Predator-prey systems offer a rich variety of prey behaviors to explore, from innate to learned responses. Prey responses to predators also vary depending on the predation strategy (Belgrad and Griffen, 2016; Palmer and Packer, 2021), as well as the prey’s abilities and the environmental context of both species (Garcia and Koelling, 1966). Additionally, prey can evaluate the cost/benefit of engaging in these antipredator behaviors, since they might impose additional costs by reducing access to food or mates (Kavaliers and Choleris, 2001). While predators kill and consume prey, they can also influence prey behavior without necessarily inflicting direct harm, in both wild and laboratory contexts (Lima, 1998; Sih, 1980). However, these changes in prey behavior often involve costs like reduced access to food or mates (Sih, 1980). For example, reintroducing wolves into Yellowstone National Park resulted in changes to the grazing patterns of female elks with calves, with more time devoted to vigilance behaviors (Childress and Lung, 2003; Laundré et al., 2001). In the laboratory setting, rats presented with cat odor spent more time in shelter than exploring, feeding, or mating (Choi and Kim, 2010; Kim et al., 2018). Laboratory experiments in model organisms can lack the natural context of predator-prey dynamics, but observation in the wild lacks the ability to link predator-prey behaviors to molecules and neural pathways. To bridge the gap between ecological relevance and mechanistic insight, we explored a predator-prey system in nematodes that brings a naturalistic predator-prey interaction into the laboratory, making it more amenable to controlled experimentation.

Caenorhabditis elegans is a nematode that lives in rotting vegetation and eats the bacteria found there (Schulenburg and Félix, 2017). With 302 neurons and a mapped connectome (White et al., 1986), it is a model well suited to study behavior with the manipulation of genes and circuits often at the resolution of a single cell. While much research in predator-prey relationships involve organisms that have vision, little is known about defensive behaviors in olfactory/mechanosensory-dependent organisms like C. elegans. With different dependencies on sensory modalities, C. elegans-specific behaviors may not necessarily mimic defensive behavior traditionally associated with sighted prey, such as freezing (Yilmaz and Meister, 2013; De Franceschi et al., 2016). C. elegans spends most of its time searching for food or eating it, as well as laying eggs, so predator threat may influence these activities. The motor sequences required for changes to navigation when searching for food, such as the frequency of turns and reversals, are subject to the integration of input from several sensory neurons, and their modulation by biogenic amine neurotransmitter signaling (Gray et al., 2005; Hills et al., 2004). Although non-predative, there are numerous examples of C. elegans altering this system of navigational decision making in response to encounters with potentially aversive stimuli. For example, C. elegans will learn to avoid pathogenic bacteria such as Serratia marcescens, a behavior mediated by serotonin signaling (Zhang et al., 2005). C. elegans will also sense and navigate away from certain metal ions such as Cu²⁺, and neurons mediating Cu²⁺ response are modulated in turn by octopamine and serotonin (Guo et al., 2015). This response is also enhanced by the presence of food which is mediated by dopaminergic signaling (Ezcurra et al., 2011). Dopaminergic signaling also impacts how an animal locomotes in response to mechanically aversive stimuli such as the touch response, which itself is again modified by the presence of food (Kindt et al., 2007). These same neurotransmitters also impact the decision of when and where to lay eggs. Exogenous serotonin is known to promote the rate of egg laying off food, meanwhile exogenous octopamine and tyramine can inhibit this behavior (Alkema et al., 2005). Dopaminergic signaling couples locomotor behavior and egg laying, promoting the rate of egg laying when animals are roaming (Cermak et al., 2020). Like other potentially aversive stimuli, predator responses may be expected to modify how an animal navigates its environment. Like these stimuli, predator-evoked changes to exploration would likely intersect with the availability of food, potentially impacting activities like egg laying, all of which is expected to be modulated by biogenic amine neurotransmitter and receptor signaling pathways.

Previous studies have shown that nematodes of the Pristionchus genus can predate on other nematodes like C. elegans (Sommer, 2006) and are found in necromenic association with beetles (Hong and Sommer, 2006; Herrmann et al., 2006) as well as in rotting vegetation along with Caenorhabditis (Félix et al., 2018; Félix et al., 2013). Members of the Pristionchus genus exhibit mouth polyphenism, with either two-toothed ‘eurystomatous’ (Eu) or one-toothed ‘stenostomatous’ (St) mouthforms (von Lieven and Sudhaus, 2000; Sudhaus et al., 2003). The Eu mouthform in P. pacificus has been shown to enable more successful killing of nematode prey like C. elegans (Serobyan et al., 2014; Wilecki et al., 2015). While P. pacificus is a relatively well-studied species within Pristionchus, it is uncertain whether C. elegans actually interacts with P. pacificus in nature. In contrast, the gonochoristic species Pristionchus uniformis has been found in the same sample with wild C. elegans isolates (Félix et al., 2018), thus P. uniformis may represent a likelier candidate for naturalistic predative antagonism to C. elegans. Although P. uniformis was first characterized as a St-only species (Fedorko and Stanuszek, 1971), it has recently been re-assessed and found to possess both a bacterivorous St and the predatory Eu mouthform (Kanzaki et al., 2014), and we too find that in standard growth conditions most P. uniformis strain JU1051 individuals have an Eu mouthform (Figure 1—figure supplement 1).

To test the hypothesis that, like other aversive stimuli, predators were able to exert an influence on patterns of C. elegans exploration, we wondered whether we could observe changes to C. elegans position and egg laying relative to food when animals experienced predator threat, and how factors like predator presence and bacterial topology intersect. As navigation and egg laying are influenced by biogenic amine signaling, we also wondered whether we could then use the molecular tools developed in C. elegans to discover the mechanisms underlying any observed changes to behavior. In this study, we show that C. elegans avoids a bacterial lawn that is occupied by its naturally cohabiting predator P. uniformis (Félix et al., 2018), and lays its eggs away from that lawn. We find that predator-exposed C. elegans potentiates the probability of egg laying off of the lawn, and this effect is sustained for many hours even after the predator is removed. This potentiation is further exaggerated when food is present outside the main bacterial lawn. Furthermore, we find that C. elegans egg laying locations are regulated by biogenic amine signaling in both baseline and predator-exposed conditions. Complete loss of dopamine synthesis resulted in overall reductions to egg laying at off lawn locations, which was restored by supplementation with exogenous dopamine. However, loss of signaling through combinations of D1 and D2 receptor homologs was able to perturb predator-induced off lawn egg laying behavior while maintaining baseline levels. Taken together we present a framework for interrogating prey behavior in nematodes, define some of the dynamics of this behavior, and identify potential molecular regulators of egg laying under predator threat.

In this study, we show that C. elegans responds to its predator P. uniformis by changing egg laying location relative to a shared food patch. When given the option to find lower density bacterial streaks off of the main lawn, C. elegans shift to laying more eggs off the lawn basally consistent with a boost in exploratory behavior when alternate food sources are present (Figures 3 and 4). When exposed to predator, C. elegans is even more likely to lay its eggs off the lawn (Figure 3) when these new food options are available and this effect is greater than either exposure to predator or the presence of these bacterial streaks alone, and persists even in the absence of predator for many hours (Figures 4 and 5).

We show that basally and in predator-exposed contexts, a shift to laying eggs off the lawn is modulated by biogenic amine signaling. Biogenic amines like dopamine and serotonin have been previously shown to play a role in driving responses to predator threat in honey bees (Nouvian et al., 2018), ants (Aonuma, 2020), and fruit flies (Gibson et al., 2015). Consistently, we find that loss of dopamine synthesis modulates baseline C. elegans egg laying which is consistent with changes to locomotion observed in these mutants (Chase and Koelle, 2007). This behavior is rescued by transgenic re-expression of cat-2 in CEP or ADE/PDE neurons or with the application of exogenous dopamine (Figures 6 and 7). Finally, we show that loss of specific combinations of dopaminergic receptors can exhibit effects to the basal rate of off lawn egg laying, but importantly also appear to modulate the magnitude of predator response (Figures 8 and 9). Other biogenic amines such as serotonin also appear to exert effects on off lawn egg laying and their contributions to predator-evoked response merit future investigation.

CEP neurons have been previously implicated in learning the size of bacterial lawns. We previously showed that C. elegans learns the size of the lawn by using high threshold sensory neurons that detect lawn edges, which in turn signal to CEP neurons to release dopamine. In this paradigm, we speculate that information about lawn size was stored in amount of dopamine released from CEP neurons (Calhoun et al., 2015). PDE neurons are involved in increasing egg laying during roaming, and dopamine release can increase the probability of egg laying in the absence of food (Nass et al., 2005), so dopamine release from PDE in this predator-prey assay could also encourage egg laying off lawn. While it is the case that effects observed in our transgenic rescue experiments could be due to artifacts of promoter usage, this known division of labor between CEP and PDE could also explain the intermediate levels of rescue to off lawn laying we observe.

We observe a role for multiple dopamine receptors in this prey response to predator threat. The C. elegans genome encodes at least four dopamine receptors (Hobert, 2013). While DOP-1 and DOP-2/3 are the C. elegans homologs of the mammalian D1-like and D2-like receptors respectively, DOP-4 is a D1-like receptor unique to invertebrates (Sugiura et al., 2005; Chase et al., 2004). We find that the dop-1; dop-2; dop-3 triple mutant animals have a reduced response to predator threat while maintaining normal off lawn egg laying behavior. Complete loss of all four receptors, or the double loss of dop-3 and dop-4, results in greatest reductions to baseline off lawn laying. Studies in mammals where pharmacology and receptor knockouts have shown that knockouts in D1- and D2-like receptors can have opposing effects on behavior (Sugiura et al., 2005; Kelly et al., 1998; Gong et al., 1999; McNamara et al., 2003). Here, we show that specific combinations of receptors can exert varying effects. While we did not identify the site of action of these receptors, we suggest that the combined action of DOP-1, -2, and -3 receptors act downstream of dopamine release to alter prey egg location in predator-exposed animals.

All data generated or analysed during this study are included in the manuscript. Raw data for experiments in Figures 1—9 and figure supplements are provided as source data files in MS Excel format. Analysis code for computation of associated effects can be found on the Shrek Lab GitHub: https://github.com/shreklab/PribadiEtAl2023 (copy archived at Shrek Lab, 2023).

1. 1. Alkema MJ
   2. Hunter-Ensor M
   3. Ringstad N
   4. Horvitz HR

   (2005) Tyramine functions independently of Octopamine in the Caenorhabditis elegans nervous system

   Neuron 46:247–260.

   https://doi.org/10.1016/j.neuron.2005.02.024

   • PubMed
   • Google Scholar
2. 1. Aonuma H

   (2020) Serotonergic control in initiating defensive responses to unexpected tactile stimuli in the trap-jaw ant Odontomachus Kuroiwae

   The Journal of Experimental Biology 223:jeb228874.

   https://doi.org/10.1242/jeb.228874

   • PubMed
   • Google Scholar
3. 1. Belgrad BA
   2. Griffen BD

   (2016) Predator–prey interactions mediated by prey personality and predator hunting mode

   Proceedings. Biological Sciences 283:20160408.

   https://doi.org/10.1098/rspb.2016.0408

   • PubMed
   • Google Scholar
4. 1. Brenner S

   (1974) The genetics of Caenorhabditis elegans

   Genetics 77:71–94.

   https://doi.org/10.1093/genetics/77.1.71

   • PubMed
   • Google Scholar
5. 1. Calhoun AJ
   2. Tong A
   3. Pokala N
   4. Fitzpatrick JAJ
   5. Sharpee TO
   6. Chalasani SH

   (2015) Neural mechanisms for evaluating environmental variability in Caenorhabditis elegans

   Neuron 86:428–441.

   https://doi.org/10.1016/j.neuron.2015.03.026

   • PubMed
   • Google Scholar
6. 1. Carvelli L
   2. McDonald PW
   3. Blakely RD
   4. DeFelice LJ

   (2004) Dopamine transporters Depolarize neurons by a channel mechanism

   PNAS 101:16046–16051.

   https://doi.org/10.1073/pnas.0403299101

   • PubMed
   • Google Scholar
7. 1. Cermak N
   2. Yu SK
   3. Clark R
   4. Huang YC
   5. Baskoylu SN
   6. Flavell SW

   (2020) Whole-organism behavioral profiling reveals a role for dopamine in state-dependent motor program coupling in C. elegans

   eLife 9:e57093.

   https://doi.org/10.7554/eLife.57093

   • PubMed
   • Google Scholar
8. 1. Chase DL
   2. Pepper JS
   3. Koelle MR

   (2004) Mechanism of Extrasynaptic dopamine signaling in Caenorhabditis elegans

   Nature Neuroscience 7:1096–1103.

   https://doi.org/10.1038/nn1316

   • PubMed
   • Google Scholar
9. 1. Chase DL
   2. Koelle MR

   (2007) Biogenic amine neurotransmitters in C. elegans

   WormBook 7:1–15.

   https://doi.org/10.1895/wormbook.1.132.1

   • PubMed
   • Google Scholar
10. 1. Childress MJ
    2. Lung MA

    (2003) Predation risk, gender and the group size effect: does ELK vigilance depend upon the behaviour of Conspecifics

    Animal Behaviour 66:389–398.

    https://doi.org/10.1006/anbe.2003.2217

    • Google Scholar
11. 1. Choi JS
    2. Kim JJ

    (2010) Amygdala regulates risk of Predation in rats foraging in a dynamic fear environment

    PNAS 107:21773–21777.

    https://doi.org/10.1073/pnas.1010079108

    • PubMed
    • Google Scholar
12. 1. Click A
    2. Savaliya CH
    3. Kienle S
    4. Herrmann M
    5. Pires-daSilva A

    (2009) Natural variation of outcrossing in the hermaphroditic nematode pristionchus pacificus

    BMC Evolutionary Biology 9:75.

    https://doi.org/10.1186/1471-2148-9-75

    • PubMed
    • Google Scholar
13. 1. De Franceschi G
    2. Vivattanasarn T
    3. Saleem AB
    4. Solomon SG

    (2016) Vision guides selection of freeze or flight defense strategies in mice

    Current Biology 26:2150–2154.

    https://doi.org/10.1016/j.cub.2016.06.006

    • PubMed
    • Google Scholar
14. 1. Duerr JS
    2. Frisby DL
    3. Gaskin J
    4. Duke A
    5. Asermely K
    6. Huddleston D
    7. Eiden LE
    8. Rand JB

    (1999) The Cat-1 Gene of Caenorhabditis elegans Encodes a vesicular monoamine transporter required for specific monoamine-dependent behaviors

    The Journal of Neuroscience 19:72–84.

    https://doi.org/10.1523/JNEUROSCI.19-01-00072.1999

    • PubMed
    • Google Scholar
15. 1. Ezcurra M
    2. Tanizawa Y
    3. Swoboda P
    4. Schafer WR

    (2011) Food sensitizes C. elegans avoidance Behaviours through acute dopamine signalling

    The EMBO Journal 30:1110–1122.

    https://doi.org/10.1038/emboj.2011.22

    • PubMed
    • Google Scholar
16. 1. Fedorko A
    2. Stanuszek S

    (1971)

    Pristionchus uniformis SP. N.(nematoda, rhabditida, diplogasteridae), a facultative parasite of leptinotarsa decemlineata say and melolontha melolontha L in poland morphology and biology

    Acta Parasitologica Polonica 19:95–112.

    • Google Scholar
17. 1. Félix M-A
    2. Jovelin R
    3. Ferrari C
    4. Han S
    5. Cho YR
    6. Andersen EC
    7. Cutter AD
    8. Braendle C

    (2013) Species richness, distribution and genetic diversity of Caenorhabditis nematodes in a remote tropical Rainforest

    BMC Evolutionary Biology 13:10.

    https://doi.org/10.1186/1471-2148-13-10

    • PubMed
    • Google Scholar
18. 1. Félix MA
    2. Ailion M
    3. Hsu JC
    4. Richaud A
    5. Wang J

    (2018) Pristionchus nematodes occur frequently in diverse rotting Vegetal substrates and are not exclusively Necromenic, while Panagrellus Redivivoides is found specifically in rotting fruits

    PLOS ONE 13:e0200851.

    https://doi.org/10.1371/journal.pone.0200851

    • PubMed
    • Google Scholar
19. 1. Flames N
    2. Hobert O

    (2009) Gene regulatory logic of dopamine neuron differentiation

    Nature 458:885–889.

    https://doi.org/10.1038/nature07929

    • PubMed
    • Google Scholar
20. 1. Flavell SW
    2. Pokala N
    3. Macosko EZ
    4. Albrecht DR
    5. Larsch J
    6. Bargmann CI

    (2013) Serotonin and the neuropeptide PDF initiate and extend opposing behavioral States in C. elegans

    Cell 154:1023–1035.

    https://doi.org/10.1016/j.cell.2013.08.001

    • PubMed
    • Google Scholar
21. Book

    1. Fox J
    2. Weisberg S

    (2018)

    An R Companion to Applied Regression

    Sage publications.

    • Google Scholar
22. 1. Garcia J
    2. Koelling RA

    (1966) Relation of cue to consequence in avoidance learning

    Psychonomic Science 4:123–124.

    https://doi.org/10.3758/BF03342209

    • Google Scholar
23. 1. Gibson WT
    2. Gonzalez CR
    3. Fernandez C
    4. Ramasamy L
    5. Tabachnik T
    6. Du RR
    7. Felsen PD
    8. Maire MR
    9. Perona P
    10. Anderson DJ

    (2015) Behavioral responses to a repetitive visual threat stimulus express a persistent state of defensive arousal in Drosophila

    Current Biology 25:1401–1415.

    https://doi.org/10.1016/j.cub.2015.03.058

    • PubMed
    • Google Scholar
24. 1. Gong W
    2. Neill DB
    3. Lynn M
    4. Justice JB Jr

    (1999) Dopamine D1/D2 agonists injected into nucleus accumbens and ventral Pallidum Differentially affect locomotor activity depending on site

    Neuroscience 93:1349–1358.

    https://doi.org/10.1016/S0306-4522(99)00235-3

    • Google Scholar
25. 1. Gray JM
    2. Hill JJ
    3. Bargmann CI

    (2005) A circuit for navigation in Caenorhabditis elegans

    PNAS 102:3184–3191.

    https://doi.org/10.1073/pnas.0409009101

    • PubMed
    • Google Scholar
26. 1. Guo M
    2. Wu TH
    3. Song YX
    4. Ge MH
    5. Su CM
    6. Niu WP
    7. Li LL
    8. Xu ZJ
    9. Ge CL
    10. Al-Mhanawi MTH
    11. Wu SP
    12. Wu ZX

    (2015) Reciprocal inhibition between sensory ASH and ASI neurons modulates Nociception and avoidance in Caenorhabditis elegans

    Nature Communications 6:5655.

    https://doi.org/10.1038/ncomms6655

    • PubMed
    • Google Scholar
27. 1. Han B
    2. Dong Y
    3. Zhang L
    4. Liu Y
    5. Rabinowitch I
    6. Bai J

    (2017) Dopamine signaling tunes spatial pattern selectivity in C. elegans

    eLife 6:e22896.

    https://doi.org/10.7554/eLife.22896

    • PubMed
    • Google Scholar
28. 1. Herrmann M
    2. Mayer WE
    3. Sommer RJ

    (2006) Nematodes of the genus Pristionchus are closely associated with Scarab beetles and the Colorado potato BEETLE in Western Europe

    Zoology 109:96–108.

    https://doi.org/10.1016/j.zool.2006.03.001

    • PubMed
    • Google Scholar
29. 1. Hilliard MA
    2. Bargmann CI
    3. Bazzicalupo P

    (2002) C. elegans responds to chemical Repellents by integrating sensory inputs from the head and the tail

    Current Biology 12:730–734.

    https://doi.org/10.1016/s0960-9822(02)00813-8

    • PubMed
    • Google Scholar
30. 1. Hills T
    2. Brockie PJ
    3. Maricq AV

    (2004) Dopamine and glutamate control area-restricted search behavior in Caenorhabditis elegans

    The Journal of Neuroscience 24:1217–1225.

    https://doi.org/10.1523/JNEUROSCI.1569-03.2004

    • PubMed
    • Google Scholar
31. 1. Hobert O

    (2013) The neuronal genome of Caenorhabditis elegans

    WormBook 1:1–106.

    https://doi.org/10.1895/wormbook.1.161.1

    • PubMed
    • Google Scholar
32. 1. Hong RL
    2. Sommer RJ

    (2006) Chemoattraction in Pristionchus nematodes and implications for insect recognition

    Current Biology 16:2359–2365.

    https://doi.org/10.1016/j.cub.2006.10.031

    • PubMed
    • Google Scholar
33. 1. Horvitz HR
    2. Chalfie M
    3. Trent C
    4. Sulston JE
    5. Evans PD

    (1982) Serotonin and octopamine in the nematode Caenorhabditis elegans

    Science 216:1012–1014.

    https://doi.org/10.1126/science.6805073

    • PubMed
    • Google Scholar
34. 1. Hothorn T
    2. Bretz F
    3. Westfall P

    (2008) Simultaneous inference in general parametric models

    Biometrical Journal. Biometrische Zeitschrift 50:346–363.

    https://doi.org/10.1002/bimj.200810425

    • PubMed
    • Google Scholar
35. 1. Kanzaki N
    2. Ragsdale EJ
    3. Herrmann M
    4. Sommer RJ

    (2014)

    Two new and two Recharacterized species from a radiation of Pristionchus (Nematoda: Diplogastridae) in Europe

    Journal of Nematology 46:60–74.

    • PubMed
    • Google Scholar
36. 1. Kavaliers M
    2. Choleris E

    (2001) Antipredator responses and defensive behavior: ecological and Ethological approaches for the Neurosciences

    Neuroscience and Biobehavioral Reviews 25:577–586.

    https://doi.org/10.1016/s0149-7634(01)00042-2

    • PubMed
    • Google Scholar
37. 1. Kelly MA
    2. Rubinstein M
    3. Phillips TJ
    4. Lessov CN
    5. Burkhart-Kasch S
    6. Zhang G
    7. Bunzow JR
    8. Fang Y
    9. Gerhardt GA
    10. Grandy DK
    11. Low MJ

    (1998) Locomotor activity in D2 dopamine receptor-deficient mice is determined by Gene dosage, genetic background, and developmental adaptations

    The Journal of Neuroscience 18:3470–3479.

    https://doi.org/10.1523/JNEUROSCI.18-09-03470.1998

    • Google Scholar
38. 1. Kim EJ
    2. Kong MS
    3. Park SG
    4. Mizumori SJY
    5. Cho J
    6. Kim JJ

    (2018) Dynamic coding of predatory information between the Prelimbic cortex and lateral amygdala in foraging rats

    Science Advances 4:eaar7328.

    https://doi.org/10.1126/sciadv.aar7328

    • PubMed
    • Google Scholar
39. 1. Kindt KS
    2. Quast KB
    3. Giles AC
    4. De S
    5. Hendrey D
    6. Nicastro I
    7. Rankin CH
    8. Schafer WR

    (2007) Dopamine mediates context-dependent modulation of sensory plasticity in C. elegans

    Neuron 55:662–676.

    https://doi.org/10.1016/j.neuron.2007.07.023

    • PubMed
    • Google Scholar
40. 1. Laundré JW
    2. Hernández L
    3. Altendorf KB

    (2001) Wolves, ELK, and bison: reestablishing the" landscape of fear" in Yellowstone National Park, USA

    Canadian Journal of Zoology 79:1401–1409.

    https://doi.org/10.1139/z01-094

    • Google Scholar
41. 1. Lima SL

    (1998) Nonlethal effects in the Ecology of predator-prey interactions

    BioScience 48:25–34.

    https://doi.org/10.2307/1313225

    • Google Scholar
42. 1. Lints R
    2. Emmons SW

    (1999) Patterning of dopaminergic neurotransmitter identity among Caenorhabditis elegans Ray sensory neurons by a Tgfbeta family signaling pathway and a Hox Gene

    Development 126:5819–5831.

    https://doi.org/10.1242/dev.126.24.5819

    • PubMed
    • Google Scholar
43. 1. Liu Z
    2. Kariya MJ
    3. Chute CD
    4. Pribadi AK
    5. Leinwand SG
    6. Tong A
    7. Curran KP
    8. Bose N
    9. Schroeder FC
    10. Srinivasan J
    11. Chalasani SH

    (2018) Predator-secreted Sulfolipids induce defensive responses in C. elegans

    Nature Communications 9:1128.

    https://doi.org/10.1038/s41467-018-03333-6

    • PubMed
    • Google Scholar
44. 1. McNamara FN
    2. Clifford JJ
    3. Tighe O
    4. Kinsella A
    5. Drago J
    6. Croke DT
    7. Waddington JL

    (2003) Congenic D1A dopamine receptor Mutants: Ethologically based resolution of behavioural topography indicates genetic background as a determinant of knockout phenotype

    Neuropsychopharmacology 28:86–99.

    https://doi.org/10.1038/sj.npp.1300008

    • PubMed
    • Google Scholar
45. 1. Nass R
    2. Hahn MK
    3. Jessen T
    4. McDonald PW
    5. Carvelli L
    6. Blakely RD

    (2005) A genetic screen in Caenorhabditis elegans for dopamine neuron Insensitivity to 6-hydroxydopamine identifies dopamine transporter Mutants Impacting transporter biosynthesis and trafficking

    Journal of Neurochemistry 94:774–785.

    https://doi.org/10.1111/j.1471-4159.2005.03205.x

    • PubMed
    • Google Scholar
46. 1. Nouvian M
    2. Mandal S
    3. Jamme C
    4. Claudianos C
    5. d’Ettorre P
    6. Reinhard J
    7. Barron AB
    8. Giurfa M

    (2018) Cooperative defence operates by social modulation of Biogenic amine levels in the honey bee brain

    Proceedings. Biological Sciences 285:20172653.

    https://doi.org/10.1098/rspb.2017.2653

    • PubMed
    • Google Scholar
47. 1. Palmer MS
    2. Packer C

    (2021) Reactive anti-predator behavioral strategy shaped by predator characteristics

    PLOS ONE 16:e0256147.

    https://doi.org/10.1371/journal.pone.0256147

    • PubMed
    • Google Scholar
48. 1. Perreault ML
    2. Hasbi A
    3. O’Dowd BF
    4. George SR

    (2014) Heteromeric dopamine receptor signaling complexes: emerging Neurobiology and disease relevance

    Neuropsychopharmacology 39:156–168.

    https://doi.org/10.1038/npp.2013.148

    • PubMed
    • Google Scholar
49. 1. Pujol N
    2. Cypowyj S
    3. Ziegler K
    4. Millet A
    5. Astrain A
    6. Goncharov A
    7. Jin Y
    8. Chisholm AD
    9. Ewbank JJ

    (2008a) Distinct innate immune responses to infection and wounding in the C. elegans epidermis

    Current Biology 18:481–489.

    https://doi.org/10.1016/j.cub.2008.02.079

    • PubMed
    • Google Scholar
50. 1. Pujol N
    2. Zugasti O
    3. Wong D
    4. Couillault C
    5. Kurz CL
    6. Schulenburg H
    7. Ewbank JJ

    (2008b) Anti-fungal innate immunity in C. elegans is enhanced by evolutionary diversification of antimicrobial peptides

    PLOS Pathogens 4:e1000105.

    https://doi.org/10.1371/journal.ppat.1000105

    • PubMed
    • Google Scholar
51. 1. Quach KT
    2. Chalasani SH

    (2022) Flexible Reprogramming of Pristionchus Pacificus motivation for attacking Caenorhabditis elegans in predator-prey competition

    Current Biology 32:1675–1688.

    https://doi.org/10.1016/j.cub.2022.02.033

    • PubMed
    • Google Scholar
52. 1. Ragsdale EJ
    2. Müller MR
    3. Rödelsperger C
    4. Sommer RJ

    (2013) A developmental switch coupled to the evolution of plasticity acts through a Sulfatase

    Cell 155:922–933.

    https://doi.org/10.1016/j.cell.2013.09.054

    • PubMed
    • Google Scholar
53. Software

    1. R Development Core Team

    (2009) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org/
54. 1. Sawin ER
    2. Ranganathan R
    3. Horvitz HR

    (2000) C. elegans Locomotory rate is modulated by the environment through a dopaminergic pathway and by experience through a serotonergic pathway

    Neuron 26:619–631.

    https://doi.org/10.1016/s0896-6273(00)81199-x

    • PubMed
    • Google Scholar
55. 1. Schafer WR

    (2005) Egg-laying

    WormBook 1:1–7.

    https://doi.org/10.1895/wormbook.1.38.1

    • PubMed
    • Google Scholar
56. 1. Schafer WF

    (2006) Genetics of egg-laying in worms

    Annual Review of Genetics 40:487–509.

    https://doi.org/10.1146/annurev.genet.40.110405.090527

    • PubMed
    • Google Scholar
57. 1. Schulenburg H
    2. Félix MA

    (2017) The natural biotic environment of Caenorhabditis elegans

    Genetics 206:55–86.

    https://doi.org/10.1534/genetics.116.195511

    • PubMed
    • Google Scholar
58. 1. Scott E
    2. Hudson A
    3. Feist E
    4. Calahorro F
    5. Dillon J
    6. de Freitas R
    7. Wand M
    8. Schoofs L
    9. O’Connor V
    10. Holden-Dye L

    (2017) An oxytocin-dependent social interaction between larvae and adult C. elegans

    Scientific Reports 7:10122.

    https://doi.org/10.1038/s41598-017-09350-7

    • PubMed
    • Google Scholar
59. 1. Serobyan V
    2. Ragsdale EJ
    3. Sommer RJ

    (2014) Adaptive value of a predatory mouth-form in a Dimorphic Nematode

    Proceedings. Biological Sciences 281:20141334.

    https://doi.org/10.1098/rspb.2014.1334

    • PubMed
    • Google Scholar
60. Software

    1. Shrek Lab

    (2023) Pribadietal2023, version swh:1:rev:7816eee41b997c82e58a885fb6abf09bc85deeab

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:30dd9b54d00c282a1fdd8fdbfcbce9b41952da5f;origin=https://github.com/shreklab/PribadiEtAl2023;visit=swh:1:snp:047d1f67564f224998b6070ee17370045b460220;anchor=swh:1:rev:7816eee41b997c82e58a885fb6abf09bc85deeab
61. 1. Sievers F
    2. Wilm A
    3. Dineen D
    4. Gibson TJ
    5. Karplus K
    6. Li W
    7. Lopez R
    8. McWilliam H
    9. Remmert M
    10. Söding J
    11. Thompson JD
    12. Higgins DG

    (2011) Fast, Scalable generation of high-quality protein multiple sequence alignments using Clustal Omega

    Molecular Systems Biology 7:539.

    https://doi.org/10.1038/msb.2011.75

    • PubMed
    • Google Scholar
62. 1. Sih A

    (1980) Optimal behavior: can Foragers balance two conflicting demands?

    Science 210:1041–1043.

    https://doi.org/10.1126/science.210.4473.1041

    • PubMed
    • Google Scholar
63. 1. Sih A

    (1987) Prey refuges and predator-prey stability

    Theoretical Population Biology 31:1–12.

    https://doi.org/10.1016/0040-5809(87)90019-0

    • Google Scholar
64. 1. Sommer RJ

    (2006) Pristionchus Pacificus

    WormBook 1:1–8.

    https://doi.org/10.1895/wormbook.1.102.1

    • PubMed
    • Google Scholar
65. 1. Sudhaus W
    2. Fürst V
    3. Lieven A

    (2003)

    A Phylogenetic classification and catalogue of the Diplogastridae (Secernentea, Nematoda)

    Journal of Nematode Morphology and Systematics 6:43–90.

    • Google Scholar
66. 1. Sugiura M
    2. Fuke S
    3. Suo S
    4. Sasagawa N
    5. Van Tol HHM
    6. Ishiura S

    (2005) Characterization of a novel D2-like dopamine receptor with a truncated splice variant and a D1-like dopamine receptor unique to Invertebrates from Caenorhabditis elegans

    Journal of Neurochemistry 94:1146–1157.

    https://doi.org/10.1111/j.1471-4159.2005.03268.x

    • PubMed
    • Google Scholar
67. 1. Sulston J
    2. Dew M
    3. Brenner S

    (1975) Dopaminergic neurons in the nematode Caenorhabditis elegans

    The Journal of Comparative Neurology 163:215–226.

    https://doi.org/10.1002/cne.901630207

    • PubMed
    • Google Scholar
68. 1. Sze JY
    2. Victor M
    3. Loer C
    4. Shi Y
    5. Ruvkun G

    (2000) Food and metabolic signalling defects in a Caenorhabditis elegans serotonin-synthesis mutant

    Nature 403:560–564.

    https://doi.org/10.1038/35000609

    • PubMed
    • Google Scholar
69. 1. von Lieven AF
    2. Sudhaus W

    (2000) Comparative and functional morphology of the Buccal cavity of Diplogastrina (Nematoda) and a first outline of the Phylogeny of this Taxon

    Journal of Zoological Systematics and Evolutionary Research 38:37–63.

    https://doi.org/10.1046/j.1439-0469.2000.381125.x

    • Google Scholar
70. 1. Werner MS
    2. Sieriebriennikov B
    3. Loschko T
    4. Namdeo S
    5. Lenuzzi M
    6. Dardiry M
    7. Renahan T
    8. Sharma DR
    9. Sommer RJ

    (2017) Environmental influence on Pristionchus Pacificus mouth form through different culture methods

    Scientific Reports 7:7207.

    https://doi.org/10.1038/s41598-017-07455-7

    • PubMed
    • Google Scholar
71. 1. White JG
    2. Southgate E
    3. Thomson JN
    4. Brenner S

    (1986) The structure of the nervous system of the Nematode Caenorhabditis elegans

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 314:1–340.

    https://doi.org/10.1098/rstb.1986.0056

    • PubMed
    • Google Scholar
72. 1. Wilecki M
    2. Lightfoot JW
    3. Susoy V
    4. Sommer RJ

    (2015) Predatory feeding behaviour in Pristionchus nematodes is dependent on Phenotypic plasticity and induced by serotonin

    The Journal of Experimental Biology 218:1306–1313.

    https://doi.org/10.1242/jeb.118620

    • PubMed
    • Google Scholar
73. 1. Yilmaz M
    2. Meister M

    (2013) Rapid innate defensive responses of mice to looming visual stimuli

    Current Biology 23:2011–2015.

    https://doi.org/10.1016/j.cub.2013.08.015

    • PubMed
    • Google Scholar
74. 1. Zhang Y
    2. Lu H
    3. Bargmann CI

    (2005) Pathogenic bacteria induce Aversive olfactory learning in Caenorhabditis elegans

    Nature 438:179–184.

    https://doi.org/10.1038/nature04216

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Dopamine signaling regulates predator-driven behavioral changes in Caenorhabditis elegans" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Piali Sengupta as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. Reviewer 2 raises some important concerns regarding the analysis and interpretation of your data. In a revised manuscript, please consider the null hypothesis that worms are simply dispersing and laying more eggs during the roaming state as a result of dopamine release. This will likely require some additional data analysis.

2. Please address reviewer 1's concern about the interpretation of the CEP-specific cat-2 rescue experiment.

Addressing these concerns does not require any new experiments.

Reviewer #1 (Recommendations for the authors):

I find the behavioral responses identified by the authors to be robust and compelling; the novelty of the finding is for me a large part of the appeal of the paper. Some aspects of the mechanistic dissection are not as strong as they could be (see detailed comments below) and I am surprised that the role of 5HT was not more carefully examined. Below are some specific comments that the authors may wish to consider, listed roughly in order of importance.

– It's interesting but a bit surprising that the eud-1 (St only) P. pacificus fail to elicit a behavioral response in C. elegans, even though they presumably still produce compounds that C. elegans perceives as aversive. I imagine the explanation is that this response isn't strong enough to bring about a change in egg distribution, but is it possible that the St form doesn't produce aversive chemical cues? If so, this would undermine the idea that the bite itself provides the trigger for behavioral change.

– The conclusions from the cat-2 rescue experiments may be overstated. Could the apparent importance of CEPs simply be a consequence of stronger expression of the CEP-specific promoter compared to the ADE/PDE promoter? It could be that the site of DA release is less important than the amount of release. The interpretation of this result should be toned down (lines 256-58, abstract, discussion).

– Figure 6 – I'm not sure I agree with the authors' interpretation of these results. The phenotype of the dop-1; dop-3 double is more than "slight", and the evidence for a meaningful role for dop-2 is weak.

– The exogenous DA experiments are fascinating but somewhat difficult to interpret. Do the authors think that exogenous DA is bypassing the effects of endogenous DA release (which would support the idea that DA signaling, at or downstream of its receptors, is altered in predator-exposed animals)? Or is it possible instead that exogenous DA is rapidly taken up by DA neurons and then released in a regulated way? In this case it's less clear how (or even if) DA signaling is being actively regulated.

– It's quite interesting that 5HT signaling is important for predator-induced behavior changes. The rationale for not studying this – that 5HT is already known to be important for egg-laying and in predator/prey behavior – is a little underwhelming. It doesn't seem obvious how the known functions of 5HT in worm behavior predict a straightforward explanation of its role here.

– It might be interesting to know whether nlp-29 has a role in the detection of the predator stimulus. The authors show that the behavioral change triggered by P. uniformus occurs without apparent induction of nlp-29::gfp, but it could be that this induction is weak/undetectable. It could also be that injury triggers an increase in nlp-29 release by the hypodermis without a strong upregulation of nlp-29 transcription.

– Line 243 – please rephrase (mutants don't contribute to the change, neurotransmitters do).

– Line 249 – please note that this expression pattern is specific to adult hermaphrodites.

– Lines 282-284 – this is an inappropriate overinterpretation of negative data. A more likely explanation for the lack of statistical support for early changes in behavior is that the experiment is underpowered.

– Lines 285-6 – "locomotor response" suggests changes in specific features of motor behavior – "navigational response" might be better here.

– Line 362 – Please provide additional details on the methods here. To conclude that the effect "lasts at least 6 hours" it would be necessary to examine the location of animals and/or eggs laid during a short window around the 6 hr time point, not cumulatively from 0-6 hours. It's hard to tell which was the case here.

– Lines 443-5 – word(s) missing from this sentence?

– Line 476 – this sentence needs editing.

– Figure S1 – legends for (b) and (c) are switched.

– Figure 6-S4 is mislabled as Figure 4-S4.

Reviewer #2 (Recommendations for the authors):

I think this is a great experimental paradigm, with a nice progression of genetic experiments to identify the role of dopamine release from CEP neurons as causing the altered behavior in response to predation. However, I think how the data are quantified and interpreted should be altered, as mentioned in my prior comments. I realize the time resolution of the experiments are not sufficient to quantify random walk dynamics, and I am normally loathed to request more experiments, especially since so many have been performed here, but perhaps the data could be reanalyzed in a way that more accurately reflects the underlying phenomenon.

For example, the authors claim more eggs are laid off the lawn, and quantify this in Figure 1-S1. Why did you not do this for all the data? This is simple, and could be easily quantified with a simple chi-square analysis. The next question is, why? Since worms inherently perform random walks, the null hypothesis would be that their effective diffusion constant has changed. The drive to perform random walks is fundamental strategy of worm navigation, even while moving toward favorable conditions. A nice quantification of this property can be found in Klein et al., eLife, 2017.

This is especially relevant since we know that the worm's effective diffusion constant is different on and off food, and that neuromodulation alters these statistics. I realize your movies don't have sufficient resolution to capture these dynamics, but you do have the spatial distribution of eggs, correct? Why not plot that distribution for the different conditions, rather than quantifying the relative distance from the food edge, which is nebulous given the effect size? You could then compare distributions, and do a bootstrap analysis to verify whether changes in distribution are different? Given what's already known about dopamine and egg laying (Cermak et al., 2020), as well as decades of work quantifying the worm's random walk behavior, a shift in the statistics of this underlying stochastic motor program should be considered the null hypothesis.

I appreciate the Appendix with the statistics, but in the absence of the raw data, it's really impossible to validate if any of this is accurate. Surely these analyses were based upon the analysis of text files/tables of data, correct? Why not include them?

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Dopamine signaling regulates predator-driven changes in Caenorhabditis elegans' egg laying behavior" for further consideration by eLife. Your revised article has been evaluated by Piali Sengupta (Senior Editor) and a Reviewing Editor.

Both reviewers feel that the revised manuscript has been substantially improved. However, before publication, we would like you to consider Reviewer #1's point about the role of dopamine in bringing about predator-induced changes in behavior. The bulk of your evidence points to a function for DA in egg-laying per se (which of course is already well-established), with its role in changes in behavior upon predator exposure being much more subtle. We suggest being a bit more cautious in some of your interpretations.

Each reviewer also has several minor comments/suggestions that you may wish to address.

Reviewer #1 (Recommendations for the authors):

The revised manuscript is significantly improved, largely due to the change in data analysis suggested by reviewer #2. The demonstration of the persistent effects of predator exposure on locomotor and egg-laying behavior is robust and fascinating.

My biggest concern about the revised manuscript is that the role of dopamine in predator-induced behavioral change is a bit overstated. Nearly all of the experiments in the paper now clearly show that the primary role of dopamine in this paradigm is to modulate baseline P(off). The direct evidence that DA is required for predator-induced changes is relatively slim. To my mind, the only compelling result is the dop-1;2;3 triple mutant in Figure 9c. In general, the experiments in Figures8 and 9 are tricky to interpret because the baseline P(off) is quite a bit lower here than in previous experiments. This raises the concern that floor effects are limiting the ability to see further reductions in baseline P(off) in mutants, which could in turn lead to artificially low values for fold-change in P(off) in the predator-exposed condition.

Essential revisions:

1. Reviewer 2 raises some important concerns regarding the analysis and interpretation of your data. In a revised manuscript, please consider the null hypothesis that worms are simply dispersing and laying more eggs during the roaming state as a result of dopamine release. This will likely require some additional data analysis.

We have fully re-analyzed egg assay data using the quantification of # of eggs laid off and on lawns as the primary metric for discerning effects across conditions (see extensive treatment in response to Reviewer 2). This is both a powerful and easily digestible statistical paradigm as we are now primarily concerned with the probability of eggs being laid on and off lawn and the interactions of various conditions on this probability.

As a result of this, a number of concerns of the reviewers specifically regarding dopamine synthesis and mutants in dopaminergic signaling are well founded. We find on the whole that mutants in dopamine synthesis do not in fact show reproducible blunting of the predator response itself, but instead seem to blunt the overall probability of laying eggs off the lawn even in control conditions. However, we have found significant effects on predator exposure with respect to combinations of dopamine receptors mutants. In light of this re-analysis, we have re-ordered the paper, replotted figures, and in general overhauled the way we treat these data. Again, please see the extensive comment to Reviewer #2.

2. Please address reviewer 1's concern about the interpretation of the CEP-specific cat-2 rescue experiment.

In light of the statistical overhaul noted above, we are not highlighting the role in these neurons in altering the predator effect itself. It is clear that cat-2 mutants blunt the baseline probability of off-lawn laying. When looking at proportions of eggs laid off the lawn, rescue in ADE/PDE partially restores this background, and CEP further elevates the probability of off-lawn laying in the control condition. All show an increased proportion of eggs laid off lawn when predator is present. Please see lines 388 – 404 for the description and discussion of these data.

Addressing these concerns does not require any new experiments.

Reviewer #1 (Recommendations for the authors):

I find the behavioral responses identified by the authors to be robust and compelling; the novelty of the finding is for me a large part of the appeal of the paper. Some aspects of the mechanistic dissection are not as strong as they could be (see detailed comments below) and I am surprised that the role of 5HT was not more carefully examined. Below are some specific comments that the authors may wish to consider, listed roughly in order of importance.

– It's interesting but a bit surprising that the eud-1 (St only) P. pacificus fail to elicit a behavioral response in C. elegans, even though they presumably still produce compounds that C. elegans perceives as aversive. I imagine the explanation is that this response isn't strong enough to bring about a change in egg distribution, but is it possible that the St form doesn't produce aversive chemical cues? If so, this would undermine the idea that the bite itself provides the trigger for behavioral change.

We find that conditioning a lawn with P. uniformis secretions is not sufficient to drive a change the probability of C. elegans off-lawn egg laying (Figure 1—figure supplement 6). We do agree with the reviewer that eud-1 mutants might still produce aversive compounds for C. elegans prey. However, our egg-laying assay is unable to identify the behavioral changes. We have included a statement about eud-1 mutants in our revised manuscript Line 177-179.

New manuscript Line 177: “These data indicate that interactions between eud-1 mutants and prey (secretions, contacts, and others) are unable to alter the locations of C. elegans eggs.”

– The conclusions from the cat-2 rescue experiments may be overstated. Could the apparent importance of CEPs simply be a consequence of stronger expression of the CEP-specific promoter compared to the ADE/PDE promoter? It could be that the site of DA release is less important than the amount of release. The interpretation of this result should be toned down (lines 256-58, abstract, discussion).

As this paper has gone through restructuring upon re-analysis of egg data for clarity we will copy original text into this rebuttal in order to reference old and new line numbers:

Original manuscript "We found that both transgenic animals restored responses to P. uniformis males, but CEP-specific expression was able to increase off-lawn egg laying to near wildtype levels (Figure 4c). These results indicate dopamine synthesis from CEPs likely play a major role in altering egg location upon exposure to P. uniformis males."

Upon re-analysis we find that primarily the effect of cat-2 mutant allele(s) is to dampen the baseline of off-lawn laying: i.e., reductions to the probability that C. elegans will lay eggs off the lawn in control (non-predator-exposed) conditions. We find (Figure 6g-i) that rescue of CAT-2 in ADE/PDE partially restores off-lawn laying under control conditions and rescue in CEP restores this behavior to beyond that of wild-type animals. This, we believe, likely underlies the apparent restoration of predator-evoked levels of off-lawn laying, or at least the two phenotypes (changes to baseline off-lawn behavior, changes to predator-evoked response) are challenging to disentangle.

We thank the reviewer for this observation. Yes, it is case that differences in expression levels brought about by using different promoters could be driving differences in rescue between the two cell populations. Due to the findings upon re-analysis and the reviewer's observation, we have softened this tone in general.

New manuscript 397 – 404: "The cohorts of cat-2 mutants used in Figure 6a-b, Figure 6c-d, as well as the results shown in Figure 7 described below, indicate that changes to the underlying probability of laying eggs off the lawn is likely driving any observed effects to predator response. Additionally, differences in promoter strength used to drive expression of cat-2 may explain why dopaminergic cell types show differing ability to restore baseline P(off). Nevertheless, it is clear that re-expression of CAT-2 protein in either ADE/PDE or CEP only is sufficient to at least partially restore baseline off lawn egg laying behavior."

– Figure 6 – I'm not sure I agree with the authors' interpretation of these results. The phenotype of the dop-1; dop-3 double is more than "slight", and the evidence for a meaningful role for dop-2 is weak.

Original manuscript Figure 6 -> New manuscript Figures 8 and 9.

These data have been fully re-analyzed using a binomial model to estimate the probability of off-lawn laying (see response to Reviewer #2). As a result, we show in new main figures 8 and 9 the single and double/triple mutant data.

When analyzed in terms of probability of off-lawn laying, there are clearly effects to the background level of off-lawn laying in control conditions. To some degree, the situation is clearest in the triple vs. quadruple mutant (new Figure 9d-f). In the triple mutant, the background (control) level of off-lawn laying is comparable to WT, but the predator response is blunted, whereas in the quadruple mutant, the control level is also depressed. Thus, although the predator response is lower in an absolute sense, it is not lower in fold change relative to its own control. We acknowledge these are not paired data, however, we believe off-lawn laying in the control condition is still a reasonable estimate of these mutants’ “baseline” of activity and a logistic regression/binomial generalized linear models can still estimate the confidence intervals of the magnitude of fold change between control and predator conditions.

With respect to the dop-1;dop-3 double mutant, we agree, this is not “slight”. In light of re-analysis, again, these results have been re-interpreted. The double mutant’s fold change in Predator – Control is a useful way to rank them with respect to their effect on predator response. This is shown in new Figure 9c. dop-3;dop-4 has the most comparable fold change compared to WT, whereas dop-1;dop-3 and dop-2;dop-3 sit at the lowest magnitude fold change. We hope this presentation makes these complex effects clearer to the reader.

Our re-interpreted results are presented in the new manuscript as follows:

New manuscript (lines 465-474): “ These combinations also had differing effects on baseline P(off) in Mock controls. Dop-1;dop-4 mutants were the most similar to WT. dop-1;dop-2, dop-2;dop-4 and dop-2;dop-3 all showed elevation of baseline off lawn egg laying activity relative to WT, and dop-1;dop-3 and dop-3;dop-4 showed reductions to baseline P(off). The agnitude of Predator Response in these mutant combinations is shown ordered from highest to lowest in Figure 9b. WT and dop-3;dop-4 double mutants show the highest fold change increase in P(off) relative to their respective Mock controls. All combinations containing dop-4 rank intermediate with dop-2;dop-3 and dop-1;dop-3 ranking lowest. Other than dop-3;dop-4, all other combinations showed reduction to predator response relative to WT. “

– The exogenous DA experiments are fascinating but somewhat difficult to interpret. Do the authors think that exogenous DA is bypassing the effects of endogenous DA release (which would support the idea that DA signaling, at or downstream of its receptors, is altered in predator-exposed animals)? Or is it possible instead that exogenous DA is rapidly taken up by DA neurons and then released in a regulated way? In this case it’s less clear how (or even if) DA signaling is being actively regulated.

Old manuscript Figure 8 -> New manuscript Figure 7

In light of re-analysis, it seems clear that what DA is rescuing is primarily the baseline of off-lawn laying activity under control conditions. In other words, cat-2 mutant rarely lay eggs off lawn at all. Thus, they increase the proportion of off-lawn eggs after predator exposure, but this is at lower proportion than WT given their baseline low activity of laying eggs off the lawn. When exogenous DA is supplemented, the baseline in the control condition comes back up to WT levels and even exceeds it.

We are unsure about the mechanism of this effect and would require additional experiments. One possible experiment would be to cross the cat-2 and dat-1 mutant backgrounds. If DA is still able to restore normal off-lawn egg laying in this double mutant background, it is then likely because exogenous dopamine is bypassing the re-uptake and regulated release. For the scope of this paper, however, we would argue that the point of this experiment would be say that it is cat-2 ‘s role as an upstream enzyme in dopamine synthesis that underlies changes to off-lawn laying and not some other effect of mutation of this gene. We have speculated a bit about possible reasons for this, as well as the roles dopaminergic receptors may be playing, in our Discussion (New manuscript Lines 554-589).

– It’s quite interesting that 5HT signaling is important for predator-induced behavior changes. The rationale for not studying this – that 5HT is already known to be important for egg-laying and in predator/prey behavior – is a little underwhelming. It doesn’t seem obvious how the known functions of 5HT in worm behavior predict a straightforward explanation of its role here.

We wanted to focus this manuscript on only one neurotransmitter rather than attempting to address both; we believe that 5HT signaling in this context is worth pursuing in the future but believe it is out of the scope for this current manuscript. Clarified this point, see line 377-379 new manuscript:

New manuscript line 377: “We focused our remaining studies on dopaminergic signaling, but future work will investigate the role of serotonin signaling as serotonin has been previously shown to modify egg laying behavior [50,51].”

– It might be interesting to know whether nlp-29 has a role in the detection of the predator stimulus. The authors show that the behavioral change triggered by P. uniformus occurs without apparent induction of nlp-29::gfp, but it could be that this induction is weak/undetectable. It could also be that injury triggers an increase in nlp-29 release by the hypodermis without a strong upregulation of nlp-29 transcription.

It is possible that nlp-29::GFP induction by P. uniformis is present but at undetectable levels. We also believe that not all injuries to the cuticle can induce nlp-29::GFP; it is possible that only injuries that pierce deeply into the hypodermis are capable of inducing nlp-29::GFP and that shallower injuries do not. However, we believe that, P. uniformis damages the cuticle less than P. pacificus, so grievous bodily harm can be ruled out as a main driver of behavioral change.

– Line 243 – please rephrase (mutants don’t contribute to the change, neurotransmitters do).

Original manuscript: " These data show that mutants in both serotonin and dopamine synthesis contribute to the egg location change in response to P. uniformis males."

New manuscript line 371: "Taken together, these data show loss of biogenic amine neurotransmitters can modify off lawn egg laying behavior, attenuating or even increasing the observed response to predator, though these two phenomena were not so clearly separable. Loss of both dopamine and serotonin neurotransmitters in cat-1 mutants, however, not only reduced the general probability of off lawn laying but also contributed to the largest blunting of the predator response.”

– Line 249 – please note that this expression pattern is specific to adult hermaphrodites.

Original manuscript: "In C. elegans, CAT-2 is expressed by eight neurons (four CEPs, two ADEs and PDEs), and dopamine signaling has been previously shown to affect modulation of locomotion as well as learning [50]."

New manuscript line 383: "In C. elegans adult hermaphrodites, CAT-2 protein is expressed by eight neurons (four CEPs, two ADEs and PDEs), and dopamine signaling has been previously shown to affect modulation of locomotion as well as learning [43]. "

– Lines 282-284 – this is an inappropriate overinterpretation of negative data. A more likely explanation for the lack of statistical support for early changes in behavior is that the experiment is underpowered.

Original manuscript: "This could be due to incomplete knockdown of dopamine synthesis in cat-2(e1112) [52], or due to an additional role for dopamine in altering egg laying dynamics [53] during predator exposure."

We have removed this interpretation. WormWatcher data has been re-analyzed using a non-parametric bootstrap approach which makes this interpretation unsound in any case. See Figure 6—figure supplement 1.

– Lines 285-6 – "locomotor response" suggests changes in specific features of motor behavior – "navigational response" might be better here.

Original manuscript: "As reducing the amount of available dopamine in the cat-2 mutant depressed locomotor response to predator, we hypothesized that increasing dopamine would increase the response to predator."

New manuscript: This general and following interpretations of what occurs with dat-1 mutation are no longer sound given re-analysis of the WormWatcher data using non-parametric bootstrap approach, and we have removed this particular transitional sentence from the new manuscript.

– Line 362 – Please provide additional details on the methods here. To conclude that the effect "lasts at least 6 hours" it would be necessary to examine the location of animals and/or eggs laid during a short window around the 6 hr time point, not cumulatively from 0-6 hours. It's hard to tell which was the case here.

We have fully shown the data in this figure over the course of 6 hours while addressing the nature of the temporal aspect of these phenotypes in light of re-analysis as proportion of eggs off lawn. See treatment in new manuscript lines: 289 – 316. There is an interesting interaction with time in these data that is worth nothing.

For orientation, this y-axis is the probability of off lawn egg laying. Trend lines over time are shown in four panels because plotting all these data in a single panel would be difficult to interpret and little messy. Data points however are only shown once per plot as this is a single experiment so as to not give the impression that data points are used for multiple analyses more than once.

Fitting the three-way interaction of time, predator exposure, and lawn condition, shows interesting effects. Namely: artificial streaking shows a positive relationship with laying eggs off lawn over time: worms are venture out to lay eggs more often over time. After predator exposure, this slope is flatter and worms are simply laying eggs off the lawn across all time points. When these two conditions are combined, not only is the time evolution flatter but also a much larger proportion of eggs are laid off lawn.

We feel the current treatment provides the best understanding of the data and is a natural way to understand competing interests for the worms: recent predator experience which may promote laying eggs at a distance, as well as bacterial concentrations existing off the main lawn which may have drawn prey to those locations. Finally, it is clearer that with predator exposure, the effect is already true even at 1 hour and persists relatively evenly across the 6 hours.

– Lines 443-5 – word(s) missing from this sentence?

Sections about mammalian homology as well as the actions of the triple and quadruple mutant are now split up. See new manuscript:

New manuscript Line 445: "C. elegans DOP-1 is a homolog of the mammalian D1-like receptors and DOP-2/3 are homologs of mammalian D2-like receptors [43]. DOP-4 is also D1-like, however this receptor belongs to a unique invertebrate family of D1-like including receptors found in Drosophila melanogaster and Apis mellifera [57]. "

New manuscript Line 477: "To test the hypothesis of the presence or absence of just dop-4 influencing predator-evoked behavior, we performed an experiment comparing triple mutant animals in dop-1;dop-2;dop-3 to quadruple mutants of all four receptors (Figure 9c). Once again, the quadruple mutant showed reduction to the baseline P(off) in the Mock control as in Figure 8, however, the triple mutant showed a comparable level of off lawn laying in the mock condition relative to WT.”

– Line 476 – this sentence needs editing.

The Ideas and Speculation section of our Discussion has been restructured.

– Figure S1 – legends for (b) and (c) are switched.

All figures and legends have been overhauled and current legend for Figure 1—figure supplement 1-4 should have all panels correctly identified.

– Figure 6-S4 is mislabled as Figure 4-S4.

Single dopamine receptors have been moved to new Figure 8, and all figures have been renamed to follow correct eLife conventions.

Reviewer #2 (Recommendations for the authors):

I think this is a great experimental paradigm, with a nice progression of genetic experiments to identify the role of dopamine release from CEP neurons as causing the altered behavior in response to predation. However, I think how the data are quantified and interpreted should be altered, as mentioned in my prior comments. I realize the time resolution of the experiments are not sufficient to quantify random walk dynamics, and I am normally loathed to request more experiments, especially since so many have been performed here, but perhaps the data could be reanalyzed in a way that more accurately reflects the underlying phenomenon.

For example, the authors claim more eggs are laid off the lawn, and quantify this in Figure 1-S1. Why did you not do this for all the data? This is simple, and could be easily quantified with a simple chi-square analysis. The next question is, why? Since worms inherently perform random walks, the null hypothesis would be that their effective diffusion constant has changed. The drive to perform random walks is fundamental strategy of worm navigation, even while moving toward favorable conditions. A nice quantification of this property can be found in Klein et al., eLife, 2017.

This is especially relevant since we know that the worm's effective diffusion constant is different on and off food, and that neuromodulation alters these statistics. I realize your movies don't have sufficient resolution to capture these dynamics, but you do have the spatial distribution of eggs, correct? Why not plot that distribution for the different conditions, rather than quantifying the relative distance from the food edge, which is nebulous given the effect size? You could then compare distributions, and do a bootstrap analysis to verify whether changes in distribution are different? Given what's already known about dopamine and egg laying (Cermak et al., 2020), as well as decades of work quantifying the worm's random walk behavior, a shift in the statistics of this underlying stochastic motor program should be considered the null hypothesis.

Thank you to the reviewer for these excellent points. We have overhauled the analysis performed on these data which highlight some of the concerns that the reviewer has here.

So, it is clear that (a) eggs have a distribution, and (b) these distributional properties are oversimplified by collapsing to a simple metric such as the mean/median/central tendency of eggs away from the lawn. A bootstrap analysis of these distributions is actually not the greatest technique here. Bootstrapping still requires a bootstrap statistic. If, for example, that statistic were the mean or the median, then the bootstrap derived empirical intervals for that statistic would still reflect estimation of that quantity and fail to capture other interesting features of the data such as the changes to dispersal of the eggs.

As it is known that worms detect the lawn edge, the binary off/on-lawn is a very reasonable approach for quantifying the eggs. When we performed this analysis, we find that indeed cat-2 mutants do respond to predator by laying eggs more frequently off the lawn (see new Figure 6). Consider the data shown in new Figure 6C for example, which is an experiment looking at two cat-2 mutant alleles. If we analyze the mean, the coefficient of variation, as well as the upper and lower quartiles of the distribution, all report that the cat-2 mutants are “not” altering their distributions with respect to exposure to predator. However, if you simply tabulate the quantity of eggs laid off the lawn, you see quite clearly that they do the respond. The reason for this statistical phenomenon is that the cat-2 mutants very rarely lay eggs off the lawn at all, even under control conditions, whereas WT animals venture off the lawn to lay eggs at least some of the time. So, when cat-2 mutants are exposed to predator they do in fact increase the proportion of their eggs laid off the lawn relative to this low background rate, and due to the rarity of these events, this effect is most simply and easily captured by quantifying the number of eggs laid on and off the lawn. This might be summarized by a distributional parameter such as a quantile, but this would require decision making about which region of the distribution to look in. This is possible but seems overly complicated or biased in light of the simpler metric of the lawn boundary and the numbers of eggs on and off the lawn.

Thus, we have proceeded in our egg assays to analysis the [# off lawn eggs, # on lawn eggs]. For a simple experiment with few independent variables, we agree that a Fisher’s exact or chi-square approach is reasonable. However, we have complex effects some of which include two- and three-way interactions between variables. Instead, we have turned to using logistic regression/binomial generalized linear modeling which gives us analogous tools to ANOVA in the form of likelihood ratio statistics/analysis of deviance. Binomial modeling uses the statistic of the Odds Ratio which is also a very natural metric corresponding to the “chance of success” – in our case defined as the probability of leaving the lawn to lay eggs versus staying on the lawn to lay eggs. This paradigm give us a simple and powerful way to quantify, and analyze effects in the data. In the binomial model, the input N are the eggs themselves which have a value of 1 or 0 depending on whether they are “success” (defined here as off the lawn) or “failure” (defined here as on the lawn), which are stratified by experimental replicate. Thus, this approach takes into account every egg in every arena and thus greatly boosts statistical power. These changes are reflected in reported statistics throughout the manuscript and in the figure legends/methods. We are now enabled to perform inference about changes to : the background level of off-lawn laying in control conditions, which varies, and “double Δ” interaction effects: i.e., change in the magnitude of change between predator and control odds of off-lawn laying, which reflects what we are terming a Predator Response (see new Methods/Statistics section, especially Equation #3). Effectively this is the change in the numbers of eggs laid off lawn in predator-exposed conditions controlling for the Mock control level of off lawn laying.

I appreciate the Appendix with the statistics, but in the absence of the raw data, it's really impossible to validate if any of this is accurate. Surely these analyses were based upon the analysis of text files/tables of data, correct? Why not include them?

As our statistical analyses have been overhauled, all raw data files are now included as source data files according to eLife submission standards.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Both reviewers feel that the revised manuscript has been substantially improved. However, before publication, we would like you to consider Reviewer #1's point about the role of dopamine in bringing about predator-induced changes in behavior. The bulk of your evidence points to a function for DA in egg-laying per se (which of course is already well-established), with its role in changes in behavior upon predator exposure being much more subtle. We suggest being a bit more cautious in some of your interpretations.

Each reviewer also has several minor comments/suggestions that you may wish to address.

Reviewer #1 (Recommendations for the authors):

The revised manuscript is significantly improved, largely due to the change in data analysis suggested by reviewer #2. The demonstration of the persistent effects of predator exposure on locomotor and egg-laying behavior is robust and fascinating.

We thank the reviewer for their feedback.

My biggest concern about the revised manuscript is that the role of dopamine in predator-induced behavioral change is a bit overstated. Nearly all of the experiments in the paper now clearly show that the primary role of dopamine in this paradigm is to modulate baseline P(off). The direct evidence that DA is required for predator-induced changes is relatively slim. To my mind, the only compelling result is the dop-1;2;3 triple mutant in Figure 9c. In general, the experiments in Figures8 and 9 are tricky to interpret because the baseline P(off) is quite a bit lower here than in previous experiments. This raises the concern that floor effects are limiting the ability to see further reductions in baseline P(off) in mutants, which could in turn lead to artificially low values for fold-change in P(off) in the predator-exposed condition.

The background, as assessed from the “absolute” control i.e. a WT animal exposed only to other C. elegans does vary a bit from assay setup to assay setup. In Figure 1, N2 animals are not used, GFP fluorescent animals are used to distinguish C. elegans eggs from the eggs of other predators. When we switch to using P. uniformis males throughout the remaining studies in this work, we were able to switch to N2 as the reference strain. However, differences in background between Figure 1 and others may be due to this strain difference.

In the remainder of the studies with the exception of Figure 4 and 5 and 7 where animals are tested for eggs only after exposure to predator (learning assays), in which the background may be affected by this change in setup, the reviewer will note that the background in Figure 8 and 9 seems not too far off the range of variability observed in other experiments. Explicitly:

WT/mock, Figure 3: P(off) estimate at 0.09

Figure 6A: P(off) estimate at 0.21

Figure 6C: P(off) estimate at 0.15

Figure 6E: P(off) estimate at 0.34

Figure 8B: P(off) estimate at 0.05

Figure 9A: P(off) estimate at 0.15

Figure 9C: P(off) estimate at 0.10

In fact, given these estimates, it is the rescue assay in Figure 6E that seems to be surprisingly high.

We do not know what accounts for this variability. It may be that there are aspects of the animal’s internal state at assay start that contribute to it. But we do not think it is unreasonable to perform inference within an experiment with respect to its own background. However future exploration of the relationship of an animal’s internal state to its propensity for laying eggs off the lawn merits study.

Author details

1. Amy Pribadi

   1. Biological Sciences Graduate Program, University of California, San Diego, La Jolla, United States
   2. Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Michael A Rieger

   Competing interests

   No competing interests declared

2. Michael A Rieger

   Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   Data curation, Formal analysis

   Contributed equally with

   Amy Pribadi

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4020-5476

3. Kaila Rosales

   Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   Data curation

   Competing interests

   No competing interests declared

4. Kirthi C Reddy

   Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

5. Sreekanth H Chalasani

   1. Biological Sciences Graduate Program, University of California, San Diego, La Jolla, United States
   2. Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing - review and editing

   For correspondence

   schalasani@salk.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2522-8338

• 346

  Page views

• 53

  Downloads

• 0

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.