Effects of an IgE receptor polymorphism acting on immunity, susceptibility to infection, and reproduction in a wild rodent

  1. Klara M Wanelik  Is a corresponding author
  2. Mike Begon
  3. Janette E Bradley
  4. Ida M Friberg
  5. Joseph A Jackson
  6. Christopher H Taylor
  7. Steve Paterson
  1. Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, United Kingdom
  2. School of Life Sciences, University of Nottingham, United Kingdom
  3. School of Environment and Life Sciences, University of Salford, United Kingdom

Abstract

The genotype of an individual is an important predictor of their immune function, and subsequently, their ability to control or avoid infection and ultimately contribute offspring to the next generation. However, the same genotype, subjected to different intrinsic and/or extrinsic environments, can also result in different phenotypic outcomes, which can be missed in controlled laboratory studies. Natural wildlife populations, which capture both genotypic and environmental variability, provide an opportunity to more fully understand the phenotypic expression of genetic variation. We identified a synonymous polymorphism in the high-affinity Immunoglobulin E (IgE) receptor (GC and non-GC haplotypes) that has sex-dependent effects on immune gene expression, susceptibility to infection, and reproductive success of individuals in a natural population of field voles (Microtus agrestis). We found that the effect of the GC haplotype on the expression of immune genes differed between sexes. Regardless of sex, both pro-inflammatory and anti-inflammatory genes were more highly relatively expressed in individuals with the GC haplotype than individuals without the haplotype. However, males with the GC haplotype showed a stronger signal for pro-inflammatory genes, while females showed a stronger signal for anti-inflammatory genes. Furthermore, we found an effect of the GC haplotype on the probability of infection with a common microparasite, Babesia microti, in females – with females carrying the GC haplotype being more likely to be infected. Finally, we found an effect of the GC haplotype on reproductive success in males – with males carrying the GC haplotype having a lower reproductive success. This is a rare example of a polymorphism whose consequences we are able to follow across immunity, infection, and reproduction for both males and females in a natural population.

Editor's evaluation

This study provides an unusually comprehensive analysis of the associations between polymorphism in an immune gene, the immunoglobulin E receptor Fcer1a, and immune responses, resistance to infection, and reproductive fitness in a wild rodent population. The investigators find that these effects appear to be sex-specific. This study provides critical observations of the possible consequences of immune polymorphisms in wild populations and should be of interest to immunologists, evolutionary biologists, and ecologists investigating genotype-phenotype relationships and potential life-history tradeoffs.

https://doi.org/10.7554/eLife.77666.sa0

Introduction

In order for an individual to control or avoid infection and ultimately contribute offspring to the next generation, they must have a well-functioning immune system (Møller and Saino, 2004). An individual’s immune function is in part determined by their genotype (e.g., Paterson et al., 1998; Wanelik et al., 2018; Turner et al., 2011). Individuals within a population differ from each other in the genotypes they carry, with the potential for these different genotypes to affect the activity of different immune components, and thereby influence susceptibility to infection and reproduction and survivorship. Indeed, variability in immune responses to infection is well-characterized. The phenotypic expression of a genotype is dependent on the environment, including the sex of an individual (intrinsic environment) and exposure to infection (extrinsic environment). Since capturing genotypic and environmental variability in controlled laboratory studies is problematic, natural wildlife populations have been proposed as models in which to understand the phenotypic expression of genetic variation and its consequences for susceptibility to infection and reproduction.

Studies of natural populations have explored the effects of genotype on immune phenotype and have observed consequences for susceptibility to infection. Most notably, variability in the genes of the major histocompatibility complex (MHC) has been associated with resistance to intestinal nematodes in domestic sheep (Paterson et al., 1998) and with resistance to malaria, hepatitis, and AIDS in humans (Hill et al., 1991; Carrington et al., 1999; Thursz et al., 1997). The role of variability elsewhere in the genome, for shaping immune phenotype, has also been studied (Wanelik et al., 2018; Turner et al., 2011). However, it remains challenging to follow the consequences of a genotypic effect for immunity, infection, and reproduction and to account for any sex-dependent expression of a genotype. This is because of the difficulty in obtaining phenotypic data across immune, infection, and reproductive traits, especially for large enough sample sizes to test for data-hungry genotype by sex interactions (Ober et al., 2008). In many cases, sex and other environmental factors are considered as a confounding variable to be controlled for in order not to hide any subtle genetic associations (Paterson et al., 1998). Other studies focus on a single sex for the sake of simplicity (Jackson et al., 2014). More recently, however, there has been a growing body of large-scale field studies of natural populations able to apply genetic and immunological methods to follow large numbers of individuals, exposed to a challenging environment and with varying genetic backgrounds, throughout their lives. This allows us to make a more complete assessment of the impacts of genotype throughout the life of an individual, whether male or female. For example, Graham et al., 2010 found evidence for heritable variation in immunity associated with sex-dependent effects on Soay sheep reproduction. However, we know of no documented example of a polymorphism affecting immunity, susceptibility to infection, and reproduction in a natural population investigated in males and females. Here, we use a wild rodent, the field vole (Microtus agrestis), as a model in which to do this. Wild rodents, in particular, offer an opportunity to quickly follow large numbers of individuals throughout their lives, given their short lifespans. They also offer the opportunity to draw on the immunological and genetics resources developed for laboratory rodents, while providing a much more realistic ecological model of human populations (Turner et al., 2014; Turner and Paterson, 2013).

Immunoglobulin E (IgE)-mediated responses are associated with defense against helminths (Gounni et al., 1994) and with allergy (Tomassini et al., 1991). They are controlled by the high-affinity IgE receptor, FCER1, which is found on the surface of various immune effector cells, for example, mast cells, basophils, and eosinophils (Daeron and Nimmerjahn, 2014). In humans, naturally occurring polymorphisms in FCER1 are known to affect an individual’s serum IgE levels, with consequences for their susceptibility to infection (Weidinger et al., 2008; Granada et al., 2012) and their risk of developing inflammatory disease (Hasegawa et al., 2003; Potaczek et al., 2006; Zhou et al., 2012; Niwa et al., 2010). Furthermore, sex differences in serum IgE levels (Weiss et al., 2006) and the incidence of IgE-mediated inflammatory disease (Chen et al., 2008) have been documented in humans, suggesting that any polymorphism in this pathway is likely to experience different contexts in males and females. Indeed, one study found evidence for a polymorphism in the Fcer1a gene (the alpha chain of FCER1) whose association with susceptibility to systemic lupus erythematosus (a chronic inflammatory disease) differed between males and females (Yang et al., 2013).

In a previous study of a natural population of M. agrestis, we found that males carrying the GC haplotype of the Fcer1a gene expressed the transcription factor GATA3 at a lower level than males carrying non-GC haplotypes (Wanelik et al., 2018). GATA3 is a biomarker of tolerance to macroparasites in mature males in our population (macroparasite infection gives rise to increased expression of GATA3, which gives rise to improved body condition and survival; Jackson et al., 2014). Here, we explore the effects of this GC haplotype further in both males and females to analyze the effect of genotype acting across immune gene expression, infection susceptibility and reproductive success.

Results

We sampled a natural population of M. agrestis in Kielder Forest, Northumberland, UK, over 3 years (2015–2017) and across seven different sites. Our study involved a cross-sectional component (n = 317 destructively sampled voles) and a longitudinal component (n = 850 marked individuals monitored through time, with n = 2387 sampling points). We tested the consequences of the GC haplotype of the Fcer1a gene using both cross-sectional and longitudinal components of our study. As well as the GC haplotype (present at a frequency of 0.08), three other haplotypes were present in our study population: AC haplotype, AT haplotype, and GT haplotype, present at frequencies of 0.81, 0.10, and 0.01, respectively. The two single-nucleotide polymorphisms (SNPs) composing the haplotype were found to be tightly linked (r2 = 0.50; D’ = 0.70).

The GC haplotype has effects on inflammation that differ between sexes

In humans, naturally occurring polymorphisms in the Fcer1a gene have previously been linked to inflammatory disease (Hasegawa et al., 2003; Potaczek et al., 2006; Zhou et al., 2012; Niwa et al., 2010). Therefore, we used the cross-sectional component of our study to test the effects of the GC haplotype on inflammation in males and females. Differential gene expression (DGE) analysis was performed on unstimulated splenocytes taken from 53 males and 31 females assayed by RNASeq, with the aim of identifying individual genes that were differentially expressed between those individuals with and without a copy of the GC haplotype. This DGE analysis showed that the identity of top differentially expressed genes differed between the sexes. In males, the top differentially expressed immune gene was the cytokine, Il33 (log fold change [logFC] = 2.76, p<0.001, q < 0.001; Appendix 1—table 2) while in females it was the suppressor of cytokine signaling Socs3 (logFC = 1.07, p<0.001, q = 0.05; Appendix 1—table 3). Looking at the ranking of each top differentially expressed immune gene in the opposite sex strengthens the case for differing effects in males and females, with Il33 ranked markedly lower in females (rank = 8224/12904, logFC = 0.29, p=0.70, q = 1.00) and Socs3 markedly lower in males (rank = 10886/12904, logFC = –0.05, p=0.84, q = 1.00). Il33 is commonly associated with the anti-helminthic response (Liew et al., 2010) and Socs3 with regulation of the immune response more broadly (Carow and Rottenberg, 2014). Given the link between Il33 and the antihelminthic response (and more generally, IgE-mediated responses and the antihelminthic response), we repeated the DGE analysis while controlling for cestode burden, but this had little effect on our results (same top differentially expressed immune genes; see Appendix 1—table 4 and Appendix 1—table 5), suggesting that these effects were not driven by differences in cestode infection.

Both Il33 and Socs3 also share an association with the inflammatory response (Carow and Rottenberg, 2014; Cayrol and Girard, 2014). While Il33 positively regulates this response (appearing in the gene set GO:0050729), Socs3 negatively regulates it (GO:0050728). To test whether these effects on the inflammatory response were limited to these genes or were more widespread, we performed a gene set enrichment analysis (GSEA) that looked at the rankings of all genes present in each of these gene sets (GO:0050729 and GO:0050728). This analysis showed that both gene sets were more highly relatively expressed in individuals with the GC haplotype than individuals without the haplotype, and that this was true for both males and females. However, males with the GC haplotype showed a stronger signal for genes that positively regulate the inflammatory response (GO:0050729: p=0.007; GO:0050728: p=0.04; Figure 1A, upper panel) while females with the GC haplotype showed a stronger signal for genes that negatively regulate the inflammatory response (GO:0050728: p=0.001; GO:0050728: p=0.04; Figure 1A, lower panel).

Effects of GC haplotype (hGC).

Upper panel: males. Lower panel: females: (A) Unstimulated immune gene expression: Barcode plots showing enrichment of the GO terms GO:0050729 (pro-inflammatory genes) and GO:0050728 (anti-inflammatory genes) in unstimulated splenocytes taken from individuals with (hGC+) vs. without (hGC-) the haplotype, showing that males with the haplotype have a pro-inflammatory bias, whereas females have an anti-inflammatory bias. In each plot, x-axis shows log fold change (logFC) in hGC+ vs. hGC-, black bars represent genes annotated with the GO terms and the worm shows relative enrichment. (B) Susceptibility to infection: association between hGC and the odds of infection with Babesia microti, showing that females with the haplotype have an increased susceptibility to infection (from a GLMM). (C) Reproduction: association between hGC and reproductive success, showing that males with the haplotype have lower reproductive success (from a GLM) (error bars represent ± standard error; *p<0.05; **p<0.01; see Table 1 for sample sizes). GLM = generalized linear model; GLMM = generalized linear mixed effects model.

To further explore these effects on the inflammatory response, we used an independent dataset for males and females whose spleens were stimulated with two immune agonists and assayed by Q-PCR (for a panel of 18 immune genes with limited redundancy; see 'Materials and methods' for how these genes were selected). From this ex vivo assay, one can gain insight into the types of immune response that could be made to a pathogen in vivo (see 'Materials and methods' for details). Although our panel of genes did not include Il33 or Socs3, it did include other genes associated with the inflammatory response, including Il17a, Ifng, Il1b, Il6, and Tnfa. We found that the effect of the GC haplotype on the stimulated expression levels of Il17a, a pro-inflammatory cytokine involved in the antibacterial response, displayed a significant interaction with sex (genotype by sex interaction in follow-up linear mixed effects model [LMM], likelihood ratio test [LRT] p=0.001). While females with the GC haplotype expressed lower levels of Il17a (estimate = –0.12, 95% CI = −0.19 to −0.04), males with the GC haplotype did not differ in their expressed levels of Il17a (estimate = 0.03, 95% CI includes zero = –0.02 to 0.08; Appendix 1—table 6 and Appendix 1—table 7).

The GC haplotype affects oxidative stress

Inflammation and oxidative stress are closely related and often lead to pathology (Biswas, 2016). To test whether the effects of the GC haplotype on inflammation may be having knock-on effects on oxidative stress, we again used the cross-sectional component of our study. We tested the effect of the GC haplotype on antioxidant superoxide dismutase 1 (SOD1) enzymatic activity in males and females. SOD1 is one of the main antioxidative enzymes within the antioxidative enzyme system (Sun et al., 1989). We found that (irrespective of sex) individuals with the AT haplotype had lower SOD1 activity compared with other haplotypes, including the GC haplotype (p=0.04; Appendix 1—table 8).

The GC haplotype affects susceptibility to Babesia infection in females

We then used the longitudinal component of our study to test the effects of the GC haplotype on the probability of infection with common parasites known to infect our study population: two micro-parasites Babesia microti and Bartonella spp. and a range of macroparasites (ticks, fleas, and cestodes). We found an effect of the GC haplotype on the probability of infection with B. microti, which displayed a marginally significant interaction with sex (genotype by sex interaction, LRT p=0.048; Appendix 1—table 9). While females with the GC haplotype were more likely to be infected with B. microti (log odds ratio = 1.25, 95% CI = 0.44–2.07; Figure 1B, lower panel), males with the GC haplotype did not differ in their susceptibility to infection (log odds ratio = 0.14, 95% CI includes zero = –0.76 to 1.03; Figure 1B, upper panel). However, we found no effect of the haplotype (interactive or not) on the probability of infection with the other parasites in our population (Appendix 1—table 10, Appendix 1—table 11 and Appendix 1—table 17).

The GC haplotype reduces male reproductive success

We genotyped both cross-sectional and longitudinal voles for 114 SNPs and used this dataset to construct a pedigree. We estimated each individual's reproductive success by counting the number of offspring it produced according to this pedigree. We then tested the effects of the GC haplotype on this measure of reproductive success. We tried to run a single model with both sexes (as above) but resulting residual variances differed significantly between the sexes, which reflects the fact that male and female reproduction are inherently different traits. This made it impossible to formally test for a genotype by sex interaction, and so instead we ran a separate model for each sex. We found that males with the GC haplotype had an average of 2.2 fewer offspring than males without the haplotype (p=0.04; Appendix 1—table 12; Figure 1C, upper panel). Despite a larger sample size and lower variability in reproductive success, females with the GC haplotype did not differ significantly in their number of offspring (genotype term did not appear in best model; Appendix 1—table 13; Figure 1C, lower panel). This is suggestive of a significant genotype by sex interaction. We ran the same models including microparasite variables, but this had little effect on our results (see Appendix 1—table 14 and Appendix 1—table 15), suggesting that these effects were not driven by differences in microparasite infection.

Discussion

In this study, we describe a polymorphism in the high-affinity receptor for IgE with effects that act across immune gene expression, oxidative stress, susceptibility to infection, and ultimately reproductive success in a natural population. This begins to reveal how genotypic effects can have multiple effects on different phenotypic or life history traits for organisms living in the natural environment. Interestingly, effects often differed between sexes, with evidence for opposing effects in the sexes (unstimulated immune gene expression) and an effect present in one sex but not the other (stimulated immune gene expression, susceptibility to infection, and reproductive success). However, we cannot rule out that, in the case of susceptibility to infection and reproductive success, there was a smaller effect present in the opposite sex (in the same or opposite direction), which our analysis did not have sufficient power to detect.

In humans, naturally occurring polymorphisms in the high-affinity IgE receptor have previously been linked to inflammatory disease (Hasegawa et al., 2003; Potaczek et al., 2006; Zhou et al., 2012; Niwa et al., 2010). Our work is consistent with this and suggests differing effects of this polymorphism on the inflammatory phenotype of males and females. While males with the GC haplotype showed a pro-inflammatory bias, females with the haplotype showed an anti-inflammatory bias. A previous study on another polymorphism in the human ortholog of Fcer1a also found evidence for sex-dependent effects on inflammatory disease (Yang et al., 2013). In order to fully understand this genotype by sex interaction, it is important to consider background differences in the inflammatory phenotypes of males and females. Females in this study were pregnant for a large proportion of their lives (57% of post-juvenile females were pregnant or lactating at the time of capture in the longitudinal component of our study). In mammals, including humans, pregnancy has been shown to be a largely anti-inflammatory state, with pregnant females suppressing inflammation. This is to protect the fetus from attack by the mother’s immune system (Wegmann et al., 1993). While testosterone in males has an anti-inflammatory effect (Bianchi, 2019), it also drives males to use more space (Davis et al., 2015) and to be more aggressive (Martínez-Sanchis et al., 2003). These behaviors might increase their rates of contact with other individuals, and with their environment, and hence their likelihood of infection and subsequent immune stimulation. We suggest that the polymorphism may be exaggerating background sex differences in inflammatory activity. Sex hormones have been implicated in the differential expression of some autosomal genes in males and females (Mayne et al., 2016), and could be driving these opposing effects. Some of the differences in immune phenotype that we observed may also be driven by difference in parasite infection (although we accounted for cestode burden in a follow-up analysis, we cannot rule this out).

The effect of Fcer1a polymorphism on inflammatory responses may also, in turn, affect individual susceptibility to infection. This is consistent with a previous association found between IgE-mediated responses and defense against helminths (Gounni et al., 1994). Although we did not find an association between macroparasite burden (including cestode burden) and the GC haplotype in this study, in our previous study, we showed that males with the GC haplotype had a lower level of an immunological marker of tolerance to ticks, fleas, and adult cestodes (i.e., they were less tolerant) (Wanelik et al., 2018). Here, we use data on infection incidence to show that, in a different context, the same haplotype is also associated with resistance to a microparasite, B. microti, in females. Although observational studies of this kind are not able to identify causal relationships, a realistic scenario is that females with the GC haplotype are more likely to be infected with B. microti (i.e., they are less resistant) as a result of a lower pro-inflammatory to anti-inflammatory cytokine ratio. Pro-inflammatory cytokines (e.g., IL-6, IFN-γ, and TNF-α) may help to resist B. microti infection (Djokic et al., 2018). A lack or imbalance of these cytokines may hamper this resistance. The panel of parasites that we have measured is not exhaustive, and previous studies have highlighted the important role of species interactions in the parasite community in driving infection risk in this study population (Telfer et al., 2010). B. microti may therefore represent a community of parasites, to which this haplotype affects resistance in females.

A fitness consequence of the GC haplotype was observed in reduced male reproductive success. A realistic scenario is that the larger cost incurred by males with the GC haplotype due to inflammation reduced their reproductive value, as reproducing and mounting a pro-inflammatory response are both costly activities (Sheldon and Verhulst, 1996). Another realistic scenario is that the positive effect of the GC haplotype on levels of oxidative stress (as indicated by a tendency for higher SOD1 activity) may have had a more detrimental effect in males, damaging sperm and reducing fertilizing success. Sperm competition can be an important feature in the reproduction of microtines, for example, meadow vole (M. pennsylvanicus) (Delbarco-Trillo and Ferkin, 2004), with males having to produce many sperm of high quality in order to successfully outcompete other males. Sperm are also highly susceptible to damage by reactive oxygen species (ROS) (Losdat et al., 2011). In addition, the heightened level of oxidative stress may have impaired olfactory sexual signals, for example, excretion of major urinary proteins (MUPs) making males less attractive to females and negatively impacting on mating success (Garratt et al., 2014).

Despite the immune system playing an important role in determining the health and reproductive fitness of an individual, natural selection has not converged on a single immune optimum. Instead, individuals in natural populations vary widely in their response to infection. Understanding why immunoheterogeneity is maintained is a key question in eco-immunology. Antagonistic selection, where a mutation is beneficial in some environmental contexts and harmful in others, is thought to be one mechanism by which balancing selection is generated, and genetic variation in immunity can be maintained within natural populations (Graham et al., 2010). In samples collected between 2008 and 2010, we previously identified four haplotypes at this locus, GC, AC, AT, and GT, at frequencies of 0.12, 0.76, 0.07, and 0.05, respectively (Wanelik et al., 2018). Seven years on, these frequencies have remained relatively unchanged (0.08, 0.81, 0.10, and 0.01). The fact that the GC haplotype remains in the population, despite its detrimental effects on male reproductive success and female infection susceptibility, suggests that it may be under balancing selection.

In order to be under balancing selection though, the GC haplotype would need to confer some advantage. We were not able to find evidence for any such advantage in this study, but we can speculate on one. Microtines show cyclical population dynamics, alternating between a 'peak' phase (associated with good conditions and high vole densities) and a 'crash' phase (poor conditions and low vole densities). Selection pressures acting during these two phases will be very different. We found that the GC haplotype was associated with a pro-inflammatory bias in males, indicating higher investment in promoting inflammation. Thus, one possible scenario is that higher investment in promoting inflammation is disadvantageous in the 'peak' phase when the risk of parasitism is low due to dilution effects, and it pays not to waste energy mounting a stronger immune response that is unnecessary. However, it might be advantageous in the 'crash' phase when conditions are poor, and the risk of parasitism is high due to the time-lagged transmission of parasite infectious stages that have accumulated during the peak phase. Vole numbers were extremely low in our study area between 2012 and 2013 (indicating a 'crash phase'). In 2014, vole numbers began to increase and by the beginning of 2015 (the start of our study) had reached a peak. Although vole numbers declined throughout our study period, they never reached the extremely low numbers recorded in 2012–2013 (X. Lambin, personal communication, 2016). This suggests that we sampled in the region of a 'peak' phase, when this investment was disadvantageous, and that if we sampled during a true 'crash' phase we might detect an advantage to the GC haplotype in males.

There is a growing interest in human genomic (or precision) medicine, with the potential to use a patient’s genotypic information to personalize their treatment. What we have shown here demonstrates that considering genotype in isolation can be misleading as the same polymorphism can have different outcomes not only for the immune gene expression, but the susceptibility to infection and ultimate reproductive success of males and females.

Materials and methods

We studied M. agrestis in Kielder Forest, Northumberland, UK, using live-trapping of individual animals from natural populations. Trapping was performed from 2015 to 2017 across seven different sites, each a forest clear-cut. Access to the sites was provided by the Forestry Commission. At each site, 150–197 Ugglan small mammal traps (Grahnab, Gnosjo, Sweden) were laid out in a grid, spaced approximately 3–5 m apart. Our study was divided into longitudinal and cross-sectional components.

Longitudinal data

Every other week, traps were checked every day, once in the morning and once in the evening. Newly trapped field voles were injected with a Passive Integrated Transponder (PIT) tag (AVID, Uckfield, UK) for unique identification. This approach allowed us to build up a longitudinal record for voles that were caught on multiple occasions. A total of 850 voles were individually marked in this way. We also took a small tissue sample from the tail for genotyping of individuals and a drop of blood from the tail which we put into 500 μl of RNAlater (Fisher Scientific, Loughborough, UK) for use in parasite detection (see below).

Parasite detection

Request a detailed protocol

We quantified infections by microparasites (B. microti and Bartonella spp.) in blood samples taken from longitudinal animals using SYBR green based two-step reverse transcription quantitative PCR (Q-PCR) targeted at pathogen ribosomal RNA genes. Expression values were normalized to two host endogenous control genes: Ywhaz and Actb. Blood samples were derived from tail bleeds. RNA was extracted from blood samples stored in RNAlater at –70°C using the Mouse RiboPure Blood RNA Isolation Kit (Thermo Fisher, Waltham, MA) according to the manufacturer’s instructions and DNAse treated. It was then converted to cDNA using the High-Capacity RNA-to-cDNA Kit (Thermo Fisher), according to the manufacturer’s instructions. B. microti primer sequences targeting the 18S ribosomal RNA gene were as follows CTACGTCCCTGCCCTTTGTA (forward primer sequence) and CCACGTTTCTTGGTCCGAAT (reverse primer sequence). Bartonella spp. primer sequences targeting the 16S ribosomal RNA gene were as follows: GATGAATGTTAGCCGTCGGG (forward primer sequence) and TCCCCAGGCGGAATGTTTAA (reverse primer sequences). Assays were pipetted onto 384-well plates by a robot (Pipetmax; Gilson, Middleton, WI) using a custom program and run on a QuantStudio 6-flex Real-Time PCR System (Thermo Fisher) at the machine manufacturer's default real-time PCR cycling conditions. Reaction size was 10 µl, incorporating 1 µl of template (diluted 1/20) and PrecisionFAST qPCR Master Mix (PrimerDesign, Chandler’s Ford, UK) with low ROX and SYBR green and primers at the machine manufacturer’s recommended concentrations. Alongside the pathogen assays, we ran assays for two host genes (see above) that acted as endogenous controls. For the pathogen assays, we used as a calibrator sample a pool of DNA extracted from 154 blood samples from different M. agrestis at our study sites in 2015 and 2016; these DNA extractions were carried out using the QIAamp UCP DNA Micro Kit (QIAGEN, Hilden, Germany) following the manufacturer’s instructions. For the Ywhaz and Actb assays, the calibrator sample was the same cDNA calibrator sample as described below for other host gene expression assays. Relative expression values (normalized to the two host endogenous control genes and presumed to relate to the expression of pathogen ribosomal RNA genes and in turn to the force of infection) used in analyses below are RQ values calculated by the QuantStudio 6-flex machine software according to the ∆∆Ct method, indexed to the calibrator samples. Melting curves and amplification plots were individually inspected for each well replicate to confirm specific amplification. We validated our diagnostic results by comparing our PCR RQ values to independent data for a subset of voles from the cross-sectional component of our study with mapped genus-level pathogen reads from RNASeq analysis of blood samples (n = 44), finding that the two datasets strongly corroborated each other (Bartonella spp., Spearman’s ρ = 0.79, p<0.001, n = 44; B. microti, Spearman’s ρ = 0.88, p<0.001, n = 43).

Cross-sectional data

For the cross-sectional component of the study (which ran from 2015 to 2016 only), animals were captured and returned to the laboratory where they were killed by a rising concentration of CO2, followed by exsanguination. As our UK Home Office license did not allow us to sample overtly pregnant females in this way, there are fewer females than males present in this dataset. This component of the study allowed us to take a more comprehensive set of measurements and to culture cells and perform stimulatory assays on them. A small tissue sample was taken from the ear of cross-sectional animals for genotyping (see below).

Parasite detection

Request a detailed protocol

After culling, the fur of cross-sectional animals was examined thoroughly under a binocular dissecting microscope to check for ectoparasites, which were recorded as direct counts of ticks and fleas. Guts of cross-sectional animals were transferred to 70% ethanol, dissected, and examined under a microscope for gastrointestinal parasites. Direct counts of cestodes (by far the dominant endohelminths in biomass) were recorded.

SOD1 measurement

Request a detailed protocol

Given the well-established link between inflammation, oxidative stress, and pathology (Biswas, 2016), we measured antioxidant enzymatic activity in blood samples taken from cross-sectional animals. We chose to measure superoxide dismutase 1 (SOD1) because it is one of the main antioxidative enzymes within the antioxidative enzyme system (Sun et al., 1989) and is clearly linked to changes in immune gene expression in laboratory mice (Marikovsky et al., 2003). Assays were carried out using the Cayman Superoxide Dismutase kit and following the manufacturer’s instructions except where otherwise indicated below. Blood pellets from centrifuged cardiac bleeds were stored at –70°C and thawed on ice prior to assay. A 20 μl aliquot from each pellet was lysed in 80 μl of ultrapure water and centrifuged (10,000 × g at 4 °C for 15 min) and 40 μl of the supernatant added to a 1.6× volume of ice-cold chloroform/ethanol (37.5/62.5 [v/v]) (inactivating superoxide dismutase 2). This mixture was then centrifuged (2500 × g at 4°C for 10 min) and the supernatant removed and immediately diluted 1:200 in kit sample buffer. A seven-point SOD activity standard was prepared (0, 0.005, 0.010, 0.020, 0.030, 0.040, and 0.050 U/ml) and the assay conducted on a 96-well microplate with 10 μl of standard or sample, 200 μl of kit radical detector solution, and 20 μl of xanthine oxidase (diluted as per the manufacturer’s instructions) per well. Plates were then covered with a protective film, incubated at room temperature for 30 min on an orbital shaker, and read at 450 nm on a VERSAmax tunable absorbance plate reader (Molecular Devices, San Jose, CA), subtracting background and fitting a linear relationship to the standard curve in order to estimate SOD activity in unknown samples.

Splenocyte cultures

Request a detailed protocol

Spleens of cross-sectional animals were removed, disaggregated, and splenocytes cultured under cell culture conditions equivalent to those used in Jackson et al., 2011. Unstimulated splenocytes, taken from 84 cross-sectional animals collected between July and October 2015, were initially used to assay expression by RNASeq (see below). We exposed splenocytes from the remaining cross-sectional animals to stimulation with anti-CD3 antibodies (Hamster Anti-Mouse CD3e, Clone 500A2 from BD Pharmingen, San Diego, CA) and anti-CD28 antibodies (Hamster Anti-Mouse CD28, Clone 37.51 from Tombo Biosciences, Kobe, Japan) at concentrations of 2 μg/ml and of 1 μg/ml, respectively, for 24 hr. Costimulation with anti-CD3 and anti-CD28 antibodies was used to selectively promote the proliferation of T-cells (Frauwirth and Thompson, 2002; Wanelik et al., 2020). We assumed that this would reflect the potential response of T-cell populations in vivo. Stimulated splenocytes were used to assay expression by Q-PCR.

RNASeq

Request a detailed protocol

Full details of the methods used for RNA preparation and sequencing can be found in Wanelik et al., 2018. Briefly, samples were sequenced on an Illumina HiSeq4000 platform. High-quality reads were mapped against a draft genome for M. agrestis (GenBank accession no. LIQJ00000000) and counted using featureCounts (Liao et al., 2014). Genes with average log counts per million, across all samples, of less than one were considered unexpressed and removed from the data (n = 8410). Following filtering, library sizes were recalculated, data were normalized, and MDS plots were generated to check for any unusual patterns in the data. The mean library size was 19 million paired-end reads (range = 3–71 million paired-end reads).

Q-PCR

Request a detailed protocol

We used SYBR green-based Q-PCR to measure the expression levels of a panel of 18 genes (Appendix 1—table 16) in splenocytes, from our cross-sectional animals, that had been stimulated with T-cell agonist anti-CD3 and anti-CD28 antibodies. We did this, in part, to validate our RNASeq results in an independent dataset. We used the observed expression profile as a general measure of the potential responsiveness of the immune system to an inflammatory stimulation in vivo. We note that the genetic focus of our study, Fcer1a, codes for the alpha chain protein of the high-affinity IgE receptor that is expressed on cells such as eosinophils, basophils, and mast cells (Daeron and Nimmerjahn, 2014). Although the high-affinity IgE receptor may not be expressed significantly by the T-cells preferentially stimulated in our cultures (but see Schadt, 2009), the principle that genetic variants functionally expressed in one cell type may affect the function of other cell types through molecular interaction networks is embodied in the modern understanding of network biology (Schadt, 2009; Barabási et al., 2011). Thus, the effect of polymorphism in Fcer1a, expressed in cells such as eosinophils, basophils, or mast cells, either in vivo prior to isolation, or in the ex vivo cultured splenocyte population, could act indirectly on T-cells through various pathways, including via cytokine signaling (Rothenberg and Hogan, 2006; Akuthota et al., 2008; Villanueva, 2015). For example, eosinophils are known to promote helper T-cell activation and proliferation in ex vivo co-culture (Liu et al., 2006; Harfi et al., 2013) and in in vivo models (Shi et al., 2004). The choice of our panel of genes was informed by (i) known immune-associated functions in mice, combined with (ii) significant sensitivity to environmental or intrinsic host variables in our previous studies (Jackson et al., 2014; Jackson et al., 2011) or in a recent DGE analysis of RNASeq data (not reported here), and (iii) the aim of limited redundancy, with each gene representing a different immune pathway.

Primers (20 sets, including 2 endogenous control genes) were designed de novo and supplied by PrimerDesign (16 sets) or designed de novo in-house (4 sets) and validated (to confirm specific amplification and 100 ± 10% PCR efficiency under assay conditions). All PrimerDesign primer sets were validated under our assay conditions before use. The endogenous control genes (Ywhaz and Sdha) were selected as a stable pairing from our previous stability analysis of candidate control genes in M. agrestis splenocytes (Jackson et al., 2011). We extracted RNA from splenocytes conserved in RNAlater using the RNAqueous Micro Total RNA Isolation Kit (Thermo Fisher), following the manufacturer’s instructions. RNA extracts were DNAse treated and converted to cDNA using the High-Capacity RNA-to-cDNA Kit (Thermo Fisher), according to the manufacturer’s instructions, including reverse transcription negative (RT-) controls for a subsample. SYBR green-based assays were pipetted onto 384-well plates by a robot (Pipetmax, Gilson) using a custom program and run on a QuantStudio 6-flex Real-Time PCR System (Thermo Fisher) at the machine manufacturer's default real-time PCR cycling conditions. Reaction size was 10 µl, incorporating 1 µl of template and PrecisionFAST qPCR Master Mix with low ROX and SYBR green (PrimerDesign) and primers at the machine manufacturer’s recommended concentrations. We used two standard plate layouts for assaying, each of which contained a fixed set of target gene expression assays and the two endogenous control gene assays (the same sets of animals being assayed on matched pairs of the standard plate layouts). Unknown samples were assayed in duplicate wells and calibrator samples in triplicate wells, and no template controls for each gene were included on each plate. Template cDNA (see above) was diluted 1/20 prior to assay. The calibrator sample (identical on each plate) was created by pooling cDNA derived from across all splenocyte samples. Samples from different sampling groups were dispersed across plate pairs, avoiding confounding of plate with the sampling structure. Gene relative expression values used in analyses are RQ values calculated by the QuantStudio 6-flex machine software according to the ∆∆Ct method, indexed to the calibrator sample. Melting curves and amplification plots were individually inspected for each well replicate to confirm specific amplification.

Longitudinal and cross-sectional data

Genotyping

Request a detailed protocol

We genotyped both cross-sectional and longitudinal animals for 346 SNPs in 127 genes. See Wanelik et al., 2018 for details of the approach used to select these SNPs. Our list included two synonymous and tightly linked (r2 = 0.50; D’ = 0.70) SNPs in the gene Fcer1a (the alpha chain of the high-affinity receptor for IgE) on scaffold 582 (CADCXT010006977 in ENA accession GCA_902806775; see Appendix 1—table 1 for genomic location information), which we had previously identified as a candidate tolerance gene in a natural population of M. agrestis. In this previous work, we had identified four haplotypes at this locus present in our population: GC, AC, AT, and GT, at frequencies of 0.12, 0.76, 0.07, and 0.05, respectively. We had also identified the GC haplotype as being of particular interest, given its significantly lower expression level of the transcription factor GATA3 (a biomarker of tolerance to macroparasites in our population) compared to the other haplotypes (Wanelik et al., 2018). We concluded that this haplotype tagged a causal mutation in the coding sequence, or in the up- or downstream regulatory regions. DNA was extracted from a tail sample (longitudinal component) or an ear sample (cross-sectional component) taken from the animal using DNeasy Blood and Tissue Kit (QIAGEN). Genotyping was then performed by LGC Biosearch Technologies (Hoddesdon, UK; http://www.biosearchtech.com) using the KASP SNP genotyping system. This included negative controls (water) and duplicate samples for validation purposes. The resulting SNP dataset was used for two purposes: (i) genotyping individuals within the locus of interest and (ii) pedigree reconstruction (see below).

Pedigree reconstruction

Request a detailed protocol

We used a subset of our SNP dataset to reconstruct a pedigree for both cross-sectional and longitudinal animals using the R package Sequoia (Huisman, 2017). SNPs that violated the assumptions of Hardy–Weinberg equilibrium were removed from the dataset. For pairs of SNPs in high linkage disequilibrium (most commonly within the same gene), the SNP with the highest minor allele frequency (MAF) was chosen. A minimum MAF cutoff of 0.1 and call rate of >0.7 was then applied, and any samples for which >50% of SNPs were missing were removed. This resulted in a final dataset including 114 SNPs – a sufficient number for very good performance of parentage assignment (Huisman, 2017).

Life history information, namely, sex and month of birth, was inputted into Sequoia where possible. Juvenile voles weighing less than 12 g on first capture were assigned a birth date 2 weeks prior to capture. Juvenile voles weighing between 12 and 15 g on first capture were assigned a birth date 4 weeks prior to capture. Finally, adult voles breeding on first capture were assigned a birth date 6 weeks prior to capture (minimum age at first breeding) (Begon et al., 2009; Burthe et al., 2010). Adult voles not breeding on first capture could not be assigned a birth date as it was not known whether they had previously bred or not. Virtually all samples (99%) were assigned a sex, and approximately half (54%) were assigned a birth month. As we sampled individuals from across seven different clear-cut areas of the forest, each several kilometers apart, these were assumed to be independent, closed populations with negligible dispersal. Site-specific pedigrees were therefore generated.

We assessed the accuracy of our reconstructed pedigrees by checking whether predicted parent–offspring pairs met expectations given the biology of M. agrestis. As expected, the majority of predicted parent–offspring pairs (87%) were born in the same year. We also expected parents and offspring to overlap in space. Again, as expected, the majority of predicted parent–offspring pairs (92%) were, at some point, found along the same transects (horizontal or vertical). We also inspected log10 likelihood ratios (LLRs) for parent pairs as recommended in the user manual for Sequoia. Almost all LLRs were positive (n = 698/720 or 97% of LLRs) indicating confidence in our assignments. Individuals with vs. without our haplotype of interest did not differ in their probability of appearing in a pedigree (χ2 = 0.09, d.f. = 1, p=0.76). For each individual that ended up in a pedigree, that is, with one or more relatives recorded (n = 652; site COL = 3; site BLB = 125; site GRD = 204; site CHE = 137; site RAV = 16; site SCP = 90; site HAM = 77), the number of offspring was counted to provide a measure of their reproductive success. Few individuals were first trapped as juveniles, with the majority trapped as adults that had already recruited into the population. Our measure of reproductive success then more closely resembles the number of recruited (rather than newborn) offspring per individual. Half of the individuals present in our pedigrees (n = 325) were found to have no offspring. We expect the majority of these to be true zeros (representing actual reproductive failure) as we generally sampled a large proportion of the total population within clear-cuts. We also minimized the chance of false zeros by excluding those individuals (e.g., at the periphery of a study grid) that did not end up in a pedigree because we identified no relatives (including offspring), likely because we had not sampled in the right place.

Statistical analyses

Not all individuals appeared in all datasets; therefore, sample sizes (reported in Table 1) vary between analyses. All analyses were performed in R statistical software version 3.5.2 (R Development Core Team, 2018).

Table 1
Model specifications including, for each main model, covariates included in the full model, datasets used, and sample sizes (F = included as a fixed effect; R = included as a random effect).
DGE analysisHaplotype association analyses
Response variable
Immune gene expressionParasite infectionReproductive successSOD1activity
Covariates
Snout-vent length
FF
Eye lens weightFF
Reproductive statusFFF
Body conditionFFF
Birth monthF
Culled or notF
SiteF; R (Il17a LMM)F (Macro); R (Micro)FF
YearFFFF
SeasonF; R (Il17a LMM)F (Macro); R (Micro)F
Individual IDR (Micro)
Assay plateR (Il17a LMM)
DatasetCCC (Macro)
L (Micro)
C+LC
Sample size♀31 ♂53Il17a LMM: ♀73 ♂220Macro: ♀82 ♂235
B. microti: ♀1075 ♂1247
Bartonella spp.: ♀1283 ♂1104
♀419
♂232
♀81
♂227
  1. C = cross-sectional; L = longitudinal; DGE = differential gene expression; LMM = linear mixed effects model.

Differential gene expression analysis

Request a detailed protocol

DGE analysis was performed on filtered and normalized count data using the R package edgeR (Robinson et al., 2010), the aim being to identify individual genes that were differentially expressed between those individuals with and without a copy of the GC haplotype (i.e., a dominant model). Only those individuals for which haplotype could be inferred with certainty could be included (n = 53 males and n = 31 females; none of which were known to have two copies of the GC haplotype, hence the choice of a dominant model). Samples from different sexes were analyzed together. To test whether the top differentially expressed genes differed between the sexes, we included a dummy variable with four levels (males with haplotype, males without haplotype, females with haplotype, females without haplotype) and inspected the contrasts of interest (males with versus without haplotype; females with versus without haplotype). As this was an exploratory analysis, used in conjunction with more targeted measurements of immune gene expression (see below), model specification was kept as simple as possible and no covariates were initially included. However, in a follow-up DGE analysis we controlled for a key parasite variable (cestode burden).

Having confirmed a sex-dependent effect of the GC haplotype on the expression of some inflammatory genes (see 'Results') in the initial DGE analysis, we ran separate DGE analyses for males and females and tested more broadly for enrichment of pro- and anti-inflammatory genes in our results; more specifically, the Gene Ontology terms ‘positive regulation of inflammatory response’ (GO:0050729; n = 143) and ‘negative regulation of inflammatory response’ (GO:0050728; n = 149). The aim here was to answer the question: are pro- or anti-inflammatory genes more highly ranked relative to other genes, when we compare individuals with and without the GC haplotype, and does this also differ between the sexes? This GSEA was performed using the R package limma (Ritchie et al., 2015), and genes were ranked on log fold change.

Haplotype association analyses

Request a detailed protocol

Following the exploratory DGE analysis, the GC haplotype was tested for associations with (i) gene expression assayed by Q-PCR to validate these results, (ii) macro- and microparasite infection to test whether the GC haplotype predicted an individual’s susceptibility to infection, and (iii) reproductive success to test whether the GC haplotype was associated with any reproductive costs. Given the well-established link between inflammation and oxidative stress (Reuter et al., 2011; Collins, 1999), we also tested for an association between the GC haplotype and SOD1 activity.

For all analyses, this was initially attempted using the R package hapassoc (Burkett et al., 2006) in order to maximize sample size (see below). Hapassoc infers haplotypes on the basis of data from single SNPs and allows likelihood inference of trait associations with resulting SNP haplotypes and other attributes. It adopts a generalized linear model framework and estimates parameters using an expectation–maximization algorithm. Hapassoc models assumed an additive genetic model. If the haplotype combination of an individual cannot, with certainty, be inferred from its genotyping data (i) because it is heterozygous at two or more markers or (ii) because it has missing data for a single marker, the approach implemented in hapassoc is to consider all possible haplotype combinations for that individual. Standard errors accounting for this added uncertainty are calculated using the Louis, 1982 method. We compared the GC haplotype against the other two major haplotypes (AC and AT). Another haplotype, the GT haplotype, was identified in the population but this was present at such low frequencies (frequency ≤ 0.01 among individuals for which haplotype could be inferred with certainty) that it was omitted from all analyses. Results reported in the text for macroparasites and SOD1 activity come from these hapassoc models.

However, there are some restrictions on model specification within hapassoc (e.g., random terms cannot be included, limited choice of error distributions), so this was followed up with regression models for some analyses. As in the DGE analysis, these regression models only included those individuals whose haplotype combination could be inferred with certainty. Results reported in the text for gene expression assayed by Q-PCR, microparasites, and reproductive success come from these regression models. Again, as in the DGE analysis, genotype was coded as the presence or absence of the GC haplotype (i.e., a dominant model). Regression models were run using the R package lme4 (Bates et al., 2015) or glmmADMB (Skaug et al., 2016; Fournier et al., 2012).

Table 1 provides a summary of (hapassoc or regression) model specifications. As we found evidence for sex-dependent effect in the initial DGE analysis, with the exception of the model for reproductive success (see below), all models included a genotype by sex interaction that was retained if it improved model fit. All models also accounted for other biological and technical covariates (the choice of which was informed by our previous work, the literature, and/or our experimental design). Full regression models (including all covariates and interactions of interest) were reduced using backward stepwise deletion of nonsignificant terms to minimize Akaike’s information criterion (AIC), following the drop1 function. This was not possible for hapassoc models. Summary tables, including all estimates, standard errors, and z-statistics, are included in Appendix 1. Associated significance values are also included in these tables, except for LMMs and GLMMs, for which significance values were based on LRTs and are reported in the main text. Throughout, residuals from regression models were checked for approximate normality and homoscedasticity, and all covariates were tested for independence using variance inflation factors (all VIFs < 3).

Association between GC haplotype and immune gene expression assayed by Q-PCR

Request a detailed protocol

As we ran a total of 18 hapassoc models (one model per gene), the Benjamini and Hochberg method of correction was applied to all p-values (Benjamini and Hochberg, 1995). Resulting q-values (FDR-corrected p-values) are reported, alongside original p-values (Appendix 1—table 6). A hapassoc model including a genotype by sex interaction was tested against a null model without an interaction term. A Gamma error distribution and log link were used. Other covariates considered potential drivers of immune gene expression and included in both models were informed by our previous work (Jackson et al., 2011). These included snout-vent length (SVL), eye lens weight (categorized into seven intervals; SVL and eye lens weight capture the combined influence of age and historical growth trajectory), reproductive status (males were considered to be reproductively active if they had descended testes; females if they were pregnant or had perforate vaginas), and body condition (estimated by regressing body weight against life history stage, SVL, and its quadratic term). Site, year, and season (four levels, designated as spring [March and April], early summer [May and June], late summer [July and August], and autumn [September and October]) were included to account for any spatial and/or temporal autocorrelation. All covariates were included as fixed effects. We did not use a multidimensional approach (such as principal component analysis) because of limited redundancy in our panel of genes.

In order to confirm these results, an LMM was run for a single immune gene for which expression appeared to be associated with genotype (q = 0.037). The LMM included random terms for site and season, as well as assay plate number (Table 1). The latter was included to account for nonindependence due to immunological assaying structure. All other covariates were the same as the hapassoc model. A Yeo-Johnson transformation (with λ = –2) was used to achieve more normal and homoscedastic residuals (Yeo and Johnson, 2000).

Association between GC haplotype and parasite infection

Request a detailed protocol

The three macroparasite measurements taken from cross-sectional animals (counts of ticks, fleas, and cestodes) were log-transformed (log10 [x+1]) and summarized as a single principal component (explaining 39% of total variation; Appendix 1—table 17) to avoid difficulties in interpretation due to multiple testing. See Wanelik et al., 2018 and Jackson et al., 2014 for full details of this approach. This combined measure of macroparasite burden was modeled using a hapassoc model with a Gaussian error distribution.

Microparasite infection status was assessed multiple times for the majority of individuals in the longitudinal component of the study (mean = 2.8; range = 1–11). Due to these repeated measures, microparasite infection could not be modeled using hapassoc. Instead, to test for an association between the GC haplotype and the probability of an individual being infected with a microparasite, we ran a GLMM with a binary response (infected or not), log link, and a random term for individual. Other covariates, considered potential drivers of both macro- and microparasite infection, were, again, informed by our previous work (Wanelik et al., 2018; Jackson et al., 2014; Taylor et al., 2018). These included body condition, reproductive status, year, season, and site (Table 1). Season and site were included as random terms in the GLMM for microparasites, as was individual identity, to account for nonindependence due to repeat sampling.

Association between GC haplotype and reproductive success

Request a detailed protocol

Our measure of reproductive success was zero-inflated (50% zeros). This is consistent with a previous study of the closely related common vole (Microtus arvalis) (Wang et al., 2019). A Poisson error distribution was therefore deemed inappropriate, and it could not be modeled using hapassoc. Instead, to test for an association between the GC haplotype and reproductive success, we ran a GLM with a quasi-Poisson error distribution. This distribution has been previously used to model predictors of reproductive success in other organisms, and accounts for the overdispersion caused by excess zeros.

We only recorded a reproductive failure (i.e., zero reproductive success) for those individuals with some other familial relationship in our pedigree, purposefully omitting those individuals (e.g., at the periphery of a study grid) for which we may have recorded no relatives (including offspring) simply because we had not sampled in the right place. Therefore, we expect all (or most) of our zeros to represent actual reproductive failure. For this reason, zero-inflated models were deemed inappropriate.

As detailed in the 'Results,' we tried to run a single quasi-Poisson GLM with both sexes but resulting residual variances differed significantly between the sexes (F test to compare variances of two samples; p=0.02), making it impossible to formally test for a genotype by sex interaction. Instead, we ran a separate model for each sex. We included birth month as a covariate in this model, given that autumn-born voles (of the closely related common vole) have been shown to have a lower chance to reproduce than spring-born voles (Wang et al., 2019). Other covariates included in this model were whether or not an individual was culled for the cross-sectional component of this study (again, reducing the opportunity to reproduce), site, and the year in which an individual was most frequently captured (Table 1). In a follow-up analysis, we also controlled for B. microti and Bartonella spp. infection status. More specifically, we included the proportion of samples taken from an individual that were Babesia-positive and the proportion of samples taken from an individual that were Bartonella-positive. All covariates were included as fixed effects. Only a single female was trapped in one of the sites (COL) and consequently caused convergence problems in the female model. This female was therefore omitted.

Association between GC haplotype and SOD1 activity

Request a detailed protocol

SOD1 activity was modeled using a hapassoc model with a Gaussian error distribution. Other covariates, considered potential drivers of SOD1 activity, were informed by the literature. Previous studies on wild rodents have shown that antioxidant levels increase with both reproductive effort (Garratt et al., 2011) and with age (Hindle et al., 2010). Studies on birds have also shown that improvements in body condition are often accompanied by increases in antioxidant activity, for example, in response to supplemental feeding (Wilcoxen et al., 2015). We therefore included SVL, eye lens weight, reproductive status, and body condition as covariates in our model. As in other models, we included site, year, and season to account for spatial and/or temporal autocorrelation in our data (Table 1). All covariates were included as fixed effects.

Appendix 1

Appendix 1—table 1
Position of single-nucleotide polymorphisms (SNPs) and other key features in the Fcer1a gene.

Features lie in scaffold CADCXT010006977 within assembly GCA_902806775.

FeatureStartEnd
Fcer1a gene1074552810745528
Exon 51074552810745881
Exon 41074681810747076
Exon 31074860910748864
Exon 21075004410750065
Exon 11075046510750642
5’ UTR1075052010750642
CDS1075046510750520
CDS1075004410750065
CDS1074860910748864
CDS1074681810747076
CDS1074572910745881
3’ UTR1074552810745729
SNP 21074871810748719
SNP 11074684610746847
Appendix 1—table 2
Top 10 annotated genes that were differentially expressed between males with vs. without the GC haplotype, including associated log fold changes (logFC), p-values, and q-values (false discovery rate [FDR]-corrected p-values).
GeneProteinLogFCp-valueq-value
Snai3Snail family zinc finger 31.6981.220 × 10–91.574 × 10–5
Pla2g4cPhospholipase A2, group IVC (cytosolic, calcium-independent)2.9046.386 × 10–94.120 × 10–5
Il33Interleukin 332.7561.474 × 10–74.755 × 10–4
Mmp13Matrix metallopeptidase 132.5714.427 × 10–71.142 × 10–3
Uba7Ubiquitin-like modifier activating enzyme 70.7715.979 × 10–71.286 × 10–3
Robo4Roundabout guidance receptor 40.8923.738 × 10–64.385 × 10–3
TtnTitin1.6658.995 × 10–67.254 × 10–3
FlncFilamin C, gamma1.5541.245 × 10–59.447 × 10–3
Crb2Crumbs family member 21.3271.567 × 10–51.063 × 10–2
Muc16Mucin 161.9371.971 × 10–51.156 × 10–2
Appendix 1—table 3
Annotated genes that were differentially expressed (q ≤ 0.1) between females with vs. without the GC haplotype, including associated log fold changes (logFC), p-values, and q-values (false discovery rate [FDR]-corrected p-values).
GeneProteinLogFCp-valueq-value
Tldc1TBC/LysM associated domain containing 11.4892.502 × 10–83.228 × 10–4
Peg3Paternally expressed 31.4405.369 × 10–50.049
Socs3Suppressor of cytokine signaling 31.0716.061 × 10–50.052
Appendix 1—table 4
Top 10 annotated genes that were differentially expressed between males with vs. without the GC haplotype when controlling for cestode burden, including associated log fold changes (logFC), p-values, and q-values (false discovery rate [FDR]-corrected p-values).
GeneProteinLogFCp-valueq-value
Il33Interleukin 333.0912.473 × 10–103.191 × 10–6
Snai3Snail family zinc finger 31.6725.259 × 10–93.393 × 10–5
Pla2g4cPhospholipase A2, group IVC (cytosolic, calcium-independent)2.8831.562 × 10–86.721 × 10–5
Uba7Ubiquitin-like modifier activating enzyme 70.8092.477 × 10–87.990 × 10–5
Mmp13Matrix metallopeptidase 132.6064.090 × 10–78.796 × 10–4
Robo4Roundabout guidance receptor 40.8928.160 × 10–69.572 × 10–3
TtnTitin1.5992.532 × 10–51.951 × 10–2
FlncFilamin C, gamma1.4932.721 × 10–51.951 × 10–2
Crb2Crumbs family member 21.2613.512 × 10–52.151 × 10–2
Appendix 1—table 5
Annotated genes that were differentially expressed (q ≤ 0.1) between females with vs. without the GC haplotype when controlling for cestode burden, including associated log fold changes (logFC), p-values, and q-values (false discovery rate [FDR]-corrected p-values).
GeneProteinLogFCp-valueq-value
Tldc1TBC/LysM associated domain containing 11.4892.890 × 10–83.730 × 10–4
Socs3Suppressor of cytokine signaling 31.0834.133 × 10–50.041
Peg3Paternally expressed 31.4116.375 × 10–50.055
Ppp1r3cProtein phosphatase 1, regulatory (inhibitor) subunit 3C1.6628.798 × 10–50.071
Appendix 1—table 6
Significance values from hapassoc models for expression of 18 genes (assayed by Q-PCR) in splenocytes.

Splenocytes were stimulated with anti-CD3 and anti-CD28 antibodies in order to promote the proliferation of T-cells. q-values (false discovery rate [FDR]-corrected p-values) are reported alongside original p-values for the genotype by sex interaction.

Genep-valueq-value
Cd40.1240.822
Cd8a0.7440.866
Foxp30.4990.845
Gata30.5630.845
Il100.8660.866
Mpo0.1730.822
Tbx210.6500.866
Tgfb10.2710.822
Ifng0.3990.822
Il17a0.0020.037
Il1b0.7490.866
Il60.2820.822
Ms4a10.2810.822
Orai10.3530.822
Tnfa0.8580.866
Il20.4110.822
Apobr0.8570.866
Arg10.5520.845
Appendix 1—table 7
Estimates, standard errors, and z-statistics from best LMM for Yeo-Johnson-transformed Il17a expression levels.
EstimateSEz
(Intercept)0.1070.0234.61
Genotype–0.1150.037–3.10
Sex male–0.0550.019–2.89
Year 20160.0840.0184.60
Genotype × sex male0.1430.0443.25
Appendix 1—table 8
Effect sizes, standard errors, z-statistics, and associated significance from Gaussian hapassoc model for SOD1 activity.
EstimateSEzp-value
(Intercept)0.6141.5140.4060.685
hAT–0.4530.223–2.0320.042
Reproductive status active–0.1120.266–0.4220.673
Sex male0.0310.2500.1240.901
SVL0.0160.0170.9450.345
Lens weight 20.5680.5311.0700.284
Lens weight 30.4400.5490.8010.423
Lens weight 40.4450.5870.7580.448
Lens weight 50.4150.6530.6360.525
Lens weight 60.3010.7520.4000.689
Lens weight 7–0.3071.095–0.2800.779
Body condition0.0200.0300.6770.498
Site CHE1.4290.4313.3160.001
Site COL2.6751.0992.4340.015
Site GRD1.4760.4373.3790.001
Site RAV0.9040.9180.9840.325
Site SCP1.1070.4192.6430.008
Year 20160.5930.2422.4570.014
Season early summer–0.3920.389–1.0070.314
Season late summer–0.0980.266–0.3690.712
Season spring–0.9690.383–2.5290.011
Appendix 1—table 9
Effect sizes, standard errors, and z-statistics from best binomial GLMM for probability of infection with Babesia microti.
EstimateSEz
(Intercept)–2.2140.377–5.87
Genotype1.2540.4163.02
Sex male0.9570.2294.19
Year 20161.2600.2774.55
Year 20171.0470.3023.46
Reproductive status active0.7540.1445.24
Body condition0.0440.0162.76
Genotype × sex male–1.1160.615–1.82
Appendix 1—table 10
Effect sizes, standard errors, and z-statistics from best binomial GLMM for probability of infection with Bartonella spp.
EstimateSEz
(Intercept)0.2360.3690.64
Year 20160.7400.1355.49
Year 20172.2800.17513.01
Appendix 1—table 11
Effect sizes, standard errors, z-statistics, and associated significance from Gaussian hapassoc model for macroparasite infection summarized by a single principal component.
EstimateSEzp-value
(Intercept)0.0500.3310.1520.879
hAC–0.1020.130–0.7860.432
hAT–0.1080.158–0.6820.495
Sex male–0.2710.126–2.1460.032
Season early summer–0.1030.182–0.5670.571
Season late summer0.0120.1320.0900.929
Season spring0.3270.1761.8540.064
Reproductive status active–0.2220.119–1.8680.062
Body condition–0.0190.014–1.3280.184
Year 2016–0.4730.124–3.8100.000
Site CHE0.7140.2263.1570.002
Site COL0.7620.5751.3270.185
Site GRD0.7070.2293.0900.002
Site RAV1.1000.4812.2880.022
Site SCP0.7470.2203.3890.001
Appendix 1—table 12
Effect sizes, standard errors, z-statistics, and associated significance from best quasi-Poisson GLM for reproductive success in males.
EstimateSEzp-value
(Intercept)1.7430.2437.1711.05 × 10–11
Genotype–0.4840.237–2.0420.042
Year 20160.5920.1853.1930.002
Year 2017–0.4370.199–2.1930.029
Birth month–0.3580.048–7.4491.97 × 10–12
Culled yes0.4220.1502.8090.005
Appendix 1—table 13
Effect sizes, standard errors, z-statistics, and associated significance from best quasi-Poisson GLM for reproductive success in females.
EstimateSEzp-value
(Intercept)1.1370.2644.2982.15 × 10–5
Year 20160.5200.2112.4660.014
Year 2017–0.1180.215–0.5490.584
Birth month–0.2980.047–6.3914.42 × 10–10
Appendix 1—table 14
Effect sizes, standard errors, z-statistics, and associated significance from best quasi-Poisson GLM for reproductive success in males when controlling for Babesia microti and Bartonella spp. infection.
EstimateSEzp-value
(Intercept)1.4670.4183.5137.64 × 10–4
Genotype–1.0810.425–2.5440.013
Year 20160.2380.2790.8540.396
Year 2017–0.9120.386–2.3380.022
Birth month–0.4640.091–5.0902.69 × 10–6
Proportion of samples Babesia-positive0.8490.2763.0760.003
Proportion of samples Bartonella-positive0.9970.4402.2680.026
Appendix 1—table 15
Effect sizes, standard errors, z-statistics, and associated significance from best quasi-Poisson GLM for reproductive success in females when controlling for Babesia microti and Bartonella spp. infection.
EstimateSEzp-value
(Intercept)1.4830.3054.8673.20 × 10–6
Birth month–0.3100.073–4.2294.38 × 10–5
Proportion of samples Babesia-positive0.5690.2472.3080.0226
Appendix 1—table 16
Panel of 18 genes for which expression levels in splenocytes stimulated with anti-CD3 and anti-CD28 antibodies were measured using two-step reverse transcription quantitative PCR (Q-PCR).
GeneProtein
Cd4T-cell surface glycoprotein CD4
Cd8aT-cell surface glycoprotein CD8 alpha chain
Foxp3Forkhead box protein P3
Gata3GATA binding protein 3
Il10Interleukin-10
MpoMyeloperoxidase
Tbx21T-box transcription factor TBX21
Tgfb1Transforming growth factor beta 1
IfngInterferon gamma
Il17aInterleukin-17a
Il1bInterleukin-1 beta
Il6Interleukin-6
Ms4a1B-lymphocyte antigen CD20
Orai1Calcium release-activated calcium channel protein 1
TnfaTumor necrosis factor alpha
Il2Interleukin-2
ApobrApolipoprotein B receptor
Arg1Arginase-1
Appendix 1—table 17
Loadings from principal component analysis summarizing infection by macroparasites (ticks, fleas, and cestodes).
MacroparasitePC1
Cestodes–0.481
Ticks–0.592
Fleas–0.646

Data availability

RNASeq data have been deposited in the European Nucleotide Archive (study accession number PRJEB51626). Longitudinal phenotypic data are available from the NERC EDS Environmental Information Data Centre (https://doi.org/10.5285/e5854431-6fa4-4ff0-aa02-3de68763c952). Cross-sectional phenotypic data are available from the University of Liverpool's Research Data Catalogue (https://doi.org/10.17638/datacat.liverpool.ac.uk/1850). Genotype data are also available from the University of Liverpool's Research Data Catalogue (https://doi.org/10.17638/datacat.liverpool.ac.uk/1849).

The following data sets were generated
    1. Paterson S
    (2022) European Nucleotide Archive
    ID PRJEB51626. RNAseq analysis of field voles (Microtus agrestis).
    1. Paterson S
    2. Jackson J
    3. Jackson I
    4. Bradley J
    5. Begon M
    6. Wanelik K
    7. Taylor C
    (2022) NERC EDS Environmental Information Data Centre
    Data on immunological expression and phenotypes in a natural population of field voles in Kielder Forest, UK 2015-2017.
    https://doi.org/10.5285/e5854431-6fa4-4ff0-aa02-3de68763c952

References

    1. Hindle AG
    2. Lawler JM
    3. Campbell KL
    4. Horning M
    (2010) Muscle aging and oxidative stress in wild-caught shrews
    Comparative Biochemistry and Physiology. Part B, Biochemistry & Molecular Biology 155:427–434.
    https://doi.org/10.1016/j.cbpb.2010.01.007
  1. Software
    1. R Development Core Team
    (2018) R: A language and environment for statistical computing
    R Foundation for Statistical Computing, Vienna, Austria.

Decision letter

  1. Sarah E Cobey
    Reviewing Editor; University of Chicago, United States
  2. Jos W van der Meer
    Senior Editor; Radboud University Medical Centre, Netherlands

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Sex-specific effects of an IgE polymorphism on immunity, susceptibility to infection and reproduction in a wild rodent" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jos van der Meer 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. Temper claims to match statistics.

2. Exclude in vitro T cell stimulation tests of the FCER1A SNP or provide better justification for the assay (please see reviewer comments below).

3. Investigate potential confounding of IL-33 expression involving GC haplotype, IgE, and parasite burdens in a revised statistical analysis. For example, one reviewer suggests IL-33 should be included as an explanatory alongside all potential confounding variables such as parasite burdens, and the resulting fitted model estimate / z score for IL-33 be used to evaluate its importance.

Reviewer #2 (Recommendations for the authors):

In general, I would recommend you tone down the claims about sex-specific effects; actually, the results presented here are of interest regardless of whether they are sex-specific or not. Given that such effects are at best marginally significant, I think this is something that is better addressed in the Discussion, rather than claimed in the title.

Reviewer #3 (Recommendations for the authors):

– Please revise and expand reporting of the immune gene expression data (Il-33 is a type 2 cytokine, not normally considered pro-inflammatory). Much of the discussion of inflammatory vs anti-inflammatory requires more nuance – there are, at a minimum, antibacterial (IL-17), antihelminthic (IL-33, GATA3), and anti-inflammatory (SOC3) pathways being flagged up, and it would also be important to test antiviral responses (interferon-stimulated genes).

– Covariates detailed in Table 1 lack infection variables. These would be essential adjustments because the hypothesis is that all else being equal (i.e., at identical infection burdens, etc.), GC carriers should be more pro-inflammatory (males) or anti-inflammatory (females).

– The differential expression of Il33 (and any other genes/pathways of interest) should be followed up with statistical analysis to determine whether it is driven by worm infection, reproductive status, or other potential confounders in addition to / rather than haplotype. A GLMM containing covariates detailed in Table 1 + infection data would seem appropriate.

– Likewise, the effect of GC on male (and female?) reproductive success should be adjusted for the same potential confounders, like infection level.

– Figure 1A is quite uninformative. Please consider representing genes and top enriched pathways in a manner that allows readers to interpret the data.

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

Thank you for resubmitting your work entitled "Effects of an IgE receptor polymorphism acting on immunity, susceptibility to infection and reproduction in a wild rodent" for further consideration by eLife. Your revised article has been evaluated by Jos van der Meer (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed.

(1) Reviewer 2 has pointed out that it is difficult to understand how Fcer1a could be linked to IL17a production. This finding should be backed in the manuscript by some reference to previous evidence, or it should be made clear that this result is unexpected and difficult to explain. Reviewer 2 also suggests removing the result, which is another option.

(2) Please see other suggestions by Reviewer 2 for improving clarity.

Reviewer #2 (Recommendations for the authors):

The authors have dealt with most of my comments in a satisfactory way, but there are still a few significant problems. In particular, the rationale for the experiment where T cells were stimulated ex vivo is highly unclear (see below for details).

The conclusions are now better matched by data, although there are still quite strong claims about sex-specific effects even though there are few formal tests of this (i.e. tests for sex-by-genotype effects). More problematic is that the justification (L 578-581) provided for the analysis of ex vivo stimulated T cells is very vague. The problem with this assay is as follows: I believe that in splenocytes, Fcer1a is mainly (only?) expressed on basophils and eosinophils; thus, an effect of a Fcer1a polymorphism needs to involve these cells in some way. In your assay you stimulated T cells (which are abundant in splenocytes) in a non-specific way. Of the 18 cytokines measured, you find an effect of Fcer1a polymorphism on expression of IL17a, which is produced by T cells (but not by eosinophils/basophils). The problem is that I just cannot see how the Fcer1a polymorphism (which should have an effect in basophils/eosinophils, but not in T cells) could be linked to IL17a production by T cells; this would need to involve signalling from activated T cells to basophils/eosinophils, and then Fcer1a polymorphism-dependent signalling from basophils/eosinophils back to T-cells to affect IL17a production; I guess it is not impossible, but I am unaware of anything like that. I think you need to provide a sound and specific rationale for this assay; just saying "it could be acting indirectly on T-cells through various pathways, including via cytokine signalling, following Fcer1a expression by other cells" (L578-581) is not enough.

The presentation of the DGE analysis and GSEA of unstimulated splenocytes is improved, but still a bit confusing (L 199-238): (i) I think it would be good to make clear in the Results that the DGE analysis and GSEA are based on comparing individuals with the GC haplotype vs all others (so that the reader does not have to go the MandM to find this), (ii) using the term "top-responding" (L 206) implies that you are measuring the transcriptional response to some treatment, while you are just comparing baseline expression of GC vs others in males and females (or have I misunderstood something?); use "differentially expressed" or something similar instead, (iii) similarly, using the term "upregulated" (L 231) implies you measured the transcriptional response to some treatment; use "more highly expressed in individuals with GC…" or similar.

When it comes to the ex vivo stimulation of T cells, I strongly suggest you remove these results (L240-254); they just don't make sense from an immunological point of view. Unfortunately, this is one of few analyses that actually shows a sex-by-genotype effects, so removing this would weaken the support for sex-specific effects of the Fcer1a polymorphism even further.

https://doi.org/10.7554/eLife.77666.sa1

Author response

Essential revisions:

1. Temper claims to match statistics.

We have removed ‘sex-specific’ from the title and substantially reworked the introduction and discussion to reflect the comments by Reviewer 2 that the results are of interest regardless of whether they are sex-specific. A more detailed response is given below.

2. Exclude in vitro T cell stimulation tests of the FCER1A SNP or provide better justification for the assay (please see reviewer comments below).

We have provided a justification for this assay – although Fcer1a is not expressed by T-cells themselves, polymorphism in this gene could be acting indirectly on T-cells through various pathways, including via cytokine signalling, following expression of Fcer1a by other cells. Again, please see more detailed response given below.

3. Investigate potential confounding of IL-33 expression involving GC haplotype, IgE, and parasite burdens in a revised statistical analysis. For example, one reviewer suggests IL-33 should be included as an explanatory alongside all potential confounding variables such as parasite burdens, and the resulting fitted model estimate / z score for IL-33 be used to evaluate its importance.

We did not measure the expression of Il33 by QPCR, only by RNASeq. Therefore, we have responded to Reviewer 3’s concerns re. the potential confounding effect of infection (particularly helminth infection) within our DGE analysis. As explained in more detail below, we have accounted for cestode burden (cestodes being the most prominent helminth infection in terms of biomass) in an extra DGE analysis. The addition of cestode burden had little effect on our results, with Il33 still emerging as a top-responding gene in males.

Reviewer #2 (Recommendations for the authors):

In general, I would recommend you tone down the claims about sex-specific effects; actually, the results presented here are of interest regardless of whether they are sex-specific or not. Given that such effects are at best marginally significant, I think this is something that is better addressed in the Discussion, rather than claimed in the title.

We agree with the Reviewer that our results are interesting regardless of whether they are sex-specific or not. As already mentioned above, we have tempered our claims about sex-specific effects.

Reviewer #3 (Recommendations for the authors):

– Please revise and expand reporting of the immune gene expression data (Il-33 is a type 2 cytokine, not normally considered pro-inflammatory). Much of the discussion of inflammatory vs anti-inflammatory requires more nuance – there are, at a minimum, antibacterial (IL-17), antihelminthic (IL-33, GATA3), and anti-inflammatory (SOC3) pathways being flagged up, and it would also be important to test antiviral responses (interferon-stimulated genes).

Please see our more detailed response above re. classification of genes. We now mention in the results that Il33 is involved in the antihelminthic response , and Il17a is involved in the antibacterial response. No interferon-stimulated genes were flagged up in our analyses so these (and antiviral responses more broadly) are not mentioned.

– Covariates detailed in Table 1 lack infection variables. These would be essential adjustments because the hypothesis is that all else being equal (i.e., at identical infection burdens, etc.), GC carriers should be more pro-inflammatory (males) or anti-inflammatory (females).

We understand the Reviewer’s concerns about our DGE analysis. As mentioned above in more detail, we have run an extra DGE analysis including cestode burden as a covariate (a parasite variable of relevance to IgE, as pointed out by the Reviewer). We found very similar results in this analysis, suggesting that the differences in immune gene expression that we see are not driven by cestode burden. We have presented the result of this extra DGE analysis as extra supplementary tables (see Appendix 1—table 4 and 5). We have also included the following caveat in our discussion:

“Some of the differences in immune phenotype that we observed may also be driven by difference in parasite infection (although we accounted for cestode burden in a follow-up analysis, we cannot rule this out).”

– The differential expression of Il33 (and any other genes/pathways of interest) should be followed up with statistical analysis to determine whether it is driven by worm infection, reproductive status, or other potential confounders in addition to / rather than haplotype. A GLMM containing covariates detailed in Table 1 + infection data would seem appropriate.

We did not measure the expression of Il33 by QPCR, only by RNASeq. Therefore we have responded to these comments within our DGE analysis. As mentioned above, we have accounted for cestode burden in an extra DGE analysis. The addition of cestode burden had little effect on the results.

– Likewise, the effect of GC on male (and female?) reproductive success should be adjusted for the same potential confounders, like infection level.

As mentioned above, we have run extra GLMs for both females and males which include two parasite variables: proportion of samples taken from an individual that tested positive for Babesia and proportion of samples taken from an individual that tested positive for Bartonella. We found no difference in the main results – males with the GC haplotype still have fewer offspring, suggesting that infection is not acting as a confounder. This additional analysis is mentioned in the text: “We ran the same models including microparasite variables, but this had little effect on our results (see Appendix 1—table 14 and 15), suggesting that these effects were not driven by differences in microparasite infection”, The model output is also included in the supplementary information, see Appendix 1—table 14 and 15. For the majority of those individuals with known reproductive success, we did not have data on macroparasite burden (which requires culling). However, given the lack of association between macroparasite burden and GC haplotype in cross-sectional animals, and the lack of effect of including cestodes in the DGE analysis, we don’t believe including macroparasite variables would change these results.

– Figure 1A is quite uninformative. Please consider representing genes and top enriched pathways in a manner that allows readers to interpret the data.

As mentioned above, barcode plots such as this are commonly used representation of GSEA results (see e.g. Sadik et al. 2020, Cell 182, 1252–1270; Kumarasingha et al. 2020, Frontiers in Immunology 11, 582358). We would expect most readers to be familiar with them. As such, we would like to keep Figure 1A as it is. In order to aid the Reviewer and those readers who are unfamiliar with barcode plots, we have included some more information in the legend:

“Unstimulated immune gene expression: Barcode plots showing enrichment of the GO terms GO:0050729 (pro-inflammatory genes) and GO:0050728 (anti-inflammatory genes) in unstimulated splenocytes taken from individuals with (hGC+) vs. without (hGC-) the haplotype, showing that males with the haplotype have a pro-inflammatory bias, whereas females have an anti-inflammatory bias. In each plot, x-axis shows log fold change (logFC) in hGC+ vs. hGC-, black bars represent genes annotated with the GO terms and the worm shows relative enrichment”.

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

The manuscript has been improved but there are some remaining issues that need to be addressed.

(1) Reviewer 2 has pointed out that it is difficult to understand how Fcer1a could be linked to IL17a production. This finding should be backed in the manuscript by some reference to previous evidence, or it should be made clear that this result is unexpected and difficult to explain. Reviewer 2 also suggests removing the result, which is another option.

We have added more justification on this point, evidenced with nine new references (please see below for details).

(2) Please see other suggestions by Reviewer 2 for improving clarity.

We have made all of Reviewer 2’s suggested changes to improve clarity (please see below for details).

Reviewer #2 (Recommendations for the authors):

The authors have dealt with most of my comments in a satisfactory way, but there are still a few significant problems. In particular, the rationale for the experiment where T cells were stimulated ex vivo is highly unclear (see below for details).

We are glad that we have satisfactorily addressed most of Reviewer 2’s comments.

The conclusions are now better matched by data, although there are still quite strong claims about sex-specific effects even though there are few formal tests of this (i.e. tests for sex-by-genotype effects). More problematic is that the justification (L 578-581) provided for the analysis of ex vivo stimulated T cells is very vague. The problem with this assay is as follows: I believe that in splenocytes, Fcer1a is mainly (only?) expressed on basophils and eosinophils; thus, an effect of a Fcer1a polymorphism needs to involve these cells in some way. In your assay you stimulated T cells (which are abundant in splenocytes) in a non-specific way. Of the 18 cytokines measured, you find an effect of Fcer1a polymorphism on expression of IL17a, which is produced by T cells (but not by eosinophils/basophils). The problem is that I just cannot see how the Fcer1a polymorphism (which should have an effect in basophils/eosinophils, but not in T cells) could be linked to IL17a production by T cells; this would need to involve signalling from activated T cells to basophils/eosinophils, and then Fcer1a polymorphism-dependent signalling from basophils/eosinophils back to T-cells to affect IL17a production; I guess it is not impossible, but I am unaware of anything like that. I think you need to provide a sound and specific rationale for this assay; just saying "it could be acting indirectly on T-cells through various pathways, including via cytokine signalling, following Fcer1a expression by other cells" (L578-581) is not enough.

We have added more justification, evidenced with nine new references, on this point, around the line numbers indicated. The reviewer is correct that expression of the Fcer1a protein in the high-affinity IgE receptor is mainly associated with cells like basophils or eosinophils – although such expression is sometimes reported, or considered to be important, in other cells. However, it should be kept in mind that the cells assayed here were derived ex vivo from a wild organism and were also cultured in a complex global mixture – within which the T-cells that were specifically stimulated would have initially occurred alongside other cell types bearing functional high-affinity IgE receptor. Thus, the stimulated T-cells would previously (in vivo) and during the assay (ex vivo) have been under the influence of high-affinity IgE receptor-bearing cells. Given the modern attitude to interaction networks between cells, and the known cytokine-producing and immunoregulatory properties of high-affinity IgE receptor-bearing cells – including demonstrated effects on helper T-cells in ex vivo co-culture and in in vivo models (see the new references) – it does not seem surprising, or unreasonable to expect, that there might be indirect interactions with T-cells. In the event, we did, in fact, find evidence of an effect of Fcer1a polymorphism on the responses in T-cell-stimulated cultures that corroborated our independent findings – and this provides its own justification.

The presentation of the DGE analysis and GSEA of unstimulated splenocytes is improved, but still a bit confusing (L 199-238): (i) I think it would be good to make clear in the Results that the DGE analysis and GSEA are based on comparing individuals with the GC haplotype vs all others (so that the reader does not have to go the MandM to find this), (ii) using the term "top-responding" (L 206) implies that you are measuring the transcriptional response to some treatment, while you are just comparing baseline expression of GC vs others in males and females (or have I misunderstood something?); use "differentially expressed" or something similar instead, (iii) similarly, using the term "upregulated" (L 231) implies you measured the transcriptional response to some treatment; use "more highly expressed in individuals with GC…" or similar.

In response to point (i): We have now clarified in the Results that we compared individuals with vs. without the GC haplotype in the DGE analysis, by adding the following text:

“Differential gene expression (DGE) analysis was performed on unstimulated splenocytes taken from 53 males and 31 females assayed by RNASeq, with the aim of identifying individual genes which were differentially expressed between those individuals with and without a copy of the GC haplotype.”

We have done the same for the GSEA by editing/adding the following text in the Results:

“This analysis showed that both gene sets were more highly expressed in individuals with the GC haplotype than individuals without the haplotype, and that this was true for both males and females.”

In response to points (ii) and (iii): Throughout, we have replaced “top-responding” with “top differentially expressed”, and “upregulated” with “more highly relatively expressed in individuals with the GC haplotype” (or similar), as suggested by the Reviewer – adding the word ‘relatively’ to be precise (we report relative expression values, as detailed in the Materials and Methods).

When it comes to the ex vivo stimulation of T cells, I strongly suggest you remove these results (L240-254); they just don't make sense from an immunological point of view. Unfortunately, this is one of few analyses that actually shows a sex-by-genotype effects, so removing this would weaken the support for sex-specific effects of the Fcer1a polymorphism even further.

This point has been addressed above – and more justification has been provided in the methods.

https://doi.org/10.7554/eLife.77666.sa2

Article and author information

Author details

  1. Klara M Wanelik

    Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom
    Contribution
    Formal analysis, Writing – original draft, Writing – review and editing
    For correspondence
    klara.wanelik@biology.ox.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1485-0340
  2. Mike Begon

    Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom
    Contribution
    Conceptualization, Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1715-5327
  3. Janette E Bradley

    School of Life Sciences, University of Nottingham, Nottingham, United Kingdom
    Contribution
    Conceptualization, Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3973-7977
  4. Ida M Friberg

    School of Environment and Life Sciences, University of Salford, Salford, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  5. Joseph A Jackson

    School of Environment and Life Sciences, University of Salford, Salford, United Kingdom
    Contribution
    Conceptualization, Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0330-5478
  6. Christopher H Taylor

    School of Life Sciences, University of Nottingham, Nottingham, United Kingdom
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4299-7104
  7. Steve Paterson

    Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom
    Contribution
    Conceptualization, Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1307-2981

Funding

Natural Environment Research Council (NE/L013452/1)

  • Mike Begon
  • Janette E Bradley
  • Joseph A Jackson
  • Steve Paterson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank all those involved in obtaining and processing samples from the field: Rebecca Turner, Lukasz Lukomski, Stephen Price, Sarah Gore, Ed Parker, Maria Capstick, Noelia Dominguez Alvarez, Susan Withenshaw, William Foster, Ann Lowe, and Benoit Poulin. We would also like to thank the Forestry Commission for access to the study sites and the Centre for Genomic Research at the University of Liverpool for sequencing samples.

Ethics

All animal procedures were performed with approval from the University of Liverpool Animal Welfare Committee and under a UK Home Office licence (PPL 70/8210 to S.P.).

Senior Editor

  1. Jos W van der Meer, Radboud University Medical Centre, Netherlands

Reviewing Editor

  1. Sarah E Cobey, University of Chicago, United States

Publication history

  1. Preprint posted: November 14, 2019 (view preprint)
  2. Received: February 7, 2022
  3. Accepted: December 22, 2022
  4. Version of Record published: January 16, 2023 (version 1)

Copyright

© 2023, Wanelik et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 157
    Page views
  • 19
    Downloads
  • 0
    Citations

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

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Klara M Wanelik
  2. Mike Begon
  3. Janette E Bradley
  4. Ida M Friberg
  5. Joseph A Jackson
  6. Christopher H Taylor
  7. Steve Paterson
(2023)
Effects of an IgE receptor polymorphism acting on immunity, susceptibility to infection, and reproduction in a wild rodent
eLife 12:e77666.
https://doi.org/10.7554/eLife.77666

Further reading

    1. Genetics and Genomics
    2. Neuroscience
    Michael J Lafferty, Nil Aygün ... Jason L Stein
    Research Article Updated

    Expression quantitative trait loci (eQTL) data have proven important for linking non-coding loci to protein-coding genes. But eQTL studies rarely measure microRNAs (miRNAs), small non-coding RNAs known to play a role in human brain development and neurogenesis. Here, we performed small-RNA sequencing across 212 mid-gestation human neocortical tissue samples, measured 907 expressed miRNAs, discovering 111 of which were novel, and identified 85 local-miRNA-eQTLs. Colocalization of miRNA-eQTLs with GWAS summary statistics yielded one robust colocalization of miR-4707–3p expression with educational attainment and brain size phenotypes, where the miRNA expression increasing allele was associated with decreased brain size. Exogenous expression of miR-4707–3p in primary human neural progenitor cells decreased expression of predicted targets and increased cell proliferation, indicating miR-4707–3p modulates progenitor gene regulation and cell fate decisions. Integrating miRNA-eQTLs with existing GWAS yielded evidence of a miRNA that may influence human brain size and function via modulation of neocortical brain development.

    1. Genetics and Genomics
    2. Microbiology and Infectious Disease
    Po Jui Chen, Anna B McMullin ... David Bates
    Research Article Updated

    Bidirectional DNA replication complexes initiated from the same origin remain colocalized in a factory configuration for part or all their lifetimes. However, there is little evidence that sister replisomes are functionally interdependent, and the consequence of factory replication is unknown. Here, we investigated the functional relationship between sister replisomes in Escherichia coli, which naturally exhibits both factory and solitary configurations in the same replication cycle. Using an inducible transcription factor roadblocking system, we found that blocking one replisome caused a significant decrease in overall progression and velocity of the sister replisome. Remarkably, progression was impaired only if the block occurred while sister replisomes were still in a factory configuration – blocking one fork had no significant effect on the other replisome when sister replisomes were physically separate. Disruption of factory replication also led to increased fork stalling and requirement of fork restart mechanisms. These results suggest that physical association between sister replisomes is important for establishing an efficient and uninterrupted replication program. We discuss the implications of our findings on mechanisms of replication factory structure and function, and cellular strategies of replicating problematic DNA such as highly transcribed segments.