Volatile and non-volatile pathogen cues shape host extracellular vesicles production in pre-infection response

Kołodziejska, Klaudia; Szczepańska, Agata; Vadlamani, Satya; Ponath Sukumaran, Ramakrishnan; Radkiewicz, Mariusz; Bringmann, Henrik; Pujol, Nathalie; Pokrzywa, Wojciech; Turek, Michał

doi:10.1038/s41467-025-67789-z

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Published: 21 December 2025

Volatile and non-volatile pathogen cues shape host extracellular vesicles production in pre-infection response

Nature Communications volume 17, Article number: 1038 (2026) Cite this article

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Abstract

In natural environments, animals encounter pathogen-derived chemicals long before infection occurs. How such anticipatory cues influence extracellular vesicle (EV) dynamics, which are central to immune regulation, intercellular communication, and stress responses, remains unknown. Using Caenorhabditis elegans, we show that pathogen-derived volatile and non-volatile compounds trigger distinct EV pathways through separate sensory and molecular mechanisms. Non-volatile secretome components, including the tripeptide Ile-Pro-Pro, activate immune-dependent EV production, whereas volatile metabolites elicit immunity-independent EV formation. Both responses require sensory input from ASK, ADL, and AWC neurons and converge on a neural circuit involving RMG, AIB, and AIA interneurons. GPCRs SRI-19, SRI-36/39, and SRR-6 mediate non-volatile responses, with SRR-6 acting in the intestine to regulate muscle EVs release. Notably, pre-exposure to pathogen volatiles enhances offspring survival during subsequent infection in an SRI-19–dependent manner, suggesting a predictive, intergenerational benefit of pathogen detection. In summary, our findings uncover that pathogen-derived chemical cues shape host EV production via specialized sensory circuits, revealing how animals anticipate infection and prime protective physiological responses.

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Introduction

Extracellular vesicles (EVs), including exosomes and microvesicles, are pivotal in the host immune response by mediating intercellular communication and regulating immune functions. Released by various cell types, EVs carry a diverse array of bioactive molecules such as antigens, signalling molecules, and genetic material, thus influencing immune cell behavior and activity^1,2. Pathogen-derived EVs often contain pathogen-associated molecular patterns (PAMPs) that can activate immune responses³, while host-derived EVs help modulate inflammation and immune activation, maintaining homeostasis and enhancing defense mechanisms against pathogens⁴. These roles for EVs have been well-documented during active infection, where pathogens are already invading the host. However, pathogens release both volatile and non-volatile compounds, which can alert and prepare the host to their exposure^5,6,7. Whether such compounds can modulate EV production prior to infection is not known.

The nematode Caenorhabditis elegans is a widely used model for studying host-pathogen interactions, particularly valuable for examining its responses to clinically significant human pathogens such as Pseudomonas aeruginosa, Serratia marcescens, and Staphylococcus aureus⁸. This organism offers several advantages due to its simplicity, genetic tractability, and well-characterized immune responses. C. elegans relies solely on innate immune pathways, making it an ideal organism for dissecting fundamental aspects of innate immunity without the confounding influence of an adaptive immune system^9,10. Additionally, its short lifespan and large brood size enable high-throughput genetic and pharmacological screens¹¹. The evolutionary conservation of many immune pathways between C. elegans and higher organisms further enhances its relevance for studying host-pathogen interactions^12,13. Moreover, C. elegans uses specialised G protein-coupled receptor (GPCR)-mediated sensory circuits to detect environmental pathogens and orchestrate systemic outputs, including immune activation and behavioral avoidance, in response to microbial metabolites such as phenazine-1-carboxamide, pyochelin¹⁴, and volatile 1-undecene¹⁵.

C. elegans has also emerged as a valuable model for studying EV production. Due to its transparent body and well-characterized genetics, C. elegans allows tracking of EV production in real-time and investigation of the regulatory pathways involved¹⁶. One example of an evolutionarily conserved class of particularly large EVs that is investigated in worms are exophers. Exophers have been shown to be involved in waste removal and material exchange between tissues, with key functions in cellular stress responses, tissue maintenance, and reproduction^{17,18,19,20,21,22}. Previous studies have demonstrated that exophers help protect neuronal activity in C. elegans by expelling toxic cellular debris¹⁷, a process also observed in mammals, where mouse cardiomyocytes release exophers to discard defective mitochondria, maintaining metabolic balance and preventing inflammation²³. In addition to waste disposal, we previously demonstrated that exophers facilitate inter-tissue material transfer to support reproductive fitness¹⁸, under the control of social cues originating from other individuals in the population²².

In this context, we aimed to investigate whether EV production in a host organism can be modulated by the mere presence of pathogens in the environment. Importantly, we focused on the response to pathogen volatile and non-volatile secretomes, allowing us to decouple this regulation from direct infection-related responses. Using exophers released by C. elegans body wall muscles as a model, we sought to explore the molecular mechanisms underlying pathogen secretomes-induced EV dynamics.

Our study uncovers a complex regulatory network governing exopher production in C. elegans, shaped by the type of interaction with pathogens. Pathogen infection or exposure to their secretomes increases exopher production. However, the molecular mechanisms regulating volatile metabolite-dependent and non-volatile secretome-dependent increases in EV production are distinctly controlled at sensory and neuronal levels. Moreover, the ability to distinguish between two types of signals triggers a context-specific physiological response, which may or may not involve immune activation, depending on the nature of the detected compounds. These findings provide new insights into the physiological mechanisms of EV production in C. elegans and suggest that similar mechanisms may be conserved across species.

Results

Infection with pathogens induces exopher production in hermaphrodite muscles

To test whether pathogen infection influences the production of EVs (exophers) in C. elegans (Fig. 1a), we exposed worms at adulthood day 1 to live Pseudomonas aeruginosa PA14 or Serratia marcescens Db10 bacteria for 3 hours. Next, we moved them back to plates seeded with a non-pathogenic Escherichia coli OP50 bacteria and, 24 hours later, we quantified the number of exophers released by body-wall muscle cells (BWMs) following a previously described methodology²⁴ (Fig. 1b). Our results show that worms exposed to both types of pathogenic bacteria produce significantly more exophers than worms that were kept on E. coli OP50 bacteria throughout the whole experiment (Fig. 1c).

**Fig. 1: Production of *C. elegans* muscle exophers is upregulated during pathogen infection.**

Given that extracellular vesicles in other organisms are often part of the immune response^1,25, we next examined whether basal production of exophers as well as increased exopher production upon exposure to pathogenic bacteria depends on specific immune pathways. We tested various mutants deficient in key nematode immune response pathways^10,26, including the insulin pathway (akt-1, daf-16)²⁷, TGF-β pathway (dbl-1)²⁸, p38 MAPK pathway (tir-1, pmk-1)^29,30, and Intracellular Pathogen Response (IPR) pathway (cul-6)^31,32 (Fig. 1d). In some of the tested immune response pathways, we observed altered levels of exophergenesis, either decreased or increased, compared to wild-type worms. This was observed under standard growing conditions on non-pathogenic E. coli OP50 bacteria after knocking out at least one component of each pathway (Fig. 1e). Surprisingly, despite their altered basal levels, most of the tested mutants still exhibited a robust, statistically significant increase in exopher production upon pathogen exposure (Fig. 1f-h). The sole exception was the cul-6 mutant with a compromised IPR pathway, which showed an increase in exopher production upon exposure to P. aeruginosa and a decrease in exopher production upon exposure to S. marcescens when compared to the control (Fig. 1i). Among the immune-deficient strains we tested, the magnitude of pathogen-induced exopher upregulation differed. Following P. aeruginosa exposure, the dbl‑1, pmk‑1, and cul‑6 mutants exhibited increases in exopher production comparable to wild‑type levels, whereas tir‑1 mutants showed an even greater induction. In contrast, daf‑16 mutants displayed a significantly diminished response relative to wild‑type, while akt‑1 mutants, defective in the negative regulator of DAF‑16, exhibited an exaggerated increase (Supplementary Fig. 1a). Because AKT‑1 inhibits DAF‑16 in the insulin pathway, these data indicate that although exopher induction by pathogen presence is broadly robust, its amplitude is fine-tuned by DAF‑16-dependent transcriptional programs as well as by p38 MAPK pathway.

A similar pattern emerged when worms were challenged with S. marcescens, with the exception that tir‑1 mutants responded like wild‑type and cul‑6 mutants showed a significant reduction in exopher induction (Supplementary Fig. 1b).

Collectively, these results indicate that exopher upregulation in response to pathogens is not driven by a single canonical immune pathway but is primarily modulated through the insulin/DAF-16 axis, with other pathways contributing in a minor or compensatory manner.

Previous work has established that exopher biogenesis requires fertility and the presence of embryos in the uterus^18,33. To determine whether pathogen exposure alone can bypass this requirement, we exposed sterile, FUdR‑treated worms to P. aeruginosa and S. marcescens. In both cases, sterile animals failed to produce exophers (Supplementary Fig. 1c), demonstrating that fertility remains a prerequisite for pathogen‑induced exophergenesis. When we examined embryo accumulation, S. marcescens exposure led to an increase in uterine embryos, whereas P. aeruginosa-exposed worms showed no change in embryo accumulation (Fig. 1j). Therefore, although both pathogens stimulate exopher production in fertile animals, only S. marcescens does so alongside embryo accumulation. This observation is critically important, as it shows that P. aeruginosa activates exophergenesis through a novel signalling pathway that is independent of the classic mechanism linked to embryo accumulation. This model allows for the isolation and study of the direct response to a pathogenic signal without the confounding influence of maternal signals. For this reason, we decided to focus our subsequent mechanistic analyses specifically on exophergenesis induced by P. aeruginosa.

Pathogen non-volatile and volatile secretomes trigger exopher production

Recent reports indicate that C. elegans can sense pathogen-derived volatile metabolites (airborne compounds emitted by the pathogen), which influence their physiology in a cell non-autonomous manner^15,34. Previous studies have also shown that exposing C. elegans to the P. aeruginosa secretome (the full complement of water‑soluble factors present in clarified culture supernatant including small metabolites, peptides, proteins, and EVs) leads to a robust immune response³⁵. We hence aimed to determine whether the increased production of EVs in response to pathogens requires direct infection or can be triggered simply by exposure to volatile or soluble bacterial components.

We first investigated whether exposure to pathogen odours alone would be sufficient to induce exopher production in muscle cells. To test this, we covered an NGM plate seeded with OP50 and containing age-synchronized L4-stage worms with another NGM plate containing P. aeruginosa and sealed both plates together with parafilm. Worms were incubated in this setup for 48 hours until they reached adulthood day 2, after which exophers were counted (Fig. 2a). Our results demonstrate that exposure to P. aeruginosa volatile metabolites indeed induces exopher production (Fig. 2b and c) and causes a slight embryo accumulation in utero (Fig. 2d).

**Fig. 2: The production of muscle exophers in *C. elegans* is regulated by both volatile and non-volatile metabolites from pathogens.**

Second, we aimed to determine whether the increased production of EVs in response to pathogens is triggered by exposure to the bacterial non-volatile secretome (Fig. 2e). As shown in Fig. 2f and g, direct infection with P. aeruginosa is not necessary; worms grown on plates with P. aeruginosa secretome also produced more exophers. Notably, this increase in exopher production is not associated with increased embryo accumulation in the worm’s uterus (Fig. 2h). The fact that P. aeruginosa non-volatile secretome fails to increase embryo accumulation in utero, in contrast to the retention induced by volatile metabolites, suggests that non‑volatile pathogen cues may actively suppress the embryo‑accumulation response typically triggered by volatiles. This opposing outcome points to distinct downstream signalling branches that modulate reproductive physiology in response to different classes of pathogen‑derived compounds. This also aligns with our observation that simultaneous exposure to volatile metabolites and the non-volatile secretome is non-additive for exopher production (Fig. 2i).

Consistent with previous results, FUdR-sterilized worms failed to upregulate exopher production in response to either P. aeruginosa volatile metabolites or non-volatile secretome (Supplementary Fig. 2a and d). This demonstrates that fertility is essential for exopher upregulation by both volatile and non‑volatile pathogen cues. Moreover, because starvation alone is sufficient to increase exopher production^18,21, we monitored pharyngeal pumping to rule out reduced feeding as a confounding factor. We found no significant change in pumping rates following exposure to either pathogen volatile metabolites or non-volatile secretome (Supplementary Fig. 2b and e), confirming that the observed exopher upregulation is not driven by altered food intake.

We further explored whether neuronal exophers, previously shown to help maintain proteostasis in neurons, are similarly regulated by the pathogen-derived secretome. Using a touch-receptor neuron reporter strain¹⁷, we exposed animals to either volatile metabolites or non-volatile P. aeruginosa secretome as described above and quantified neuronal exophers. Strikingly, we found that volatile metabolites, but not non-volatile secretome, significantly upregulate exopher formation in neurons (Supplementary Fig. 2c and f), indicating that the signalling machinery controlling EV production differs between muscle and neuronal cells.

Volatile and non-volatile cues engage distinct immune-regulatory mechanisms

Next, we investigated whether the increased muscle exopher production in our new experimental setups is influenced by innate immunity signalling pathways. We first assessed each immune response mutant against its own unexposed baseline and then compared the magnitude of P. aeruginosa‑induced changes to wild‑type responses. On one hand, our data show that insulin, TGF-β, p38 MAPK, and IPR pathways are not individually required for the robust increase in exopher production triggered by volatile metabolites as each mutant mirrored wild‑type induction (Fig. 3a and c), although we cannot exclude the possibility of redundant or compensatory interactions. However, there is a notable discrepancy in the role of these pathways when considering the non-volatile secretome-dependent increase in EV production. Specifically, while the insulin pathway does not by itself appear to be involved, the TGF-β, IPR, and partially p38 MAPK pathways are essential for the non-volatile secretome-dependent increase in exopher production (Fig. 3b and c). On the other hand, comparison of mutant versus wild-type animals showed that pmk-1 and akt‑1 mutants displayed an even greater increase in exophers than wild-type upon volatile metabolites and non-volatile secretome exposure, while dbl-1 mutants exhibited a blunted response to both types of compounds (Supplementary Fig. 3a and b). Together, these results indicate that C. elegans relies on distinct immune signalling mechanisms for EV production depending on the chemical nature of the stimulus: volatile P. aeruginosa metabolites trigger exophergenesis that is independent of multiple pathways yet quantitatively fine‑tuned by insulin, TGF-β, and p38 MAPK signalling, whereas non‑volatile secretome‑driven upregulation requires TGF-β, IPR, and partially p38 MAPK pathways.

**Fig. 3: Exopher production in response to pathogen volatile metabolites and non-volatile secretome is governed by distinct regulatory mechanisms.**

Tripeptide Ile-Pro-Pro present in P. aeruginosa non-volatile secretome upregulates exopher production

During the course of our study, we observed a sudden decline in the reliability of our non-volatile secretome-based exopher upregulation assay. Troubleshooting traced this inconsistency to a switch in the membrane filters used for supernatant clarification. We confirmed this by testing five different filter types (two previously used and three additional ones) and found that only two permitted robust exopher induction (Fig. 4a). We leveraged this filter sensitivity as a bioassay to pinpoint the active component(s) in P. aeruginosa supernatant (Fig. 4b). Using liquid chromatography–mass spectrometry, we compared the metabolite profiles of exopher-inducing versus non-inducing filtrates from the same bacterial liquid culture and identified 65 statistically significant, differentially abundant compounds (Supplementary Data 1). Interestingly, the active supernatant was enriched in several small peptides, dipeptides, tripeptides, and tetrapeptides, all of which contained at least one proline residue. Moreover, inspired by previous work demonstrating that N,N-dimethyltryptophan–containing dipeptide can modulate C. elegans physiology, we selected Ile-Pro-Pro (Fig. 4c), a tripeptide known to be produced by lactic acid bacteria³⁶, for functional testing.

**Fig. 4: Untargeted metabolomics revealed Ile-Pro-Pro tripeptide role in exopher upregulation.**

When synthetic Ile-Pro-Pro was added to standard NGM medium, exopher production increased in a dose-dependent manner (Fig. 4d). At 50 µM, we observed a 17% rise over vehicle control, rising to 29% increase at 100 µM. These results identify the tripeptide Ile-Pro-Pro as a specific, pathogen-associated molecule capable of inducing exopher production in the host. While likely acting in concert with other secreted factors, Ile-Pro-Pro represents a key molecular signal through which C. elegans can sense the metabolic state of surrounding bacteria.

Multiple types of sensory neurons regulate exopher production in response to pathogen secretome

Olfactory neurons in C. elegans detect numerous environmental signals that influence various aspects of the worm’s life, including behavior, physiology, and reproduction³⁷. Additionally, our recent findings have shown that olfactory neurons regulate pheromone-dependent increases in EV production²². To identify the sensory neurons responsible for regulating the pathogen non-volatile secretome-dependent and volatile metabolites-dependent increases in EV production, we utilized nematodes with genetic ablations of sensory neurons known to mediate pathogen recognition^38,39,40 and in exopher regulation²². Systematic analysis using animals carrying established genetic ablations of specific sensory neurons^{41,42,43,44,45}, revealed that removal of ADL, ASK, or AWC neurons completely abolished exopher upregulation induced by both volatile metabolites and the non-volatile secretome (Fig. 5a-c). Conversely, the removal of AQR/PQR/URX, which we previously identified as regulators of exophergenesis²², had no effect in either assay, similar to ASJ ablation (Fig. 5a-c). Interestingly, we observed discrepancies among ASI, ASH, and AWB neurons. While ASI, ASH, and AWB neurons were crucial for the non-volatile secretome-induced increase, they do not play a role in the volatile metabolites-dependent increase in EV production (Fig. 5a-c). Notably, ablation of ADL or ASK also reduced baseline exopher levels (Fig. 5a and c), consistent with our previous work showing that these neurons contribute to exophergenesis regulation at the basal level²². Finally, when comparing each ablation to wild type under non‑volatile secretome exposure, all sensory‑neuron mutants except ASI and AQR/PQR/URX showed a significantly attenuated exopher response, with ASK and ADL mutants exhibiting the largest reductions, whereas AQR/PQR/URX-ablated and ASI-ablated worms produced more exophers than wild type and the same number as wild type, respectively (Supplementary Fig. 4b). For volatile metabolites, AQR/PQR/URX and ASI ablations likewise enhanced exopher induction, AWB ablation had no measurable effect, and the response was reduced in all other neuron‑ablated strains (Supplementary Fig. 4a). Altogether, these findings show that multiple types of sensory neurons regulate exopher production in response to pathogen-produced compounds in a context-dependent manner, with ADL, ASK, and AWC neurons playing pivotal roles.

**Fig. 5: Chemosensory neurons regulate exopher production in response to pathogen metabolite exposure.**

A hierarchical neural circuit regulates exopher production in response to pathogen secretomes

To map the downstream interneuron circuit, we took a dual approach. A literature search for a central integrator of the previously identified sensory neurons pointed to the RMG interneuron, a known “hub-and-spoke” network center⁴⁶. Separately, a search of the C. elegans connectome for downstream partners highlighted the AIA and AIB interneurons⁴⁷. Notably, AIA and AIB, along with the AIB-specific neuropeptide receptor NPR-9, have previously been implicated in regulating C. elegans immunity^48,49, making them prime candidates for mediating this physiological response.

Our functional data confirmed the critical role of the RMG hub. Genetic ablation of RMG not only reduced basal exophergenesis but also completely abolished the induced response to both volatile and non-volatile pathogen cues (Fig. 6a–c, and Supplementary Fig. 5a and b), establishing it as an essential, central integrator.

**Fig. 6: AIA, RMG, AIB interneurons and AIB-specific NPR-9 receptor integrate sensory responses to mediate exopher production.**

Next, we examined the roles of the candidate processing interneurons. Ablation of AIA revealed a striking, context-dependent function. While AIA removal caused a modest, non‑significant reduction in basal exopher levels (Fig. 6d), AIA‑deficient worms still responded to volatile metabolites with a significant but attenuated increase in exopher production (Fig. 6e, and Supplementary Fig. 5a). In contrast, exposure to the non-volatile secretome failed to elicit any induction (Fig. 6f, and Supplementary Fig. 5b). These results indicate that AIA is essential for non‑volatile secretome-driven exophergenesis and plays a modulatory role in the volatile metabolites‑mediated response (Fig. 6k).

Genetic ablation of AIB did not affect basal exopher production, whereas loss of the inhibitory receptor NPR‑9, a condition that is known to activate AIB⁴⁸, reduced baseline exopher levels (Fig. 6g). Consistent with the results from the npr-9 mutant, optogenetic activation of AIB for 4 hours via ReaChR⁵⁰ also significantly decreased exopher production (Fig. 6h). Upon exposing worms to P. aeruginosa secretomes, the removal of the AIB neurons or NPR-9 receptor significantly reduced the increased exophergenesis induced by pathogen volatile metabolites (Fig. 6i, Supplementary Fig. 5a) and non-volatile secretome (Fig. 6j, and Supplementary Fig. 5b), with a more pronounced effect for volatile metabolites exposure. These results suggest that the AIB interneurons and NPR-9 receptor are essential for mediating exopher production in response to pathogen signals (Fig. 6k).

In summary, our findings delineate a multi-layered circuit that processes pathogen threat information (Fig. 6k). Increased exopher production due to volatile metabolites sensing relies primarily on the ASK, ADL, and AWC sensory neurons, which signal through the AIB and RMG interneurons, with AIA interneurons acting as a modulator (Fig. 6l). In contrast, increased exopher production due to non‑volatile secretome sensing engages an expanded circuit that includes ASI, AWB, and ASH inputs, alongside ASK, ADL, and AWC, converging on the same AIB and RMG core, but with stronger AIA involvement (Fig. 6m).

Identification and functional characterization of GPCRs mediating pathogen-induced exophergenesis

G-protein-coupled receptors (GPCRs) are essential mediators that enable cells to interact with their environment by transmitting signals. In both vertebrates and invertebrates, GPCRs play a key role in coordinating immune responses^51,52. Because exopher induction is triggered by chemical cues from pathogenic bacteria, we hypothesised that specific GPCRs act as sensors linking pathogen-derived signals to neuronal circuits controlling exophergenesis. To identify such receptors, we adopted a transcriptomic approach combining published datasets and new RNA-sequencing experiments.

First, we analysed published transcriptomics data from worms exposed to P. aeruginosa compared to worms grown on E. coli OP50⁵³. Among the most significantly upregulated GPCRs were sri-36 and sri-39, with fold changes of 480 and 79.5, respectively. According to a study by Vidal et al. ⁵⁴., these GPCRs are expressed in ASK and ASI cells, which we have shown to play an important role in exopher upregulation (Fig. 5a-b). Additionally, our prior RNA sequencing, which identified the GPCR str-173 as a regulator of socially-mediated exopher production, also ranked sri-36 and sri-39 among the top candidates associated with exopher regulation²², further justifying their selection for validation.

To broaden this search and identify receptors associated with natural variation in exopher output, we conducted RNA sequencing on age-synchronized wild-type worms that exhibited either high (more than 20) or low (fewer than 2) number of exophers on adulthood day 2. A similar strategy, utilizing gene expression heterogeneity within isogenic populations, was recently employed to study the processes that generate lifespan variation⁵⁵. The results of this RNA sequencing show that worms producing a high number of exophers differ significantly at the transcriptomic level from worms producing low number of exophers (Fig. 7a). Specifically, 609 genes were upregulated and 665 genes were downregulated (p adj <0.05; -1> log2 fold change > 1) (Fig. 7b and Supplementary Data 2). GO enrichment analysis revealed upregulation of “cilium assembly” (Fig. 7d), consistent with the central role of ciliated neurons in exopher regulation²². Among the significantly altered genes were three GPCRs; str-173, srr-6, and sri-19, whose expression correlated with exopher number (Fig. 7c). As str-173 had been validated previously as a pheromone-responsive regulator²², srr-6 and sri-19 were selected for functional analysis alongside sri-36 and sri-39, yielding four prime receptor candidates for experimental validation.

**Fig. 7: Comparative transcriptomic analysis of worms producing high and low number of exophers per individual.**

To examine their functions, we generated CRISPR/Cas9 mutants for each locus (Supplementary Fig. 6). Because sri-36 and sri-39 are separated by only 1.9 kb, with one gene situated between them, they were deleted together to produce an sri-36/39 mutant. Under baseline conditions, exopher levels were similar to wild type in sri-19 and sri-36/39 mutants (Fig. 8a and e), while it was mildly upregulated in srr-6 mutant (Fig. 8i). Upon exposure to pathogen cues, a clear functional divergence emerged. All receptor mutants exhibited a statistically significant increase in exopher production following exposure to volatile metabolites (Fig. 8b, f, j). The responses of sri-36/39 and srr-6 mutants were comparable to wild type (Supplementary Fig. 7c, e), whereas the sri-19 mutants showed a significantly attenuated response (Supplementary Fig. 7a). In stark contrast, the response to the non-volatile secretome was completely suppressed in all tested mutants (sri-19, sri-36/39, and srr-6), indicating they are all essential for this pathway (Fig. 8c, g, k, Supplementary Fig. 7b, d, f). Underscoring its central role, the sri-19 mutant also failed to upregulate exopher production upon exposure to live P. aeruginosa bacteria (Supplementary Fig. 7g).

**Fig. 8: Exopher production induced by PA14 non-volatile metabolites is regulated by multiple GPCRs.**

Expression analysis provided the crucial link between these molecular players and the previously identified circuits. The wrmScarlet CRISPR/Cas9-mediated transcriptional knock-in of the sri-19 gene revealed its expression in ADL and PHA neurons (Fig. 8d, and Supplementary Fig. 7h, i). Furthermore, transcriptional reporters using fluorescent proteins for the sri-36 and sri-39 genes indicated expression in anterior ciliated neurons (Fig. 8h), which, based on available literature⁵⁴, correspond to ASI and ASK neurons. However, we must note that we used a double deletion at the sri-36/39 locus, the observed phenotype in exopher production could be driven by loss of only one receptor, with the other making little or no contribution. Nonetheless, the expression pattern of all three receptors is consistent with our data, underscoring the critical role of ADL, ASK, and ASI neurons in pathogen-induced exopher production and establishing its molecular mechanism.

Finally, the expression of the srr-6 receptor in neurons of the retrovesicular ganglion (RVG), alimentary tract, and excretory system (Fig. 8l), as well as the expression of sri-36 and sri-39 in the alimentary tract (Fig. 8h), suggest that one or more of these tissues may also play a role in regulating exopher production in muscles. To test this, we drove srr-6 expression specifically in the intestine. This intestinal rescue not only restored the non-volatile secretome-dependent exopher response but also reduced basal exopher levels, a result consistent with array-mediated overexpression, demonstrating that SRR-6 functions in the intestine to regulate muscle exopher production (Supplementary Fig. 7j) and revealing a novel gut-muscle signalling axis.

Offspring of animals exposed to pathogen volatile metabolites show improved survival post-infection

We aimed to investigate the functional consequences of extracellular vesicles release when worms detect the pathogen in their environment. Prompted by a recent study showing that perception of Pseudomonas vranovensis volatiles confers intergenerational protection in C. elegans⁵⁶, we examined both offspring and maternal outcomes following pre-exposure to P. aeruginosa cues. Specifically, we assessed progeny survival and developmental rate during P. aeruginosa infection following parental pre-exposure to volatile metabolites or non-volatile secretomes, and evaluated how this preconditioning impacted hermaphrodite mothers’ survival upon subsequent P. aeruginosa infection (Fig. 9a). For these assays, P0 worms were exposed to P. aeruginosa volatile or non-volatile secretomes for 48 hours beginning at the L4 stage. To assess progeny outcomes, F1 embryos were collected at the 3-fold stage, transferred to P. aeruginosa–seeded plates at 20 °C, and scored after 48 hours for survival and developmental progression (scored as younger than L4, or L4 and older). For maternal survival, P0 adults (exposed to secretomes until day 2 of adulthood) were transferred to fresh P. aeruginosa plates and monitored every few hours until death. Pre-exposure of P0 adults to P. aeruginosa volatiles significantly increased F1 survival under infection (Fig. 9b) without altering their rate of development to L4/adulthood (Fig. 9c). However, this effect did not persist in the F2 generation, where no significant differences in survival or development were observed (Supplementary Fig. 8a and b).

**Fig. 9: The SRI−19 receptor is essential for the increased progeny resilience observed after maternal pre-exposure to *P. aeruginosa* volatile cues.**

P0 pre-exposure to non-volatile secretomes had no effect on either progeny survival or developmental timing (Fig. 9d and e). For P0 mothers, volatile preconditioning produced a slight, non-significant decrease in survival on P. aeruginosa (Fig. 9f), whereas non-volatile preconditioning had no measurable impact (Fig. 9g).

We next asked whether the SRI-19 receptor, essential for exopher induction by P. aeruginosa and its secretomes, also influences progeny outcomes. Following P0 pre-exposure to P. aeruginosa volatiles or secretomes, sri-19 mutants displayed no differences in F1 survival or developmental rate (Fig. 9h-k). This result indicates that the enhanced offspring resilience conferred by parental exposure to P. aeruginosa volatiles depends on the SRI-19-mediated pathway.

Elevated temperature of 25 °C both enhances exopher production in C. elegans²² and increases P. aeruginosa virulence. Because wild worms encounter pathogens in a range of environmental temperatures, we assessed how F1 survival and development are affected at 25 °C. As shown in Supplementary Fig. 9a–d, these high-temperature outcomes differ markedly from those at 20 °C (Fig. 9b–e). Progeny of parents pre-exposed to P. aeruginosa volatiles developed faster (Supplementary Fig. 9b) but showed unchanged survival (Supplementary Fig. 9a), whereas progeny of parents exposed to P. aeruginosa non-volatile secretome suffered both reduced survival and delayed development compared to unexposed controls (Supplementary Fig. 9c and d). In P0 adults, pre-exposure to P. aeruginosa volatiles significantly decreased survival upon subsequent P. aeruginosa infection (Supplementary Fig. 9e), while pre-exposure to non-volatile secretome had no effect (Supplementary Fig. 9f). Notably, sri-19 mutants, accumulated fewer embryos in utero (Supplementary Fig. 9g), and are unable to upregulate exophers in response to P. aeruginosa, but survived significantly longer during infection (Supplementary Fig. 9h). Likewise, knockdown of the yolk receptor RME-2, which nearly abolishes exopher production¹⁸, and reduces embryo accumulation in utero (Supplementary Fig. 9i) also conferred enhanced resistance to P. aeruginosa (Supplementary Fig. 9j). Together, these data demonstrate that parental sensing of P. aeruginosa volatiles enhances offspring resilience at 20 °C, while elevated environmental temperature abolishes this effect, underscoring its context-dependent nature. Finally, SRI-19’s role in this pathway directly links the machinery of exopher biogenesis to survival outcomes, highlighting how EV production and organismal fitness are mechanistically intertwined.

Discussion

Our findings support a unified model (Fig. 10), in which C. elegans employs adaptive exophergenesis as a sophisticated response mechanism to pathogen-derived environmental cues. We demonstrate that worms effectively discriminate pathogen cues based on their chemical nature, volatile versus non-volatile. Volatile metabolites are small, low‑molecular‑weight compounds with high vapour pressures, diffusing rapidly through air or aqueous films to create broad, low‑concentration plumes. In contrast, compounds present in non‑volatile secretomes are larger, more polar molecules that remain close to their source, forming steep, localized concentration peaks. We propose that distinguishing between these two types of compounds could enable worms to accurately infer pathogen proximity and subsequently mount tailored physiological responses. This response is orchestrated by immune signaling in a stimulus-specific manner: exopher production in response to volatile metabolites is modulated by insulin, TGF-β, and p38 MAPK but proceeds independently of IPR, whereas non-volatile secretomes require TGF-β, p38 MAPK, and IPR. The partial overlap of these pathways^57,58 suggests coordinated or compensatory interactions that fine-tune exophergenesis according to the nature and intensity of the pathogen cue. The sensory neurons ADL, ASK, and AWC detect both cues, whereas ASI, ASH, and AWB neurons specialize in detecting non-volatile compounds. Signal integration occurs downstream via RMG hub, AIA, and AIB interneurons, with the AIB-specific GPCR NPR-9 functioning as one of the regulatory components. Both neuronal and non-neuronal GPCRs (SRI-19, SRI-36/39, and SRR-6) are essential for transducing the non-volatile secretome signal into exopher release, and SRI-19 is responsible for modulation of the response to volatile metabolites. Moreover, P. aeruginosa released tripeptide Ile-Pro-Pro functions as an inducer of exopher production. Functionally, we demonstrate that maternal exposure to volatile pathogen metabolites triggers an SRI-19-dependent increase in offspring survival at 20 °C, linking the regulation of exopher formation with survival outcomes and suggesting that both processes are coordinated through shared signaling mechanisms.

**Fig. 10: Model of exopher regulation from muscles by pathogen non-volatile secretome and volatile metabolites.**

Our findings on pathogen-induced exophergenesis gain broader significance when considered alongside our recent work on its regulation by social cues, revealing a unifying framework in which C. elegans leverages the same physiological process, muscle-derived exopher production, to navigate radically different environmental contexts. In both cases, exophergenesis seems to serve as a form of maternal resource allocation that supports offspring fitness, yet the initiating stimuli represent opposite poles of the ecological spectrum: pathogenic threat versus reproductive opportunity. The male-produced ascaroside pheromone ascr#10 enhances maternal provisioning in anticipation of mating, whereas pathogen-derived metabolites provoke a terminal investment in progeny under threat of infection. This functional reuse of a conserved output module underscores the evolutionary versatility of the exopher pathway. A striking point of convergence across these two systems is the ASK and ADL sensory neurons, which emerge as master integrators capable of detecting important environmental information. For instance, in the social context, ASK detects ascr#10 via the GPCR STR-173; in the context of pathogen exposure, ASK, together with ADL, detects non-volatile pathogen secretomes likely via distinct GPCRs such as SRI-36/39 and SRI-19. This dual role positions ASK and ADL as a key node in the sensory network that interprets both reproductive cues and pathogen-derived danger signals, illustrating the remarkable contextual plasticity of the sensory code. However, beyond this shared component, the upstream sensory receptors and downstream processing circuits diverge almost completely, demonstrating the evolution of parallel, specialised neural architectures for environmental decision-making. The pathogen-responsive circuit engages a distributed network comprising ASK, ADL, and AWC neurons, integrated through the RMG, AIB, and AIA interneurons hub mediated through canonical immune pathways such as TGF-β, p38 MAPK, and the IPR system. In contrast, the social cue circuit recruits the AQR, PQR, and URX sensory neurons and is modulated by neuropeptides including FLP-8 and FLP-21. This divergence could be explained by a broader principle of C. elegans sensory neuroscience: worms employ combinatorial coding and distributed receptor repertoires to extract meaning from complex environmental landscapes. Rather than relying on a single labelled-line logic, the nervous system interprets unique activation patterns across multiple neurons⁵⁹, allowing for nuanced discrimination between identity and concentration of an odorant stimulus. In this context, a similar mechanism could underlie the discrimination between pathogen threats and imminent reproductive opportunities. The evolution of dual sensory systems converging on exopher production, each with distinct inputs and physiological consequences, illustrates how a single effector process can be co-opted to serve adaptive roles across diverse ecological scenarios.

Importantly, the nature of the exopher response is determined not only by the chemical nature of the stimulus (volatile vs. non-volatile) but also by the associated signalling architecture. Non-volatile cues require canonical immune modules, including TGF-β, p38 MAPK, and the IPR pathway, signalling axes that are deeply conserved across metazoans and underscore an immune-centric, mode of defensive mobilisation. In stark contrast, volatile-triggered responses proceed largely independently of these immune effectors, reflecting an alternative regulatory strategy of pathogen anticipation. Interestingly, we did not observe an additive effect when both volatile and non-volatile pathogen-derived cues were present simultaneously. This suggests that these two types of signals may converge onto a shared regulatory or effector pathway that defines an upper limit for exopher production, rather than acting through independent mechanisms. This distinction also raises the possibility that exophers may constitute distinct subtypes with unique molecular compositions and functions. The chance that quantitatively similar exophers could be functionally distinct opens new research avenues to isolate and characterize these vesicles in detail. By analysing their content in response to both volatile and non-volatile secretomes, we could uncover their specific roles in organismal health and pathogen defense. This principle echoes findings in mammalian extracellular vesicle biology, where cargo composition and functional outcome are determined by the upstream signalling context⁶⁰.

In this study, we focused on key immune signalling pathways in C. elegans, including p38 MAPK, insulin signalling, TGF-β, and the intracellular pathogen response (IPR) pathway, which are well established as evolutionary conserved central mediators of pathogen-induced stress responses. Although additional transcriptional regulators such as SKN-1⁶¹, ELT-2^62,63, and HLH-30⁶⁴ are also known to participate in immune and stress-related processes, their functions are highly pleiotropic and extend beyond immunity to encompass broader roles, for instances in stress responses⁶⁵, detoxification⁶⁶, and autophagy⁶⁷. Nevertheless, the potential involvement of SKN-1, ELT-2, and HLH-30 in pathogen compounds-dependent EV regulation remains an intriguing avenue for future investigation.

Interestingly, while volatile metabolites like 1-undecene have been shown to increase lifespan in C. elegans under non-pathogenic conditions⁶⁸ or to protect worms against a subsequent infection with P. aeruginosa¹⁵, our results demonstrate that volatile-induced exopher production can lead at higher temperature to decreased survival during infection. The differences in the type, timing, and context of exposure likely contribute to this contradiction. In previous studies^15,68, a specific compound and shorter exposure periods were used. In contrast, our study involved prolonged exposure to a mixture of volatile metabolites from pathogens, which may trigger different physiological responses. The involvement of multiple G protein-coupled receptors in exopher production indicates that exophergenesis is driven by a complex mixture of pathogen-derived signals, rather than a single metabolite, although some receptors may share common ligands. This complexity may allow the organism to fine-tune its response depending on the nature of the threat it perceives, enabling a more nuanced and context-dependent reaction to environmental challenges.

Previous studies have shown that increased exopher release can benefit the organism by removing protein aggregates¹⁷, extending lifespan²⁰, or promoting faster offspring development¹⁸. However, when exopher release is elevated in response to pathogen exposure at higher temperature, we observe an opposite effect, leading to decreased survival upon infection. Intriguingly, our data reveal tissue-specific exopher responses: muscle-derived exophers are induced by both volatile and non-volatile cues, whereas neuronal exophers respond exclusively to volatile signals. The apparent paradox between muscle- and neuron-derived exophers may reflect distinct functional roles: neuronal exophers promote longevity via cell-autonomous proteostasis, while muscle exophers serve as transporters of biological material. Moreover, this raises the possibility that exophers may function under a hormetic response: while moderate exopher production may be beneficial, too much exopher release might impose a burden on the organism, potentially due to changes in the content of these vesicles under different environmental conditions.

Our findings suggest that pathogen-induced exophergenesis contributes to offspring fitness by promoting non-genetic maternal investment strategies. One possibility is that exophers serve as vehicles for protective cargos that are ultimately transferred to developing oocytes. Alternatively, elevated exopher output may reflect a systemic reconfiguration of maternal physiology that enhances oocyte provisioning through shared metabolic or stress-response pathways without the direct involvement of EVs in this process. Addressing this will require not only profiling exopher content but also functional testing, such as isolating exophers and injecting them into naïve mothers to assess whether they confer fitness advantages to offspring in the absence of infection. Resolving this will yield fundamental insights into the mechanisms of non-genetic, transgenerational immunity and into the broader role of EV as adaptive mediators of host-pathogen interactions.

In conclusion, our study highlights the complexity of exopher production in response to pathogen-derived signals. The ability of C. elegans to detect and respond to volatile and non-volatile compounds in a context-dependent manner suggests that exophers and possibly other EVs play a crucial role in mediating the organism’s physiological response to environmental stressors. Future research should focus on isolating and characterizing exophers produced under different conditions to determine their specific molecular contents and functions. Understanding how these vesicles contribute to the long-term offspring fitness will provide deeper insights into the adaptive roles of EVs in host-pathogen interactions.

Methods

Worm Maintenance and Strains

C. elegans strains were maintained at 20 °C (unless otherwise indicated) on nematode growth medium (NGM) plates seeded with Escherichia coli OP50 as a food source⁶⁹. Only hermaphrodites were used in the experiments. A comprehensive list of all strains and chemicals used in the study is provided in Supplementary Data 3 and 4, respectively.

Pathogen procedures

Pathogen bacteria media plates preparation

Worms were exposed to either Pseudomonas aeruginosa PA14 or Serratia marcescens Db10 strains. Pathogen-seeded plates were prepared following standard protocols^70,71. All the experiments with the pathogenic bacteria were conducted in Biosafety level 2 laboratory.

Pseudomonas aeruginosa PA14 “slow-killing” (SK) plates preparation

The PA14 strain was revived from frozen stock by streaking onto a 10-cm LB agar plate and incubating at 37 °C for 12–18 hours. Plates were stored at 4 °C and used within one week. A single PA14 colony was inoculated into 5 mL of LB broth and incubated overnight (maximum 16 hours) at 37 °C with shaking at 250 rpm. Following incubation, 10 μL of the overnight PA14 culture was spotted onto the center of 35 mm “slow killing” plates (SK plates), which were incubated at 37 °C for 24 hours, followed by 25 °C for another 24 hours to maximize virulence.

Serratia marcescens Db10 plates preparation

For S. marcescens Db10, the strain was similarly streaked from frozen stock onto a 10-cm LB agar plate, incubated at 37 °C for 12–18 hours, and stored at 4 °C for up to one week. A single Db10 colony was inoculated into 5 mL of LB broth and cultured at 37 °C with shaking for 8–10 hours. To prepare NGM infection plates, 25 μL of the S. marcescens culture was seeded onto 35 mm NGM plates, which were incubated at 37 °C overnight.

Short-time infection assay

Worms were age-synchronized by isolating 3-fold stage embryos (hand-picked under a stereomicroscope). Upon reaching the first day of adulthood (AD1), they were transferred either to control plates seeded with E. coli OP50 or to plates seeded with the pathogens PA14 or Db10. Worms were exposed directly to the pathogens for 3 hours at 25 °C, after which they were transferred back to OP50 seeded plates. Exopher levels were assessed the following day, on the second day of adulthood (AD2).

Pathogen non-volatile secretome assay

To collect non-volatile secretome, a single colony of PA14 was inoculated into 5 mL of LB media and cultured overnight for a maximum of 16 hours at 37 °C. After incubation, bacterial cells were removed by passing the culture through a 0.22 μm filter (Sarstedt, Filtropur S, no. 83.1826.001), and the bacterial supernatant was collected. Age-synchronized L4 stage worms (n = 30), grown on 60 mm NGM plates seeded with OP50, were treated with 50 μL of the filtered bacterial supernatant, while control worms received 50 μL of LB broth. Plates were sealed with parafilm, and exophers were scored 48 hours later, when the worms reached AD2.

Volatile metabolites chamber assay

3-fold stage embryos were placed on 35-mm NGM plates seeded with E. coli OP50, with 10 embryos per plate. Once the embryos developed into L4 stage worms, a chamber with volatile metabolites was assembled by placing a 35 mm SK plate containing PA14 or a control 35 mm NGM plate with OP50 on the bottom. The plate with synchronized worms was positioned on top, and the two plates were sealed together with parafilm. Worms were incubated with volatile metabolites of the bacteria for 48 hours. Exophers were evaluated on the second day of adulthood.

Parental generation survival

The parental generation (P0) of C. elegans was synchronized by transferring 10 3-fold stage embryos onto NGM plates seeded with Escherichia coli OP50. When the worms reached the L4 larval stage, they were subjected to the “Volatile Metabolites Chamber Assay” or “Pathogen non-volatile secretome assay” as previously described. After 48 hours of exposure to the volatile or non-volatile secretomes, exopher production was quantified in the P0 generation at AD2. The P0 worms were then moved to SK plates seeded with PA14 and maintained at 20 °C (Fig. 9f, g) or 25 °C (Supplementary fig. 8e-h). The use of 5-fluoro-2′-deoxyuridine (FUdR) was omitted from all plates. Survival of the P0 population was monitored at multiple time points using a touch-provoked movement assay. Worms that did not respond to touch were recorded as deceased, removed from the killing plates, and documented. Those that responded to touch were considered alive and were counted accordingly. Survival data were collected at least twice daily until the death of the entire population.

Offspring survival and development assay

Once the parental generation (P0) of worms reached the L4 larval stage, they were subjected to the “Volatile Metabolites Chamber Assay” or “Pathogen non-volatile secretome assay”. After 48 hours of secretome exposure, exopher production was quantified in the P0 generation on the second day of adulthood (AD2).

To assess intergenerational effects, 3-fold stage embryos of the F1 generation, derived from P0 worms were collected and transferred to 35 mm SK plates seeded with PA14 bacteria (10–15 embryos per plate). The plates were incubated at 20 °C (Fig. 9b-e, h-k) or 25 °C (Supplementary fig. 8a-d). Offspring survival and development were assessed after 48 h by scoring the total number of worms and determining their developmental stages.

Embryo accumulation assay

First, AD1 age-synchronized worms were placed on either a lawn of pathogenic bacteria (PA14 and Db10) or a control lawn of OP50 for 3 hours. After exposure, worms were transferred to OP50 plates and cultured until they reached the AD2 stage. In the next step, hermaphrodites were treated with 1.8% hypochlorite solution. Once the worms dissolved, the embryos retained in the uterus were counted.

RNA Interference Assay

RNA interference (RNAi) in C. elegans was performed using the standard RNAi feeding method⁷². Briefly, RNAi plates were prepared by supplementing NGM with 12.5 µg/mL tetracycline, 100 µg/mL ampicillin, and 1 mM IPTG. These plates were seeded with E. coli HT115 bacteria expressing double-stranded RNA (dsRNA) targeting the gene of interest. Control worms were fed E. coli HT115 containing the empty vector L4440.

Age-synchronized 3-fold stage embryos were transferred onto the freshly prepared RNAi plates and cultured until day 2 of adulthood, at which point exophers were quantified. The developmental stage of the worms was confirmed at the L4 stage, and any individuals that were developmentally advanced or delayed were excluded from the experiment.

Metabolomic analysis

All solvents were prepared prior to analysis: extraction solvent was prepared by mixing 1 mL of LC-MS grade methanol, 4.5 mL of LC-MS grade acetonitrile and 4.5 mL of LC-MS grade isopropanol; 90% acetonitrile was prepared by mixing 9 mL of LC-MS grade acetonitrile with 1 mL of MQ grade water; mobile phase A (MPA) was prepared by adding 1 mL of formic acid and filling it to 1 L with MQ grade water; mobile phase B (MPB) was prepared by adding 1 mL of formic acid and filling it to 1 L with LC-MS grade acetonitrile. Metabolite extraction was achieved by transferring 50 µL of the sample to an Eppendorf LoBind tube and adding 250 µL of ice-cold extraction solution of methanol:acetonitrile:isopropanol (2:9:9, v/v/v). Samples were shaken for 10 min at 1500 RPM in RT and subsequently centrifuged for 10 min at 14 000 RPM at 4 °C. Supernatants were transferred to new polypropylene sample tubes and evaporated to dryness under nitrogen stream at 50 °C using Caliper TurboVap LV evaporator. Dry residue was reconstituted by addition of 100 µL of 90% acetonitrile, vortexed and centrifuged.

LC-MS analysis was performed using ACQUITY UPLC system (Waters) coupled with Q-Exactive Orbitrap high resolution mass spectrometer (Thermo Scientific). Chromatographic separation was achieved using ACQUITY UPLC BEH HILIC column (130 Å, 1.7 µm, 2.1 mm × 100 mm). MPA was 0.1% formic acid in MQ grade water and MPB was 0.1% formic acid in LC-MS grade acetonitrile. The initial gradient conditions were 100% B with mobile phase flow rate at 0.4 mL/min and was held for 1 min. The mobile phase composition changed to 90% B over next 6 min, then dropped to 50% B at 9 min, and to 10% B at 10 min to be kept for next 4 min. Column was re-equilibrated by changing the gradient back to initial conditions. The total method run time was 20 min. The mass spectrometer operated in separate positive (ESI+) and negative (ESI-) electrospray ionization. The tune parameters were as given: sheath gas flow rate 55.0 [a.u.], auxiliary gas flow rate 15.0 [a.u.], sweep gas flow rate 4.0 [a.u.], spray voltage in ESI+ was 4.0 and in ESI- was 3.5, S-lens RF 30.0 [a.u.]. Capillary temperature was set to 320 °C and auxiliary gas temperature to 350ׄ °C. Data acquisition was proceeded in Data Dependent Acquisition (DDA) mode, MS1 resolution set to 70,000, MS1 AGC Target 1.0 × 10⁶, MS 1 Maximum Injection Time 90 ms, scan range 120 to 1600 m/z, and MS2 resolution was set to 35,000, MS2 AGC Target to 1.0 × 10⁵, MS2 Maximum Injection Time 100 ms, TopN was 8, isolation window 2.0 m/z and normalized stepped collision energy of 20, 30 and 40 was used to collect fragmentation spectra.

Raw data were uploaded to Compound Discoverer 3.3.3 (Thermo Scientific). An in-house protocol for metabolomic search and identification was employed. The workflow tree consisted of: Input Files → Select Spectra (any polarity, precursor mass 100–1500 Da, MS(n-1) precursor selection) → Align Retention Times (adaptive curve alignment model, max mass tolerance 10 ppm, max shift time 2.0 min) → Detect Compounds (mass tolerance 15 ppm, signal/noise> 5, max peak width 0.3 min, base ions [M + H]⁺ and [M-H]^-) → Merge Features (mass tolerance 15 ppm and RT tolerance 0.5 min) → Group Compounds (mass tolerance 10 ppm, RT tolerance 0.2 min, all peaks used for area integration, preferred ions [M + H]⁺ and [M-H]^-) → Fill Gaps (mass tolerance 5 ppm, signal/noise> 3) → Normalize Areas (Median Absolute Deviation model) → Mark Background (max sample/blank=5). Predict Composition node was set to mass tolerance of 5 ppm, isotope instensity tolerance of 30%, and minimum pattern coverage of 90%. Metabolomika Pathways, mzVault, mzCloud and ChemSpider databases were selected for analytes identification and assignment. Identifications were analyzed using mzLogic node with minimum match threshold of 30 and high resolution fragment mass tolerance 15 ppm.

XCalibur 3.0.63 (Thermo Scientific) software was used to design LC-MS workflow and acquire sample batch, as well to analyze raw mass spectra using built-in Qual Browser. OpenMS 3.4.0 software was used to visualize the raw fragmentation spectra. ACD/ChemSketch 2024.2.3 (ACD Labs) was employed to determine the fragmentation pattern of isoleucyl-prolyl-proline tripeptide.

Ile-Pro-Pro tripeptide assay

Worms were age synchronized from 3-fold stage embryos and cultured on 60 mm NGM plate seeded with E.coli OP50 bacteria. A total of 50 µL Ile-Pro-Pro solution (containing 50 µM or 100 µM) was applied to plates with worms on adulthood day 1. Plates were left open under the hood to dry for 30 minutes and incubated for further 24 hours at 20 °C. BWM exophers were scored on day 2 of adulthood, after 24 h exposure to tripeptide. Synthetic, concentrated stock solution of Ile-Pro-Pro was stored in H₂O at −80 °C. The stock was diluted to working solutions with M9 buffer.

Transcriptome analysis

C. elegans RNA extractions, library preparations, and sequencing were performed by Azenta US, Inc (South Plainfield, NJ, USA) as follows:

RNA extraction

Total RNA was extracted using Qiagen RNeasy Plus mini kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions.

Library preparation with polyA selection and Illumina sequencing

RNA samples were quantified using the Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA), and their integrity was assessed with the Agilent TapeStation 4200 (Agilent Technologies, Palo Alto, CA, USA). RNA sequencing libraries were constructed using the NEBNext Ultra II RNA Library Prep Kit for Illumina, following the manufacturer’s protocol (NEB, Ipswich, MA, USA). In brief, mRNA was enriched using Oligo(dT) beads and subsequently fragmented at 94 °C for 15 minutes. First and second-strand cDNA synthesis was performed, followed by end repair and adenylation of the cDNA fragments at their 3’ ends. Universal adapters were then ligated to the cDNA fragments, and libraries were enriched through a limited-cycle PCR with the addition of indexing sequences. The resulting sequencing libraries were validated using the Agilent TapeStation and quantified with both the Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA) and quantitative PCR (KAPA Biosystems, Wilmington, MA, USA). The libraries were multiplexed and loaded onto the Illumina NovaSeq 6000 platform according to the manufacturer’s instructions. Sequencing was performed with a 2×150 paired-end (PE) configuration using v1.5 reagents. Image analysis and base calling were carried out using the NovaSeq Control Software v1.7. The raw sequence data (.bcl files) generated were converted into fastq files and de-multiplexed using the Illumina bcl2fastq software (version 2.20), allowing for one mismatch in index sequence identification.

Sequencing data analysis

Following quality assessment of the raw sequencing data, reads were trimmed to remove potential adapter sequences and low-quality nucleotides using Trimmomatic v0.36. The cleaned reads were then aligned to the Caenorhabditis elegans reference genome from ENSEMBL using the STAR aligner v2.5.2b. STAR is a splice-aware aligner that identifies splice junctions and integrates them into the alignment process to enhance the accuracy of read mapping. This step resulted in the generation of BAM files. Unique gene hit counts were calculated using the featureCounts function from the Subread package v1.5.2, considering only reads uniquely aligned to exon regions.

The gene hit counts were subsequently used for downstream differential expression analysis. DESeq2 was employed to compare gene expression levels between different sample groups. The Wald test was used to calculate p-values and log2 fold changes. Genes with an adjusted p value < 0.05 and an absolute log2 fold change > 1 were considered differentially expressed in each comparison.

Scoring of Muscle Exophers and Fluorescence Microscopy

Exopher quantification was performed as described in Turek et al. ¹⁸ and Banasiak et al.²⁴ (Bio-Protocol, detailed, step-by-step guide). In brief, exophers were scored using a Zeiss Axio Zoom.V16 stereomicroscope equipped with filter sets 63 HE and 38 HE. Worms were age-synchronized by isolating 3-fold stage embryos (hand-picked under a stereomicroscope) and maintained until day 2 of adulthood. On the second day of adulthood, worms were observed on NGM plates, and the number of exophers per freely moving worm was counted.

Figure 1f-i experiments were performed using different experimental setup. Exophers were scored using a Leica DMRBE microscope equipped with a 20× objective and RFP filter. Worms were age-synchronized by isolating 3-fold stage embryos or L4 stage larvae and maintained until day 2 of adulthood. During the infection period with PA14 or Db10 (or OP50 as a control), worms were briefly incubated at 25 °C for three hours. On day 2 of adulthood, animals were immobilized on 2% agarose pads with a 50 mM NaCl drop (10–20 µl), and the number of exophers per worm was scored.

Scoring of neuronal exophers

Neuronal exophers were scored using a Leica STELLARIS 8 FALCON confocal system with a 40× oil immersion objective. For imaging, worms were immobilized on 5% agarose pads (dissolved in M9), 10 μM tetramisole as an anesthetic.

Optogenetics assays

For optogenetic activation, both control and experimental 35 mm NGM plates seeded with E. coli OP50 bacteria were covered with 0.2 µM all-trans retinal (ATR) and left to dry at 20 °C and darkness for 16 hours. Ten to twelve age-synchronized worms were picked per plate from optogenetic strains expressing ReaChR in AIB at adulthood day 2. After a 3-hour incubation at 20 °C and in darkness, BWM exophers were scored. Next, experimental plates were placed under the source of white light (light intensity measured at 594 nm = 0.02 mW/mm2) for 4 hours. Control plates were shielded from light. The muscle exophers were counted immediately after illumination. Control and treated groups were randomized before the start of the experiment.

Fluorescence Microscopy

Representative fluorescence images in Fig. 1a, Fig. 2b, and Fig. 2f were acquired using a Leica STELLARIS 8 FALCON confocal microscope equipped with an HC PL APO CS2 40×/1.30 oil-immersion objective. GFP and RFP fluorescence were excited using 494 nm and 575 nm laser lines, respectively. Images in Fig. 1a were captured in LIGHTNING deconvolution mode to enhance spatial resolution. The left and middle panels show single optical sections, while the right panels present a three-dimensional reconstruction of the corresponding z-stack generated in LAS X 3D Viewer (Leica Microsystems). Images in Fig. 2b and Fig. 2f were acquired in standard confocal mode and represent maximum-intensity projections of z-stacks.

Representative fluorescence images in Fig. 7d, Fig. 7h, Fig. 7l, and Supplementary Fig. 7i were obtained using a Zeiss LSM 800 laser-scanning confocal microscope equipped with an EC Plan-Neofluar 40×/1.30 Oil DIC M27 objective. GFP and RFP were excited with 488 nm and 561 nm laser lines, respectively, and all panels represent maximum-intensity projections of confocal z-stacks.

For imaging, worms were mounted on 5% agarose pads and immobilized using either 10 μl of 0.05 μm polystyrene microspheres (PolySciences) or 25 μM tetramisole as an anesthetic.

Generation of sri-19, sri-36/39, and srr-6 mutant strains

CRISPR/Cas9 genome editing was employed to generate the sri-19 mutants (sri-19(wwa15) and sri-19(wwa16)) following previously established protocols⁷³. The crRNA sequence used to target the sri-19 locus was ATAATTGGTGGATATACAAATGG. Mutations were mapped to the first exon, resulting in frame shifts that likely produce molecular null alleles.

To create srr-6 mutants, a previously described STOP-IN strategy⁷⁴ was used, which introduces an early stop codon within the first exon. The crRNA sequence used to target the srr-6 locus was ACTCCAAGTCCTGAAGTCGT, and the STOP-IN cassette sequence was GGGAAGTTTGTCCAGAGCAGAGGTGACTAAGTGATAA, flanked by AATCTTTTTCAACAATGATTACTCCAAGTCCTGAAGTCGT (left) and GGGTGACGAGGTTTCGTGTCCATCTATTATTGAAATACCT (right).

For the deletion of both the sri-36 and sri-39 genes, the entire 5968 bp region encompassing both genes (including the intercalated gene F33H12.7) was removed and replaced with a STOP-IN cassette (GGGAAGTTTGTCCAGAGCAGAGGTGACTAAGTGATAA). The flanking sequences were TGGTTACTTTTTATTTTTATTTTTC (left) and AATCCTAATCTAATATGAATCAATT (right). The crRNA sequences used for this deletion were ACAATGCTAGTTGTAGGTAC and ATTCATATTAGATTAGGATT.

All mutant strains were backcrossed twice with N2 wild-type worms to remove background mutations and verified using Sanger sequencing.

Generation of srr-6 transgenic rescue strains

Plasmids cloning

Rescue construct was generated by simultaneously inserting two fragments, promoter and gene of interest, into the linearized pMT27 vector using the SLiCE method (Seamless Ligation Cloning Extract). The pMT27 plasmid contains the unc-54-3’UTR and an unc-119(+) rescuing sequence. Restriction sites used for plasmid linearization were HpaI and PciI). Promoter region was amplified by PCR from plasmid templates. For the gene insert, cDNA from C. elegans was used as a template.

The SLiCE reaction (10 µl total volume) consisted of 100 ng of linearized vector and promoter/gene inserts in a molar ratio approximately equimolar to the vector, ensuring ~30 bp overlapping sequences between vector and inserts. The reaction mixture also contained NEBuffer 2.1 (NEB) and 0.5 µl of T4 DNA polymerase (NEB). The mixture was incubated at room temperature for 2 minutes, then placed on ice to stop the reaction, followed by bacterial transformation using the MH1 cloning strain. Plasmid sequences were verified for correctness using Nanopore sequencing.

Transgenic lines preparation

Transgenic strain was generated by germline transformation by injecting rescuing construct for srr-6 gene (100 ng/µl of eft-3p::srr-6::unc-54-3’UTR and 10 ng/µl of pGLOW77(myo-2p::mNeonGreen) from Addgene as co-injection marker) to srr-6(wwa49) mutant.

Data Analysis and Visualization

Data analysis was performed using Microsoft Excel and GraphPad Prism 9. Graphical representations of the results were generated in GraphPad Prism 9.

Statistical Analysis

No prior statistical methods were applied to determine sample sizes. Worms were randomly allocated to experimental groups, and all experiments were conducted in a blinded fashion for the datasets presented in Fig. 2d, i; Fig. 3a, b; Fig. 4a, d; Fig. 5a; Fig. 6a–j; Fig. 8a–k; Fig. 9b–k; Supplementary Fig. 2a, d; Supplementary Fig. 3a, b; Supplementary Fig. 4a, b; Supplementary Fig. 5a, b; Supplementary Fig. 7a–g, j; Supplementary Fig. 8a–h. Because the data were not normally distributed, we employed nonparametric statistical tests. For pairwise comparisons, the two-tailed Mann–Whitney tests were used, while Kruskal-Wallis tests followed by Dunn’s multiple comparisons test were applied for multi-group comparisons. A p value of <0.05 was considered statistically significant.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The authors declare that the main data supporting the findings of this study are available within the article and its supplementary files. The RNAseq data generated in this study have been deposited in the GEO database under accession code GSE312284. Data underlying the figures is deposited at RepoOD with (https://doi.org/10.18150/YBLA2I) (https://repod.icm.edu.pl/dataset.xhtml?persistentId=doi:10.18150/YBLA2I). Source data are provided with this paper.

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Acknowledgements

Some strains were provided by the CGC, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440). We thank Justyna Polaczyk, Monika Woźniak, Natalia Wasiak, Małgorzata Śledź, Filip Kozłowski, and Tomasz Strumiński for assistance with worms maintenance; Adam Kłosin for discussions and comments on the manuscript. Work in the MT Laboratory was mainly funded by a National Science Center SONATA grant (2019/35/D/NZ3/04091 to MT) and additionally supported by a National Science Center SONATA BIS grant (2021/42/E/NZ3/00358 to MT). This work was supported by the Norwegian Financial Mechanism 2014-2021 operated by the Polish National Science Center (2019/34/H/NZ3/00691 to WP) and by the French National Research Agency (ANR-22-CE13-0037-01 to NP).

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These authors contributed equally: Satya Vadlamani, Ramakrishnan Ponath Sukumaran.

Authors and Affiliations

Laboratory of Animal Molecular Physiology, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland
Klaudia Kołodziejska, Agata Szczepańska, Satya Vadlamani, Ramakrishnan Ponath Sukumaran & Michał Turek
Doctoral School of Molecular Biology and Biological Chemistry at Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland
Klaudia Kołodziejska, Satya Vadlamani & Ramakrishnan Ponath Sukumaran
Mass Spectrometry Facility, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland
Mariusz Radkiewicz
Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
Henrik Bringmann
Aix Marseille Univ, INSERM, CNRS, CIML, Turing Centre for Living Systems, 163 Avenue de Luminy, case 906, 13009, Marseille, France
Nathalie Pujol
Laboratory of Protein Metabolism, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
Wojciech Pokrzywa

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Klaudia Kołodziejska
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Agata Szczepańska
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Satya Vadlamani
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Ramakrishnan Ponath Sukumaran
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Mariusz Radkiewicz
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Henrik Bringmann
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Nathalie Pujol
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Wojciech Pokrzywa
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Michał Turek
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Contributions

Conceptualization: M.T. Data curation: M.T.; Formal analysis: K.K., A.S., S.V., R.P.S., M.R., M.T., W.P., N.P., H.B.; Funding acquisition: M.T., W.P., N.P.; Investigation: K.K., A.S., S.V., R.P.S., M.R., M.T.; Methodology: M.T., N.P., W.P., K.K., A.S., S.V., R.P.S., M.R., H.B.; Project administration: M.T.; Resources: M.T., N.P., M.R., W.P., H.B.; Supervision: M.T.; Validation: M.T., W.P., K.K., A.S., N.P., S.V., R.P.S., M.R.; Visualization: K.K., A.S., S.V., R.P.S, M.R., M.T.; Writing— original draft: M.T.; Writing—review & editing: M.T., W.P., K.K., A.S., N.P., S.V., R.P.S., H.B., M.R.

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Correspondence to Michał Turek.

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Kołodziejska, K., Szczepańska, A., Vadlamani, S. et al. Volatile and non-volatile pathogen cues shape host extracellular vesicles production in pre-infection response. Nat Commun 17, 1038 (2026). https://doi.org/10.1038/s41467-025-67789-z

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Received: 15 November 2024
Accepted: 09 December 2025
Published: 21 December 2025
Version of record: 27 January 2026
DOI: https://doi.org/10.1038/s41467-025-67789-z