ORIGINAL RESEARCH article

Front. Mar. Sci., 21 January 2022

Sec. Aquatic Physiology

Volume 8 - 2021 | https://doi.org/10.3389/fmars.2021.736362

miRNA–mRNA Integrative Analysis Reveals the Roles of miRNAs in Hypoxia-Altered Embryonic Development- and Sex Determination-Related Genes of Medaka Fish

  • 1. Laboratory of Environmental Pollution and Integrative Omics, Guilin Medical University, Guilin, China

  • 2. Department of Chemistry, City University of Hong Kong, Kowloon, Hong Kong SAR, China

  • 3. State Key Laboratory of Marine Pollution, City University of Hong Kong, Kowloon, Hong Kong SAR, China

  • 4. State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China

  • 5. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States

  • 6. Center for Promotion of International Education and Research, Faculty of Agriculture, Kyushu University, Fukuoka, Japan

  • 7. Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, China

Abstract

Recent studies have shown hypoxia to be an endocrine disruptor that impairs sex differentiation and reproductive function, leading to male-biased F1 populations in fish. However, the molecular mechanisms through which hypoxia alters fish sex differentiation and therefore sex ratios remain poorly understood. In order to understand the potential role of miRNAs in mediating hypoxia-altered sex determination and differentiation in fish, we conducted small RNA sequencing and transcriptome sequencing on marine medaka (Oryzias melastigma) embryos that were exposed to hypoxia (2.0 ± 0.2 mg O2 L1) for 40 h (encompassing a critical window of sex determination). We identified dysregulated miRNAs and mRNAs in the hypoxia-exposed embryo, and bioinformatic analysis of the integrative small RNA sequencing and transcriptome sequencing results revealed hypoxia to cause alterations of genes related to embryonic development through miRNA regulation. Importantly, we have identified miRNA-mRNA pairs that were reported to play roles in gonad development (novel miR-145-col9a3 and novel miRNA-94- arid5b), in sex hormone response (novel miRNA-210-ca2, novel miRNA-106-nr2f2, nbr-miR-29c-nr4a1, and ola-miR-92b-akr1d1), and in sex characteristic development (novel miRNA-145-mns1, nle-miR-20-sord, and ipu-miR-219b-abcc8). Our findings highlighted the possible roles of miRNA–mRNA in regulation of embryonic development and sex determination in response to hypoxic stress.

Introduction

Hypoxia is a widespread and pressing environmental concern in aquatic habitats, causing severe habitat damage and major disruption to aquatic ecosystems worldwide. The occurrence of hypoxia in water bodies has increased globally over the past 30 years due to escalating eutrophication and organic pollution. Presently, over 500 hypoxic areas (<2 mg O2 L–1) spanning hundreds of thousands of square kilometers have been reported worldwide (Thrash et al., 2017), and is likely to worsen in the future, as a result of global warming and rapid coastal development, thereby threatening the sustainability of natural populations (Pörtner and Peck, 2010). Compared with mammals, fishes possess an exceptional range of sex determination and differentiation processes, which are modulated by a variety of environmental factors such as population density, social behaviors, pH, and dissolved oxygen (; Reddon and Hurd, 2013; Yamamoto et al., 2014).

Previous studies from our group have shown that hypoxia is an endocrine disruptor that impairs reproductive activities and affects sexual differentiation in fish, leading to male-biased F1 generations (Shang et al., 2006; ). Similar findings on sex ratio alterations have been observed in other studies which showed that the forkhead transcription factor (foxl2) and doublesex and mab3-related transcription factor 1 (dmrt1) were downregulated following exposure to hypoxia (Pelley, 2006; ). Importantly, a largescale field study from the Gulf of Mexico, one of the largest hypoxic dead zones in the world, reported similar endocrine disruption phenomena − extensive reproductive disruption, ovarian masculinization and male-biased Atlantic croaker sex ratios − occurring in natural fish populations (Thomas and Rahman, 2012). DNA methylation of the gonadal cyp19a (aromatase) gene has been linked to temperature-dependent sex ratio shifts in European sea bass (Navarro-Martín et al., 2011), suggesting that epigenetic modifications in response to environmental changes may also play a role in sex determination. Additionally, several recent studies have reported regulatory roles of miRNAs in sex differentiation. For example, let-7 and miR-21 have been shown to regulate egg development in rainbow trout (), and miR-141 and miR-429 have been found to play crucial roles in regulating testis development and spermatogenesis in yellow catfish (). Distinct subsets of miRNAs have also been observed in both male and female Nile tilapia embryos () and gonads (Wang et al., 2016), indicating potential regulatory roles for miRNAs in the sexual differentiation of fish. Overall, the abovementioned studies strongly implicate miRNAs as a new and important class of effector molecules controlling gonadal development and sexual differentiation in vertebrates.

Marine medaka Oryzias melastigma (synonyms: Oryzias dancena) is a model marine fish species for ecotoxicological studies (). The sex-determining gene DMY is absent in the O. melastigma (). Genetic studies using a progeny test of sex-reversed individuals indicated the presence of XX/XY sex-determination system absent in the O. melastigma (). In addition, SRY-related HMG-Box gene 3 (sox3) has been co-opted as the male sex determination gene in the O. melastigma (). Our group has previously identified several gonad-specific miRNAs in marine medaka, O. melastigma, as well as specific miRNAs from medaka embryos that are responsive to hypoxia (). Likewise, differentially expressed miRNAs was found to play important roles during fish ovarian development (). A genome-wide study identified novel ovarian-predominant miRNAs in the medaka fish (Mishima et al., 2008). And, miR-202 was reported to control female fecundity by regulating medaka oogenesis (), suggesting that tissue-specific miRNAs may play specific roles in gonadal functions. Using marine medaka embryos as a model, together with integrative omics analysis, including small-RNA sequencing and transcriptome sequencing, we investigated the role of specific hypoxia-responsive miRNAs in directly targeting sex determination genes. Our results identify miRNA–mRNA pairs that could be important for controlling sexual differentiation, resulting in hypoxia-altered sex ratios in fish.

Materials and Methods

Fish Embryo Maintenance and Hypoxic Exposure

All animal research procedures were approved by the Animal Ethics Committee on Research Experiments involving Animal Subjects (A-0244) of the City University of Hong Kong. Oryzias melastigma embryos used in our experiments were obtained from State Key Laboratory of Marine Pollution, City University of Hong Kong. Embryos within 1 day old (< 24 h after fertilization) were collected from parental marine medakas that were maintained in seawater with salinity of 3.5% under optimal growth and breeding conditions (5.8 mg O2 L–1, 28 ± 2°C, pH 7.2 in a 14-h light: 10-h dark cycle). The collected embryos were randomly selected under a dissecting microscope. Filaments attached to the clustered embryos were carefully removed via constant rolling of the embryos on a net with fingertips and pulling of forceps until the embryo was fully separated and clean. Abnormal or unfertilized embryos were removed from the batch. The selected embryos were then distributed into 12 separate 50-mL beakers, each holding 30 embryos. Six beakers of embryos were exposed to normoxia (5.8 ± 0.2 mg O2 L–1; outside the hypoxic chamber) and six beakers of embryos were exposed to hypoxia (2.0 ± 0.2 mg O2 L–1; inside the hypoxic chamber) continuously for 40 h. The desired dissolved oxygen (DO) level was achieved by a constant flow of premixed air and nitrogen (0.5% oxygen) inside a hypoxic chamber. The DO level was measured and monitored using a DO meter (YSI model 580). After the exposure period, the DO level of the hypoxic water was measured again to ensure DO levels were maintained at 2.0 ± 0.2 mg O2 L–1. Water temperature was maintained at 26 ± 2°C by placing the hypoxic chamber inside an incubator.

Embryo Survivability and Hatchability

After the exposure to normoxic or hypoxic conditions, embryos from each condition were transferred back to normoxic condition. The number of hatched eggs and dead eggs was recorded daily for 4 weeks. Temperature was maintained at 26 ± 2°C under a 14:10 light:dark photoperiod. Statistical analyses were performed using the paired t-test in GraphPad Prism 9.1.0 (GraphPad Software).

RNA Sample Preparation and Small RNA Sequencing

After the normoxic or hypoxic exposure, one beaker of embryos from each condition was used as one biological replicate for the RNA preparation, and there were three replicates per condition. Total RNA was extracted immediately using the mirVanaTM miRNA isolation kit (Applied Biosystems) following the manufacturer’s instructions. RNA quality was assessed using the Agilent 2100 Bioanalyzer system and samples with an RNA Integrity Number (RIN) greater than 8 were used to construct the small RNA and complementary DNA (cDNA) libraries (Supplementary Table 1). Five μg of total RNA were used from each sample. Short RNA transcripts (18–30 nucleotides long) were resolved and isolated using polyacrylamide gel electrophoresis (PAGE) gels. The isolated small RNA molecules were ligated with 3′ and 5′ adapters, and single-stranded cDNA was synthesized using SuperScript II Reverse Transcriptase (Invitrogen). The cDNA was then amplified using indexing primers. Following quantitative real-time PCR (qRT-PCR) amplification, the library size was determined using the Bioanalyzer (Agilent Technologies 2100) and library concentrations were assessed using qRT-PCR (EvaGreen). The libraries were then processed by Novogene. Single-end reads [50 base-pair (bp) read length] were sequenced using the Illumina Novaseq 6000 sequencer to produce at least 20 M clean reads per sample. The adaptor sequences were trimmed and bases with a Phred quality score less than 20 were removed.

Prediction of Mature miRNA Sequences

Raw sequencing reads were trimmed to remove adapters using Cutadapt 2.8 software (). Trimmed reads were processed using the mapper.pl function of miRDeep2 software () to remove reads shorter than 18 nucleotides and collapsed reads. The processed reads were then mapped onto the genome assembly of O. melastigma (Om_v0.7.RACA) with Bowtie 1.2.2 software (), using the following parameters: -n 1 -e 80 -l 18 -a –best –strata, producing mapped reads in BWT format. The BWT files were converted to mirDeep2 ARF format and parsed to remove unmatched nucleotides at the 3′ end, using the convert_bowtie_output.pl and parse_mappings.pl functions of mirDeep2 respectively. The parsed mapped reads were used to predict miRNA sequences using the miRDeep2.pl function of mirDeep2. The miRNA prediction results from all samples were merged and parsed into a single list of unique predicted mature miRNA sequences, which were then matched to known miRNAs by nucleotide BLAST to the miRBase database () using the blastn function of BLAST + 2.6.0 () with the following parameters: -task blastn-short -strand plus -num_alignments 1. The BLAST hits were filtered using the following parameters: the first 2–7 nucleotides are identical between the predicted miRNA and known miRNA sequences, hit coverage was ≥90%, no indels present, and a maximum of two mismatches.

Expression Analysis of Small RNAs

Trimmed sequencing reads were remapped onto the mature miRNA sequences using Bowtie with the following parameters: -v 2 -a -m 1 –norc –best –strata, with mapped reads output in SAM format. The SAM files were converted to BAM format using samtools (). The number of reads mapped to each mature miRNA sequence was then counted, producing a read counts table. Differential expression analysis of the miRNA read counts was performed using DESeq2 (). Differentially expressed miRNAs (DEmiRNAs) were determined using cutoff values of | log2 (fold change: treatment/control)| > 1 and B&H corrected p-value < 0.05.

miRNA Target Genes Prediction

Target mRNAs of significant differentially expressed miRNAs were predicted using IntaRNA v2:3:1 (; ; ; ; ; ; ; Wright et al., 2014; ; ; ; Raden et al., 2018)[20, 21] and miRanda v3:3a (), with default parameters. Target mRNAs were treated as true targets when they met the following criteria: (1) targets were predicted by both tools; (2) interaction sites overlapped between predictions.

Transcriptome Sequencing and Bioinformatic Analysis

Total RNA extracted from the same set of embryos (three replicates each from normoxic and hypoxic conditions) were subjected to cDNA library construction. The libraries were processed by Novogene. Paired-end reads (150 bp × 2) were sequenced on the Illumina Novaseq 6000 sequencer to produce at least 23 M clean reads per sample. The raw sequencing reads were checked for quality using FastQC (). The sequence reads were then mapped onto the genome assembly of O. melastigma (Om_v0.7.RACA) using Spliced Transcripts Alignment to a Reference (STAR) 2.7.1a (), and aligned reads were output in BAM format. Aligned reads were annotated to exon features of the Om_v0.7.RACA gene annotation file using the htseq-count function of HTseq 0.11.2 (), and the resulting read-count data were subjected to differential expression analysis using DESeq2. Genes with | log2 (fold change: treatment/control)| > 1 and B&H corrected p-value < 0.05 were considered differentially expressed genes (DEGs).

Assignment of Human Gene Symbols to Differentially Expressed Gene and Functional Analysis of Differentially Expressed Gene

Differentially expressed gene were assigned human gene symbols by reciprocal best hit (RBH) matching of the human and O. melastigma proteomes. Briefly, protein BLAST of the human (GRCh38.p12) protein sequences was performed against the medaka (Om_v0.7.RACA) protein sequences, and vice versa, using BLAST + 2.6.0 (). The top hit, with the lowest e-value, for each query sequence was extracted and compared between the two sets of BLAST queries; Protein sequences where the query and top hit matched in both directions were identified as RBHs. The DEGs were then subjected to gene ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using DAVID 6.81, enriched biological processes and KEGG pathways (p < 0.05) were identified.

Complementary DNA Synthesis and Quantitative PCR Analysis

One μg of total RNA was converted to cDNA by using SuperScript™ VILO™ cDNA Synthesis Kit (Invitrogen). Then qPCR analysis was performed on the cDNA from normoxia and hypoxia groups using StepOnePlus™ Real-Time PCR System (Thermofisher). The primer sequencing was shown as Supplementary Table 2.

Results

Exposure to Hypoxia Did Not Affect the Hatchability and Survival Rate of Medaka Embryos

Following normoxia or hypoxia exposure, the viability and hatchability of the embryos were measured for 4 weeks (28 days). Our results indicated that exposure to hypoxia did not affect the hatchability (Figure 1A) and survival rates (Figure 1B) of the medaka embryos, as compared to the normoxic group.

FIGURE 1

Hypoxia Was Predicted to Alter Embryonic Development Through miRNA Dysregulation

To identify changes to the miRNA profile in the hypoxia-exposed embryos, small RNA sequencing was employed. For this, we obtained 72.8 and 79.3 million quality-trimmed raw reads from normoxic and hypoxic-exposed embryos, respectively, translating to a total of 4.36 Gb of qualified data (Table 1). The clean sequencing reads were mapped to the O. melastigma genome to determine the miRNA content of marine medaka embryos. We identified 253 conserved miRNAs and 572 novel miRNAs (Supplementary Table 3) in the medaka embryo. When we compared the expression level of these miRNAs in the normoxic and hypoxic-exposed embryos, we found 131 miRNAs with altered regulation, including 50 upregulated and 81 downregulated miRNAs (Figure 2A and Table 2). Of these, 75 and 56 dysregulated miRNAs were conserved and novel miRNAs, respectively (Table 2).

TABLE 1

Sample nameNormoxia 1Normoxia 2Normoxia 3Hypoxia 1Hypoxia 2Hypoxia 3
Number of raw reads26,132,79625,721,29221,373,91622,951,65734,874,18321,931,340
Number of trimmed reads26,059,73225,589,06821,114,71322,877,50234,752,22121,650,001
Number of raw bases1,306,639,8001,286,064,6001,068,695,8001,147,582,8501,743,709,1501,096,567,000
Number of trimmed bases746,846,191755,768,344609,584,250659,360,186984,978,164604,696,719

Statistics for small RNA sequencing.

FIGURE 2

TABLE 2

Conserved/novel miRNAmiRNA sequencelog2 fold change (hypoxia/normoxia)Adjusted p-value
Novel miRNA_409TGTGTAAAGAGATGGTCGACGGT4.960.0035
Novel miRNA_428TACATTGGATCTGTGTGTGCGCGCC4.930.016
Novel miRNA_569TCATGACTGACTACCTGGCTCAGGT4.930.011
Novel miRNA_18ACCCTCCGGATCGGCTGCCTCCGCC4.720.0065
Novel miRNA_66TTCGGTTTCCTGTGTTTTACATC4.290.0028
Novel miRNA_503TGCTCTGAGGGACGTGGACTTGG4.240.0054
Novel miRNA_94TGCTCCCCTCTCCCTGCTCCTGG4.170.0060
Novel miRNA_518GATCATGCGGACACAGCGGGCCT3.970.043
Novel miRNA_296TTCGGTCTGGGGCTGACGCTCA3.890.00018
Novel miRNA_422TCCGTCGGTCCTGCTCGGGTCC3.804.39E-05
Novel miRNA_349CTGTCCGTCATTCTGCACTCGC3.781.76E-11
Novel miRNA_565TGCCTGTGGATCCCTTGTGAAGAG3.491.25E-06
Novel miRNA_258TGGATTGTGAAGGAGAAAAGCG3.420.043
Novel miRNA_311AGGACCCGACTTGTAACTTTGA3.311.15E-07
Novel miRNA_380TTAGCGGTCCACTAACTTTGTTT3.270.028
Novel miRNA_416TGTGTGTCTGTAGATCAGGAGC3.180.0033
Novel miRNA_338TCTCCAGATCTGGTCTCTAGTC2.911.58E-05
Novel miRNA_345TCTGAACTGCAGCGCCACCTGC2.890.040
Novel miRNA_106TGAGTGGTGCTGTAGCTGGCTGGCC2.770.00012
Novel miRNA_537TGAAGAGTGTGCAGCGGCTGGACGT2.770.0030
xla-miR-210-5pAAGCCACTGACTAACGCACATT2.681.64E-07
Novel miRNA_319TTGATGTCTACTTGGGTTCTGGAGT2.610.00067
Novel miRNA_494GGTCTCTAGTCTCCAGGTCTGG2.610.0050
Novel miRNA_197TGAGGTTGGGAGCTCAGACGGG2.530.0054
Novel miRNA_158TCGTGTATCAGATCTAGGGAGACTA2.330.00038
Novel miRNA_137TTTGAGAAGACTCGGAGGCGGTGG2.330.043
Novel miRNA_423TTCAGATCTCCTGTAGAGCAG2.150.041
Novel miRNA_145TCCGTGTCTCTCCTGTGCCTGGCG2.030.0024
Novel miRNA_510TTTGGTGTGGCCTGCTGTGTGTC2.020.0060
Novel miRNA_507TTTGTGACCTGTTATACTGCTA2.020.0076
Novel miRNA_320TTGGATAAACTGCTGTAACTCAGC2.020.0022
Novel miRNA_450TCAAGGACGTGCTGGTCAAGG1.960.011
Novel miRNA_134TCGATTGTGGAGGAGAAAAGTG1.950.0090
Novel miRNA_548TTCTGTAACGAAGAGGCTGAGACGT1.900.0019
Novel miRNA_290TCAGACTTGTACAGACTCCGCAGGG1.870.00028
Novel miRNA_207TCCAATCTCAGGGTGAACACTCTA1.870.0065
Novel miRNA_417TGAAGACTTTTGGGATTATCTAAA1.860.017
Novel miRNA_414TTGGCGTGATGCGAGCTTGGCTTG1.834.39E-05
Novel miRNA_556CGAGACCTGGAGTTTAACATCT1.690.035
Novel miRNA_60TTTCTTGGGTCTTGCATGGACA1.640.00012
Novel miRNA_292TCCAATCTCAGGGTGGACACTCTA1.590.021
cli-miR-1388-3pATCTCAGGTTCGTCAGCCCATG1.490.0085
Novel miRNA_62TAGGATAATGAGGTCTCTTTAAGG1.430.017
gmo-let-7d-5pTGAGGTAGTTGGTTGTATGGTT1.410.0079
Novel miRNA_360TATTGTTGATTGGTGGAATCCA1.370.016
Novel miRNA_44ACCCCAACATGTAGCACTTACT1.370.0084
Novel miRNA_210TGTGACTGAAGCGCTTTGGGCCCTC1.360.0040
Novel miRNA_31CGTGTAATGTGCGTTCAAGAAC1.340.040
oga-miR-25CATTGCACTTGTCTCGGTCTGA1.160.017
Novel miRNA_344AAAGTGCTTCTTGTTGGGTTGG1.150.0079
ocu-miR-183-5pTATGGCACTGGTAGAATTCACT–1.030.0079
oga-miR-26bTTCAAGTAATCCAGGATAGGTT–1.180.042
Novel miRNA_247CCGGGAGTGGGACTGTTTGCACT–1.180.013
mmr-miR-204TTCCCTTTGTCATCCTATGCCT–1.180.014
gmo-miR-216b-5pTAATCTCTGCAGGCAACTGTGA–1.190.042
oga-miR-17CAAAGTGCTTACAGTGCAGGTAG–1.250.041
xla-miR-27b-3pTTCACAGTGGCTAAGTTCTGCA–1.250.016
ocu-miR-7a-5pTGGAAGACTAGTGATTTTGTTGTT–1.350.00078
xla-miR-203-3pGTGAAATGTTTAGGACCACTTG–1.360.0024
oni-miR-217TACTGCATCAGGAACTGATTGGC–1.360.017
oga-miR-16TAGCAGCACGTAAATATTGGC–1.370.0012
xla-miR-200b-3pTAATACTGCCTGGTAATGATGAT–1.380.00029
Novel miRNA_139TTTGAGTGCGGTACCACATCTG–1.410.016
Novel miRNA_547GCTGCTCAGCACTCCAAACGCG–1.500.011
Novel miRNA_363GATTTCAGTGGATTGAAGAGTA–1.540.0020
ocu-miR-205-5pTCCTTCATTCCACCGGAGTCTG–1.540.0024
ccr-miR-10cTACCCTGTAGATCCGGATTTGTG–1.600.0013
gmo-miR-15b-5pTAGCAGCGCATCATGGTTTGAAAC–1.670.00027
nle-miR-30cTGTAAACATCCTACACTCTCAGCT–1.698.55E-06
cli-miR-455-5pTATGTGCCCTTGGACTACATCGT–1.710.00010
hhi-miR-10dTACCCTGTAGAACCGAATGTGTG–1.740.00078
mle-miR-1-3pTGGAATGTAAAGAAGTATGTAT–1.780.0060
ocu-miR-10b-5pTACCCTGTAGAACCGAATTTGTG–1.800.0019
nle-miR-454TAGTGCAATATTGCTTATAGGGTGT–1.820.00044
dre-miR-181a-5-3pACCATCGACCGTTGACTGTGCC–1.830.00016
oga-miR-30dTGTAAACATCCCCGACTGGAAGCT–1.841.40E-05
xla-miR-130c-5pGCCCTTTTTCTGTTGCACTACT–1.850.0022
gmo-miR-126-3pTCGTACCGTGAGTAATAATGCA–1.940.00066
pny-miR-725TTCAGTCATTGTTTCTGGTCGT–1.972.56E-05
ocu-miR-107-3pAGCAGCATTGTACAGGGCTATCA–2.010.0084
oga-miR-190TGATATGTTTGATATATTAGGTTG–2.060.00021
ssa-miR-148a-3pTCAGTGCATTACAGAACTTTGTT–2.120.0061
gmo-miR-152-3pTCAGTGCATAACAGAACTTTG–2.131.39E-05
gmo-miR-20b-5pAAAAGTGCTCACAGTGCAGATA–2.140.00029
Novel miRNA_33TGGGCTCACATCAACCTCTTCATC–2.170.00076
nbr-miR-29cACTGATTTCCTCTGGTGCTTAGA–2.180.0077
Novel miRNA_20GTTTTTTTAGGTTTTGATTTTC–2.232.94E-05
pny-miR-7132a-3pTGAGGCGTTTAGAACAAGTTCA–2.260.00037
nle-miR-20TAAAGTGCTTATAGTGCAGGTAG–2.271.54E-05
oga-miR-181aAACATTCAACGCTGTCGGTGAGT–2.383.68E-06
Novel miRNA_135CAAACCATAATGTGCTGCCTCT–2.400.0069
ipu-miR-219bGGAGTTGTGGATGGACATCACGC–2.410.016
cli-miR-181a-2-3pACCATCGACCGTTGACTGTACC–2.442.17E-07
ola-miR-301b-5pGCTCTGACAATGTTGCACTACT–2.461.78E-05
Novel miRNA_168CAGAACTTAGTTCATTAGTGAGCA–2.570.0024
ocu-miR-124-5pCGTGTTCACAGCGGACCTTGATT–2.628.60E-05
ola-miR-92bTATTGCACTTGTCCCGGCCTCC–2.680.0060
ola-miR-194-3pCCAGTGGAGGTGCTGTTACCTG–2.700.00010
pny-miR-218bTTGTGCTTGATCTAACCATGCA–2.740.033
sbo-miR-214TACAGCAGGCACAGACAGGCAG–2.770.0071
ocu-miR-181b-5pAACATTCATTGCTGTCGGTGGGTT–2.771.99E-08
gmo-miR-216a-3pCACAATGGCCTCTGGGATTATG–2.784.39E-05
ocu-miR-499-5pTTAAGACTTGCAGTGATGTTTA–2.795.14E-05
cpo-miR-200a-3pTAACACTGTCTGGTAACGATGTT–2.808.84E-10
novel miRNA_530AATGAGACTGATGTTCTTCCTCTGC–2.831.41E-08
gmo-miR-19d-3pTGTGCAAACCCATGCAAAACTG–2.838.95E-06
oni-miR-101bGTACAGTACTATGATAACTGAA–2.832.78E-06
pny-miR-458ATAGCTCTTTAAATGGTACTGC–2.851.97E-06
oga-miR-19bTGTGCAAATCCATGCAAAACTG–2.934.09E-16
ocu-miR-153-5pTCATTTTTGTGATGTTGCAGCT–2.950.011
oga-miR-30eTGTAAACATCCTTGACTGGAAGCT–2.961.88E-09
ocu-miR-140-5pCAGTGGTTTTACCCTATGGTAG–3.071.41E-08
oga-miR-18TAAGGTGCATCTAGTGCAGATAG–3.152.57E-08
pny-miR-106TAAAGTGCTTACAGTGCAGGTAG–3.161.43E-10
pny-miR-301aTAGTGCAATAGTATTGTCAAAGC–3.180.033
gmo-miR-7552-5pTTACAATTAAAGGATATTTCTG–3.193.65E-05
gmo-miR-449a-5pAGGCAGTGTCTCGTTAGCTGGA–3.280.0066
gmo-miR-93-5pAAAAGTGCTGTTTGTGCAGGTAG–3.301.48E-14
gmo-miR-135d-5pTATGGCTTTTTATTCCTACGTGA–3.342.46E-05
pny-miR-734TAAATGCTGCAGAATTGTGCTC–3.501.43E-16
ocu-miR-199a-5pCCCAGTGTTCAGACTACCTGTTC–3.529.46E-07
ssa-miR-2188-3pGCTGTGTGAGGTCAGACCTATC–3.580.00011
ocu-miR-133a-5pAGCTGGTAAAATGGAACCAAATC–3.840.027
gmo-miR-181d-5pAACATTCATTGCTGTCGCTGGGTT–3.925.83E-06
xla-miR-449-5pAGGCAGTGCAATGTTAGCTGGC–3.960.033
abu-miR-135a-5pTATGGCTTTTTATTCCTATCTGA–3.992.39E-12
pny-miR-8159TCAGTAACTGGAATCTGTCCCTGCA–4.060.0016
pny-miR-219aAGAATTGTGTATGGACATCTGT–4.434.86E-23
Novel miRNA_208TTACTGTTTTATCTCTTATTTTTAG–4.510.00049
gga-miR-205c-5pACTTCACACCACTGAAATCTGG–5.040.013
mle-miR-219-5pTGATTGTCCAAACGCAATTCTTG–5.607.09E-13

Hypoxia dysregulated miRNA in medaka embryos.

Bioinformatic analysis using the miRanda and IntaRNA algorithms was used to predict the dysregulated miRNA target genes. By combining the results of the 2 algorithms, we found that the hypoxia-dysregulated miRNAs were predicted to target 2466 genes in the embryo (Supplementary Table 4). The predicted target genes were subjected to Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8 analysis to further understand the effect of hypoxia-dysregulated miRNA. Our results showed that the hypoxia exposure could alter the gene cluster involved in many biological processes related to embryo development, such as cartilage development, microtubule cytoskeleton organization, liver development, neural crest cell migration, myelination in the peripheral nervous system, and pectoral fin development (Figure 2B). More importantly, fertility and sex determination-related cell signaling including the retinoic acid receptor signaling pathway and the steroid hormone-mediated signaling pathway were highlighted in our analysis (Figure 2B). Although, the hypoxia exposure had no effect on the GSI and HSI indexes (Supplementary Figure 1).

Hypoxia Altered Embryonic Development and Sex Determination Through the Regulation of miRNA–mRNA Pairs

As miRNAs are one of the major mediators of gene expression, we applied comparative transcriptomic analysis to determine the differential gene expression occurring as a result of hypoxia exposure. A total of 163.6 million quality-trimmed raw reads, translating to 8.18 Gb of data were obtained from the RNA sequencing (Table 3). In the comparative transcriptome analysis, we found 3009 DEGs, including 1543 upregulated genes and 1466 downregulated genes in the embryos exposed to hypoxia (Figure 3A and Supplementary Table 5).

TABLE 3

Sample nameNormoxia 1Normoxia 2Normoxia 3Hypoxia 1Hypoxia 2Hypoxia 3
Number of raw reads25,665,90930,833,25431,167,24025,663,81726,464,10323,853,887
Number of bases7,699,772,7009,249,976,2009,350,172,0007,699,145,1007,939,230,9007,156,166,100
% GC content50.0849.8449.6650.4850.3250.61
Number of reads mapped to genome23,444,75928,474,12028,582,19823,973,60424,686,36021,986,391

Statistics for transcriptome sequencing.

FIGURE 3

We then integrated the miRNA and mRNA sequencing results to look at the reversed expression of miRNA and mRNA. Our results indicated that hypoxia could lead to the dysregulation of 167 miRNA–mRNA interaction pairs including 31 downregulated miRNA-upregulated mRNA pairs (Table 4) and 136 upregulated miRNA-downregulated mRNA pairs (Table 5). The miRNA-targeted mRNA was subjected to DAVID analysis to understand the effect of hypoxia-dysregulated miRNA–mRNA pairs in the embryo. Our results showed that hypoxia exposure could result in alterations to miRNA-mediated genes related to different developmental processes, such as adipose tissue development, skeletal muscle tissue growth, and post-embryonic development (Figure 3B). Additionally, hypoxia exposure altered the gene cluster related to neuron migration and amacrine cell differentiation, leading to the interference of brain development through the control of miRNA–mRNA interactions (Figure 3B). Finally, we further assessed the possible impact of hypoxia on sex determination and differentiation through miRNA-mRNA regulation. Using gene ontology analysis, we identified miRNA-mRNA pairs that may be involved in biological functions related to sex determination and differentiation including novel miR-145-col9a3 and novel miRNA-94- arid5b in gonad development, novel miRNA-210-ca2, novel miRNA-106-nr2f2, nbr-miR-29c-nr4a1, and ola-miR-92b-akr1d1 in sex hormone response, and novel miRNA-145-mns1, nle-miR-20-sord, and ipu-miR-219b-abcc8 in sex characteristic development (Figure 3C). The differential gene expression was further validated by using quantitative PCR (qPCR), our data showed that the result of qPCR matched with the finding of RNA sequencing (Figure 3D). Taken together, our data suggest that hypoxia may alter sex determination and differentiation through the regulation of miRNA-mRNA pairs.

TABLE 4

Downregulated miRNAUpregulated mRNA
ccr-miR-10crimbp2
gmo-miR-93-5pMPZL3
ocu-miR-199a-5pap3d1
sbo-miR-214ngef
sbo-miR-214atp10b
sbo-miR-214hdac4
sbo-miR-214best1
sbo-miR-214GPR137B
gmo-miR-181d-5pCDKL5
gmo-miR-181d-5pgrm1a
oga-miR-26bahcyl1
ocu-miR-7a-5psi:dkey-288a3.2
abu-miR-135a-5pf2rl1.2
Novel miRNA_247ablim2
Novel miRNA_247poln
Novel miRNA_247bmper
nbr-miR-29cnr4a1
oga-miR-19bCTSS
nle-miR-20sord
mmr-miR-204fnip1
mmr-miR-204hkdc1
mmr-miR-204tfeb
ipu-miR-219babcc8
gmo-miR-449a-5pFBLN2
gmo-miR-449a-5pvwc2
gmo-miR-449a-5pkcnd2
ocu-miR-124-5pshtn1
ocu-miR-124-5pefhc2
Novel miRNA_547ppp1r9alb
ola-miR-92bahdc1
ola-miR-92bAKR1D1

Hypoxia-induced downregulated miRNA-upregulated mRNA pairs in medaka embryos.

TABLE 5

Upregulated miRNADownregulated mRNA
Novel miRNA_503mdga1
Novel miRNA_503dhx57
Novel miRNA_503ulk1a
Novel miRNA_503nkain2
Novel miRNA_503adck1
Novel miRNA_503chp2
Novel miRNA_349unm_sa821
Novel miRNA_349apobec2a
Novel miRNA_349stk36
Novel miRNA_349lrig1
Novel miRNA_349PDZRN4
Novel miRNA_349neurod1
Novel miRNA_349cdh15
Novel miRNA_416bnc2
Novel miRNA_416mcm3
Novel miRNA_537VAT1
Novel miRNA_537CHRNA1
Novel miRNA_537zgc:63863
Novel miRNA_414JMY
Novel miRNA_414tepsin
Novel miRNA_414c1qbp
Novel miRNA_414txlnba
Novel miRNA_565atp6v0a2a
Novel miRNA_565gmpr2
Novel miRNA_292dhx36
Novel miRNA_292WDR77
Novel miRNA_510acp6
Novel miRNA_510prmt9
Novel miRNA_510chodl
Novel miRNA_510ANXA2
Novel miRNA_510asmt
Novel miRNA_296lrp5
Novel miRNA_338her8.2
Novel miRNA_338si:dkey-40m6.8
Novel miRNA_338tp53inp1
Novel miRNA_338EPHA6
Novel miRNA_94ccdc15
Novel miRNA_94myom2a
Novel miRNA_94sphkap
Novel miRNA_94ogfrl1
Novel miRNA_94mep1b
Novel miRNA_94zmp:0000000760
Novel miRNA_94lap3
Novel miRNA_94pprc1
Novel miRNA_94ttll6
Novel miRNA_94sema3c
Novel miRNA_94pcsk2
Novel miRNA_94map3k12
Novel miRNA_94s1pr3a
Novel miRNA_94itga10
Novel miRNA_94adgra2
Novel miRNA_94lhx4
Novel miRNA_94arid5b
Novel miRNA_94casp2
Novel miRNA_94clybl
Novel miRNA_94fkbpl
Novel miRNA_94gtf2f1
Novel miRNA_94c1qtnf6b
Novel miRNA_94tmem204
Novel miRNA_94rab33a
Novel miRNA_94uncx4.1
Novel miRNA_94phf2
Novel miRNA_319cass4
Novel miRNA_319MARK1
Novel miRNA_319adamts14
Novel miRNA_319SLC4A11
Novel miRNA_319ppp1r9a
Novel miRNA_290dis3l2
Novel miRNA_507klhl2
Novel miRNA_409actr5
Novel miRNA_409rbm24a
Novel miRNA_18ino80
Novel miRNA_18FRMPD4
Novel miRNA_18fam234b
Novel miRNA_18tonsl
Novel miRNA_18smarcc1b
Novel miRNA_18ccne2
Novel miRNA_450crb1
Novel miRNA_134hic1
Novel miRNA_134tox2
Novel miRNA_134lrp3
Novel miRNA_134cul2
Novel miRNA_134nup93
Novel miRNA_197vgll2b
Novel miRNA_197fam124b
Novel miRNA_197ino80da
Novel miRNA_197RANGAP1
Novel miRNA_197tprkb
Novel miRNA_197carmil2
Novel miRNA_197pik3cd
gmo-let-7d-5pints6l
gmo-let-7d-5psi:dkey-119f1.1
gmo-let-7d-5plpl
gmo-let-7d-5pnwd2
gmo-let-7d-5piqsec1b
Novel miRNA_345ints6
Novel miRNA_345pik3ap1
Novel miRNA_210zfr2
Novel miRNA_210ca2
Novel miRNA_210glra2
Novel miRNA_210PLXNA2
Novel miRNA_210si:dkey-175g6.2
Novel miRNA_518btbd9
oga-miR-25msh3
Novel miRNA_344sox6
Novel miRNA_494usp28
Novel miRNA_494slx4
Novel miRNA_494PIK3CA
Novel miRNA_494plxnb1a
Novel miRNA_494aldh18a1
Novel miRNA_106slc35e4
Novel miRNA_106pex5lb
Novel miRNA_106tlx2
Novel miRNA_106babam1
Novel miRNA_106PTPRD
Novel miRNA_106nr2f2
Novel miRNA_106pcloa
Novel miRNA_106elavl4
Novel miRNA_106cacng6b
Novel miRNA_106ccdc106a
Novel miRNA_422pcsk1
Novel miRNA_422tppp2
Novel miRNA_422zgpat
Novel miRNA_422gtf2h1
Novel miRNA_145col9a3
Novel miRNA_145cdk5rap1
Novel miRNA_145mns1
Novel miRNA_145rgmb
Novel miRNA_145syt4
Novel miRNA_145gap43
Novel miRNA_145actn2b
Novel miRNA_145si:ch211-283g2.1
Novel miRNA_145neurod4
Novel miRNA_290si:ch211-87j1.4
Novel miRNA_290dync1li2
Novel miRNA_290chrnd

Hypoxia-induced upregulated miRNA-downregulated mRNA pairs in medaka embryos.

Discussion

Using small-RNA sequencing analysis, we identified 253 conserved miRNA and 572 novel miRNAs in the medaka embryo. The larger number of novel miRNAs being identified in this study compared to conserved miRNAs suggests that many marine medaka miRNAs are yet to be identified. When we compared our results to the limited miRNAs previously identified in marine medaka which is approximately 200 novel miRNAs identified in different organs such as brain, liver, and gonads (). Many novel miRNAs in embryos are important in fish embryonic development, such as bone and gonadal development (; ).

We then looked at the miRNAs altered by hypoxia exposure by comparing the miRNA profile of normoxic and hypoxic exposed embryos. We observed some conserved miRNAs, which are reported to play important roles in embryonic development. For example, the downregulated miR-214 is a developmental regulator, which controls the polycomb protein, Ezh2, in skeletal muscle and embryonic stem cells (). Additionally, an in vitro study demonstrated that miR-214 expression levels are controlled by the transcription factor Twist-1 in the development of specific neural cell populations (Presslauer et al., 2017). Another hypoxia-downregulated miRNA − miR-29c reportedly affects lateral development and cardiac circulation through the Wnt4/β-catenin signaling pathway in zebrafish (). The involvement of miR-29c has also been implicated in bovine blastocyst development and embryo implantation of rats with endometriosis (; Shen et al., 2020), suggesting the importance of hypoxia dysregulated miR-29c in embryonic development.

Our results also highlighted miR-19b, which was reduced following hypoxia exposure, and is reported to impair cardiac development in zebrafish by targeting ctnnb1(). Our results in medaka are also concordant with a bird study of miRNAs in great tits that found miRNA-19b to be a hypoxia-responsive miRNA, which regulated MAPK1 expression in embryonic fibroblasts (). In addition, the expression level of miRNA-19b is also associated with embryo quality (). We also found hypoxia to suppress the expression of miR-204; this miRNA is reported to alter neuronal migration and cortical morphogenesis during embryonic development in mouse embryos (), and both human and zebrafish studies have demonstrated miR-204-mediated control of developmental lymphangiogenesis (). The reduction of these miRNAs by hypoxia is suggestive of the possible effects of hypoxia exposure on embryonic development through miRNA regulation. Following analysis of miRNAs for which expression was induced by hypoxia exposure, we found elevated expression levels in a large number of novel miRNAs, but not conserved miRNAs. This result made determining the possible effect of the novel miRNA profile change complex. Therefore, we applied two algorithms (MiRanda and IntaRNA) to predict miRNA target genes. To strengthen our findings, we also conducted comparative transcriptome sequencing followed by the miRNA and mRNA integrative analysis, to further determine miRNA-mRNA interaction pairs. Hundred and ninety-nine miRNA–mRNA pairs were identified, and DAVID analysis was performed on the miRNA target genes to determine the effect of hypoxia-dysregulated miRNA-mRNA pairs. For the data analysis, we focused primarily on the endpoint of embryonic development and sex determination and differentiation. In the functional characterization, novel miRNA-145-col9a3 was reported to be involved in gonad development, as Col9a3 encodes the α3 chain of type IX collagen, which is expressed during mouse testis development (Venø et al., 2017; ). Another mouse study demonstrated the specified expression of Col9a3 in testicular cords during the early stages of gonadal differentiation (). More importantly, Col9a3 expression was markedly upregulated in the male, but remained very low in the female during embryo development (), suggesting the possible role of Col9a3 in sex differentiation.

Our findings also highlighted some hypoxia-dysregulated miRNA-mRNA pairs such as novel miRNA-210-ca2, novel miRNA-106-nr2f2, and nbr-miR-29c-nr4a1, which could contribute to sex-steroid hormone response. The novel miRNA, miRNA-210, mediates carbonic anhydrase II (CA2), which is a metalloenzyme responsible for the maintenance of the acid-base balance in body systems (). Rat studies have demonstrated an association between CA2 and sex-steroid hormones, for example, CA2 expressed in the rat lateral prostate and seminal vesicles is under testosterone regulation (Perera et al., 2001), and it is involved in bicarbonate production − a function that particularly characterizes the rat lateral prostate (Perera et al., 2001). The expression level of CA2 is also differentially regulated by testosterone in the dorsal and lateral prostate in rats (Sanyanga et al., 2019). Other than male sex hormones, CA2 is also regulated by female sex hormones. Hepatic carbonic anhydrase is reportedly induced by estrogen in rat models (), and estrogen and progesterone differentially regulate CA2 in ovariectomized rat uteri (). The other hypoxia-altered miRNA-mRNA pair, novel miRNA-106-nr2f2, was also found to be associated with the development of sex organs. Nuclear Receptor Subfamily 2 Group F Member 2 (nr2f2), encodes a ligand-inducible transcription factor and is expressed during early ovarian development of embryogenesis in ovarian somatic cells (). A review of genetic disease studies showed that rare disease differences or disorders of sex development (DSD) are associated with mutations in NR2F2 (). Furthermore, a study of human genetic disorders demonstrated that NR2F2 loss of function caused defects in testis development in individuals with DSD (Rastetter et al., 2014).

As well as novel miRNAs, our result also highlighted the conserved miR-29c-nr4a1, which is also involved in sex differentiation. Nr4a1, an orphan nuclear receptor, is important for hormone-induced steroidogenesis in Leydig cells, where it plays a pivotal role in regulating the expression of several genes involved in male sex differentiation (). Nr4a1 can be activated by many transcription factors such as myocyte enhancer factor 2 to control Leydig cell gene expression (). It has also been reported that Nr4a1 is a regulator of Insulin-like 3 (INSL3) transcription (), which is a hormone produced by fetal Leydig cells of the testis to regulate testicular descent during fetal life. In adults, Nr4a1 acts as a germ cell survival factor ().

Finally, novel miR-145-mns1 and nle-miR-20-sord pairs are reported to play roles in male sex characteristics. Meiosis-specific nuclear structural protein 1 (Mns1) is necessary for spermiogenesis and motile cilia function, such as sperm flagella, through its interaction with ciliary proteins (Robert et al., 2006; ). Studies of Mns1-deficient mice demonstrate that homozygous loss-of-function mutations in Mns1 resulted in laterality defects and male infertility (). This finding was supported by a human population study combining genome-wide SNP mapping and whole-exome sequencing analysis that identified MNS1 variant as a cause of male infertility in humans (Zhou et al., 2012).

For nle-miR-20-sord, sorbitol dehydrogenase (Sord) expression is reported to be regulated by androgens in the human prostate, suggesting that this is the location of the physiological role of sord (Ta-Shma et al., 2018). Furthermore, Sord expression is induced to support epididymal sperm motility during spermatogenic differentiation in mouse spermatogenesis (). Accordingly, reduced expression of Sord in the mouse epididymis results in inhibition of sperm maturation through the impairment of secretory functions of the epididymis (Szabó et al., 2010).

Conclusion

Our integrative analysis of small RNA sequencing and transcriptome sequencing identified a cluster of miRNA–mRNA pairs that may involve in embryonic development. Our findings have also provided important insights into the molecular mechanisms through which miRNAs interact with different target genes that may regulate the reproductive axis and mediate hypoxia-altered plasticity of sex determination and differentiation in fish. But further studies are required to confirm the role of miRNA–mRNA pairs in physiological processes of hypoxia-altered sex biased in fish.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA706118.

Ethics statement

The animal study was reviewed and approved by all animal research procedures were approved by the Animal Ethics Committee on Research Experiments involving Animal Subjects (A-0244) of the City University of Hong Kong.

Author contributions

KL, SC, and RK contributed to conception and design of the study. NT, YK, and WT organized the database. CT, CL, and YK performed the experiments. YC, XL, and TC performed the bioinformatics and statistical analysis. KL and RK wrote the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Funding

This study was fully supported by a grant from the General Research Fund (CityU 11102918) of the Research Grants Council of Hong Kong SAR, People’s Republic of China. KL was supported by the Hong Kong SAR, Macao SAR, and Taiwan Province Talented Young Scientist Program of Guangxi.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars.2021.736362/full#supplementary-material

References

Summary

Keywords

hypoxia, fish, sequencing, miRNA, bioinformatics

Citation

Lai KP, Tam NYK, Chen Y, Leung CT, Lin X, Tsang CF, Kwok YC, Tse WKF, Cheng SH, Chan TF and Kong RYC (2022) miRNA–mRNA Integrative Analysis Reveals the Roles of miRNAs in Hypoxia-Altered Embryonic Development- and Sex Determination-Related Genes of Medaka Fish. Front. Mar. Sci. 8:736362. doi: 10.3389/fmars.2021.736362

Received

08 July 2021

Accepted

14 December 2021

Published

21 January 2022

Volume

8 - 2021

Edited by

Youji Wang, Shanghai Ocean University, China

Reviewed by

Jae-Sung Rhee, Incheon National University, South Korea; Bindhu Paul, Amrita Vishwa Vidyapeetham, India

Updates

Copyright

*Correspondence: Keng Po Lai, Richard Yuen Chong Kong,

This article was submitted to Aquatic Physiology, a section of the journal Frontiers in Marine Science

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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