Abstract
Parents can have profound effects on offspring fitness. Little, however, is known about the mechanisms through which parental care variation influences offspring physiology in natural systems. White-throated sparrows Zonotrichia albicollis (WTSPs) exist in two genetic morphs, tan and white, controlled by a large polymorphic supergene. Morphs mate disassortatively, resulting in two pair types: tan male x white female (TxW) pairs, which provide biparental care and white male x tan female (WxT) pairs, which provide female-biased care. To investigate the effects of parental care variation, we performed RNA-seq on WTSP nestlings sampled from nests of both pair types. Pair type had the largest effect on nestling gene expression, with 881 genes differentially expressed (DE) and seven correlated gene co-expression modules. The DE genes and modules up-regulated in nests with female-biased parental care primarily function in metabolism and stress-related pathways resulting from the overrepresentation of stress response and proteolysis genes. These results show that parental genotypes, a proxy for parental care in this system, alter nestling physiology and highlight avenues of further research investigating the ultimate implications of alternative parental care strategies. Nestlings also exhibited morph-specific gene expression, driven by innate immunity genes and co-expression of genes located in the supergene. Remarkably, we identified the same regulatory hub genes in these blood-derived expression networks as were previously identified in WTSP brains (EPM2A, BPNT1, TAF5L). These hub genes were located within the supergene, highlighting the importance of this gene complex in structuring regulatory networks across diverse tissues.
Introduction
Parents can have profound impacts on offspring development and fitness. Parental effects can manifest throughout the developmental period, both pre- and postnatally (reviewed in Meaney 2001, Lupien et al. 2009). Postnatal effects can be particularly critical in altricial species, where offspring rely entirely on their parents for proper development and growth. Parental effects in these species can be mediated through genetic and physiological aspects of the parent or result from parental behaviors during early development (Trivers 1972).
A major component of these parental effects is mediated by the mother, termed maternal effects. In altricial species, maternal effects are crucial in the prenatal stage, where the mother’s genotype and/or phenotype directly influence the offspring phenotype (reviewed in Gluckman et al. 2008). In this case, offspring develop in a maternally created environment and are exposed and influenced by maternal hormones (Wolf & Wade 2009). Maternal hormones can accumulate in the prenatal environment (e.g. uterus, egg) and influence offspring physiology (reviewed in Cottrell & Secki 2009).
Beyond hormone mediated maternal effects, parental behaviors (both maternal and paternal) can also impact offspring. These parental effects can appear prenatally during nest building. Nest location will determine susceptibility to impacts from temperature or sun exposure, humidity, food availability, pollution, and predation (e.g. Lloyd & Martin 2004, Sofaer et al. 2012, Mulholland et al. 2018). However, the magnitude of parental effects is likely largest during the postnatal period, where offspring rely entirely on the parents for provisioning and must compete with siblings during feeding bouts. Thus, this postnatal environment, mediated through parental care, can be a potential source of early life stress (ELS) in offspring, which may result in life-long fitness effects.
A prominent area of ELS research focuses on neural development and neuroendocrine signaling (McEwen et al. 2007). The HPA axis is among the most well studied physiological mechanisms of the stress response. ELS is associated with impaired neuroendocrine function and corresponding impaired HPA response, which leads to lifetime consequences in behavior and fitness (e.g. Heim et al. 2008, Crespi et al. 2012, Spencer 2017). Additionally, increased levels of ELS can result in altered behaviors as organisms develop and mature including symptoms of anxiety and depression in the postnatal environment (Noguera et al. 2017) and result in impaired behavior as reproductive adults, with ultimate consequences in fitness. For example, ELS can slow brain development in songbirds, leading to altered song learning and production, which directly impacts the ability to produce and recognize songs as adults, a crucial aspect of songbird reproduction (Spencer et al. 2003, MacDougall-Shackleton & Spencer 2012, Sewall et al. 2018)
Epigenetic impacts of ELS have also been extensively studied (Szyf et al. 2007, Szyf 2009). In particular, the quality of parental care can have profound impacts on offspring health. Classic studies assessing maternal licking behavior in mice revealed strong epigenetic effects and corresponding physiological consequences in offspring receiving poor parental care (Liu et al. 1997, Meaney et al. 2001, Weaver et al. 2004). While there are a variety of epigenetic marks, including histone modification and chromatin accessibility, changes in DNA methylation are often used as an indicator of ELS. Specifically, methylation of the exon 1F promoter for the glucocorticoid receptor, NR3C1, has received considerable attention. NR3C1 is often hypermethylated in offspring experiencing ELS, which has been linked to physiological disorders later in life (McGowan et al. 2009, Romens et al. 2014, Turecki & Meaney 2016). In general, these epigenetic modifications are thought to alter transcriptional activity of genes in the modified genomic region. However, very few studies assess genome-wide transcription under ELS, particularly in the context of parental effects (but see: Weaver et al. 2006).
In this study, we studied the white-throated sparrow (WTSP, Zonotrichia albicollis) in order to assess the role of parents and ELS on offspring gene expression. WTSPs exist in two plumage morphs, tan (T) and white (W), that are found in both sexes and in roughly equal frequencies (Lowther 1961). These morphs are genetically determined by alternative alleles of a supergene (Schwander et al. 2014), resulting from a complex chromosomal rearrangement comprising multiple inversions (hereafter referred to as “inversion” or “inverted”). This inverted region contains ~1,100 genes on chromosome two, termed ZAL2m (Throneycroft 1975, Thomas et al. 2008, Romanov et al. 2009, Tuttle et al. 2016). W morphs are nearly always heterozygous for the inversion (ZAL2/ZAL2m) and T morphs are always homozygous (ZAL2/ZAL2) and do not contain the inversion (Thorneycroft 1966, 1975). The morphs of both sexes differ dramatically in behavior, where W morphs are highly territorial, sing frequently, and maintain higher levels of hormones at different times of the breeding season. T morphs are far less territorial and aggressive and maintain lower levels of circulating hormones (Lowther 1962, Kopachna & Falls 1993, Tuttle 2003, Spinney et al. 2006, Swett & Breuner 2009, Horton & Holbertson 2010, Horton et al. 2014). Importantly, males of each morph differ in paternal investment (Knapton & Falls 1983). W morph males are promiscuous and provision nestlings very little. T morph males defend their within-pair paternity through mate guarding and are highly paternal. Additionally, morphs nearly always mate disassortatively, resulting in two stable pair types: T male x W female (TxW) and W male x T female (WxT) (Lowther 1961, Tuttle 2003, Tuttle et al. 2016). Because males differ in paternal investment, this results in two distinct parental care strategies. TxW pairs provide biparental care and WxT pairs provide female-biased parental care.
Thus, WxT pairs may be a source of ELS (via reduced provisioning) for nestlings. We expect nestlings in WxT nests to elicit a distinct transcriptional stress response relative to nestlings in TxW nests. Alternatively, W morph females may produce higher levels of oestradiol resulting from singing and territorial behavior (e.g. Horton et al. 2014), and this may act as a source of hormone mediated maternal effects in TxW nests. In this case, we may expect to see the opposite pattern, with a transcriptional stress response in nestlings from TxW nests as they cope with the immunosuppressive effects of estrogens (e.g. al-Alfaleq & Homeida 1998). Furthermore, each nest will produce nestlings of both morphs, offering the unique opportunity to investigate the interaction between offspring and parental morphs. As adults, W morphs must cope with high levels of energy expenditure via singing and territorial behavior relative to T morphs. Thus, we predicted W morph nestlings may be better suited to handle ELS mediated through female-biased parental care in WxT nests, or maternal effects in TxW nests, and we would see an elevated stress response in T morph nestlings in these nests. Lastly, we investigate morph-specific gene expression, independent of the pair type of the nestling, to assess the role of nestling morph (i.e. presence or absence of ZAL2m inversion) on nestling gene expression.
Methods
Field based sample collection
All nestling samples in this study came from a breeding population of WTSPs at the Cranberry Lake Biological Station in northern New York, USA (SUNY-ESF, 44.15°N, 74.78°W) and were collected during the 2015 breeding season. We only utilized samples collected during the first clutch (June 6 - June 14, 2015), as WTSP males may increase paternal investment in replacement broods (Horton et al. 2014). Nestlings were measured daily (tarsus length, mass) from hatch date or upon locating a nest with nestlings. Nestlings were banded on days 5–7 post-hatch and ~80μL blood was collected in capillary tubes via brachial venipuncture.
Approximately 60μL blood was preserved in Longmire’s lysis buffer (Longmire et al. 1992) for genotyping and ~20μL was immediately placed in RNAlater. Within six hours of collection, samples were placed temporarily into liquid nitrogen, before being shipped overnight on dry ice to −80°C storage until RNA extraction. All animal sampling protocols were approved by the Indiana State University Institutional Animal Care and Use Committee (IACUC 562158-1:ET/RG, 562192-1:ET/RG).
Molecular sexing & genotyping
Nestling DNA was extracted from erythrocytes using the DNA IQ® magnetic extraction system (Promega Corp, Madison, WI USA). To determine sex and morph, we used PCR to fluorescently label and amplify a region of the chromo-helicase-DNA-binding gene, and a region of the vasoactive intestinal peptide following Griffiths (1998) and Michopolous et al. (2007). The PCR products were run and analyzed on an ABI PRISM™ 310 genetic analyzer.
RNA extraction, library preparation, & sequencing
We sampled a total of 32 nestlings for RNA extraction and sequencing. These samples represent 23 nestlings from eight TxW pairs and nine nestlings from three WxT pairs. Additionally, these data represent 18 females, 14 males, 15 T morph, and 17 W morph individuals.
We removed RNAlater and homogenized whole blood tissue samples with Tri-Reagent (Molecular Research Company). Total RNA was purified with a Qiagen RNeasy mini kit (Valencia, CA, USA), followed by DNase treatment and further purification. We quality assessed RNA with an Agilent Bioanalyzer (Wilmington, DE, USA). Both library preparation and sequencing were performed at the University of Illinois Roy J. Carver Biotechnology Center. A library was prepared for each RNA sample using the Illumina HT TruSeq (San Diego, CA, USA) stranded RNA sample prep kit. Libraries were distributed into four pools with equimolar concentrations and quantitated via qPCR. Each of the pools was sequenced on an individual lane of an Illumina HiSeq 2500 using the Illumina TruSeq SBS sequencing kit v4 producing 100 nucleotide single-end reads.
Creation of masked reference genome
The WTSP reference genome was generated from a male T morph individual (Tuttle et al. 2016). Thus, the reference genome does not contain any sequence data from the ZAL2m inversion. To avoid any potential bias in mapping reads derived from W morph individuals onto a T morph genome, we generated a masked reference genome for this study. To do so, we used previously published whole genome sequences from three W morph adults (Tuttle et al. 2016). Reads were adapter trimmed with Trim Galore! v0.3.8 (https://github.com/FelixKrueger/TrimGalore) and aligned to the WTSP reference genome with bwa mem v 0.7.10-r789. We converted and sorted the resulting SAM alignment to BAM format with samtools view and samtools sort, respectively (samtools v1.2, Li et al. 2009). We then merged all genomic scaffolds corresponding to the ZAL2m inversion with samtools merge. We called SNPs within the inversion using samtools mpileup and bcftools call v 1.2 (Li 2009). We only kept SNPs that were heterozygous in each of the three individuals with SnpSift v 4.3p (Cingolani et al. 2012) and used these SNPs to mask the reference genome with bedtools maskfasta v 2.21.0 (Quinlan & Hall 2010).
Quality control, read mapping, differential expression, & gene ontology
We trimmed Illumina sequencing adapters from each of the 32 libraries with Trim Galore! v 0.3.8 which uses Cutadapt v1.7.1 (Martin 2011). Trimmed reads were then mapped to the masked reference genome with STAR v2.5.3a (Dobin et al. 2013). The mapping results were then quantified and assigned gene IDs with htseq-count v0.6.0 (Anders et al. 2015) specifying ‘-s reverse’ and ‘-i gene’. Genes with an average read count of ≥ 5 were used for downstream analyses.
All statistical analyses were performed with R v3.5.0 (R Core Team 2013). We identified outlier samples as having a normalized connectivity below −2.5 (Horvath 2011). Two samples, one T female and one T male representing an entire TxW nest, were identified as outliers and removed from all future analyses (Figure S1). We normalized reads accounting for sequencing depth and assessed differential expression (DE) with DEseq2 (Love et al. 2014). We performed variance stabilizing transformation of reads in DEseq2 and performed PCA and hierarchical clustering of gene expression profiles with pcaExplorer v2.6.0 (Marini 2018). DE analyses utilized pairwise comparisons between nestling morph and pair type (i.e. parental morphs). In each case, we controlled for sex, morph, and/or nest ID. We did not include nestling age in analyses, as most samples were 6 days old (n=21), limiting comparisons with nestlings aged Day 5 (n=3) or Day 7 (n=6). Network analysis (see below) did not reveal any effect of age on variables of interest (morph, pair type; data not shown). We also tested for an interaction between nestling morph and pair type utilizing a grouping variable as outlined in the DEseq2 manual. DEseq2 determines DE with a Wald test followed by Benjamini & Hochberg (1995) FDR correction. Genes were considered DE if the FDR corrected p-value was < 0.10. Details for each model run, including the R code used, are in this project’s GitHub repository.
We next grouped DE genes into gene ontology (GO) categories with GOrilla (Eden et al. 2007, 2009). For each DEseq2 comparison, we ordered the list of genes based on ascending FDR values, excluding any genes in which DEseq2 did not assign a FDR value. The WTSP genome is not completely annotated, so any loci without a gene symbol were excluded from GO analyses (n=1,926). GOrilla places greater weight on genes located at the top of the list (i.e. DE genes), while accounting for the contribution of each gene in the given comparison. GO categories were considered significantly enriched if the FDR corrected p-value <0.05. GOrilla does not support WTSP annotation; so, all analyses were based on homology to human gene symbols.
Weighted gene co-expression network analysis (WGCNA)
We used the WGCNA package in R (Zhang & Horvath 2005, Langfelder & Horvath 2008) to identify modules of co-expressed genes in our dataset. We first exported variance stabilizing transformed (vst) read counts from DEseq2, removed genes with an average vst < 5 across all 30 samples, and imported the subsequent list of 8,982 genes into WGCNA. To build the co-expression matrix, we chose a soft thresholding power (β) value of 12, at which the network reaches scale-free topology (Figure S2). We generated a signed network with minimum module size of 30 genes and merged highly correlated modules (dissimilarity threshold = 0.20, which corresponds to R2 = 0.80). We then correlated the eigengene, which is the first principal component of a module, of these merged modules with external traits (pair type, morph, sex, nest ID). Modules with p < 0.05 were considered significantly correlated with a given trait.
To visualize the interaction of genes within a module, we generated the intramodular connectivity (IM) score for each gene, which represents the interconnection of module genes. We exported all IM scores for modules of interest and imported into VisAnt v5.51 (Hu et al. 2013) for visualization. To maximize network clarity, we only plotted the top 300 interactions based on IM scores. Thus, we only visualized the most connected genes. To identify hub genes, we visualized the Degree Distribution (DD) for the network and selected the most connected genes above a natural break in the distribution. This resulted in one to nine hub genes per module.
Lastly, to understand the biological function of modules correlated with traits of interest, we performed a target vs background GO analysis in GOrilla. For each module, we tested the assigned genes for each module against the entire list of 8,982 genes used for the WGCNA analysis. GO categories were significant with a FDR corrected p-value < 0.05.
Results
Sequencing results
We sequenced each sample to an average depth of 29.4 million reads (range = 16.2–58.5 million reads). The 32 libraries were distributed into four pools in equimolar concentration. One pool contained only four samples, which corresponded to the four samples with lowest RNA concentrations. This pool was sequenced to an average depth of 56.17 million reads per library. The remaining three pools were sequenced to an average depth of 25.62 million reads per library. Samples mapped to our masked genome at an average rate of 91.08% (range = 88.19%-92.87%) (Table S1). A total of 8,982 genes had count values ≥ 5 across all samples. Samples do not segregate by pair type or morph in clustering analyses (Figures S3, S4).
Differential Expression – Morph
Testing for morph-specific expression resulted in 92 genes DE. Sixty-five of these genes were in the supergene (Table S2). Additionally, many of these 92 genes were up-regulated in W morph nestlings and function in innate immunity (e.g. IFIT5, IL20RA, EIF2AK2, RSAD2). There was GO enrichment of four categories, two of which are immunity related: “immune response” (p = 0.019) and “defense response to virus” (p = 0.049) (Table S3).
Differential Expression – Pair Type
Pair type had the largest effect on gene expression, with 881 genes differentially expressed (DE) between offspring from the two different pair types (FDR < 0.10, Table S2). Many genes associated with stress responses were up-regulated in nestlings in WxT nests, including the glucocorticoid receptor (NR3C1), superoxide dismutase (SOD)1 & SOD2, DEP domain-containing mTOR-interacting protein (DEPTOR), and several ubiquitin-mediated proteolysis pathway genes (e.g. UBE2D3, PSMD3, PSMD6). Additionally, several immune system related genes were also up-regulated in WxT nests, including cytokines (e.g. IL2RA, IL7R), suppressor of cytokine signaling 1 (SOCS1), and five putative major histocompatibility complex (MHC) class I loci. No GO categories were significantly enriched, however.
We next tested for a morph-specific response to pair type. Within WxT nests, 40 genes were DE (p <0.10) between T and W morph nestlings, 12 of which are in the supergene. Additionally, 34/40 genes are uniquely DE between morphs in WxT nests and do not overlap with the overall list of 92 genes DE between morphs described above, suggesting a prominent role of the WxT pair type on nestling morph-specific gene expression. Only two genes (THSD7B & CFAP44) were DE between morphs within TxW nests, both of which are uniquely DE between morphs in TxW nests. No GO categories were enriched in either comparison.
WGCNA – Morph
WGCNA revealed 26 modules, five of which were correlated with morph (Table 1, Figure 1). The light cyan module (183 genes, R2=0.67, p=5×10−5) and white module (72 genes, R2=−0.66, p=9×10−5) contained genes up-regulated and down-regulated respectively in W morph nestlings. These genes are primarily located in the chromosomal inversion (light cyan = 70/183 genes, white = 40/72) (Figure S5). The hubs of each of these modules are also located in the chromosomal inversion (Table 1, Figure S5). Additionally, the sky blue module (58 genes, R2=0.53, p=0.003) and dark red module (102 genes, R2=0.47, p=0.009) (Figure S6) contained genes up-regulated in W morph nestlings and many of these genes overlap with the immune related genes described in the morph DE tests above. Additionally, the hubs of these networks (e.g. sky blue: EIF2AK2, IFIT5, OASL; dark red: TRAF5) (Table 1) reflect a conserved innate immunity network structure in avian blood (Kernbach et al., in review) (Figure S6).
WGCNA − Pair Type
We found seven modules correlated with pair type (Table 2, Figure 1). The blue module represented genes that are up-regulated in nestlings from WxT nests (1,142 genes, R2 = −0.45, p=0.01). This module contained both the largest number of genes and correspondingly strongest functional enrichment. Many of these GO enrichments were related to protein function, resulting from the presence of ribosomal genes. Interestingly, several GO categories for metabolism, catabolism, and proteolysis were also enriched, driven by genes encoding ubiquitin-conjugating enzymes and proteasome subunits (e.g. “proteasomal protein catabolic process”, p=2.34×10−4; “proteasome-mediated ubiquitin-dependent protein catabolic process”, p=5.32×10−4) (Table S4). Many of these (e.g. PSMF1, PSMD3, PSMD6, UBE2D2, UBE2D3, UBE3C) were also DE between offspring of the two pair types (Figure 2). Lastly, the blue module contains one hub gene, NDUFB3 (DD=42) (Figure 2), which is involved in the mitochondrial electron transport chain.
The remaining modules were not enriched for any GO categories, but the tan and light green modules represented candidate stress response networks. These modules showed contrasting expression patterns in nestlings from WxT nests. Within the tan module (335 genes, R2=−0.61, p=3×10−4), genes were up-regulated and DEPTOR was the single hub (DD=39), which functions as an inhibitor of the mTOR pathway in response to stress (e.g. Desantis et al. 2015) (Figure 3). The tan module also contained NR3C1, which is activated in response to increased glucocorticoid secretion. Lastly, the light green module (116 genes, R2=0.60, p=4×10−4) contained genes down-regulated in TxW nests. There were three hub genes (DD > 28), CDK19, CHD4, and EPG5, each with previously described roles in the stress response (Figure 4). We did not observe modules correlated with pair type that were also correlated with nestling morph or sex, suggesting there is no morph or sex-specific response to a given pair type at the network level.
Discussion
By assessing genome-wide transcription in nestlings raised by different WTSP pair types, a proxy for parental investment, we have identified distinct transcriptomic signatures that suggest WxT pairs (female-biased parental care) induce a stress response in developing nestlings relative to TxW pairs. This is reflected both by differential expression of several genes involved in protein degradation as well as networks of co-expressed genes with stress response hubs. Additionally, we identified morph-specific gene expression driven by innate immunity genes and genes located in the chromosome 2 supergene. As adults, the genes within the supergene strongly influence the WTSP neural transcriptome (Balakrishnan et al. 2014, Zinzow-Kramer et al. 2015). Our results here suggest that as nestlings, parental genotypes and associated behaviors, rather than nestling genotype, have the strongest influence on the nestling transcriptome.
Gene expression differences resulting from pair type, a proxy for parental care
WTSP morphs differ in the amount of paternal provisioning; with W morph males providing less than their T male counterparts. Therefore, we expected nestlings in WxT nests to be stressed nutritionally or to suffer increased sibling competition due to uniparental feeding visits (e.g. Yosef et al. 2012). Indeed, we find 881 genes DE between nestlings raised under the two pair types. Many of these genes function in the proteasome or ubiquitin-mediated proteolysis. Cells naturally use the proteasome for degradation of proteins targeted by the ubiquitination process, but genes involved in proteasome formation (e.g. PSMD6, PSMD11) and ubiquitination (e.g. UBE2B) are up-regulated in cells experiencing mild oxidative stress (Aiken et al. 2011, Shang & Taylor 2011, Livneh et al. 2016) or organisms experiencing abiotic stress (Dhanasiri et al. 2013, Tomalty et al. 2015). Thus, up-regulation of these genes in nestlings from WxT nests suggests they are responding to oxidative stress. As a result, there is a physiological cost to having a W morph father and T morph mother at the nestling stage, since W morph males invest more in reproduction and territory defense than provisioning.
To complement our differential expression approach, we also constructed co-expression networks with WGCNA. WGCNA identifies modules of co-regulated genes blind to the experimental design. These modules are then correlated with external traits, offering a systems level view into how conditions impact transcriptional networks. Within these networks, we can then perform GO analyses as described above and identify network hubs, which are the most highly connected genes within that network. Using this approach, we identified 26 modules of co-regulated genes in this dataset (Figure 1). Seven of these modules were significantly correlated with parental pair type. The blue module contains genes that are up-regulated in nestlings in WxT nests. The blue module hub gene was NDUFB3 (Module Membership [MM]=0.938, DD=42) (Figure 2), which encodes a subunit of the mitochondrial membrane respiratory chain. Interestingly, many of the same proteolysis-related genes highlighted in the differential expression results are also present in this module, resulting in the enrichment of several metabolism and stress-related GO categories (Table S4).
Two modules, light green and tan, contained stress responsive hub genes. The light green module contains genes that are down-regulated in nestlings in WxT nests, with three hub genes: CDK19, CHD4, and EPG5 (Figure 4). The absence of EPG5 expression (via knockout) and reduction in CHD4 expression (via knockdown) has been associated with increased DNA damage (Zhao et al. 2013, Larsen et al. 2010). Similarly, down-regulation of CDK19 following knockdown is associated with an increased stress response (Audetat et al. 2017). Down-regulation of these genes in these nestlings could be indicative of increased cellular damage. The tan module contains genes up-regulated in nestlings from WxT nests and contains one hub gene, DEPTOR, which is an inhibitor of mTOR signaling (Figure 3). The exact role of DEPTOR remains unclear, but up-regulation likely inhibits the mTORC1 pathway to reduce endoplasmic reticulum stress, promote cell survival, and avoid apoptosis (Peterson et al. 2009, Desantis et al. 2015, Catena et al. 2016).
Up-regulation in these nestlings and the high connectivity of DEPTOR to other co-expressed genes provides further support for a transcriptional stress response within WxT nests. The tan module also contains two well-studied stress responsive genes, superoxide dismutase 2 (SOD2) and the glucocorticoid receptor (NR3C1). SOD2 mitigates the effects of exposure to reactive oxygen species by scavenging free radicals (Zelko et al. 2002). NR3C1 binds glucocorticoids and has primarily been studied in the context of ELS and methylation of an upstream promoter. NRC3C1 methylation is often associated with down-regulation of NR3C1 (e.g. McGowan et al. 2009) and impairment of the HPA axis, but up-regulation following methylation has also been observed as part of the stress response (Turner et al. 2006, Bockmühl et al. 2015). Up-regulation observed here directly implicates the HPA axis and suggests these nestlings may be activating SOD2 and NR3C1 to cope with elevated levels of reactive oxygen species and corticosterone, respectively. However, further work is needed to investigate stress physiology, corticosterone levels, and uncover the epigenetic state of NR3C1 in these nestlings and how this may relate to ELS.
Importantly, we did not measure provisioning by the parents of these nestlings but instead used pair type as a proxy for parental care. Reduced provisioning by W morph males appears to be stable across populations resulting in female-biased parental care in WxT nests (Knapton & Falls 1983, Horton et al. 2014). Therefore, reduced parental care is a likely a source of behaviorally mediated maternal or paternal effect (see Crean & Bonduriansky 2014). We cannot, however, ignore the possibility that provisioning rates did not differ between the nests we sampled. Previous work revealed no effect of parental pair type on nestling mass (Knapton et al. 1984, Tuttle et al. 2017), and nestlings did not differ in mass at time of sampling between the TxW and WxT nests used in this study (Smith et al. in review). Increased provisioning by females to compensate for reduced care by males could explain this observation. In this scenario reduced brooding and increased maternal separation; could also negatively impact nestling physiology and act as a source of ELS (reviewed in Ledón-Rettig et al. 2013). Surprisingly, given the gene expression findings described above, a recent study in the same study population did not detect differences in reactive oxygen metabolites in plasma of offspring of the two different pair types (Grunst et al. 2018b). Our finding of transcriptional differences in stress-responsive genes in the absence of significant phenotypic differences highlights the utility of RNA-seq to uncover subtle changes in physiology.
Our study was carried out in the field as part of a long-term study and is limited by the fact that was a non-experimental study. We aimed to mitigate potential environmental confounds by restricting sampling of nestlings to a short time period of nine days. Certainly, the environment may influence gene expression in our samples, but consistent changes among the samples in the two pair types suggest the role of parents is a significant driver of nestling gene expression, rather than temporal or spatial environmental variation.
Hormone-mediated maternal effects provide another potential driver of the observed expression differences among pair types. In previous studies of WTSP, only oestradiol has been shown to differ between adult female morphs during the breeding season and is higher in W morph females during the pre-laying and laying stages (Horton et al. 2014). No baseline differences in any other hormone measured to date (corticosterone, testosterone, DHEA, DHT) have been described during the breeding season (Spinney et al. 2006, Swett & Breuner 2009, Horton & Holberton et al. 2010, Horton et al. 2014), which would suggest that hormone deposition into eggs does not dramatically differ between the morphs. Additional work is needed to investigate the potential role of maternal effects in the WTSP, including measuring hormone levels in both the egg and as nestlings. Although we cannot rule out hormone-mediated maternal effects as a source of expression differences observed in offspring, given the current knowledge of the system the differences we observed are likely driven by differences in parental care.
Morph-specific gene expression
We were also interested in morph-specific gene expression and how morphs may respond to differences in pair type. We found 92 genes DE between morphs, including many innate immune-related genes and genes located within the supergene (65/92 genes, Table S2). WGCNA revealed five modules correlated with morph (Figure 1). These included two innate immunity-related modules with up-regulation in W morphs (Dark Red & Sky Blue) and two modules predominantly containing genes located in the supergene (White = 40/72, Light Cyan = 70/183) (Figures S5, S6). The sky blue module contains nine hub genes and the dark red module contains one hub gene, both of which include well-studied anti-viral genes (e.g. sky blue: OASL, RSAD2; dark red: TRAF5). These genes also form a co-expression module in avian blood following West Nile virus infection (Kernbach et al., in review). Adult WTSP morphs differ in their ability to clear infection (Boyd et al. 2018), so the immune activation here may be indicative of an increased parasite load in W morph nestlings, although further investigation is required. The light cyan module contains genes up-regulated in W morph nestlings and contains eight hub genes, each located in the supergene (Table 1). Three of these, EPM2A, BPNT1, and TAF5L, were also identified as hub genes in neural tissues of adult W morph males (Zinzow-Kramer et al. 2015). These nestlings thus exhibit transcriptional changes driven by the inversion prior to any phenotypic or behavioral differences. Additionally, the conservation of network hub genes in a different tissue and life stage highlights avenues for further investigation into WTSP transcription.
Given adult W morphs are highly territorial, aggressive, sing frequently, and maintain higher levels of stress hormones, we predicted W morph nestlings might be primed to handle stress and fare better in WxT nests than their T morph siblings. Despite broad gene expression differences between the morphs, within pair types morph-specific expression was limited. In part due to small sample size, nestlings in TxW nests only have two genes DE between morphs. There is a larger effect of morph within WxT nests, where the number of DE genes increased to 40. These genes encompassed a wide range of gene functions without any obvious stress-related candidate genes. Of these 40 genes, 34 are uniquely DE within WxT nests and do not overlap with the overall list of 92 genes DE between morphs using all samples. Interestingly, glucocorticoid-induced transcript 1 (GLCCI1) is up-regulated in W morph nestlings in WxT nests. The function of GLCCI1 remains unclear (Kim et al. 2016), but expression differences between morphs observed here implicates the role of glucocorticoids in response to pair type. This suggests that nestling morphs may respond differently to the parental pair type though larger sample sizes will be needed to explore this further.
Conclusions
Using the WTSP, a system with alternative parental care strategies, we show that nestlings in WxT nests (female-biased parental care) have increased expression of stress-related genes, and parental genotypes may act as a source of ELS in the species. Nestling morph also influences transcription, but pair type appears to have the greatest effect on their transcriptome. Combined, this supports the parental effects hypothesis (Wade 1998, Schrader et al. 2018), where offspring phenotypes are primarily a result of the nest environment and care received, rather than from offspring genotypes (i.e. T vs W). Nearly 54% of observed pairs have been WxT (Tuttle et al. 2016). Thus, roughly half of the nestlings in every population will experience reduced parental care. Our results suggest that these differences in parental care have at least short-term consequences on offspring physiology. While we have identified impacts at the level of transcription, an integrative approach assessing nestling WTSP physiology, for example by combining epigenetic and neuroendocrine approaches, will further elucidate the consequences of variation in parental pair type. Importantly, it remains unclear whether female-biased parental care or differences in maternal effects translate into long-term fitness consequences for offspring. WTSPs have been studied extensively as adults, but very rarely in other life stages. W morph males and T morph females exhibit earlier reproductive and actuarial senescence, potentially resulting from the high energy expenditure lifestyle of W morph males and biased parental care given by T morph females (Grunst et al. 2018a, Grunst et al. 2018c). There also appears to be seasonal variation in fitness between the morphs as adults. Following cold, wet winters, W morph males exhibit lower recruitment in the breeding grounds, leading to an overproduction of W morph male nestlings, potentially to stabilize morph frequencies in the population (Tuttle et al. 2017). Thus, there is a cost associated with parental genotype, as this less cooperative reproductive strategy accelerates senescence. We show here that this cost is also translated into nestlings within WxT nests via increased stress-related gene expression. This work sets the stage to further explore morph-specific fitness consequences in nestlings experiencing alternative parental care strategies.
Data Accessibility
The 32 RNAseq libraries used in this study will be submitted to the NCBI Sequence Read Archive (SRA). All files needed to produce these results, including code and counts files, will be uploaded to this project’s GitHub page: https://github.com/danielnewhouse/wtsp
Author Contributions
DJN designed and performed research, analyzed the data, and wrote the paper. MBS performed research, contributed samples, and reviewed drafts of the paper. EMT designed and performed research and contributed samples. RAG designed and performed research, contributed samples, and reviewed drafts of the paper. CNB designed and performed research, contributed reagents, and reviewed drafts of the paper.
Acknowledgements
We acknowledge Lindsay Forrette, Andrea Grunst, and Melissa Grunst for assistance in the field, Sarah Ford for assistance with molecular work, Rachel Wright for WGCNA code, and Cranberry Lake Biological Station. Funding provided by East Carolina University, Indiana State University, The National Science Foundation (grant no. DUE-0934648) and the National Institutes of Health (grant no. 1R01Gm084229 to E.M.T and R.A.G.) and a Sigma Xi Grants in Aid of Research award to DJN. Birds were banded with color bands and a Fish and Wildlife band (Master Banding Permit 22296 to EMT and permit 24105 to RAG). Dr. Alvaro Hernandez and Chris Wright provided guidance and oversight on sequencing carried out at the University of Illinois. All methods were conducted in accordance with legal and ethical standards and were approved by Indiana State University’s Institutional Animal Care and Use Committee (protocols 562158-1:ET/RG and 562192-1:ET/RG).