Profiling microRNA expression in Atlantic killifish (Fundulus heteroclitus) gill and responses to arsenic and hyperosmotic stress

Abstract

The Atlantic killifish (Fundulus heteroclitus), native to estuarine areas of the Atlantic coast of the United States, has become a valuable ecotoxicological model as a result of its ability to acclimate to rapid environmental changes and adapt to polluted habitats. MicroRNAs (miRNAs) are highly conserved small RNAs that regulate gene expression and play critical roles in stress responses in a variety of organisms. Global miRNA expression in killifish and the potential roles miRNA have in environmental acclimation have yet to be characterized. Accordingly, we profiled miRNA expression in killifish gill for the first time and identified a small group of highly expressed, well-conserved miRNAs as well as 16 novel miRNAs not yet identified in other organisms. Killifish respond to large fluctuations in salinity with rapid changes in gene expression and protein trafficking to maintain osmotic balance, followed by a secondary phase of gene and protein expression changes that enable remodeling of the gills. Arsenic, a major environmental toxicant, was previously shown to inhibit gene expression responses in killifish gill, as well the ability of killifish to acclimate to a rapid increase in salinity. Thus, we examined the individual and combined effects of salinity and arsenic on miRNA expression in killifish gill. Using small RNA sequencing, we identified 270 miRNAs expressed in killifish, and found that miR-135b was differentially expressed in response to arsenic and at 24 hours following transfer to salt water. Predicted targets of miR-135b are involved in ion transport, cell motility and migration, GTPase mediated signal transduction and organelle assembly. Consistent with previous studies of these two environmental stressors, we found a significant interaction (i.e., arsenic dependent salinity effect), whereby killifish exposed to arsenic exhibited an opposite response in miR-135b expression at 24h post hyperosmotic challenge compared to controls. By examining mRNA expression of predicted miRNA targets during salinity acclimation and arsenic exposure, we found that miR-135b targets were significantly more likely to decrease during salinity acclimation than non-targets. Our identification of a significant interaction effect of arsenic and salinity on miR-135b expression supports the hypothesis that arsenic alters upstream regulators of stress response networks, which may adversely affect the killifish response to osmotic stress. The characterization of miRNAs in this ecotoxicological model will be a valuable resource for future studies investigating the role of miRNAs in response to environmental stress.

1. Introduction

The euryhaline teleost Fundulus heteroclitus (killifish) is native to the Atlantic coast of North America and able to acclimate to rapid and extreme changes in environmental conditions, such as salinity, that occur daily in estuaries. The ability of killifish to tolerate wide variations in temperature, pH, dissolved oxygen and salinity allows for a broad distribution of populations along the Atlantic coast, where it is one of the most abundant fish species in intertidal marshes (Burnett et al., 2007). In addition to the ability to acclimate to rapid environmental changes on the individual scale, distinct populations of killifish have evolved adaptations that allow survival under persistent adverse environmental conditions such as industrial pollution (Whitehead et al., 2017). Because they are non-migratory and have a small home range, distinct populations can be identified in relatively close geographic proximity to each other. This, in combination with their hardiness and amenability to laboratory conditions, has made killifish a particularly valuable vertebrate model for ecotoxicological studies (Bello et al., 2001; Di Giulio and Clark, 2015; Osterberg et al., 2018; Reid et al., 2017, 2016; Reitzel et al., 2014; Shaw et al., 2014; Whitehead et al., 2017). The genetic tools available for gene-environment interaction studies in killifish have recently expanded with the publication of a killifish reference genome (Burnett et al., 2007; Reid et al., 2017). The sequenced genome enables investigation of gene regulatory mechanisms at multiple levels (genetic, epigenetic, transcriptional, post-transcriptional) that facilitate the ability of killifish to acclimate to changes in their environment, and in particular the role of non-coding RNAs, which have been identified as mediators of survival responses in organisms tolerant of environmental stress (Biggar and Storey, 2015).

MicroRNAs (miRNAs) are a class of short (~22 nucleotides), highly conserved non-coding RNAs that negatively regulate post-transcriptional gene expression by destabilizing mRNA or repressing translation (Jonas and Izaurralde, 2015; Lee et al., 1993). MiRNAs are expressed in organisms throughout the plant and animal kingdoms, and direct mRNA silencing by binding with perfect or imperfect complementarity to mRNA target sequences (Bartel, 2004; Tarver et al., 2013; Taylor et al., 2014). The expanding list of functions identified for miRNAs includes fine-tuning transcriptional responses, reducing variability in lowly expressed proteins, and mediating feedback loops with coexpressed genes (Bartel, 2009; Chen et al., 2004; Schmiedel et al., 2015). MiRNAs mediate normal physiological processes that require coordinated actions, including embryonic development, organ morphogenesis, angiogenesis and tissue regeneration as well as pathological conditions such as cancers and cardiovascular disease (Harfe et al., 2005; King and Yin, 2016; Kuehbacher et al., 2008; Wojciechowska et al., 2017). While the majority of miRNAs are not critical to early embryonic development or cell lineage specification in vertebrates, they play important roles in many stress responses (Mendell and Olson, 2012). We hypothesized that miRNAs, which had yet to be characterized in Atlantic killifish, would also be important mediators of acclimation responses of this species particularly within the context of osmotic stress and the extensive remodeling of gill tissue that allows long-term acclimation to a wide range of salinity. We employed an additional stressor, arsenic, which we have previously shown inhibits salinity acclimation (Hampton et al., 2018; Shaw et al., 2007; Stanton et al., 2006), to identify miRNAs that are critical to this process.

Killifish acclimate to a sudden increase in salinity with an acute response (< 1h) to maintain osmotic balance, followed by a second phase (days) of extensive remodeling of the gill tissue to a salt water phenotype that is capable of excreting excess chloride (Cozzi et al., 2015; Hwang et al., 2011). Much is known about the physiology of this process, and studies at various time points have identified distinct groups of genes involved in the immediate and long term acclimation responses in the gills (Scott et al., 2004; Shaw et al., 2014; Whitehead et al., 2011). We therefore focused this study on the gill, although other organs, such as kidney and intestine, are also important during salinity acclimation and for maintaining osmotic balance. A critical acute response to hyperosmotic conditions is translocation of the cystic fibrosis transmembrane conductance regulator (CFTR) from intracellular vesicles to the plasma membrane, where it mediates chloride secretion (Katoh and Kaneko, 2003). Genes encoding nucleosome and ion transport proteins, including Nkcc1 and Atp1a1 also increase transiently in the osmotic stress response (Scott et al., 2004). The second phase of salinity acclimation involves de novo synthesis of ion transport proteins and morphological transformation of mitochondrion-rich ionocytes to secrete NaCl (Cozzi et al., 2015; Hwang et al., 2011). This energy-intensive process is accompanied by up-regulation of mitochondrial genes (Brennan et al., 2015) and a sustained increase in Cftr mRNA mediated by the glucocorticoid receptor (Singer et al., 2008). Two observations from previous mRNA expression studies suggest post-transcriptional mechanisms are involved in the regulation of gene expression during salinity acclimation, particularly during the remodeling phase. The first is a lack of correlation between mRNA and protein expression, (Scott et al., 2004); the second is lack of overlap in genes differentially expressed at 1 vs 24 hours (Shaw et al., 2014). Post-transcriptional regulation by miRNAs could cause both of these observations by increasing degradation of mRNA targets, leading to a lack of protein expression, and facilitating rapid clearance of mRNAs involved in the early response to osmotic stress. Given the many established roles of miRNAs in repressing expression of target genes during complex physiological processes (Harfe et al., 2005; King and Yin, 2016), we hypothesized that miRNAs are important mediators of gill remodeling in killifish, and may be affected by stressors that disrupt this process.

We recently reported that arsenic exposure dampens both the early and long-term gene expression responses to salinity (Hampton et al., 2018). This interesting finding supports previous studies demonstrating that arsenic inhibits salinity acclimation in killifish as well as responses to stress in other organisms ( davey et al., 2008, 2007; Nayak et al., 2007; Shaw et al., 2010). Arsenic exposure is a major public health concern in human populations, and has been associated with adverse effects on development, reproduction, cardiac, metabolic, immune and respiratory systems as well as multiple types of cancer (Reviewed in (Naujokas et al., 2013)). Studies in laboratory organisms have additionally found that low concentrations of arsenic induce synergistic toxicity when combined with other environmental stressors, such as bacterial and viral infection (Lage et al., 2006; Nayak et al., 2007; Shaw et al., 2010). In killifish acclimated to fresh water, exposure to 12 ppm arsenic was not acutely toxic, but significantly decreased expression of CFTR protein in the gill and the ability to respond to osmotic stress, resulting in increased mortality upon transfer to salt water (Shaw et al., 2007). Further investigation determined that CFTR translocation to the opercular membrane was mediated by SGK1, and that arsenic inhibited SGK1 induction (Notch et al., 2012; Shaw et al., 2010). The mechanism by which arsenic inhibits SGK1 induction remains unknown. However, one mechanism by which arsenic could inhibit SGK1 and the induction of salinity response genes in general is by altering expression of miRNAs, each of which can target hundreds of mRNAs. In humans, arsenic exposure is associated with altered miRNA expression profiles (Marsit et al., 2006; Rager et al., 2013), and arsenic alters expression of microRNAs that target oxidative stress response genes in rats (Ren et al., 2015). Thus, we hypothesized that arsenic may disrupt gill remodeling gene networks and decrease immediate and long-term responses to salinity in killifish by altering expression of miRNAs that regulate these networks.

Vertebrate genomes contain hundreds of miRNAs, in addition to other short RNAs such as transfer RNAs (Fromm et al., 2015). MiRNAs are characterized by expression of two mature 20–26 nucleotide-long sequences, generated from a hairpin RNA precursor, with at least 16 nt complementarity between the 3p and 5p arms (Fromm et al., 2015). Mature products are incorporated into the RNA induced silencing complex where the seed region (nucleotides 2–8) of the mature miRNA directs the complex to target mRNA by sequence complementary (Lewis et al., 2005). Each microRNA is predicted to target hundreds to thousands of protein-coding genes, resulting in an estimated 30% of vertebrate protein coding genes under regulation by miRNAs (Lewis et al., 2005; Lim et al., 2005). While miRNAs have only a weak impact on the expression of most of their targets, many genes are targeted by multiple miRNAs, which can have a cumulative effect (Schmiedel et al., 2015). Several miRNAs mediate osmoregulatory processes and responses to osmotic, hypoxic and oxidative stress in teleosts and other aquatic species (Bizuayehu and Babiak, 2014; Huang et al., 2015; Yan et al., 2012a, 2012b; Zhao et al., 2016). MiR-30c responds to osmotic stress in tilapia kidney by targeting HSP70, while the miR-8 family regulates ion transporter trafficking in the zebrafish osmotic stress response by targeting Nherf1 (Flynt et al., 2009; Yan et al., 2012a). Profiling miRNAs expressed in killifish gill paves the way for investigating roles of specific miRNAs in response to environmental stress in this species.

We present here the first characterization of miRNAs expressed in Atlantic killifish gill tissue, a valuable model for gene-environment interaction studies. MiRNAs in killifish were highly conserved with other teleosts, but included 16 novel miRNAs that have not been identified in other species. In addition to the 16 novel miRNAs, we also identified 27 paralogs of previously annotated miRNAs not yet identified in the genome of another teleost, the zebrafish. We found that miRNA expression in the gill was largely stable during salinity acclimation, while a small group of miRNAs were differentially expressed in response to environmental stressors. Both hyperosmotic stress and a non-toxic level of arsenic induced differential expression of miR-135b, which was predicted to target mRNAs involved in the immediate ion regulatory response to salinity change and subsequent gill remodeling. Together, these data identify miRNAs in killifish gills and provide evidence that they regulate responses to arsenic and hyperosmotic stress.

2. Materials and Methods

2.1. Animals

Studies were performed in compliance with institutional animal care and use guidelines approved by MDI Biological Laboratory and Geisel School of Medicine at Dartmouth. Killifish, Fundulus heteroclitus, were collected from Northeast Creek, Bar Harbor, ME and acclimated at the MDI Biological Laboratory in aquaria with running sea water (27 ppt) for at least 2 weeks. Following initial acclimation and prior to all experiments, they were transitioned to “soft” fresh water (0.3 ppt) using a protocol for gradual acclimation described previously (Shaw 2007). Briefly, fish were transitioned to 10% seawater for 2 weeks, followed by very “soft” fresh water (48 mg/L NaHCO3, 30 mg/L MgSO₄, 2mg/L KCl, pH 7.5–8) for an additional 2 weeks (American Society for Testing and Materials, 1985). All studies were conducted with adult male killifish (2–6 grams).

2.2. Arsenic and salinity exposures

Killifish were exposed to 100 μg/L total arsenic, as sodium (meta)arsenite (Sigma-Aldrich, St. Louis, MO) or control in “soft” fresh water (described above, to control for potential confounding effects of water hardness) for 48 hours, and then collected (0 hour time point) or transferred to sea water containing the same levels of arsenic for 1 or 24 hours. This concentration and duration of arsenic exposure was previously shown to induce gene expression changes but no overt toxicity in adult killifish (Shaw et al., 2014, 2007). Fish were then euthanized and gills dissected for RNA isolation. To increase biological representation of the population in the study, gills from three individual fish were pooled per RNA sample, for a total of four RNA samples (n = 4) per treatment group.

2.3. RNA isolation and Small RNA-seq

RNA was isolated from pooled gill tissue using TRIzol reagent (Invitrogen) and quantified with a Nanodrop spectrophotometer. RNA Integrity was examined on an Agilent 2100 Bioanalyzer, all samples had an RNA Integrity Number > 8. Barcoded NEXTflex Illumina Small RNA Seq libraries (New England BioLabs, Ipswich, ) were prepared, pooled and sequenced on anIlluminaHiSeq2000(Illumina,SanDiego,CA)usingasingleflowcelllaneatDelawareBiotechnnologyInstitute(Newark,DE)followingmanufacturer’sprotocols.

2.4. Alignment and microRNA identification

Following sequencing, 3p-adapters (5p-TGGAATTCTCGGGTGCCAAGG-3p) were clipped from the 50bp small RNA sequencing reads for each sample and then filtered by length to obtain sequences that were between 20–26 nucleotides using the FASTX Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/). iRNA were annotated using the miRMiner software (Tarver et al., 2013; Wheeler et al., 2009) and the killifish genome assembly (Reid et al., 2017). Using miRMiner, the clipped and length-filtered reads from all samples were collapsed into unique sequence tags. Sequence tags were aligned to miRBase stem-loop precursors in miRBase (version 21) (Kozomara and Griffiths-Jones, 2014) using NCBI BLAST (Altschul et al., 1990) to identify tags with high similarity to previously annotated miRNAs. Next, sequence tags were aligned to the genome assembly using BLAT (Kent, 2002). Alignment coordinates were analyzed to generate multiple sequence alignments of the mature miRNA products along genomic segments predicted to form stable stem-loop secondary structures by RNAFold from within the ViennaRNA ackage version 1.7 (Lorenz et al., 2011). These multiple sequence alignments were examined to annotate miRNA where at least one sequence tag mapped to both the mature and star mature arms consistent with miRNA in other animals (Fromm et al, 2016). Each annotated miRNA was assigned a stem-loop precursor sequence, mature and star sequences according to the evidence in the multiple sequence alignments. miRNA homologs were identified in zebrafish (Danio rerio) miRNA families compared with eight other vertebrates in miRGeneDB (http://mirgenedb.org/) (Fromm et al., 2015). Following miRNA annotation, the expression level of miRNAs, expressed as read counts, in each sample was calculated by aligning clipped and length filtered sequences to the annotated stem-loop precursor sequences using CLCBio Genomics Workbench version 9.0 (QIAGEN Bioinformatics, Aarhus, Denmark). Sequence data, annotated miRNA sequences and miRNA read counts per sample are accessible in the NCBI Gene Expression Omnibus record, GSE118062.

2.5. microRNA expression analysis

Expression data (counts) of identified miRNAs were analyzed in R using the edgeR package version 3.22.3 (Robinson et al., 2010). The mean number of counts obtained per sample was 1.50 0.57 million. One sample was excluded from further analysis because of low sample quality and read counts. We removed from further analysis miRNAs that were not reliably detectable at this sequencing depth by filtering for at least 2 counts per million (cpm) in all samples of at least 2 treatment groups. Library normalization was performed in edgeR using the trimmed mean of M-values (TMM), and the Cox-Reid (CR) method was used to estimate tagwise dispersions for negative binomial generalized linear models (GLM). The quasi-likelihood F-test (glmQLF est) was used to test for differential expression of miRNAs in response to the main effects of arsenic and time in salt water, as well as the interaction. Power analysis on the small RNA-seq dataset was conducted with the ssizeRNA package (version 1.2.9) (Bi and Liu, 2016).

2.6. cDNA synthesis and QPCR

cDNA for microRNA specific QPCR was synthesized from total RNA using Exiqon Universal cDNA Synthesis kits (Exiqon, Vedbaek Denmark). QPCR reactions were performed with Exiqon ExiLENT SYBR® Green Master mix and microRNA-specific Exiqon probes (Table 1) according to manufacturer instructions on an Agilent StepOne thermocycler. Data were normalized to U6 snRNA and fold change calculated using the ddCt method. No significant differences in U6 snRNA Ct values were observed between treatment groups.

Table 1.

Mature miR-135b-1 and miR-10a sequences used for QPCR validation (probe sets purchased from Exiqon, Vedbaek Denmark)

miRNA	Sequence
Fhe-miR-135b-1	TATGGCTTTTTATTCCTATCTGA
Fhe-miR-10a	CACCCTGTAGATCCGAATTTGT

2.7. Target prediction and ontology analysis

Transcript 3’ UTR sequences were obtained for all annotated genes in the Fundulus heteroclitus genome (Reid et al., 2017) and the miRanda miRNA targeting algorithm was used to predict targets of miR-135b (Enright et al., 2003). A minimum score of 145 and maximum binding energy −15 kcal/mol were used as criteria for target identification. Uniprot IDs of human and zebrafish orthologs of predicted target genes were obtained from the Fundulus heteroclitus genome and filtered to include only genes expressed in gill (Reid et al., 2017). The complete set of annotated transcripts expressed in gill was used as the background gene set for all enrichment analyses. Uniprot IDs were used directly for Gene Ontology and KEGG pathway analysis with GOrilla and DAVID software, and were mapped to Entrez gene IDs with the org.Hs.eg.db (version 3.4.1) package for gene ontology analysis with the goana and kegga functions (limma package version 3.32.10) in R (Ritchie et al., 2015). Enrichment analyses were compared across the three platforms; GO terms and KEGG pathways were filtered to exclude those that represented fewer than 5 genes and were considered significantly enriched at an FDR-corrected P value < 0.05.

2.8. Expression analysis of microRNA targets

To refine predicted biological effects of microRNA targeting, we examined the expression of predicted miRNA targets in response to arsenic, 0 and 24h salt water, and the combined exposure using transcriptional data from a previous study in killifish (Shaw et al., 2014). We identified significantly differentially expressed transcripts in this whole genome microarray dataset using the limma package (version 3.32.10) in R (Ritchie et al., 2015). Models were fit to identify main effects of salt water at 1 and 24 hours, arsenic exposure, and interaction effects. Effects with P < 0.05 were considered significant. A one-tailed Fisher’s exact test was used to test for enrichment of significant differential expression among predicted targets of miR-135b. We additionally tested the hypothesis that miR-135b targets would be enriched for decreased expression at 1 and 24h. Enrichment of biological processes of differentially expressed miRNA targets were identified using goana (limma package) in R with all annotated gill transcripts on the microarray as the background gene set.

3. Results

3.1. Identification of microRNAs in Fundulus heteroclitus gill

We identified 270 miRNAs expressed in killifish gill tissue (Table S1). Of these, 254 have homologs in zebrafish and belong to 92 known miRNA families, while 16 were novel (Table S2). For each novel miRNA, we required mature reads to align to each stem loop arm and folded stem loop structure with a 2 nucleotide offset (Figure S1) (Tarver et al, 2013). Among the 254 miRNAs that belonged to known miRNA families, there were 27 paralogs of mi NAs that have not yet been identified in other teleosts in miRGeneDB (Table S3). When compared with eight other vertebrate taxa in miRGeneDB, miRNA families identified in killifish shared the most similarity with zebrafish, as expected (Figure 1A). Zebrafish have miRNAs from 12 additional families, as well as 8 novel miRNAs, for which we did not identify killifish homologs in this study (Figure 1B).

Figure 1.

miRNAs in Fundulus heteroclitus gill A) miRNA families identified in Fundulus heteroclitus and eight other vertebrates in miRGeneDB were scored for presence/absence across the nine taxa, and the most parsimonious neighbor-joining tree was identified using parsimony analysis in (ape and phanghorn packages) with the Florida lacelet (B. floridae) as an outgroup. Taxa are labeled with number of miRNA families identified in this study (F. heteroclitus) or present in miRGeneDB. B) Comparison of miRNA families in killifish ( F. heteroclitus) and zebrafish (D. rerio) displaying miRNAs unique to each species.

A comparison of miRNA expression across all samples in the dataset identified miR-146a as most highly expressed (highest median expression), followed by miR-203a-2, miR-203a-1, miR-30e-2, miR-30e-1, miR-21–1, miR-21–2, miR-22a-1 and miR-22a-2. Together these highly expressed miRNAs accounted for over 50% of total miRNA counts in the gill. MiR-10012 was the most highly expressed novel miRNA, followed by miR-10016 and miR-10003. We examined expression patterns across all samples in the dataset using Pearson distance, and found that some miRNA families, such as the LET-7 and IR-17 families, exhibited highly similar expression patterns (Figure 2). In contrast, members of the IR-130 family, as well as the novel killifish miRNAs, had more divergent expression patterns, suggesting they are not co-regulated.

Figure 2.

miRNA expression patterns among miRNA families. Correlation of miRNA expression patterns was examined using Pearson distance, which was calculated using log2 expression values across all samples in the experiment. Distance is shown in the dendrogram which was created using single linkage clustering to identify clusters of miRNAs with similar expression patterns. miRNAs families with at least 4 members are represented by individual colors, all families with < 4 members are in gray. Many miRNA families, such as the miR-7 family (maroon) exhibited highly correlated expression, while others did not (miR-130 family, pink).

3.2. Expression of miRNAs in killifish gill during salinity acclimation and arsenic exposure

We analyzed 226 miRNAs that met minimal expression criteria (2 counts per million) for differential expression analysis in response to salinity, arsenic, and the combined exposure. 44 miRNAs were detected in the gill tissue but not abundant in enough samples for differential expression analysis. As expected in animals collected from a wild population, expression of each miRNA across biological replicate samples was variable (Oleksiak et al., 2002). However, the overall pattern of miRNA expression was highly correlated between samples with a mean Pearson correlation of normalized miRNA expression between all sample pairs of 0.98 ± 0.01. We tested for significant effects of arsenic, salinity at 1 and 24h, and interactions using generalized linear models and the quasi-likelihood F-Test in edgeR. No miRNA expression changes were identified that met significance criteria of an FDR-corrected P Value < 0.05. Power analysis of the data predicted that a 4-fold change in expression could be detected with an FDR-corrected Value < 0.05 (power = 0.8). While no significant miRNA expression changes of this magnitude were observed, we identified individual and combined effects of arsenic and salinity on miRNA expression that met MA/QC criteria of log2 fold change > 1 and a nominal P-value < 0.05 (Shi et al., 2006). These targets have the potential to be biologically significant but required further validation. No changes in miRNA expression at 1 h post-transfer to salt water met these expression criteria (Figure 3A). At 24 h, three miRNAs were significantly differentially expressed (Figure 3B); miR-10a and miR-200a decreased (−2.2 and −1.5 log2 fold change, respectively), while miR-135b-1 increased (2 log2FC).

Figure 3.

Effect of salt water transition and arsenic on miRNA expression.Statistical significance (displayed as –log10 Values) versus log2 expression change in response to (A) 1 h salt water, (B) 24 h salt water, (C) arsenic, and (D) interaction effect in the combined 24 h salt water and arsenic exposure. Individual points represent all miRNAs detected in the experiment; miRNAs that met differential expression criteria (fold change >2 or < −2, P < 0.05) are highlighted in blue. E) Venn diagram of unique and overlapping miRNAs for which a significant response to arsenic, salt water at 24h, or arsenic and 24h salt water interaction was detected (P < .05, log2FC >1). F) Mean expression of the six differentially expressed miRNAs is displayed across treatment groups. Two miRNAs, miR-135b-1 and miR-10a, exhibited a significant response to arsenic, 24 h salt water, and significant interaction in the combined exposure (black boxes). Expression data in the heatmap is normalized to the mean for each miRNA. Overall miRNA abundance (mean of all samples) is represented in the dot plot.

We next examined miRNAs that were differentially expressed in gills of killifish exposed to 100 μg/L total arsenic for 48 hours. One miRNA, miR-135b-1, increased in expression, while miR-122, miR-10a, and miR-190a-2 decreased in response to arsenic (Figure 3C). We additionally tested for an interaction between the main effects of arsenic and salinity to determine whether the individual effects of arsenic and salinity behaved in an additive manner in the combined exposure. We found a significant interaction effect between arsenic and 24 h salt water for 7 miRNAs, signifying that the effects of arsenic and salinity were not additive in the combined exposure (Figure 3D). We compared all miRNAs differentially expressed in response to individual main effects (salt water and arsenic) and interactions, and identified two miRNAs (miR-10a and miR-135b-1) that were differentially expressed under both conditions alone and had a significant interaction (Figure 3E). In control fish (no arsenic), miR-135b-1 increased significantly in response to 24h salt water. Arsenic exposure alone in fresh water also induced a significant increase in miR-135b-1. In the combined exposure (arsenic-exposed fish transferred to salt water), miR-135b decreased in response to 24h salt water (Figure 3F). The opposite pattern was observed for miR-10a, which decreased in response to arsenic and 24h salt water alone, but increased in response to the combined exposure (Figure 3F). We focused additional analysis on these two miRNAs because they were altered by both the main effects of arsenic and salinity and displayed a significant and intriguing interaction.

3.3. Arsenic induces miR-135b and reduces response to hyperosmotic stress

We used quantitative PCR (QPCR) to validate expression patterns of miR-10a and miR-135b-1 in response to individual and combined salinity and arsenic exposures. In the RNA-seq dataset, miR-135b-1 increased at 24 h post salt water transition in fish that were not exposed to arsenic (Figure 4). Arsenic exposure alone increased miR-135b-1 expression, and miR-135b-1 decreased during salinity acclimation in arsenic-exposed fish (Figure 4A, interaction effect). Analysis of mature miR-135b expression with QPCR confirmed the significant effect of salinity and interaction with arsenic (Figure 4B). In contrast, miR-10a decreased during salinity acclimation in the RNA-seq dataset, but this effect did not occur when arsenic was present (Figure 4C). Though similar trends were observed in the QPCR validation (Figure 4D), these effects were not significant and, therefore, miR-10a was not included in target prediction analysis.

Figure 4.

QPRvalidationofmiR-135 band miR-10a expression changes Expression changes detected by RNA-seq (in log2 counts per million) are displayed next to expression measured by QPCR (log2 fold changed compared to mean of 0 hr controls) for miR-135b (A and B) and miR-10a (C and D). P values for RNA-seq data are derived from analysis in EdgeR, QPCR data were analyzed using linear models in R.

3.4. MiR-135b is predicted to target cell morphogenesis and signaling genes in killifish

We used miRanda to predict mRNA targets of miR-135b in killifish, using 3’ UTR sequences from the recently published killifish genome (Reid et al., 2017). We filtered predicted targets for mRNAs that were expressed in gill and conducted Gene Ontology (GO) term and KEGG pathway analysis to identify potential biological effects of changes in expression of this miRNA. We conducted GO and KEGG analysis using DAVID functional annotation clustering and GOrilla web-based software as well as goana and kegga in R and obtained consistent results across the three platforms. Predicted targets of miR-135b in killifish gill were significantly associated with signal transduction and regulation of cell morphogenesis/cell projection organization. Signal transducer activity and voltage-gated channel activity were significant molecular functions, and plasma membrane cellular components were significantly enriched. The Phospholipase D signaling pathway, Axon guidance, Pancreatic cancer and Endocytosis were the top KEGG pathways associated with miR-135b targets. Because miRNAs regulate targets by decreasing mRNA stability or inhibiting translation, the increase in miR-135b during salinity acclimation is predicted to inhibit these biological processes and pathways.

3.5. miR-135b targets are differentially expressed between 1 h and 24 h

To refine our understanding of the biological effects of miR-135b expression changes, we examined expression of predicted mRNA targets during salinity acclimation and arsenic exposure. 1,555 (60%) of the predicted miR-135btargetsweidentifiedinthekillifishgenome were represented on microarrays in a previous study of arsenic effect on salt water acclimation in Atlantic killifish (Shaw et al., 2014). We tested whether these genes were significantly more likely to be differentially expressed during salinity acclimation and arsenic exposure than transcripts on the array that were not predicted targets of miR-135b. If miRNAs drive mRNA expression changes by targeting them for degradation, we would expect the increase in miR-135b that occurred during salt water acclimation (Figure 4) to result in significant down-regulation of its mRNA targets. At 1h, when miR-135b expression was not yet significantly increased, miR-135b targets were equally up and down-regulated, with no significant trend in direction of change (Figure 5A). Surprisingly, however, we found that more miR-135b predicted targets were significantly differentially expressed at 1h than would be expected by chance (Figure 5A). The 105 significant differentially expressed targets were approximately equally up and down-regulated, and were enriched for ion transmembrane transporter activity (Table 2). They included homologs of potassium voltage-gated channels (KCNB2 and KCNJ14), a Na⁺/H⁺ antiporter (SLC9A1), a monocarboxylate transporter (SLC16A4), and multiple potential glycerophospholipid acyltranferases (TMEM68 homologs) (Table S1).

Figure 5.

Differential expression of predicted miR-135b targets. A) Direction of expression change (increase or decrease, regardless of statistical significance), and significant differential expression (regardless of direction of change) of predicted targets of miR-135b was compared with non-targets, and odds ratios and significant enrichment of increased expression calculated with Fisher’s exact test, * P < .05, **P < .01, ***P < .001. Predicted targets of miR-135b were significantly less likely to increase in expression at 24 h than expected by chance and were significantly more likely to be differentially expressed at 1h (compared to 0h control). miR-135b targets were significantly more likely to have a significant interaction effect between arsenic and salt water at 24h than non-targets. B) Conceptual model in which miR-135b targets a co-regulated 1h response gene, causing its return to baseline at 24h. C) Conceptual model in which miR-135b mediates decreased expression of a target mRNA at 24h.

Table 2.

Gene ontology enrichment of predicted targets of miR-135b that were significantly differentially expressed in response to arsenic and salinity. Significant GO terms were identified using the limma package in R, and were filtered for terms that included at least 5 genes from the dataset. Ontology terms are categorized by biological process (BP), cellular component (CC) and molecular function (MF).

GO ID	Term	Ontology	Genes	P value
miR-135b targets with significant arsenic effect
GO:0007017	microtubule-based process	BP	8	8.1E-05
GO:0006928	movement of cell or subcellular component	BP	11	3.1E-04
GO:0007010	cytoskeleton organization	BP	9	4.8E-04
GO:0048870	cell motility	BP	9	5.8E-04
GO:0005856	Cytoskeleton	CC	12	2.2E-04
miR-135b targets with significant SW 1h effect
GO:0031226	intrinsic component of plasma membrane	CC	11	2.80E-05
GO:0015075	ion transmembrane transporter activity	MF	7	0.00095
GO:0022892	substrate-specific transporter activity	MF	8	0.00142
miR-135b targets with significant SW 24h effect
GO:0019725	cellular homeostasis	BP	6	2.9E-03
GO:0098660	inorganic ion transmembrane transport	BP	5	4.5E-03
GO:0007165	signal transduction	BP	16	7.2E-03
GO:0005886	plasma membrane	CC	15	1.6E-03
GO:0004872	receptor activity	MF	6	3.4E-03

At 24 h, when miR-135b expression was significantly increased, predicted targets were more likely to be decreased compared to control than expected by chance (Figure 5A), but they were not enriched in significant differential expression (Figure 5 ). Among the 60 predicted miR-135b targets that were significantly differentially expressed, 58% decreased. This gene set was enriched in cellular communication, signaling, cellular homeostasis, cadherin binding and molecular transducer activity (Table 1) and included the mineralocorticoid receptor (NR3C2) (Table S1). Together, our observations of significant differential expression of miR-135b targets at 1h, a significant increase in miR-135b at 24h, and significant repression of targets at 24h, are consistent with a model where miR-135b is induced in a feedback loop to dampen the immediate hyperosmotic shock response and facilitate the transition to gill remodeling.

Arsenic alone also slightly increased miR-135b expression, which was predicted to result in decreased expression of miR-135b targets. We found that miR-135b targets were slightly more likely to be decreased and differentially expressed in response to arsenic exposure, but the effects did not reach significance. The 54 predicted target transcripts significantly affected by arsenic were enriched for microtubule-based processes, movement of cell, and cytoskeleton organization (Table S1). Because a strong interaction effect between arsenic and salinity acclimation was observed for miR-135b, we tested whether this also occurred in predicted targets, and found that they were more likely to have a significant interaction at both 1 h and 24 h than would be expected by chance (Figure 5A). These targets included multiple potassium voltage-gated channel transcripts (KCNJ14 homologs), a synaptosome-associated protein involved in membrane trafficking (SNAP29), an epithelial apoptosis and differentiation gene (JADE1), member of the endosomal sorting complex (CHMP2B), a mitochondrial ribosomal protein (MRPL14), an ATPase (TOR1A), a FK506 binding protein (FKBP7), and a GTPase activating protein (RANGAP1) (Table S1). The 49 predicted targets with a significant interaction at 1 h were enriched for ion transmembrane transporter activity, while the 40 targets with significant interaction at 24 h were enriched for regulation of cellular component organization, cytoskeleton and chromosome organization, and locomotion (Table 2).

4. Discussion

In the first whole-genome analysis of miRNAs expressed in in Atlantic killifish gill, we identified 270 miRNAs. The miRNAs were identified by sequence homology from features of the killifish genome that would allow for formation of a stem-loop structure, and do not include other types of short RNAs. The number of miRNAs identified in this study is smaller than the 385 zebrafish miRNAs currently listed in the MirGeneDB database of curated miRNAs (mirgenedb.org) (Fromm et al., 2015). However, expression of miRNAs is tissue-specific (Lagos-Quintana et al., 2002; Wienholds et al., 2005), and the zebrafish mi As have been curated from 11 tissues, whereas samples in this study were exclusive to killifish gill. A recent study reported 224 miRNAs in Atlantic salmon (Salmo salar) blood, while the miRbase database lists 371 Atlantic salmon miRNAs, representing at least 11 tissues (Kozomara and Griffiths-Jones, 2014; Kure et al., 2013). More miRNAs have been identified in Atlantic cod (Gadus morhua), which has 401 miRNAs from at least nine tissues in miRBase (Andreassen et al., 2016; Kozomara and Griffiths-Jones, 2014). The number of miRNAs identified in these species is influenced by multiple factors, including the diversity of tissues examined, depth of sequencing, and the stringency of criteria used to define bona-fide miR As. The number of miRNAs we identified in this study of killifish gill is within the range previously observed for a single tissue of a teleost species. 16 miRNAs we identified in killifish gill were novel and not represented in miRBase (version 21) for any organism. Some of the novel miRNAs were well-expressed, but they did not change significantly in response to the individual or combined stressors of salinity and arsenic exposure.

While they are not generally required for survival of model organisms under normal laboratory conditions, miRNAs are thought to be critical mediators of stress responses (Mendell and Olson, 2012). Essential roles of miRNAs have been identified in temperature acclimation, osmotic stress, salinity changes and cardiac overload in multiple organisms (Leung and Sharp, 2010). In zebrafish embryos, miR-8 family miRNAs are expressed in ionocytes and involved in osmoregulation by targeting Nherf1 (Flynt et al., 2009). miRNAs similarly regulate responses to osmotic stress in other fish species as well as mammals. We therefore hypothesized that miRNAs would be differentially expressed during salinity acclimation in Atlantic killifish. We found that miRNA expression was highly variable between individual samples in our study, consistent with the large variation in mRNA expression between individuals that has been observed in Atlantic killifish populations (Oleksiak et al., 2002). Given the variation among samples, we estimated that this dataset was powered to detect a 4-fold change in expression. In the Shaw et al. dataset, over 80 transcripts with at least 4-fold change were significantly differentially expressed at 1h, and ranged up to an 11 fold change in expression (Shaw et al., 2014). In our investigation of miRN s using the same experimental paradigm, there were no changes in miRNA expression of this magnitude at the 1h timepoint (Figure 3A). The lack of significant changes in miRNA expression at 1h suggests that miRNAs are not critical to the early stress response, but rather play a role in secondary remodeling, and perhaps help mediate the transition between these phases. It is important to note, however, that our study does not exclude the possibility that stably expressed miRNAs have critical biological functions in the response to hyperosmotic stress. Abundant miRNAs in the gill, such as miR-146a, could constitutively regulate stress-responsive mRNAs and prevent excessive tissue damage. However, we focused further investigation of predicted targets on miR-135b because of the interesting interaction effect that arsenic and salinity exposures elicited on expression of this miRNA.

MiR-135b, which increased during salinity acclimation, is upregulated in a variety of cancers and exerts pro-angiogenic activity in human cells by targeting NR3C2 (the mineralocorticoid receptor) and FIH, a regulator of HIF-1α (Yang et al., 2016; Zhang et al., 2013, 2017). We predicted targets of miR-135b in killifish by using miRanda to identify potential binding sites within the 3’ UTR regions of all genes in the reference genome (Reid et al., 2017), and examined gene set enrichment and differential expression of predicted targets in response to the individual and combined stressors of arsenic and salinity acclimation. This analysis assumed that miR-135b exerts significant biological effects by binding to 3’ UTRs of target genes, resulting in transcriptional degradation. While miR-135b could have additional activity by binding within coding regions of genes or blocking translation, we hypothesized that enrichment of biological effects would be detectable within the set of targets that were predicted based on the primary mechanism of miRNA targeting that has been defined in vertebrates (Hafner et al., 2010; Jonas and Izaurralde, 2015).

The set of predicted miR-135b targets in killifish was enriched in transmembrane transport, signal transduction and cell morphogenesis biological functions, and was significantly enriched in differential expression at 1 h. Previous studies of whole genome expression changes during salinity acclimation in killifish have identified enrichment of transmembrane transport among genes differentially expressed during the early response to osmotic stress (Shaw et al., 2014; Whitehead et al., 2012). Based on the observed increase in miR-135b at 24 h (Figure 4) and the expression of its predicted targets (Figure 5A) we propose a model in which miR-135b functions in a feedback loop to repress or “turn off” gene that increased in the immediate hyperosmotic response (Figure 5B). miR −135b also was predicted to target genes that decreased significantly at 1h, in which case it may reinforce or maintain repression. This is consistent with our observation that at 24h, predicted miR-135b targets were significantly more likely to be decreased in comparison to 0h control than expected by chance. One predicted target that decreased significantly was the mineralocorticoid receptor (NR3C2), which is a negative regulator of cell morphogenesis. By targeting this and other negative regulators such as FIH, increased miR-135b at 24 h may facilitate the initiation of angiogenesis and other processes that are essential for the second phase of remodeling gill tissue to secrete excess chloride. This proposed model of miR-135b repressing repressors of remodeling is represented in Figure 5C.

Arsenic exposure also increased expression of miR-135b expression. While we hypothesized that arsenicinduced decreases in Sgk1 and Cftr, genes critical for salinity acclimation, would be mediated by miRNAs, neither they nor transcriptional regulator Mapk14 were predicted targets of miR-135b (Notch et al., 2012; Shaw et al., 2010). Though miRNAs could still be involved indirectly or via targeting mechanisms not included in our prediction, more research is needed to identify a mechanism by which arsenic inhibits Sgk1 induction (Notch et al., 2012). The set of miR-135b targets that were significantly affected by arsenic was enriched in microtubule-based processes and cell movement, suggesting that miR-135b is involved in regulation of an arsenic-induced stress response. However, miR-135b targets were not enriched for significant differential expression in response to arsenic alone. This is not surprising, based on our previous studies which have shown that this low concentration of arsenic exerts a minor effect on gene expression alone, but has a substantial interaction with salinity (Hampton et al., 2018; Shaw et al., 2014).

When arsenic-exposed fish were challenged with increased salinity, miR-135b expression changed in the opposite direction of fish unexposed to arsenic, indicative of a significant interaction between the two conditions. Interaction genes have been proposed to enable the phenotypic plasticity that is essential for osmotic tolerance and is disrupted by arsenic exposure (Hampton et al., 2018; Shaw et al., 2010, 2014; Stanton et al., 2006). The responses of many miR-135b targets, including several potassium voltage-gated channel homologs, to 24h salt water were similarly inhibited under the combined exposure condition with arsenic. These individual targets may be important mediators of hyperosmotic response in killifish and help explain how arsenic reduces osmotic tolerance. Furthermore, we hypothesized that if miR-135b were to exert a meaningful biological effect in this system, enrichment of differential expression would be expected among its targets. We tested this hypothesis, and found that predicted targets of miR-135b were significantly more likely to have an interaction effect between arsenic and salinity than expected by chance.

The enrichment of biological functions important for salinity acclimation among the predicted targets of miR-135b points to a regulatory role in gill remodeling and response to hyperosmotic stress. Further studies will be required to validate direct targeting relationships, and to determine how miR-135b fits into the complex regulatory networks that coordinate immediate and long-term responses to hyperosmotic stress. For example, the increase at 24 h may facilitate angiogenesis by repressing NR3C2, but could also be important for turning off genes involved in the early (1h) stress response. In human macrophages, for example, miR-146a is induced by NFkB and functions in a negative feedback loop by targeting molecules involved in NFkB activation (Taganov et al., 2006). By identifying significant changes in mRNA expression of predicted targets, we refined predicted targeting relationships to those with evidence of biological effects on gene signaling networks. We believe the predicted relationships identified here provide the most promising directions for further investigation of the role of miR-135b during salinity acclimation. Other miRNAs we identified may have more subtle roles during salinity acclimation, and the novel miRNAs identified to be unique to killifish will be interesting targets of future studies to elucidate biological functions.

5. Conclusions

MicroRNAs expressed in killifish gill include 254 highly conserved genes as well as 16 miRNA genes novel to killifish. 27 paralogs of known miRNAs that have not yet been identified in other teleosts in miRGeneDB were identified in the killifish genome. Osmotic stress did not disrupt expression of the most highly expressed miRNAs in gill but did induce a significant increase in miR-135b. Arsenic, which inhibits osmotic tolerance, disrupted expression of miR-135b and its response to salinity in a similar manner as has been observed with mRNA expression. Accordingly, this study provides new insight into the gene regulatory networks that allows this euryhaline fish to acclimate to new environments, and how arsenic exposure can disrupt miRNA regulation. By characterizing miRNAs in Atlantic killifish, we identified miRNAs conserved within other species, novel miRNAs, and predicted mRNA targeting relationships during salinity acclimation that will be interesting for future investigations.

Highlights.

• RNA-seq analysis identified 270 microRN s expressed in Atlantic killifish gill

• miR-135b was induced by salinity and predicted to target osmoregulatory genes

• Arsenic exposure disrupted miR-135b expression alone and in response to salinity