Immunity-longevity tradeoff neurally controlled by GABAergic transcription factor PITX1/UNC-30.

A body of evidence indicates that metazoan immune and aging pathways are largely interconnected, but the mechanisms involved in their homeostatic control remain unclear. In this study, we find that the PITX (paired-like homeodomain) transcription factor UNC-30 controls the tradeoff between immunity and longevity from the nervous system in Caenorhabditis elegans. PITX/UNC-30 functional loss enhances immunity in a GATA/ELT-2- and p38 MAPK/PMK-1-dependent manner and reduced longevity by activating MXD/MDL-1 and the C2H2-type zinc finger transcription factor PQM-1. The immune inhibitory and longevity stimulatory functions of PITX/UNC-30 require the sensory neuron ASG and a signaling pathway controlled by NPR-1, which is a G protein-coupled receptor related to mammalian neuropeptide Y receptors. Our findings uncover a suppressive role of GABAergic signaling in the neural control of a biological tradeoff where energy is allocated toward immunity at the expense of longevity.

INTRODUCTION

The activation of an immunological response is an essential and costly energetic physiological process that results in the reallocation of resources, causing changes in the mechanisms that control general somatic maintenance (Ayres, 2020; Ganeshan and Chawla, 2014; Ganeshan et al., 2019; Jung et al., 2019). A tightly controlled immune activation is critical because its dysregulation has a major impact on a number of vital biological functions (Bird, 2019; Ganeshan et al., 2019; Jung et al., 2019; Kuchroo et al., 2012; Libert et al., 2006). In addition, the significant metabolic challenge posed by immune activation, immunopathology, and immune homeostasis necessitates an allocation of nutrients and energy that may lead to shortened longevity (Ayres, 2020; Bäckhed et al., 2019; Franceschi et al., 2018; Kaiser et al., 2020; Riera and Dillin, 2015; Solana et al., 2006; Tracey, 2002; Wani et al., 2019; Wu et al., 2019). Hence, the imbalance caused by immune activation results in a tradeoff between immunity and longevity that is evolutionarily conserved throughout the animal kingdom.

Although the aforementioned changes to maintain homeostasis suggest the existence of a tradeoff between immunity and longevity across metazoans, the specific pathways involved in those tradeoffs and the underlying mechanisms that control them remain unknown. Several conserved pathways, including the DAF-2 (homolog of insulin-like growth factor 1 [IGF1]), AGE-1 (homolog of phosphatidylinositol 3-kinase [PI3K]), DAF-16 (homolog of FOXO transcription factor), and HFS-1 (homolog of heat shock transcription factor 1 [HSF1]) pathways, control both immunity and longevity in the nematode Caenorhabditis elegans (Chávez et al., 2007; Garsin et al., 2003; Hajdu-Cronin et al., 2004; Libina et al., 2003; Mohri-Shiomi and Garsin, 2008; Morris et al., 1996; Murphy et al., 2003; Singh and Aballay, 2006). However, whether and how these interconnected immune and longevity pathways are regulated by tradeoff mechanisms still needs to be determined. The nervous system, which can integrate different cues in milliseconds and interpret conflicting stimuli, is well-equipped to effectively regulate tradeoffs.

To investigate the role of the nervous system in the control of the immunity-longevity tradeoff, we studied the GABAergic transcription factor PITX/UNC-30 in C. elegans. PITX/UNC-30 transcriptionally regulates the expression of GAD/UNC-25 and SLC32A1/UNC-47 that are critical in the biosynthesis, packaging, and trafficking of GABA (Cinar et al., 2005; Eastman et al., 1999; Garcia et al., 2007; Gendrel et al., 2016; McIntire et al., 1997, 1993a, 1993b). We found that UNC-30 loss-of-function mutations led to enhanced resistance to killing by Gram-negative and Gram-positive bacterial pathogens. Conversely, UNC-30 loss-of-function mutations resulted in reduced longevity. The resistance to pathogen infection was mediated by the GATA transcription factor ELT-2 and the p38 mitogen-activated protein kinase PMK-1. The reduced longevity phenotype of UNC-30 mutants was due to higher activity of the MAX dimerization protein MXD/MDL-1 and the C2H2-type zinc finger transcription factor PQM-1. We further found that neuropeptide signaling mediated by the NPYR/NPR-1 pathway acts downstream of UNC-30 and the amphid sensory neuron ASG to control the tradeoff. Our findings highlight a neuronal network that controls the tradeoff between immunity and longevity. Given the conservation of GABAergic signaling and the longevity and immune pathways involved in the tradeoff, our findings raise the possibility that a similar process may occur in higher metazoans, including humans.

RESULTS

Neuronal UNC-30 controls the immunity-longevity tradeoff

As a first step to understand the role of UNC-30 in the control of the immunity-longevity tradeoff, we studied the survival of unc-30 loss-of-function animals infected with the human opportunistic pathogen Pseudomonas aeruginosa strain PA14. We found that unc-30(ok613) mutants exhibited enhanced survival against P. aeruginosa-mediated killing compared to wild-type animals (Figure 1A). We also found that the unc-30(ok613) mutants exhibited less visible bacterial colonization and significantly reduced colony-forming units compared to wild-type animals (Figures 1B and 1C). Different unc-30 strains also displayed enhanced survival against P. aeruginosa compared to wild-type animals (Figure S1A). When exposed to E. coli, which is the food source of C. elegans in the laboratory, unc-30(ok613) mutants exhibited enhanced survival compared to wild-type animals (Figures 1D and S1B). Because proliferating live E. coli is a cause of death in C. elegans and animals deficient in the immune response are persistently colonized and killed by the bacterium (Garigan et al., 2002; Otarigho and Aballay, 2020; Sutphin and Kaeberlein, 2009), we first investigated the survival of unc-30(ok613) animals on killed E. coli. Because animals fed heat-killed E. coli exhibit bloated intestinal lumens that result in upregulation of immune pathways (Singh and Aballay, 2019b), we exposed the animals to lawns of E. coli killed by ultraviolet light (UV) that does not cause any intestinal distension (Figure S2). As shown in Figure 1E, the longevity of unc-30(ok613) animals is shorter than that of wild-type animals when exposed to UV-killed E. coli. Because ampicillin was added to prevent the growth of any bacteria that might have escaped the UV treatment, we studied whether the addition of the antibiotic would impact the lifespan of the animals. As shown in Figure S1B, ampicillin did not affect the lifespan of unc-30(ok613) mutants.

Figure 1.

UNC-30 functional loss exhibits enhanced immunity and reduced longevity

(A) Wild-type (WT) and unc-30(ok613) animals were exposed to P. aeruginosa partial lawn and scored for survival (n = 60). WT versus unc-30(ok613), p < 0.0001.

(B) Colonization of WT, unc-30(ok613), and unc-30(ok613);Prab-3::unc-30 animals by P. aeruginosa-GFP after 24 h at 25°C (n = 10). Scale bar, 200 μm.

(C) Colony-forming units per animal (WT, unc-30(ok613), and unc-30(ok613);Prab-3::unc-30) grown on P. aeruginosa-GFP for 24 h at 25°C (n = 10). Bars represent means while error bars indicate SD; ****p < 0.001 and NS, not significant (p > 0.05). Ten animals were used for each condition.

(D) WT, unc-30(ok613), and unc-30(ok613);Prab-3::unc-30 animals were exposed to live E. coli and scored for survival (n = 120). WT versus unc-30(ok613), p < 0.0001; unc-30(ok613);Prab-3::unc-30, p = NS.

(E) WT, unc-30(ok613), and unc-30(ok613);Prab-3::unc-30 animals were exposed to UV-killed E. coli and scored for survival (n = 120). WT versus unc-30(ok613), p < 0.0001; unc-30(ok613);Prab-3::unc-30, p = NS.

(F) RNAi intestine-specific strain MGH171, MGH171;unc-30 RNAi, and unc-30(ok613) animals were exposed to P. aeruginosa full lawn and scored for survival. EV, empty vector RNAi control (n = 60). MGH171 EV versus unc-30(ok613) EV, p < 0.0001; MGH171;unc-30 RNAi, p = NS.

(G) WT, unc-30(ok613), unc-30(ok613);Punc-30::unc-30, and unc-30(ok613);Prab-3::unc-30 animals were exposed to P. aeruginosa partial lawn and scored for survival (n = 60). WT versus unc-30(ok613), p < 0.0001; unc-30(ok613);Punc-30::unc-30, and unc-30(ok613);Prab-3::unc-30, p = NS.

(H) WT, unc-30(ok613), unc-30(ok613);Punc-30::unc-30, and unc-30(ok613);Prab-3::unc-30 animals were exposed to S. enterica partial lawn and scored for survival (n = 60). WT versus unc-30(ok613), p < 0.0001; unc-30(ok613);Punc-30::unc-30, and unc-30(ok613);Prab-3::unc-30, p = NS.

(I) WT, unc-30(ok613), unc-30(ok613);Punc-30::unc-30, and unc-30(ok613);Prab-3::unc-30 animals were exposed to S. aureus partial lawn and scored for survival (n = 60). WT versus unc-30(ok613), p < 0.0001; unc-30(ok613);Punc-30::unc-30, and unc-30(ok613);Prab-3::unc-30, p = NS.

(J) WT, unc-30(ok613), unc-30(ok613);Punc-30::unc-30, and unc-30(ok613);Prab-3::unc-30 animals were exposed to P. aeruginosa full lawn and scored for survival (n = 60). WT versus unc-30(ok613), p < 0.0001; unc-30(ok613);Punc-30::unc-30, and unc-30(ok613);Prab-3::unc-30, p = NS.

Figures are representative of three independent experiments. “n” represents the number of animals for each experimental condition.

UNC-30 is mainly expressed in three sensory neuronal cells (ASG, PVD, and OLL), three interneurons (PVP, RID, and RIH), and two motor neurons (DD and VD), as well as in the intestine (Cinar et al., 2005; Eastman et al., 1999; Jin et al., 1994; Kurup and Jin, 2016; Walton et al., 2015; Westmoreland et al., 2001). To study whether UNC-30 may act cell autonomously in the intestine to control immune pathways, we knocked down UNC-30 in the intestine of an RNAi intestine-specific strain, MGH171. The lack of a significant difference between control and unc-30 RNAi animals (Figure 1F) suggests that unc-30 does not function in the intestine to control immunity, and it may control the immunity-longevity tradeoff from the nervous system. To study whether UNC-30 controls the immunity-longevity tradeoff in a cell non-autonomous manner from the nervous system, we rescued unc-30 under the control of a pan-neuronal promoter and exposed the animals to P. aeruginosa. As a control, unc-30 was also expressed using its native promoter. The results obtained show that UNC-30 expression driven by its own promoter or by a pan-neuronal promoter rescued both the enhanced survival of unc-30(ok613) animals to P. aeruginosa-mediated killing (Figure 1G) and the reduced longevity of the animals grown on UV-killed E. coli (Figure 1E). To study the specificity of pathogen susceptibility of unc-30(ok613) animals, we infected them with other human bacterial pathogens, including the Gram-negative bacterium Salmonella enterica strain 1344 and the Gram-positive bacteria Staphylococcus aureus strain NCTCB325. Loss of UNC-30 enhanced the nematode’s survival against all bacterial pathogens studied (Figures 1H and 1I), suggesting that UNC-30 suppresses C. elegans general defense against bacterial pathogens. Because C. elegans naturally exhibits an avoidance behavior when exposed to P. aeruginosa, which can be observed in conditions in which the animals can freely enter and exit the bacterial lawn, we used plates that were completely covered by bacteria to control for avoidance (full-lawn). The survival of unc-30(ok613) animals was also significantly higher than that of wild-type and unc-30 rescued animals (Figure 1J). We also observed similar resistance to pathogen-mediated killing when the animals were exposed to full-lawns of S. enterica or S. aureus (Figures S3A and S3B). The neural expression of unc-30 rescued the survival defect of unc-30(ok613) animals (Figures 1G–1J). Consistent with the idea that the resistance of unc-30(ok613) animals is not due to enhanced pathogen avoidance, we found that the lawn occupancy of unc-30(ok613) mutants is comparable to that of wild-type animals (Figure S3C).

C. elegans feeds on bacteria, and the pharyngeal contraction/pumping is a direct measure of food intake (Cao et al., 2017; Sellegounder et al., 2019; Singh and Aballay, 2019b; Styer et al., 2008). Thus, we asked whether the resistance to infections and reduction of the lifespan of unc-30(ok613) animals could be due to a reduction in pathogen intake. We, therefore, measured the pharyngeal pumping rates of unc-30(ok613) and wild-type animals on P. aeruginosa bacterial lawns as well as on UV-killed E. coli. We found that unc-30(ok613) animals exhibited pumping rates comparable to that of wild-type animals (Figure S3D), indicating that the dose of pathogens is similar in both cases. It has recently been demonstrated that bacterial accumulation in animals defective in the defecation motor program (DMP) causes intestinal distension that elicits a robust immune response (Singh and Aballay, 2019b) and modulates longevity (Kumar et al., 2019). We found that the defecation cycle of unc-30(ok613) animals is indistinguishable from that of wild-type animals (Figure S3E). Certain gene mutations in C. elegans affect the reproductive system, which could lead to reduced progeny or cause total sterility, resulting in increased resistance to pathogen infection (Berman and Kenyon, 2006; Powell and Ausubel, 2008). In addition, the tradeoff between immunity and longevity can be dependent on the reproductive dynamics of some mutant animals (Amrit et al., 2019). Thus, we compared the brood size of unc-30(ok613) and wild-type animals and did not observe any difference in the numbers of neither laid eggs nor brood size (Figures S3F and S3G). Taken together, these results suggest that unc-30 functions in the nervous system to control a tradeoff between immunity and longevity.

UNC-30 inhibits the expression of immune and age-related genes

To dissect the UNC-30-dependent immune and longevity mechanisms involved in the control of defense against pathogen exposure and longevity, we employed transcriptomic analyses to identify dysregulated immune genes and pathways in uninfected and P. aeruginosa-infected unc-30(ok613) and wild-type animals (Table S1). To identify gene groups that were controlled by UNC-30, we performed an unbiased gene enrichment analysis using a WormBase enrichment analysis tool, https://wormbase.org/tools/enrichment/tea/tea.cgi, (Angeles-Albores et al., 2016), that is specific for C. elegans gene data analyses. The 10 Gene Ontology clusters with the highest enrichment score of vital biological functions for upregulated and downregulated genes in both non-infected and infected groups are shown in Figures 2A, S4A, and S4B. Overall, the gene expression data showed that the most enriched and highly significant upregulated genes were a part of neuropeptide signaling and immune/defense response pathways in both non-infected and infected groups (Figure 2A; Table S2). Other genes linked to biological processes such as metabolism, response to biotic stimuli, and IRE1-mediated unfolded protein response, were also upregulated in unc-30(ok613) animals. Consistently with the role of UNC-30 in the control of longevity (Figure 1E), genes involved in aging were also significantly enriched (Figure 2A).

Figure 2.

PITX1/UNC-30 regulates immune and age determination pathways

(A) Gene Ontology analysis of upregulated genes in unc-30(ok613) versus WT in both non-infected and P. aeruginosa-infected animals (n ≈ 1,000). The cutoff is based on the filtering thresholds of p < 0.05 and arranged according to the representation factor.

(B) Representation factors of immune pathways for the upregulated immune genes in unc-30(ok613) versus WT in both non-infected and P. aeruginosa-infected animals.

(C) qRT-PCR analysis of immune gene expression in WT, unc-30(ok613), and unc-30(ok613);Prab-3::unc-30 animals (n ≈ 1,000). Bars represent means while error bars indicate SD of three independent experiments; *p < 0.05, **p < 0.001 and ***p < 0.0001.

(D) Representation factors of age determination pathways for the upregulated aging genes in unc-30(ok613) versus WT in non-infected animals.

(E) Venn diagram showing the number of genes in each age determination pathway for the upregulated aging genes in unc-30(ok613) versus WT in non-infected animals.

(F) qRT-PCR analysis of age determination genes expression in WT, unc-30(ok613), and unc-30(ok613);Prab-3::unc-30 animals (n ≈ 1,000). Bars represent means while error bars indicate SD of three independent experiments; *p < 0.05, **p < 0.001 and ***p < 0.0001.

(C) and (F) are representative of three independent experiments. “n” represents the number of animals for each experimental condition.

To identify the potential immune pathways that may play a role in the pathogen resistance to the UNC-30-deficient animals, we performed a gene enrichment analysis using the immune gene subset by employing the Worm Exp tool (https://wormexp.zoologie.uni-kiel.de/wormexp/) (Yang et al., 2016), which integrates all published expression datasets for C. elegans to analyze and generate pathways. We found that pathways required for C. elegans defense against bacterial infections were highly enriched, including the ELT-2, PMK-1, PQM-1, and DAF-2/DAF-16 insulin pathways (Figure 2B; Table S3) (Aballay et al., 2003; Garsin et al., 2003; Head et al., 2017; Huffman et al., 2004; Kerry et al., 2006; Kim et al., 2002; Murphy et al., 2003; Rajan et al., 2019; Shapira et al., 2006; Singh and Aballay, 2006, 2009; Troemel et al., 2006). We also noticed that several of the PMK-1 and DAF-16-dependent genes were also controlled by ELT-2 (Figures S4C and S4D; Table S3), indicating that ELT-2-dependent genes may play a major role in the enhanced resistance to pathogen phenotype of unc-30(ok613) animals. Thus, we confirmed the role of neural UNC-30 in the control of ELT-2-dependent immune genes, including lys-1, K12H4.7, lys-4, lys-7, and lys-8 (Figure 2C). These results indicate that UNC-30 regulates C. elegans defense against bacterial infections mainly by activating immune genes, several of which are controlled by the ELT-2 immune transcription factor.

We further employed the Worm Exp tool (https://wormexp.zoologie.uni-kiel.de/wormexp/) (Yang et al., 2016) to analyze the age determination genes to identify specific pathways that could be transcriptionally regulated by UNC-30. We found that the SKN-1, MDL-1, and PQM-1 age determination pathways were highly enriched (Figure 2D; Table S4) (Riesen et al., 2014; Tepper et al., 2013, 2014; Tullet et al., 2008). Although the SKN-1 cluster had the highest representation factor (Figure 2D), most of the genes upregulated in unc-30(ok613) animals were under the control of the MDL-1 pathway (Figure 2E). Because the evolutionarily conserved transcription factors, MDL-1 and PQM-1, are known to suppress longevity and their inactivation results in lifespan extension (Riesen et al., 2014; Tepper et al., 2013, 2014), the upregulation of MDL-1- and PQM-1-dependent genes in unc-30(ok613) animals could explain the reduced longevity of the animals. We confirmed the role of UNC-30 in the control of MDL-1- and PQM-1-dependent genes (Figure 2F). In summary, the transcriptomic analyses revealed that although neural expression of UNC-30 downregulates the expression of immune genes controlled by ELT-2 and PMK-1, it upregulates age-related genes controlled by MDL-1 and PQM-1, indicating that UNC-30 controls a tradeoff between immunity and longevity.

UNC-30 functional loss enhances immunity via ELT-2 and PMK-1 and reduces longevity via MDL-1 and PQM-1

To test the hypothesis that the enhanced resistance to P. aeruginosa infection of unc-30(ok613) animals is due to the upregulation of immune genes, we studied the role of suppression by RNAi of the immune pathways shown in Figure 2B. We inactivated elt-2, pqm-1, pmk-1, and daf-16 in WT and unc-30(ok613) animals and exposed them to P. aeruginosa. Unlike daf-16 and pqm-1 RNAi (Figures 3A and 3B), elt-2 RNAi was able to completely suppress the enhanced resistance to P. aeruginosa infection in unc-30(ok613) animals (Figure 3C). We also observed partial suppression of the enhanced pathogen resistance of unc-30(ok613) animals by pmk-1 RNAi (Figure 3D). The lack of suppression of the phenotype of unc-30(ok613) animals by RNAi inhibition of daf-16 and pqm-1 was confirmed using daf-16 and pqm-1 mutant animals (Figures 3E and 3F). We also confirmed the partial suppression of the enhanced resistance to pathogen of unc-30(ok613) animals by inhibition of the PMK-1 pathway using nsy-1(ag3) and sek-1(km4) animals, which carry mutations in the genes encoding two kinases that function upstream PMK-1 (Figures 3G and 3H). These results indicate that loss of UNC-30 enhances immunity against infection by increasing the activity of ELT-2 and the PMK-1 pathway.

Figure 3.

Functional loss of UNC-30 enhances immunity via GATA/ELT-2 and p38 MARK/PMK-1

(A) WT, daf-16 RNAi, unc-30(ok613), and unc-30(ok613);daf-16 RNAi animals were exposed to P. aeruginosa and scored for survival (n = 60). EV, empty vector RNAi control. WT EV versus unc-30(ok613) EV, p < 0.0001; daf-16 RNAi, p = NS; unc-30(ok613);daf-16 RNAi, p < 0.0001.

(B) WT, pqm-1 RNAi, unc-30(ok613), and unc-30(ok613);pqm-1 animals were exposed to P. aeruginosa and scored for survival (n = 60). EV, empty vector RNAi control. WT EV versus unc-30(ok613) EV, p < 0.0001; pqm-1 RNAi, p < 0.05; unc-30(ok613);pqm-1 RNAi, p < 0.0001.

(C) WT, elt-2 RNAi, unc-30(ok613), and unc-30(ok613);elt-2 RNAi animals were exposed to P. aeruginosa and scored for survival (n = 60). EV, empty vector RNAi control. WT EV versus unc-30(ok613) EV, p < 0.0001; elt-2 RNAi, p < 0.0001; unc-30(ok613);elt-2 RNAi, p < 0.0001. elt-2 RNAi versus unc-30(ok613) elt-2 RNAi, p = NS.

(D) WT, pmk-1 RNAi, unc-30(ok613), and unc-30(ok613);pmk-1 RNAi animals were exposed to P. aeruginosa and scored for survival (n = 60). EV, empty vector RNAi control. WT EV versus unc-30(ok613) EV, p < 0.0001; pmk-1 RNAi, p < 0.0001; unc-30(ok613);pmk-1 RNAi, p < 0.05. pmk-1 RNAi versus unc-30(ok613) pmk-1 RNAi, p < 0.0001.

(E) WT, daf-16(mu86), unc-30(ok613), and daf-16(mu86);unc-30(ok613) animals were exposed to P. aeruginosa and scored for survival (n = 60). WT versus unc-30(ok613), p < 0.0001; daf-16(mu86), p = NS; daf-16(mu86);unc-30(ok613), p < 0.0001.

(F) WT, pqm-1(ok485), unc-30(ok613), and pqm-1(ok485);unc-30(ok613) animals were exposed to P. aeruginosa and scored for survival (n = 60). WT versus unc-30(ok613), p < 0.0001; pqm-1(ok485), p < 0.05; pqm-1(ok485);unc-30(ok613), p < 0.0001.

(G) WT, nsy-1(ag3), unc-30(ok613), and nsy-1(ag3);unc-30(ok613) animals were exposed to P. aeruginosa and scored for survival (n = 60). WT versus unc-30(ok613), p < 0.0001; nsy-1(ag3), p < 0.0001; nsy-1(ag3);unc-30(ok613), p = NS.

(H) WT, sek-1(km4), unc-30(ok613), and sek-1(km4);unc-30(ok613) animals were exposed to P. aeruginosa and scored for survival (n = 60). WT versus unc-30(ok613), p < 0.0001; sek-1(km4), p < 0.0001; sek-1(km4);unc-30(ok613), p = NS.

Figures are representative of three independent experiments. “n” represents the number of animals for each experimental condition.

The Venn diagram shown in Figure 4A indicates a poor overlap between aging and immune genes, suggesting that the misregulation of different pathways causes the immune and longevity phenotypes of animals deficient in UNC-30. Thus, we hypothesized that the upregulation of the age-related genes in uninfected unc-30(ok613) mutants compared with wild-type animals (Figure 2A) could be responsible for the reduced longevity of unc-30(ok613) animals. Consistent with this idea, a number of the age-related genes that are upregulated in unc-30(ok613) animals have been reported to extend lifespan when mutated or inactivated by RNAi (Figure 4B; Table S4). Moreover, the majority of age-related genes upregulated in unc-30(ok613) mutants compared with wild-type animals are controlled by MDL-1 and PQM-1 (Figure 2E), which are known to suppress longevity. Thus, we studied whether the inactivation of MDL-1 or PQM-1 could suppress the decreased longevity of unc-30(ok613) animals. As shown in Figures 4C and 4D, mdl-1(tm311) and pqm-1(ok485) mutations improved the lifespan of unc-30(ok613) animals. However, the lifespan of the double mutants mdl-1(tm311);unc-30(ok613) and pqm-1(ok485);unc-30(ok613) is shorter than that of the single mutants mdl-1(tm311) and pqm-1(ok485), respectively (Figures 4C and 4D). These results suggest that mdl-1 and pqm-1 may function parallel to unc-30 or that both may be required for the reduced lifespan caused by loss of unc-30. We reasoned that if the latter was the case, the lifespan of unc-30(ok613) animals lacking mdl-1 and pqm-1 should not be different than that of wild-type animals lacking mdl-1. Indeed, the longevity of mdl-1 RNAi animals is not significantly different than that of pqm-1(ok485);unc-30(ok613);mdl-1 RNAi animals (Figure 4E).

Figure 4.

Functional loss of UNC-30 reduces longevity via MXD3/MDL-1 and PQM-1 pathways

(A) Venn diagram of the immune and aging genes upregulated in unc-30(ok613) versus WT non-infected animals.

(B) Number of age determination genes upregulated in unc-30(ok613) versus WT for which mutant phenotypes have been reported.

(C) WT, mdl-1(tm311), unc-30(ok613), and mdl-1(tm311);unc-30(ok613) animals were exposed to UV-killed E. coli and scored for survival (n = 120). WT versus unc-30(ok613), p < 0.0001; mdl-1(tm311), p < 0.0001; mdl-1(tm311);unc-30(ok613), p = NS. mdl-1(tm311) versus mdl-1(tm311);unc-30(ok613), p < 0.001.

(D) WT, pqm-1(ok485), unc-30(ok613), and pqm-1(ok485);unc-30(ok613) animals were exposed to UV-killed E. coli and scored for survival (n = 120). WT versus unc-30(ok613), p < 0.0001; unc-30(ok613);pqm-1(ok485), p =NS . unc-30(ok613) versus unc-30(ok613);pqm-1(ok485), p < 0.05.

(E) WT, unc-30(ok613), mdl-1 RNAi, and unc-30(ok613);pqm-1(ok485);mdl-1 RNAi animals were exposed to UV-killed E. coli and scored for survival (n = 120). EV, empty vector RNAi control. WT EV versus unc-30(ok613) EV, p < 0.0001; unc-30(ok613);pqm-1(ok485);mdl-1 RNAi, p < 0.001. unc-30(ok613);pqm-1(ok485);mdl-1 RNAi versus mdl-1 RNAi, p = NS.

(F) WT, skn-1 RNAi, unc-30(ok613), and unc-30(ok613);skn-1 RNAi animals were exposed to UV-killed E. coli and scored for survival. EV, empty vector RNAi control (n = 120). WT EV versus unc-30(ok613) EV, p < 0.0001; skn-1 RNAi, p < 0.001; unc-30(ok613);skn-1 RNAi, p < 0.0001. skn-1 RNAi versus unc-30(ok613);skn-1 RNAi, p < 0.001.

(C)–(F) are representative of three independent experiments. “n” represents the number of animals for each experimental condition.

Inhibition of SKN-1 by RNAi reduced the longevity of wild-type animals but did not suppress the reduced longevity of unc-30(ok613) mutants (Figure 4F). This is expected because, unlike MDL-1 and PQM-1 that suppress longevity, SKN-1 promotes longevity. SKN-1 inhibition slightly but significantly reduced the longevity of unc-30(ok613) mutants, suggesting that reduced SKN-1 activity does not contribute to the longevity phenotype of unc-30(ok613) mutants. This is consistent with the absence of SNK-1-dependent genes in the subset of genes downregulated in unc-30(ok613) relative to wild-type animals (Table S1). Taken together, these results indicate that neuronal UNC-30 suppresses ELT-2- and PMK-1 mediated immunity while prolonging longevity through the inhibition of MDL-1 and PQM-1.

Neuronal UNC-30 regulates the immunity-longevity tradeoff via neuropeptide signaling

The subset of misregulated genes most significantly enriched in either infected or uninfected unc-30(ok613) animals corresponds to neuropeptide signaling genes (Figure 2A; Table S2). To identify potential neurotransmitter pathways that may be involved in the control of the tradeoff between immunity and longevity, we performed gene enrichment analysis using the neuropeptide genes subset (Table S5). Our findings show that most of the neuropeptide genes are transcriptionally controlled by NPR-1 (Figure 5A; Table S5). NPR-1 is a G protein-coupled receptor similar to neuropeptide Y receptor (NPYR) that controls C. elegans immune response against pathogens (Nakad et al., 2016; Reddy et al., 2009, 2011; Singh and Aballay, 2019b; Styer et al., 2008). Other genes are controlled by miRNA and LIN-28 (Figure 5A; Table S5). Figure 5B shows confirmation of the misregulation of NPYR/NPR-1-dependent genes in unc-30(ok613) mutants compared to wild-type animals. We also found that the pan-neuronal expression of unc-30(ok613) rescued the changes in expression of NPR-1-dependent neuropeptide genes (Figure 5B).

Figure 5.

PITX1/UNC-30 regulates immunity and longevity via neuropeptide signaling

(A) Representation factors of neuropeptide genes upregulated in non-infected and P. aeruginosa-infected unc-30(ok613) versus WT animals.

(B) qRT-PCR analysis of neuropeptide gene expression in WT, unc-30(ok613), and unc-30(ok613);Prab-3::unc-30 animals (n ≈ 1,000). Bars represent means while error bars indicate SD; *p < 0.05, **p < 0.001, ***p < 0.0001 and ns, not significant (p > 0.05).

(C) WT, npr-1(ad609), unc-30(ok613), and npr-1(ad609);unc-30(ok613) animals were exposed to P. aeruginosa and scored for survival (n = 60). WT versus unc-30(ok613), p < 0.0001; npr-1(ad609), p < 0.001; npr-1(ad609);unc-30(ok613), p < 0.05. npr-1(ad609) versus npr-1(ad609)::unc-30(ok613), p = NS.

(D) WT, npr-1(ad609), unc-30(ok613), and unc-30(ok613);npr-1(ad609) animals were exposed to UV-killed E. coli and scored for survival (n = 120). WT versus unc-30(ok613) p < 0.0001; npr-1(ad609), p = NS. While npr-1(ad609) versus unc-30(ok613), p < 0.0001; unc-30(ok613);npr-1(ad609), p = NS.

(E) Representation factors of immune genes downregulated in npr-1(ad609) versus WT.

(F) qRT-PCR analysis of expression of age determination genes in WT, npr-1(ad609), unc-30(ok613), and unc-30(ok613);npr-1(ad609) animals (n ≈ 1,000). Bars represent means while error bars indicate SD; *p < 0.05, **p < 0.001, ***p < 0.0001, and ns, not significant (p > 0.05).

(B)–(D) and (F) are representative of three independent experiments. “n” represents the number of animals for each experimental condition.

Because most unc-30(ok613) upregulated neuropeptide genes are NPYR/NPR-1-dependent, we asked whether NPR-1 may be part of the UNC-30 pathway that controls the immunity-longevity tradeoff. We hypothesized that if npr-1 acts downstream of unc-30, npr-1 mutation should suppress the enhanced immunity and reduced longevity of unc-30(ok613) animals. Indeed, we found that the npr-1(ad609) mutation was able to suppress the immunity and longevity phenotypes of unc-30(ok613) animals (Figures 5C, 5D, and S5). We further studied NPR-1-regulated genes and found that, in addition to controlling the immune PMK-1 pathway (Styer et al., 2008), NPR-1 controls immune genes that are ELT-2- and PQM-1-dependent (Figure 5E; Table S6). Consistent with the suppression of the reduced longevity of unc-30(ok613) animals by a mutation in npr-1, we found that the npr-1(ad609) mutation suppressed the missregulation of age-related genes observed in unc-30(ok613) animals (Figure 5F). Because the expression of age-related genes studied does no change in npr-1(ad609) mutants compared to wild-type animals, we believe that there is an NPR-1-independent mechanism that controls their basal expression.

GABAergic ASG neurons control the UNC-30-modulated immunity-longevity tradeoff

Next, we asked which UNC-30-expressing neuronal cells could be involved in the control of the immune-longevity tradeoff. As a first step, we employed a two-component system to kill cells by using a reconstituted caspase (Chelur and Chalfie, 2007) to generate a PVP neuron ablated strain and that was exposed to P. aeruginosa. We focused on PVP because it is well connected to other UNC-30-expressing neurons, including DD and VD (Figure S6A). We found no significant difference between the PVP(−) and wild-type animals (Figure 6A), suggesting that the immuno-modulatory function of UNC-30 is not linked to PVP neurons. However, we cannot rule out the role of DD, VD, and VC in immunity.

Figure 6.

ASG neurons control the immunity-longevity tradeoff

(A) WT, PVP(−), and unc-30(ok613) animals were exposed to P. aeruginosa and scored for survival (n = 60). WT versus PVP(−), p = NS.

(B) WT, unc-30(ok613), ASG(−), and ASG−);unc-30(ok613) animals were exposed to P. aeruginosa and scored for survival (n = 60). WT versus ASG(−), p < 0.0001; ASG−);unc-30(ok613), p < 0.0001. While ASG(−) versus ASG(−);unc-30(ok613), p = NS.

(C) WT, unc-30(ok613), unc-30(ok613);Pgyc-15::unc-30 animals were exposed to P. aeruginosa and scored for survival (n = 60). WT versus unc-30(ok613);Pgyc-15::unc-30, p = NS.

(D) qRT-PCR analysis of immune gene expression in WT and ASG(−) animals (n ≈ 1000). Bars represent means while error bars indicate SD; **p < 0.001 and ***p < 0.0001.

(E) WT, ASG(−), PVP(−), and unc-30(ok613) animals were exposed to UV-killed E. coli and scored for survival (n = 120). WT versus ASG(−), p < 0.0001; unc-30(ok613), p < 0.0001.

(F) WT, ASG(−), ASG−);unc-30(ok613), and unc-30(ok613) animals were exposed to UV-killed E. coli and scored for survival (n = 120). WT versus ASG(−), p < 0.0001; unc-30(ok613), p < 0.0001. unc-30(ok613) versus ASG(−)::unc-30(ok613), p < 0.001.

(G) WT, unc-30(ok613), unc-30(ok613);Pgyc-15::unc-30 animals were exposed to UV-killed E. coli and scored for survival (n = 120). WT versus unc-30(ok613);Pgyc-15::unc-30, p = NS.

Figures are representative of three independent experiments. “n” represents the number of animals for each experimental condition.

We next studied whether other UNC-30-expressing cells (ASG, PVD, or OLL sensory neurons) could control the defense response against pathogen infection. We employed an interactive tissue-gene expression prediction tool (https://worm.princeton.edu) to predict the expression enrichment level across different cells and tissues. The results obtained revealed that unc-30 is highly expressed in ASG, which is an amphid neuron (Figure S6B) (Li and Kim, 2008; Pereira et al., 2015; Pocock and Hobert, 2010). We noticed that some of the neuropeptide genes, including flp-6, flp-13, flp-22, and ins-1, that are upregulated in unc-30(ok613) animals are expressed in ASG neurons (Li and Kim, 2008; Pereira et al., 2015; Pocock and Hobert, 2010). Thus, we studied the effect of ASG ablation in the control of defense against P. aeruginosa infection in wild-type and unc-30(ok613) animals. We found that ASG(−) animals exhibited resistance to P. aeruginosa-mediated killing similar to that of unc-30(ok613) animals (Figure 6B). The resistance to P. aeruginosa infection of unc-30(ok613) and ASG(−)::unc-30(ok613) animals were also indistinguishable (Figure 6B). In addition, expression of unc-30 only in ASG neurons completely suppressed the pathogen resistance of unc-30(ok613) animals (Figure 6C), suggesting that the GABAergic ASG neuron plays a key role in the UNC-30 modulation of immunity. To further confirm the role of ASG neurons in the control of immunity, we quantified the expression of immune genes and found that they were upregulated in ASG(−) animals (Figure 6D).

We also investigated the lifespan of animals lacking ASG(−) or PVP(−). As shown in Figure 6E, ASG(−), but not PVP(−), animals exhibited a longer lifespan than control animals. We also found that ASG ablation partially rescued the short lifespan of unc-30(ok613) animals, suggesting that ASG neurons participate in the control of longevity by UNC-30 (Figure 6F). We also found that unc-30 expression in ASG neurons rescued the short lifespan of unc-30(ok613) animals (Figure 6G). In summary, our results suggest that neuronal UNC-30 and neuropeptide signaling regulate the tradeoff between immunity and longevity controlling the ELT-2 and PMK-1 immune pathway and the MDL-1 and PQM-1 aging pathways (Figure 7).

Figure 7.

Model for neuronal control of the tradeoff between immunity and longevity via neuropeptide signaling in C. elegans

Neuronal PITX1/UNC-30, via NPR-1, inhibits immunity by preventing the expression of ELT-2- and PMK-1-dependent immune genes and promotes longevity by preventing the expression of MDL-1 and PQM-1-dependent age-related genes.

DISCUSSION

Biological tradeoffs are widely employed by metazoans to maximize the allocation of precious resources that are critical for organismal homeostasis. However, the mechanisms that control them at the whole animal level remain obscure. The nervous system provides a perfect partner to help fine-tune tradeoffs because it can rapidly respond to many types of stimuli and is adept at controlling opposite functions (Eastman et al., 1999; Jin et al., 1994; Westmoreland et al., 2001). Here, we show that UNC-30 functions in the nervous system from where, together with neuropeptide signaling, it can control the tradeoff between immunity and longevity. The absence of UNC-30 results in the upregulation of the expression of age-related genes controlled by MDL-1 and PQM-1 and immune genes controlled by the ELT-2 and PMK-1. Furthermore, the GABAergic neuron ASG and the NPR-1 neuropeptide signaling are part of the UNC-30 pathway that controls the balance between immune activation and longevity.

Previous studies linked neurotransmitters such as dopamine and octopamine to the control of immunity via the PMK-1 pathway (Cao and Aballay, 2016; Pinoli et al., 2017; Sellegounder et al., 2019; Sun et al., 2011) and the X-box binding protein 1 (XBP-1) branch of the canonical UPR pathway (Cao et al., 2017). Acetylcholine has also been connected to the control of immunity via the intestinal epithelial Wnt signaling (Labed et al., 2018). Furthermore, serotonin was also found to modulate the immune response via G-protein GOA-1 (Gαo) in rectal epithelial cells (Anderson et al., 2013; Hoffman and Aballay, 2019). Unlike other neurotransmitter pathways, the GABAergic signaling pathway modulates the immune response to pathogens via the control of the expression of immune genes regulated by ELT-2 and PMK-1. The UNC-30 pathway also controls genes that are involved in aging (Apfeld and Kenyon, 1999, 2004; Jeong et al., 2012; Kenyon, 2010; Wolkow et al., 2000). The G protein-coupled GABA receptor GBB-1 modulates longevity through G protein-PLCb, which transmits longevity signals to FOXO/DAF-16 (Chun et al., 2015; Yuan et al., 2019). Our studies highlight a role for neural UNC-30 in the integration of signals that control the balance between the activation of both immune and longevity pathways that have opposite effects. Because UNC-30 suppresses immunity by inhibiting the expression of ELT-2- and PMK-1-dependent genes, it may simply increase lifespan by reducing the metabolic challenge caused by immune activation. However, we also found that UNC-30 may directly enhance longevity by inhibiting the activity of MDL-1 and PQM-1, which are negative regulators of longevity (Johnson et al., 2014; Nakamura et al., 2016; Riesen et al., 2014; Templeman et al., 2018; Tepper et al., 2013, 2014). PQM-1 functions together with DAF-16 as a key transcriptional regulator of DAF-2-mediated longevity (Tepper et al., 2013), but the lack of significant enrichment in DAF-16-controled genes regulated by UNC-30 suggests that PQM-1 may also control longevity in a DAF-16-independent manner.

Our findings suggest that ASG neurons play a key role in controlling the tradeoff between immunity and longevity. They also express neuropeptides flp-6, flp-13, flp-22, and ins-1 (Li and Kim, 2008; Pereira et al., 2015; Pocock and Hobert, 2010), which we found are upregulated in unc-30(ok613) animals. Furthermore, ASG cells respond to neuropeptides flp-18 and flp-21 via npr-1 (Bargmann, 2006; Li and Kim, 2008; Pereira et al., 2015; Pocock and Hobert, 2010; Rex et al., 2005; Sugiura et al., 2005), which were upregulated in unc-30(ok613) animals. These results argue strongly that ASG neurons are critical in the control of the longevity-immunity tradeoff. Both PMK-1 and ELT-2 function in the intestine to control immunity (Bolz et al., 2010; Head et al., 2017; Kerry et al., 2006; Kim et al., 2002; Shapira et al., 2006). In addition, the role of PQM-1 and MDL-1 in longevity has been linked to the intestine (Riesen et al., 2014; Tepper et al., 2013), suggesting that UNC-30 controls the immunity-longevity tradeoff in a cell non-autonomous manner. A recent example of a tradeoff mechanism indicates that dietary restriction extends lifespan by downregulating the PMK-1-mediated immune signaling (Wu et al., 2019). Further work will be required to establish whether this and other tradeoffs are controlled cell non-autonomously by the nervous system.

Our results provide evidence that specific genes and neurons in the nervous system act in a cell non-autonomous manner to coordinately control immune and aging pathways. Organisms widely rely on tradeoff mechanisms when a given trait cannot increase without decreasing another to maintain homeostasis. Given the evolutionary conservation of UNC-30 and the immune and aging pathways that are neutrally controlled, the identification and characterization of the cues that the nervous system uses to control the immunity-longevity tradeoff in C. elegans should yield insights into similar mechanisms used across metazoans.

STAR★METHODS

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Alejandro Aballay (aballay@ohsu.edu).

Materials availability

This study did not generate new unique reagents.

Data and code availability

The datasets generated during this study are available at the Gene Expression Omnibus (GEO) repository, with accession numbers, GEO: GSE149742 and GSE149862.

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Materials and Methods

Bacterial strains

The bacterial strains used in this study are Escherichia coli OP50, E. coli HT115(DE3) (Brenner, 1974), Pseudomonas aeruginosa PA14, P. aeruginosa PA14-GFP (Tan et al., 1999), Salmonella enterica Serovar Typhimurium 1344 (Wray and Sojka, 1978), and Staphylococcus aureus strain NCTCB325 (Sifri et al., 2003). Gram-negative bacteria were grown in Luria-Bertani (LB) broth. Staphylococcus aureus strain NCTCB325 was grown in Tryptic Soy Agar prepared with Nalidixic acid. All bacteria were grown at 37°C.

C. elegans Strains and Growth Conditions

Hermaphrodite C. elegans (var. Bristol) wild-type (N2) was used as control unless otherwise indicated. C. elegans strains CF1038 daf-16(mu86), KU25 pmk-1(km25), MGH171 alxIs9 [vha-6p::sid-1::SL2::GFP], VC295 unc-30(ok613), RB711 pqm-1(ok485), DA609 npr-1(ad609), CB318 unc-30(e318), CB845 unc-30(e191), SD39 unc-30(e596), GR2245 skn-1(mg570), RB711 pqm-1(ok485), KU4 sek-1(km4) and AU3 nsy-1(ag3) were obtained from the Caenorhabditis Genetics Center (University of Minnesota, Minneapolis, MN). mdl-1(tm311) was obtained from National Bioresource Project (NBRP), Japan. npr-1(ad609);unc-30(ok613), pqm-1(ok485);unc-30(ok613), daf-16(mu86);unc-30(ok613), sek-1(km4);unc-30(ok613), nsy-1(ag3);unc-30(ok613), mdl-1(tm311);unc-30(ok613), and ASG(-)::unc-30(ok613) were obtained by standard genetic crosses. Rescued strain unc-30(ok613);Punc-30::unc-30, neuronal rescued strain unc-30(ok613);Prab-3::unc-30 and unc-30 rescued in ASG neuron-specific, unc-30(ok613);Pgcy-15::unc-30 were generated as described below. The ASG(-) ablated Ex[Ptax-2::CZ::ced-3(p17)::unc-54 3′UTR + Plim-6::ced-3(p15)-NZ::unc-54 3′UTR, pRF4] and PVP(-) ablated Ex[Podr-2b::CZ::ced-3(p17)::unc-54 3′UTR, Punc-53::ced-3(p15)-NZ::unc-54 3′UTR, pRF4] were also generated as described below. The strains were crossed with the wild-type laboratory N2. Mutant C. elegans and transgenic strains used in this study are listed in the Key Resources Table. Young adult stage animals were used in all experiment, except for gene expression experiments where L4 animals were used.

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Bacterial and virus strains
Escherichia coli OP50	Caenorhabditis Genetics Center (CGC)	OP50
Pseudomonas aeruginosa PA14	Laboratory of Frederick M. Ausubel	PA14
Salmonella enterica serovar Typhimurium 1344	Laboratory of Frederick M. Ausubel	Typhimurium 1344
Enterococcus faecalis OG1RF	Laboratory of Danielle A. Garsin	OG1RF
Enterococcus faecium E007	Laboratory of Danielle A. Garsin	E007
Staphylococcus aureus NCTC8325	National Collection of Type Cultures	NCTC8325
E. coli HT115 (D3) containing plasmid pL4440	Source BioScience	HT115 L4440
Ahringer RNAi Libraries in E. coli HT115 (D3)	Source BioScience	N/A
Chemicals, peptides, and recombinant proteins
5-Fluoro-2′-deoxyuridine	Sigma-Aldrich	Cat# CAF0503
Restriction Enzymes	New England BioLabs	N/A
Q5 High-Fidelity DNA Polymerase	New England BioLabs	Cat# M0491S
Isopropyl-β-D-thiogalactoside	Research Products International	CAT# L56000
QIAzol	QIAGEN	CAT# 79306
Proteinase K	QIAGEN	CAT# 1019499
SYBR Green PCR Master Mix	Thermo Fisher Scientific	CAT# 4309155
Deposited data
RNA Sequencing of unc-30(ok613) and wild type (WT) N2 C elegans	This paper	GEO: GSE149742
RNA Sequencing of unc-30(ok613) and wild type (WT) N2 C. elegans in Pseudomonas aeruginosa (PA14) infection	This paper	GEO: GSE149862
Experimental models: organisms/strains
C. elegans: N2 wild-type	CGC	N2 Bristol
daf-16(mu86)	CGC	CF1038
pmk-1(km25)	CGC	KU25
alxIs9 [vha-6p::sid-1::SL2::GFP],	CGC	MGH171
unc-30(ok613)	CGC	VC295
pqm-1(ok485)	CGC	RB711
npr-1(ad609)	CGC	DA609
unc-30(e318)	CGC	CB318
unc-30(e191)	CGC	CB845
unc-30(e596)	CGC	SD39
skn-1(mg570)	CGC	GR2245
pqm-1(ok485)	CGC	RB711
sek-1(km4)	CGC	KU4
nsy-1(ag3)	CGC	AU3
mdl-1(tm311)	NBRP	FX00311
npr-1(ad609);unc-30(ok613)	This paper	N/A
pqm-1(ok485);unc-30(ok613)	This paper	N/A
daf-16(mu86);unc-30(ok613)	This paper	N/A
sek-1(km4);unc-30(ok613)	This paper	N/A
nsy-1(ag3);unc-30(ok613)	This paper	N/A
mdl-1(tm311);unc-30(ok613)	This paper	N/A
ASG(−)::unc-30(ok613)	This paper	N/A
unc-30(ok613);Punc-30::unc-30	This paper	N/A
unc-30(ok613);Prab-3::unc-30	This paper	N/A
unc-30(ok613);Pgcy-15::unc-30	This paper	N/A
Ex[Ptax-2::CZ::ced-3(p17)::unc-54 3′ UTR + Plim-6::ced-3(p15)-NZ::unc-54 3′UTR, pRF4]	This paper	N/A
Ex[Podr-2b::CZ::ced-3(p17)::unc-54 3′UTR, Punc-53::ced-3(p15)-NZ::unc-54 3′UTR, pRF4]	This paper	N/A
Oligonucleotides
See Table S1 for primers used in the study	Integrated DNA Technologies	Integrated DNA Technologies
Software and algorithms
GraphPad Prism 7	GraphPad Software	https://www.graphpad.com/scientificsoftware/prism
PhotoScape X	PhotoScape Tools	http://www.photoscape.org/ps/main/index.php
Fiji - ImageJ	ImageJ	https://imagej.net/Fiji
Galaxy	Galaxy	https://usegalaxy.org
SimpleMine	Wormbase	https://wormbase.org//tools/mine/simplemine.cgi
Enrichment Analysis	Wormbase	https://wormbase.org/tools/enrichment/tea/tea.cgi
WormExp	WormExp tool	http://wormexp.zoologie.uni-kiel.de/wormexp/
DAVID	DAVID Bioinformatics Database	https://david.ncifcrf.gov/tools.jsp
InteractiVenn	InteractiVenn	http://www.interactivenn.net
Venn Diagram	Bioinformatics and evolutionary genomics tool	http://bioinformatics.psb.ugent.be/webtools/Venn/
Tissue-gene expression prediction	Princeton.edu	https://worm.princeton.edu
Neuron wiring tools	Database of synaptic connectivity of C. elegans for computation	http://ims.dse.ibaraki.ac.jp/ccep-tool/
SnapGene	SnapGene Software	https://www.snapgene.com
Microsoft Excel	Microsoft	https://office.live.com/start/Excel.aspx
Fluorescent Stereo Microscope Leica M165 FC software	Leica	https://www.leica-microsystems.com/products/stereo-microscopes-macroscopes/p/leica-m165-fc/gallery/
BioRender	BioRender tool	https://biorender.com

All strains were grown at 20°C on nematode growth medium (NGM) plates seeded with E. coli OP50 as the food source (Brenner, 1974) unless otherwise indicated. The recipe for the control NGM plates is: 3 g/l NaCl, 3 g/l peptone, 20 g/l agar, 5 μg/ml cholesterol, 1 mM MgSO4, 1 mM CaCl2, and 25 mM potassium phosphate buffer (pH 6.0). The NGM plates were without antibiotics except indicated.

METHOD DETAILS

RNA Interference (RNAi)

Knockdown of targeted genes was obtained using RNAi by feeding the animal with E. coli strain HT115(DE3) expressing double-stranded RNA (dsRNA) homologous to a target gene (Fraser et al., 2000; Timmons and Fire, 1998. RNAi was carried out as described previously (Sun et al., 2011; Singh and Aballay, 2017). Briefly, E. coli with the appropriate vectors were grown in LB broth containing ampicillin (100 μg/mL) and tetracycline (12.5 μg/mL) at 37°C overnight and plated onto NGM plates containing 100 μg/mL ampicillin and 3 mM isopropyl β-D-thiogalactoside (IPTG) (RNAi plates). RNAi-expressing bacteria were grown at 37°C for 12-14 hours. Gravid adults were transferred to RNAi-expressing bacterial lawns and allowed to lay eggs for 2-3 hours. The gravid adults were removed, and the eggs were allowed to develop at 20°C to young adults. This was repeated for another generation (except for ELT-2 RNAi) before the animals were used in the experiments. The RNAi clones were from the Ahringer RNAi library.

C. elegans Survival Assay on Bacterial Pathogens

P. aeruginosa and S. enterica were incubated in LB medium. S. aureus was incubated in TSA medium with nalidixic acid (10 μg/mL). The incubations were done at 37°C with gentle shaking for 12 hours. P. aeruginosa and S. enterica were grown on modified NGM agar medium (0.35% peptone). For partial lawn assays, 20 μL of the overnight bacterial cultures were seeded at the center of the relevant agar plates without spreading. For full lawn experiments, 20 μL of the bacterial culture were seeded and spread all over the surface of the agar plate. No antibiotic was used for P. aeruginosa and S. enterica, while nalidixic acid (10 μg/mL) was used for the TSA plates for S. aureus. The seeded plates were allowed to grow for 12 at 37°C. The plates were left at room temperature for at least 1 hr before the infection experiments. 20 synchronized young adult worms were transferred to the plates for infection, and three technical replicate plates were set up for each condition (n = 60 animals) and the experiments were performed in triplicate (Table S7). The plates were then incubated at 25°C. Scoring was performed every 12 hr for P. aeruginosa and S. aureus, while 24 hr for S. enterica. Worms were scored as dead if the animals did not respond to touch by a worm pick or lack of pharyngeal pumping. Live animals were transferred to fresh pathogen lawns each day. All C. elegans killing assays were performed three times independently.

Bacterial lawn avoidance assay

Bacterial lawn avoidance assays were performed by 20 mL of P. aeruginosa PA14 on 3.5-cm modified NGM agar (0.35% peptone) plates and cultured at 37°C overnight to have a partial lawn. The modified NGM plates were left to cool to room temperature for about 1 hour, twenty young adult animals grown on E. coli OP50 were transferred to the center of each bacterial lawn after it. The number of animals on the bacterial lawns was counted at 12 and 24 hours after exposure.

Pharyngeal pumping rate assay

Wild-type and unc-30(ok613) animals were synchronized by placing 20 gravid adult worms on NGM plates seeded with E. coli OP50 and allowing them to lay eggs for 60 min at 20°C. The gravid adult worms were then removed, and the eggs were allowed to hatch and grow at 20°C until they reached the young adult stage. The synchronized worms were transferred to NGM plates fully seeded with P. aeruginosa for 24 hours at 25°C. Worms were observed under the microscope with a focus on the pharynx. The number of contractions of the pharyngeal bulb was counted over 60 s. Counting was conducted in triplicate and averaged to obtain pumping rates.

Defecation rate assay

Wild-type and mutant animals were synchronized by placing 20 gravid adult worms on NGM plates seeded with E. coli OP50 and allowing them to lay eggs for 60 min at 20°C. The gravid adult worms were then removed, and the eggs were allowed to hatch and grow at 20°C until they reached the young adult stage. The synchronized worms were transferred to NGM plates fully seeded with P. aeruginosa for 24 hours at 25°C. Worms were observed under a microscope at room temperature. For each worm, an average of 10 intervals between two defecation cycles was measured. The defecation cycle was identified as a peristaltic contraction beginning at the posterior body of the animal and propagating to the anterior part of the animal followed by feces expulsion.

Brood Size Assay

The brood size assay was done following the earlier described methods (Kenyon et al., 1993; Otarigho and Aballay, 2020). Ten L4 animals from egg-synchronized populations were transferred to individual NGM plates (seeded with E. coli OP50) (described above) and incubated at 20°C. The animals were transferred to fresh plates every 24 hours. The progenies were counted and removed every day.

C. elegans Longevity Assays

Longevity assays were performed on NGM plates containing live, heat killed, or UV-killed E. coli strains HT115 or OP50 as described earlier (Otarigho and Aballay, 2020; Sun et al., 2011; Kumar et al., 2019; Sutphin and Kaeberlein, 2009). Animals were scored as alive, dead, or gone each day. Animals that failed to display touch-provoked or pharyngeal movement were scored as dead. Experimental groups contained 60 to 100 animals and the experiments were performed in triplicate (Table S7). The assays were performed at 20°C.

Intestinal Bacterial Loads Visualization and Quantification

Intestinal bacterial loads were visualized and quantified as described earlier (Otarigho and Aballay, 2020; Sun et al., 2011). Briefly, P. aeruginosa-GFP lawns were prepared as described above. The plates were cooled to ambient temperature for at least an hour before seeding with young gravid adult hermaphroditic animals and the setup was placed at 25°C for 24 hours. The animals were transferred from P. aeruginosa-GFP plates to the center of fresh E. coli plates for 10 min to eliminate P. aeruginosa-GFP on their body. The step was repeated two times more to further eliminate external P. aeruginosa-GFP left from earlier steps. Subsequently, ten animals were collected and used for fluorescence imaging to visualize the bacterial load while another ten were transferred into 100 μL of PBS plus 0.01% Triton X-100 and ground. Serial dilutions of the lysates (101-1010) were seeded onto LB plates containing 50 μg/mL of kanamycin to select for P. aeruginosa-GFP cells and grown overnight at 37°C. Single colonies were counted the next day and represented as the number of bacterial cells or CFU per animal.

Fluorescence and bloating Imaging

Fluorescence and bloating imaging were carried out as described previously (Otarigho and Aballay, 2020; Singh and Aballay, 2019a, 2019b). Briefly, animals were anesthetized using an M9 salt solution containing 50 mM sodium azide and mounted onto 2% agar pads. The animals were then visualized for bacterial load using a Leica M165 FC fluorescence stereomicroscope. The diameter of the intestinal lumen was measured using Fiji-ImageJ software. At least 10 animals were used for each condition.

RNA Sequencing and Bioinformatic Analyses

Approximately 40 gravid WT and unc-30(ok613) animals were placed for 3 hours on 10-cm NGM plates (seeded with E. coli OP50) (described above) to have a synchronized population, which developed and grew to L4 larval stage at 20°C. Animals were washed off the plates with M9 and frozen in QIAzol by ethanol/dry ice and stored at −80 prior to RNA extraction. Total RNA was extracted using the RNeasy Plus Universal Kit (QIAGEN, Netherlands). Residual genomic DNA was removed using TURBO DNase (Life Technologies, Carlsbad, CA). A total of 6 μg of total RNA was reverse-transcribed with random primers using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA).

The library construction and RNA sequencing in Illumina NovaSeq 6000 platform was done following the method described by Zhu et al. (2018) and Yao et al. (2018) pair-end reads of 150 bp were obtained for subsequent data analysis. The RNA sequence data were analyzed using a workflow constructed for Galaxy (https://usegalaxy.org) as described (Afgan et al., 2018; Afgan et al., 2016; Amrit and Ghazi, 2017). The RNA reads were aligned to the C. elegans genome (WS271) using the aligner STAR. Counts were normalized for sequencing depth and RNA composition across all samples. Differential gene expression analysis was then performed on normalized samples. Genes exhibiting at least two-fold change were considered differentially expressed. The differentially expressed genes were subjected SimpleMine tools from wormbase (https://www.wormbase.org/tools/mine/simplemine.cgi) to generate information such as wormBase ID and gene name, which are employed for further analyses.

Gene ontology analysis was performed using the WormBase IDs in DAVID Bioinformatics Database (https://david.ncifcrf.gov) (Dennis et al., 2003) and validated using a C. elegans data enrichment analysis tool (https://wormbase.org/tools/enrichment/tea/tea.cgi). Immune and age determination pathways were obtained using the Worm Exp version 1 (http://wormexp.zoologie.uni-kiel.de/wormexp/) (Yang et al., 2016) using the transcription factor target category. The Venn diagrams were obtained using the web tool InteractiVenn (http://www.interactivenn.net) (Heberle et al., 2015) and bioinformatics and evolutionary genomics tool (http://bioinformatics.psb.ugent.be/webtools/Venn/). Tissue-gene expression prediction was used to simulate the expression pattern of UNC-30 (https://worm.princeton.edu) (Kaletsky et al., 2018). While neuron wiring was done using the database of synaptic connectivity of C. elegans for computation (White et al., 1986) http://ims.dse.ibaraki.ac.jp/ccep-tool/.

RNA Isolation and Quantitative Reverse Transcription-PCR (qRT-PCR)

Animals were synchronized and total RNA extraction was done following the protocol described above. qRT-PCR was conducted using the Applied Biosystems One-Step Real-time PCR protocol using SYBR Green fluorescence (Applied Biosystems) on an Applied Biosystems 7900HT real-time PCR machine in 96-well-plate format. Twenty-five-microliter reactions were analyzed as outlined by the manufacturer (Applied Biosystems). The relative fold-changes of the transcripts were calculated using the comparative CT(2−ΔΔ) method and normalized to pan-actin (act-1, −3, −4). The cycle thresholds of the amplification were determined using StepOnePlus Real-Time PCR System Software v2.3 (Applied Biosystems). All samples were run in triplicate. The primer sequences were available upon request and presented in Table S8.

Generation of transgenic C. elegans

To generate unc-30 rescue animals, the unc-30 DNA was amplified from the genomic DNA of Bristol N2 C. elegans adult worms. Plasmid pPD160_Punc-30_unc-30 was constructed by linearization of plasmid pPD160_Punc-30 using XbaI and SmaI restriction digestion enzymes. The amplified unc-30 DNA was cloned behind its native promoter in the plasmid pPD160_Punc-30, between XbaI and SmaI sites. For the neuronal rescue strain, the plasmid pPD95_77_Prab-3_unc-30 was constructed by cloning the amplified unc-30 DNA into KpnI/HindIII digested pPD95_77_Prab-3 under the promoter of rab-3. The constructs were purified and sequenced. Young adult hermaphrodite unc-30(ok613) C. elegans were transformed by microinjection of plasmids into the gonad as described (Mello and Fire, 1995; Mello et al., 1991). Briefly, a mixture containing pPD160_Punc-30_unc-30 (40 ng/μl) and Pmyo-3::mCherry pCFJ104 (5 ng/μl) that drives the expression of mCherry to the muscle as a transformation marker were injected into the animals. For the neuronal rescue, a mixture containing pPD95_77_Prab-3_unc-30 plasmids (40 ng/μl) and Pmyo-2::mCherry (5 ng/μl) pCFJ90 that drives the expression of mCherry to the pharynx as a transformation marker were injected into the animals.

The neuron ablated strains were generated by employing a two-component system reconstituted caspase (recCaspase) for selective ablation of targeted cells (Chelur and Chalfie, 2007). The ASG ablated neuron was done by separately cloning the promoters of tax-2 and lim-6 in the recCaspases plasmids. Briefly, a 1817bp segment of the tax-2 promoter was cloned in the CZ-ced-3(p17):)::unc-54 3′UTR plasmid at the Sph I and EcoR I site to generate the Ptax-2::CZ::ced-3(p17)::unc-54 3′UTR plasmid, and a 5001bp segment of the Lim-6 promoter was cloned in the ced-3(p15)+NZ:: unc-54 3′UTR plasmid at the Sph I and EcoR I site to generate the Plim-6::ced-3(p15)-NZ::unc-54 3′UTR plasmid. A mixture containing Ptax-2-CZ-ced-3(p17)-unc-54 3′UTR, Plim-6-ced-3(p15)-NZ-unc-54 3′UTR of 25ng/μL, and the roller marker pRF4 rol-6(su1006) (50ng/μL) was injected into the N2 young animals. To rescue unc-30 in ASG neurons, gcy-15 promoter (1987bp) and unc-30 (5042bp) were amplified from gDNA. The unc-30 fragment was cloned downstream of the Pgcy-15 by in-fusion PCR to obtain Pgcy-15::unc-30, which was cloned into pPD95.77 between the Sph I and EcoR I sites to generate pPD95_77_Pgcy-15::unc-30. The ASG neuron–specific rescue strain unc-30(ok613);Pgcy-15::unc-30 was generated by injecting plasmid pPD95_77_Pgcy-15::unc-30 (25 ng/μl) with Pmyo-2::mCherry (10 ng/μl) as a co-injection marker into the unc-30(ok613) animals.

The PVP ablation was done by separately cloning the promoters of odr-2 b isoform and unc-53 in the recCaspases plasmids. Briefly, a 2661bp segment of the odr-2 b isoform was cloned in the CZ::ced-3(p17)::unc-54 3′UTR plasmid at the Sph I and EcoR I site to obtain the Podr-2b::CZ::ced-3(p17)::unc-54 3′UTR plasmid and a segment of 3012bp of unc-53 was cloned in the ced-3(p15)-NZ::unc-54 3′UTR plasmid at the Sph I and EcoR I site to obtain the Punc-53-ced-3(p15)-NZ-unc-54 3′UTR plasmid. A mixture containing 25ng/μL of the Podr-2b-CZ-ced-3(p17)-unc-54 3′UTR plasmid, 25ng/μL of the Punc-53-ced-3(p15)-NZ-unc-54 3′UTR plasmid, and 50ng/μL of the roller marker pRF4 rol-6(su1006) was injected into N2 young animals. The successful transformation was determined by the identification of the selection marker. At least three independent lines carrying extrachromosomal arrays were obtained for each construct. Only worms with dominant roller phenotypes were selected for further experiment. Primers used in the generation of transgenic animals are shown in Table S8.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analysis was performed with Prism 8 version 8.1.2 (GraphPad). All error bars represent the standard deviation (SD). The two-sample t test was used when needed, and the data were judged to be statistically significant when p < 0.05. In the figures, asterisks (*) denote statistical significance as follows: ns, not significant, *, p < 0.05, **, p < 0.001, ***, p < 0.0001, as compared with the appropriate controls. The Kaplan-Meier method was used to calculate the survival fractions, and statistical significance between survival curves was determined using the log-rank test. All experiments were performed at least three times.