotype (Fig. 3B). This approach requires as many measurements as the number of neurons being characterized. Utilizing a compressed sensing based approach, we can instead formulate our optogenetic screen results as an underdetermined set of equations . The matrix, M, is an incoherent measurement matrix of size 29 by 87 measurement matrix (Fig. 3C), with each row corresponding to each Archaerhodopsin line (experiments were performed on 29 lines) and each column corresponding to neural identity. The matrix element, M_ij is equal to 1 if the ith line drives expression in neuron j and is 0 otherwise. corresponds to neural weights, i.e. how much each neuron contributes to the phenotype, and is the phenotype vector. The relative contributions (weights) of 87 neuron types to a phenotype can be determined using an L1 norm from these 29 measurements.

Fig. 3.

We evaluated the phenotype vector for the two behaviors of interest by quantifying the changes to the dynamics of re-entry and exit from the lawns of pathogen. For both exit and re-entry, we calculated differences between optogenetically inhibited and non-inhibited worms for each transgenic line that showed statistically significant effects of inhibition. For lawn exit, the phenotype was the increase in the rate constant of exit due to neural inhibition. For re-entry, the phenotype was the difference between the latency in re-entry caused by neural inhibition. Lines that did not show significant behavioral changes in response to inhibition were assigned a phenotype value of zero.

We inferred neural weights from our matrix equation using Lasso regression^44,45, which allowed us to solve the underdetermined set of linear equations while simultaneously imposing sparsity constraints on the weight vector by minimizing the sum of the mean squared error and the L1 norm of the solutions, thus minimizing where λ is the sparsity parameter. Using this approach, we inferred a set of candidate neurons for lawn exit and lawn re-entry (Fig. 3D,E). We focused primarily on neurons governing lawn re-entry (Fig. 1F). For lawn re-entry, 4 key neurons were inferred over a wide range of sparsity parameters: AVK, SIA, AIY and MI. Some neurons were assigned negative weights by this analysis (suggesting that their inhibition promotes lawn re-entry). However, the contributions of these neurons decreased as the sparsity parameter increased, suggesting that these neurons were less important (Fig S2).

Compressed-sensing analysis of lawn-exit behavior identified contributions from six neuron classes to this behavior: CEPs, HSNs, RIAs, RIDs, and SIAs. Some of these neurons have previously been implicated in lawn retention or dwelling. RIAs are required for learned avoidance of Pseudomonas lawns^46,26. CEPs and HSNs are also known to promote dwelling on bacterial lawns through release of dopamine and serotonin, respectively⁴⁷. Notably, this analysis suggested that the neural circuit governing lawn-exits is distinct from the neural circuit governing re-entry.

We next performed several tests to determine the robustness of our solutions, focusing on lawn re-entry behavior. To determine whether variation in archaerhodopsin expression might affect identification of neurons that govern this behavior, we tested the solutions to corrupted versions of the measurement matrix. The four neurons AVK, SIA, AIY, and MI were robustly identified regardless of matrix corruption (Fig. S3). We next tested whether random removal of promoters would alter our solutions, i.e., whether a small subset of strains was driving the identification of neurons. Neuron identification was robust to removal of up to 5 of the 29 promoters (Fig. S4). Finally, we determine false-positive and false-negative rates for compressed sensing based inference (Fig. S5A), as well as the recovery rate and true positive rate for each of the four neurons (AVK, SIA, AIY and MI) (Fig. S5B-E). While all 4 neurons identified have high recovery rates, MI, SIA and AVK can have true recovery rates below 50%. Thus, we would expect at least one of these neurons to be a false positive. To directly test and determine the roles of the neurons implicated by compressed sensing, we next focused on measuring the activities from these neuron types in freely moving animals.

Neurons identified by compressed sensing encode the experience of pathogen exposure as a reduction in neural activity

We measured calcium signals in three neuron subtypes (AVK, SIA, and AIY) in unrestrained worms before and after exposure to pathogenic Pseudomonas. We excluded MI from this analysis because of its role as a pharyngeal motor neuron; perturbation of MI might have affected lawn re-entry by modulating bacteria ingestion⁴⁸.

To accurately measure calcium dynamics in AVK, SIA, and AIY we used a custom real-time image-stabilization microscope capable of tracking and measuring neural dynamics in freely moving worms (Fig. 4A). The microscope can track worms with 1μm precision in all three dimensions while performing rotational stabilization by tracking a marker neuron (AWC^on, Pstr-2::mKO). We demonstrated the capability of this system by imaging neural activity in freely moving C. elegans at high magnification for up to an hour without affecting animal behavior³².

Fig. 4.

We tracked neurons in transgenic animals expressing the calcium sensor GCaMP6s in AVK, SIA, or AIY interneurons. Naive animals (pre-pathogen exposure) were imaged on an empty agar plate for approximately 40 minutes. These worms were then placed onto a PA14 lawn for 24 hours, recovered to an empty assay plate and imaged for approximately 40 minutes to determine how pathogen exposure affected neural activity. Pnpr-4::GCaMP6s was utilized to measure the activity of AVKs and SIAs, and Pttx-3::GCaMP6s was used to measure the activity of AIYs. We found that AIY (Fig. 4C, D), AVK (Fig. 4F, G), and SIA (Fig. 4I, J) all showed reduced neural activity after pathogen exposure. These results were evident both in analyses of populations of animals (Fig. 4C, F, I), but were also clearly observed in individual worms (Fig. 4 C, F, I inset and Fig. 4D, G, J). Pathogen exposure had different effects on different interneurons. AIY neurons of naive animals showed a significant increase in calcium approximately 30 minutes after transfer to assay plates (Fig. 4D). By contrast, AIY neurons of animals that had been exposed to pathogen remained quiescent. AVK and SIA neurons of naive animals displayed continuous high-frequency calcium signals. Post-exposure, AVKs and SIAs displayed long periods of quiescence. This data indicated that the activity of neurons that regulate a behavior critical for learned pathogen avoidance is strongly affected by exposure to pathogen. These neurons thus appear to encode the history of exposure to pathogenic bacteria in their neural activity state.

Modulation of candidate neurons validates their role in inhibition of pathogen lawn re-entry

We next tested how manipulating the activity of neurons identified by compressed sensing as regulators of lawn re-entry affects how naïve animals interact with bacterial lawns. We inhibited each of the three candidate neuron types using Archaerhodopsin and measured how acute neuronal inhibitions affected re-entry into a lawn of pathogen. We performed parallel measurements of a control set of worms that had not been treated with the opsin cofactor ATR. To target specific neurons, we used a DLP mirror array to restrict illumination to cells of interest as previously described³².

We found that acute inhibition of AIY in naive animals increased the latency of re-entry onto pathogenic Pseudomonas, mimicking the effect of prior pathogen exposure (Fig. 5B). This effect was not the result of a general defect in locomotion or lawn-entry behavior; entry into lawns of non-pathogenic E. coli was not affected by AIY inhibition (Fig. 5B). Inhibition of AVK failed to produce any effect on re-entry behavior (Fig. 5D). Inhibition of npr-4-expressing neurons also increased latency to lawn entry on PA14(Fig. 5E). To validate that this effect was due to SIA, we projected patterned light onto Pnpr-4:Arch3 animals (Fig. 5F) to selectively inhibit SIA/SIB. Inhibition of SIA using this method deterred entry of worms onto PA14 lawns (Fig. 5G), resulting in increased latency in entry (Fig. 5H). Overall, our results show that two out of the three key neurons identified by compressed sensing were able to elicit a change in lawn entry dynamics through inhibition. Together, our neural imaging and selective inhibition suggested that reduced activity of AIY, SIA, and SIB drive the reduction in lawn occupancy triggered by pathogen exposure.

Fig. 5.

We next tested whether activation of these neurons would suffice to reverse this behavioral switch. To test this, we optogenetically activated AIY, SIA and SIB using transgenes expressing the light gated ion channel channelrhodopsin-2 driven by the two promoters Pttx-3::ChR2 and Pnpr-4::ChR2. Transgenic lines were fed ATR and placed on lawns of pathogenic bacteria for 24 hours to induce lawn evacuation. Blue light (467 nm, 1mW/mm² was then used to activate these neurons and the rate of re-entry onto the lawn was quantified. For both Pttx-3::ChR2 (Fig. 5J) and Pnpr-4::ChR2 (Fig. 5L), activation of these neurons dramatically increased the re-entry rate of experienced animals onto the PA14 lawn. Animals that re-entered the lawn also rapidly exited the lawn. Thus, the increased re-entry rate did not result in sustained increases in lawn occupancy (Fig. S6). These results further illustrate that lawn exit and re-entry are controlled by a distinct set of neurons (Fig. 2C, D) and demonstrate that both behavioral modules must change in order to evacuate the bacterial lawn.

Discussion

After experiencing pathogenic bacteria, worms switch their foraging behavior and evacuate the bacterial lawn. We found that this learned pathogen avoidance behavior is driven in part by changes to lawn re-entry behavior. Unlike naive animals, which rapidly re-enter a lawn of pathogen after they exit, animals previously exposed to pathogen dramatically delay re-entry upon encountering the pathogenic bacteria lawn. Such contact-dependent pathogen aversion depends in a graded manner on the extent of pathogen exposure; the latency to lawn re-entry increases with increased time of pathogen exposure. Contact-dependent inhibition of lawn re-entry is a previously unappreciated behavioral response to pathogen exposure revealed by our study. This behavior is distinct from associative olfactory learning and modulation of chemotactic behaviors reported by other studies^16,26. In our study, continuous monitoring of worms exposed to pathogen revealed that animals that leave the pathogen lawn are capable of chemotaxis back to the lawn but stall upon contacting the lawn and do not re-enter. We further found that neurons previously identified as being important in aversive olfactory learning, e.g. AIB, RIA, AIZ and RIM²⁶, were not essential for control of lawn re-entry. Our study indicated that the transition in pathogen lawn re-entry behavior occurs through modulation of two key neurons, AIY and SIA, which both decrease their neural activity in response to pathogen exposure.

If contact-dependent lawn aversion represents a modality of pathogen aversion distinct from aversive olfactory learning, what sensory system governs this behavior? While we have yet to establish the signals involved in this process, some hints as to what might be responsible for this aversion can be inferred from looking at synaptic inputs to the neurons that we identified as key for this behavior. SIAs are connected to two sensory neurons that might play a role in driving neural activity changes following pathogen exposure – URX and CEP⁴⁹. URX is a potent regulator of foraging behavior⁵⁰, is one of the few neurons with contact with the pseudocoelomic fluid of C. elegans, and has been previously linked to regulation of metabolic signals and innate immune responses^51,52. CEPs mediate mechanosensory detection of bacteria and potently inhibit locomotion^47,53. By being downstream of these two neurons, SIA might integrate chemosensory and mechanosensory stimuli, two signals known to be important in modulating lawn evacuation from prior studies^24,30. Other neurons that we identified-the AIYs - are part of the olfactory learning circuit and may thus represent a chemotactic component of this contact dependent pathogen aversion^26,46. In addition, AIY acts as a central hub neuron that is downstream of multiple sensory neurons and may thus also act as an integrator for multiple sensory modalities^54,55,56. Interestingly, investigation of molecular modulators of this aversion behavior by looking at neuropeptides that are highly and uniquely expressed in our neurons of interest reveals a candidate neuropeptide PDF-2 which is highly expressed in AIY⁵⁷. Knocking out PDF-2 increases contact dependent lawn avoidance (Fig. S7B, C). PDF-2 has been implicated in gut to neuron signaling through the Rictor/TORC2 pathway⁵⁸, suggesting a potential mechanism through which pathogen infection data could be communicated to AIY to influence PDF-2 signaling to modulate behavior.

One remarkable aspect of the neurons that control lawn aversion is the fact that early perturbation of these neurons (within the first one to three hours of the animal’s deposition on the pathogenic lawn) produce long term changes in pathogen avoidance behavior. Effects of this early neural inhibition could be seen in differences in lawn occupancy 15 hours later, suggesting that early suppression of neural activity patterns have long term consequences of behavior. How could SIA and AIY produce such long-term effects? One possible explanation is that reduction in AIY and SIA neural activity might serve as an internal cue. The reduction in activity could drive a bistable circuit including AIY and SIA, causing extended suppression of their activity and a long-term change in their neural activity patterns. This bistability could be accomplished through positive autoregulatory feedback. PDF-2, which appears to be involved in bacteria re-entry, might provide such a mechanism to provide this feedback mechanism. Both PDF-2 and its receptor PDFR-1 are expressed in AIY⁵⁹, providing a potential feedback loop for bistability.

While contact dependent re-entry represents a novel form of pathogen aversion, it is not the only behavioral transition driving lawn evacuation. Using our compressed sensing based approach, we were able to rapidly assay the nervous system to not only discover the key neurons controlling entry, but also exit from the pathogen lawn. We find there is little overlap between the sets of neurons controlling these two subbehaviors. Consistent with this we were able to modulate the subbehavior independently. Together, these results suggest that neural control of lawn evacuation is highly modular, with different sets of neurons governing the individual behavioral transitions needed for lawn evacuation. This high degree of modularity in control over net pathogen aversion is similar to that seen in several forms of aversive olfactory learning of C. elegans in response to pathogenic bacteria. For example, imprinting of a memory of pathogenic bacteria in larvae stage worms requires distinct sets of neurons for the establishment of the memory and the expression of that memory⁶⁰, while aversive learning in adult worms carry distinct neural circuits for naïve versus learnt olfactory preference²⁶.

Method details

Strains

All lines used in this work are listed in supplementary tables. Lines used for the measurement matrix are in Supplementary Table 1. Lines used for neural activation and halorhodopsin can be seen in Supplementary Table 2. Finally, lines used for neural imaging can be seen in Supplementary Table 3. PDF-2 mutant used was wSR897: nlp-37(tm4780).

Lawn evacuation assays

PA14 lawn evacuation assay plates were prepared as follows. PA14 was inoculated into LB media and allowed to grow for 15 hours without shaking at 37°C until culture reached an OD of 0.1 to 0.2. 5uL of this culture was then pipetted onto NGM Agar plates and the colonies were allowed to grow for 18 hours at room temperature. Each colony was surrounded by a ring of filter paper to prevent worms from escaping to the edges of the plate. OP50 lawn evacuation plates were prepared in the same manner as PA14 evacuation plates, however, OP50 bacteria was grown for 18 hours at 37°C with shaking. This longer growth period was used to roughly match the thickness of the PA14 and OP50 colonies.

To begin lawn evacuation, 10 worms were transferred onto the bacteria lawn and given one hour to settle. All worms used for this assay were 1 day old hermaphroditic adults grown on OP50 bacteria. The assay was then imaged at 3fps for a total of 18 hours to assay lawn avoidance. Occupancy rate was calculated as (Number of worms on lawn)/(Total number of worms). Worms were counted as being on the lawn if any part of the body was in contact with the lawn.

Lawn re-entry assay

PA14 bacteria colonies were generated as described for the lawn evacuation assay. Assays were initiated by placing 5-10 worms 5mm away from the lawn edge, and then imaging the worms at 3fps for 1 hour. Worms were infected prior to the assay by placing them onto a PA14 lawn evacuation colony for 15 hours. Worms that evacuated the colony over this time period were removed from the plate and used in the assay. Control worms were left on an OP50 lawn during this timeframe.

Optogenetic assay for lawn evacuation

All transgenic lines used for this assay were generated by fusing Archaerhodopsin-3 to the relevant promoters via fusion PCR and then injecting the resulting constructs into worms. Worms used for optogenetic assays were fed on the rhodopsin cofactor all-trans retinal (ATR) for +12hours before the assay. One day old adults were used for all behavioral assays.

Plates were set up as described for the lawn evacuation assays above. Following this, we performed optogenetic inhibition with pulsed (1sec on, 1 sec off) 5mW/mm² green light for 2 hours using a ScopeLED G250 to activate archaerhodopsin. Assays were imaged for a total of 18 hours either at 1 frame per 3minutes for the full assay, or 3fps to assay entry and exit sub-behaviors.

Targeted inhibition of neurons during re-entry

Targeted inhibition of SIA and SIB was carried out as described in previous work. Worms were cultured for 12+hours on ATR. Worms were then placed 5 mm away from the edge of a PA14 lawn generated as described above. SIA and SIB were located at the center of the fluorescent pattern Pnpr-4::Arch3 line. A circular pattern of light was projected from the DLP projector to selectively target SIA/SIB. Worms were tracked in the frame of view and imaged until they fully entered the PA14 colony or for a total of 1 hour after the worms first contacted the lawn.

Quantification of entry and exit rate for optogenetic screening

Lawn exit rate was calculated by evaluating the number of exit events during the 2 hour timeframe of neural inhibition. Lawn evacuation movies were analyzed in 1 minute intervals and any exit events occurring (as defined by a worm completely leaving contact with the bacteria) were noted. The exit events per hour were calculated from this. Lawn entry timescale was also evaluated by measuring the time from first contact of the worm to the bacteria lawn to time of full entry of the worm onto the lawn.

GCAMP imaging and analysis

Calcium imaging was performed using a custom built microscope as previously described in past work at 15.625 frames per second. Worms were first imaged on an empty NGM plate for 40 minutes. Imaged worms were then transferred onto a colony of PA14 bacteria as used for the lawn evacuation assay and allowed to remain there for 24 hours. Following this, infected worms were imaged again for another 40 minutes.

GCaMP intensity information was extracted using custom software written in MATLAB to extract intensity data given segments encapsulating the neurons of interest. GCaMP imaging data was compiled for all imaged worms. Worm to worm variability in GCAMP expression was normalized by dividing all data by the bottom 5 percentile of fluorescence intensity in healthy worms. GCaMP data was smoothed over a 6 sec window.

Re-entry with neural activation

PA14 lawn evacuation plates were prepared as described above, and 10 ATR treated worms expressing channelrhodopsin-2 under the relevant promoters were seeded onto each colony. Channelrhodopsin was activated using 1mW/mm² blue light for 1 hour. Re-entry rate (defined as the rate at which worms fully entered the lawn) and contact rate were evaluated and quantified over this time.

Promoter removal and additions

The robustness of the solutions to removal of promoters was tested as follows. 1 to 5 promoters were removed from the measurement matrix at random. The resulting measurement matrix and phenotype vector were used to infer neuronal weights for the re-entry phenotype. This process was repeated 200 times for each of the 1 to 5 promoters, or 1000 times in total for 1 to 5 promoters. Our results demonstrated robustness of AIY, AVK, SIA and MI to these removals.

Robustness of solutions to corruption of measurement matrix

The robustness of the solutions to corruption of the measurement matrix was tested by randomly altering a small fraction of the measurement matrix and re-inferring the neural contributions. 10% of the non-zero entries in the measurement matrix were altered to a value between 0 and 0.5. 1000 such corrupted measurement matrices were generated and solutions to each corrupted matrix were inferred via Lasso regression. Our results demonstrated that corruption generally did not result in alteration of the inferred neurons, with AIY, AVK, MI and SIA robustly being inferred even with matrix corruption.

Calculation of inverse participation ratio and relative expression

Single cell RNA seq data from S. R. Taylor et al. was used to calculate the average expression of each neuropeptide within each neuron type. The relative expression of each of the neuropeptides in each neuron was calculated by dividing expression in each neuron by the maximum expression over all neurons. Inverse participation ratio (IPR) was calculated as:

Where E_i is the expression in the ith neuron.

Acknowledgements

We would like to thank members of the Ringstad and Ramathan lab for their feedback and advice on the manuscript. This work is supported by 5R01NS117908-03 (SR, NR).

Declaration of Interests

The authors declare no competing interests.

Supplementary Materials

Fig. S1.

Fig. S2.

Fig. S3.

Fig. S4.

Fig. S5.

Fig. S6.

Fig. S7.

Supplementary Table 1.

Supplementary Table 2.

Supplementary Table 3.