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. 2024 May 26;103(8):103895. doi: 10.1016/j.psj.2024.103895

Comprehensive analysis of the differential expression of mRNAs, lncRNAs, and miRNAs in Zi goose testis with high and low sperm mobility

Hongrun Hao *,†,1, Xiaofang Ren *,†,1, Zhigang Ma ‡, Zhifeng Chen ‡, Kun Yang ‡, Qiuju Wang *,†, Shengjun Liu *,†,2
PMCID: PMC11255893  PMID: 38917609

Abstract

Sperm mobility (SM) is an objective index for measuring sperm motility; however, the mechanisms underlying its regulation in geese remain unclear. The present study sought to elucidate the genetic mechanism underlying SM traits in Zi geese (Anser cygnoides L.). To this end, three successive experiments were performed. In Experiment I, SM was determined in 40 ganders; the 3 ganders with the highest mobility and three with the lowest mobility were assigned to the high and low sperm mobility rank (SMR) groups, respectively. In Experiment II, the differences in fertility between the two SMR groups were assessed within two breeding flocks comprising the selected six ganders from Experiment I and 30 females (each flock had 3 ganders and 15 females). In Experiment III, the testes of the 6 ganders were harvested for histological observation and whole-transcriptome sequencing. Results revealed better fertility, well-developed seminiferous tubules, and abundant mature sperm in the high-SMR-flock compared to those of the low-SMR-flock (89 vs. 81%) (P < 0.05). Differential expression (DE) analysis identified 76 mRNAs, 344 lncRNAs, and 17 miRNAs between the SMR groups, with LOC106049708, XPNPEP3, GNB3, ADCY8, PRKAG3, oha-miR-182-5p, and ocu-miR-10b-5p identified as key mRNAs and miRNAs contributing to SM. Enrichment analysis implicated these DE RNAs in pathways related to ATP binding, cell metabolism, apelin signaling, Wnt signaling, and Adherens junctions. Additionally, competing endogenous RNA (ceRNA) networks comprising 9 DE mRNAs, 17 DE miRNAs, and 169 DE lncRNAs were constructed. Two ceRNA network pathways (LOC106049708–oha-miR-182-5p–MSTRG.2479.6 and PRKAG3–ocu-miR-10b-5p–MSTRG.9047.14) were identified as key regulators of SM in geese. These findings offer crucial insights into the identification of key genes and ceRNA pathways influencing sperm mobility in geese.

Key words: Zi goose, testis, sperm mobility, mRNA, non-coding RNA

INTRODUCTION

Sperm mobility (SM)—a critical parameter for measuring sperm quality—is an objective measure of the motility index of sperm to resist fluids (Froman and Feltmann, 1998). As a heritable quantitative trait in poultry, SM characterizes the static motility parameters of the sperm population, serving as the most appropriate metric to indicate sperm motility. Indeed, SM has been used to predict chicken (Froman, et al., 1999), turkey (Donoghue, et al., 2003), and geese (Wang, et al., 2023) fecundity, which impacts the efficiency of poultry production. However, the mechanisms underlying the regulation of SM traits in geese remain unclear.

Genetic factors may be instrumental in regulating SM (Froman, et al., 2002; Jarrell, et al., 2020). Studies have suggested that sperm motility is primarily regulated by intracellular signal transduction processes, including G protein-coupled receptors, adenylate cyclase (AC), ion channels, ATP, and flagellation-related proteins (Böttinger, 2010; Miller, et al., 2015; Asano and Priyadarshana, 2022; Murat, et al., 2023). These molecules and pathways regulate the ion balance in sperm cells, structure and movement of the cytoskeleton, and flagellar swing, ultimately impacting sperm motility, and fertilization. The testes are the site of spermatogenesis, maturation, and storage (Estermann et al., 2021). Unlike mammals, bird sperm can acquire fertilization capacities within the testes, highlighting the critical role of testicular function in SM development (Asano and Priyadarshana, 2022).

Messenger RNAs (mRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) are widely expressed in various germ cells of the testes and are crucial in spermatogenesis and sperm mobility. Guo et al. (2023) and Sun et al. (2019) identified and screened key genes and pathways influencing sperm motility in chickens using the transcriptome sequencing method. However, geese present distinct characteristics in the seminal quality parameters compared to those of chickens. For instance, geese exhibit lower semen density (3.40 - 10.20 × 108/mL) and a higher sperm abnormality rate (15–45%) compared to those of chickens, where their semen density ranges from 20.0 to 40.0 × 108/mL and the sperm abnormality rate is typically 10 to 20% (Liu, 2021; Lukaszewicz, et al., 2021; Prabakar, et al., 2022; El Sabry, et al., 2023). Moreover, seminal quality parameters exhibit considerable variations within the same flock and even among individual geese during different semen collection attempts (Łukaszewicz, et al., 2022; Liu et al., 2023). Given these variations, it is likely that the regulatory mechanisms of sperm motility identified in chickens may not directly apply to geese.

Wu et al. (2023) screened 71 differentially expressed (DE) miRNAs and 660 DE mRNA in the testis tissues of Yili geese with high and low sperm motility. Functional enrichment analysis showed that these miRNAs were involved in arginine and proline metabolism, glycolysis/gluconeogenesis, and fructose and mannose metabolism pathways. The interaction network revealed that miR-140/miR-140-3p–NKAIN3, let-7d–BTG1, and miR-145-5p/miR-145a-5p–CLEC2E regulate testicular development and spermatogenesis. However, many studies have demonstrated that competing endogenous RNAs (ceRNAs), including mRNAs and lncRNAs, can regulate each others' expression by competitively binding to miRNAs through shared miRNA response elements (MREs) (Salmena, et al., 2011, Kong, et al., 2022). Moreover, the ceRNA mechanism has been rarely utilized to explore the interactions and transcriptional regulation between coding and non-coding RNAs during the reproductive process in geese.

This study investigated the genetic factors affecting SM using high-throughput whole-transcriptome sequencing and bioinformatics technology to identify the key genes and related signaling pathways, and comprehensively analyzed the regulatory mechanisms of mRNA, miRNA, and lncRNA in geese sperm mobility. The results of this study provide a reference for improving the reproductive efficiency of geese.

MATERIAL AND METHODS

Ethics Statements

The study protocol was approved by the Ethics Committee of Science and Technology of the Heilongjiang Bayi Agricultural University (Daqing, China) (Number: DWKJXY2023071). The nucleotide sequence data reported in this article have been submitted to NCBI SRA (http://www.ncbi.nlm.nih.gov/sra) nucleotide sequence database (accession number: PRJNA1041964; Release data: 2025-06-30).

Animals and Experimental Design

The experiments were conducted at the Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, Qiqihar City, Heilongjiang Province, China (latitude: 47°19′; longitude: 123°45′; altitude: 155 m). The subjects were Zi geese (Anser cygnoides L., a small-type breed) that were approximately 1-y old, each with similar physical conditions. The study comprised three successive experiments.

Experiment I: Following the protocol outlined by Zhang et al. (2021), semen samples were collected from 40 ganders on five occasions, once per week, and SM was assessed for each gander. Based on SM rankings, ganders were categorized into high- and low-sperm mobility rank (SMR) groups, comprising 3 ganders each with the highest and lowest SM, respectively. High- and low-SMR ganders were defined as those with SM values one standard deviation above or below the average mobility, respectively. Detailed results are provided in Supplementary Table S1-1.

Experiment II: To compare the differences in fertility between the two SMR groups, two breeding flocks were created with the six selected ganders and 30 females (each flock 3 ♂: 15 ♀).

Experiment III: Following the protocols outlined by Liu et al. (2017) and Wang et al. (2021), the testes of the six ganders from experiment II were collected for histological observation and whole-transcriptome sequencing analysis.

Sperm Mobility, Semen Quality, and Fertility Measurements

SM was measured using the Accudenz procedure as described by Liu et al. (2023) and Froman and McLean (1996). From this assessment, the three ganders (HSM1, HSM2, and HSM3) with the highest SM and three ganders (LSM1, LSM2, and LSM3) with the lowest SM were selected. Their semen quality was assessed as described by Wang et al. (2023), including determination of semen volume (SV), sperm concentration (SC), and sperm abnormality (SA).

Eggs were collected daily, and a total of 200 eggs were obtained from the two SMR breeding flocks (100 eggs from each flock). These eggs were then placed in incubators set at 37.5°C and a relative humidity of 69%. On the 15th d of incubation, the number of fertilized eggs was counted, and the fertility rate was calculated for each SMR flock based on the following formula:

Fertility = number of fertilized eggs per flock/number of eggs collected per flock × 100% (Wang, et al., 2023).

Testicular Tissue Section Preparation and Histological Observation

The testes collected from each gander in the SMR groups were weighed using an electronic balance (Sartorius AZ212, Gottingen, Germany) with an accuracy of 0.01 g and were divided into two portions. One portion was used for paraffin sectioning and hematoxylin and eosin staining. The seminiferous tubule diameters and seminiferous epithelium thickness were measured at 40, 100, 200, and 400 × magnification using Image-pro plus 6.0 software (Media Cybernetics, Inc., Rockville, MD). The remaining portion was subjected to a 30-min liquid nitrogen treatment and subsequently transferred to a cryogenic freezer at -80°C for long-term storage before performing whole-transcriptome sequencing and validation experiments.

RNA and Small-RNA Sequencing Libraries Preparation

Total RNA was isolated from sample obtained from each gander in both the SMR groups using the Trizol Reagent (Invitrogen Life Technologies, China), and the concentration, quality, and integrity were determined using a NanoDrop spectrophotometer (Thermo Scientific, China) (Supplementary Table S2-1). RNA sequencing (RNA-seq) libraries (mRNA + lncRNA) were prepared from total RNA and modified with the Epicentre Ribo-Zero rRNA Removal Kit (Epicentre, Lindenhurst, IL), DNA polymerase I (New England Biolabs, Inc., Ipswich, MA), USER enzyme (New England Biolabs, Inc., Ipswich, MA), and AMPure XP beads (Merck, Germany) according to the manufacturers’ instructions. Additionally, small-RNA sequencing (sRNA-seq) libraries targeting miRNA were prepared using the NEBNext Multiplex sRNA-seq Library Prep Set for Illumina (New England Biolabs, Inc., Ipswich, MA), according to the manufacturer's instructions. Subsequently, the sequencing libraries were quantified using an Agilent high-sensitivity DNA assay on a Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA) and sequenced on a NovaSeq 6000 platform (Illumina, San Diego, CA); the resulting image files were converted to Raw Data in FASTQ format using the software provided with the Illumina HiSeq sequencing platform. RNA-seq was performed using the paired-end sequencing mode, while sRNA-seq was performed using the single-end sequencing mode.

Quality Control, Alignment, and Assembly of RNA and Small-RNA Sequencing Data

Sequencing raw data filtering was performed using Cutadapt v4.0 (Kechin, et al., 2017) to discard sequences containing adapters at the 3′-end and to remove reads with average quality scores below Q20 to obtain clean data (Supplementary Table S2-2 and 3). The reference genome index file was constructed using the Bowtie2 v2.5.0 (Langmead and Salzberg, 2012) and reference genome file (Anser_cygnoides.GooseV1.0.dna.toplevel.fa, https://ftp.ensembl.org/pub/release-111/fasta/anser_cygnoides/dna). The clean reads were aligned to the reference genome index using HISAT2 v2.2.1 (Kim, et al., 2015) (Supplementary Table S2-4). Subsequently, SamTools v1.1.0 (Li, et al., 2009) was utilized to convert the SAM format into BAM format. The BAM files for each sample were individually assembled and gene annotated by mapping them to the genome structure annotation file (Anser_cygnoides.GooseV1.0.111.gtf, https://ftp.ensembl.org/pub/release-111/gtf/anser_cygnoides) using StringTie v2.2.0 (Pertea, et al., 2015). Subsequently, a comprehensive transcript set was constructed by integrating transcripts from all samples using StringTie v2.2.0.

Identification and Expression Analyses of mRNAs, lncRNAs, and miRNAs

The HTSeq v1.0 and union protocol parameters (Anders, et al., 2015) were used to compute the read counts for each protein-coding gene, serving as the mRNA expression levels. The raw gene expression levels were normalized using the fragments per kilobase million (FPKM) method to investigate the variations in gene expression among different ganders (Supplementary Table S2-5). Genes with FPKM values greater than 1 were considered expressed. The differences in mRNA expression between two SMR groups were assessed using the DESeq v1.18.0 package (Anders and Huber, 2010) (Supplementary Table S3-1). The screening conditions of the differentially expressed genes (DEGs) were as follows: |log2 Fold Change| > 1, and significance P-value < 0.05. Fold Change = mean expression of a specific gene in the high-SMR group/mean expression of the gene in the low-SMR group (Love, et al., 2014).

A rigorous filtering pipeline was used to identify lncRNAs. First, transcripts ≥ 200 bp, with an exon number ≥ 2, and class_code = “x,” “I,” and “u” type, occurring more than 3 times in the transcript set of at least one sample, were selected. The remaining assembled transcript sets that passed the filtering steps were analyzed using CNCI (Sun, et al., 2013), PLEK (Li, et al., 2014), and Pfamscan (Madeira, et al., 2022) packages in R v4.2.3 (Supplementary Table S2-6, 7 and 8). The transcripts lacking coding potential, as identified by the three packages, were classified as high-confidence lncRNAs (Supplementary Table S2-9). The read counts of lncRNAs were quantified based on transcript expression levels using StringTie V2.2.0 and normalized by FPKM. The differences in lncRNA expression levels between the two SMR groups were evaluated using the DESeq (Supplementary Table S3-2). The screening conditions of the DEGs were as follows: |log2 Fold Change| > 1, and significance P-value < 0.05.

The sRNA-seq raw data were processed for adapter removal and quality control using the Trimmomatic package in TBtools v2.042 (Chen, et al., 2023). The clean data were aligned to the Rfam13 (Kalvari, et al., 2018) and miRBase (Griffiths-Jones, et al., 2006) databases using BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi); reads aligned to rRNA, tRNA, snRNA, and snoRNA were excluded. Conserved miRNAs were filtered using mature miRNA sequences from all animals in BLAST and miRBase. Non-annotated reads were used to predict novel miRNAs using MIREAP (Wan, et al., 2012) (Supplementary Table S2-11). The read counts of miRNAs were based on the number of reads aligned to conserved miRNA sequences and normalized using the counts per million (CPM) method. The screening conditions of the DEGs were as follows: |log2 Fold Change| > 1, and significance P-value < 0.05.

Validation of RNA and Small-RNA Sequencing using qPCR

The RNA-seq and sRNA-seq results were verified by quantifying the expression trends of randomly selected DE mRNAs, DE lncRNAs, and DE miRNAs using quantitative reverse transcription-PCR (qPCR) technology.

According to the manufacturer's protocol, 1 μg of total RNA was reverse-transcribed using ABScript III RT Master Mix for qPCR with the gDNA Remover Kit (Abclonal, Wuhan, China). The 2X Universal SYBR Green Fast qPCR Mix Kit (Abclonal, Wuhan, China) and CFX Connect Real-Time PCR Detection System (Bio-Rad, CA) were used for individual qPCR. The total volume of the qPCR system was 20 μL: 10 μL of Mix, 1 μL of cDNA, 0.4 μL of each primer (10 μM), and 8.2 μL of ddH2O. The qPCR program was as follows: 95 °C for 3 min; 95 °C for 5 s and 60 °C for 30 s for 40 cycles; and dissolution curves were generated from 65 to 95 °C. GAPDH was used as an internal reference gene for mRNA and lncRNA quantification, while U6 was used as an internal reference gene for miRNA quantification. The primers used for qPCR are listed in Table 1, and were synthesized by Sangon Biotech (https://www.sangon.com).

Table 1.

Primers sequence used for the qPCR analyses of Zi geese samples.

Gene Name Primer sequence (5′–3′)
ADCY8 F: GGGAAAACAGAACACTCTGGC
R: TCCGTAAGTCGATGGTGTGC
CYSLTR1 F: ACTGCTGAACCGGTGACATT
R: ATGAATTTCAGGCCCGGGAG
FGF10 F: TTGACCCCCTGCAAATAAGGG
R: TCTTAGATGCAGAAAGGTAAGTCA
LOC106044170 F: CAGCACCCTACAGATGACCC
R: CAGCTGGGCTGGTTCTTACA
CHST7 F: GACGATGGAAGGGACACGTT
R: GGGTCGCAGTTTCACGTCTA
MSTRG.2982.3 F: AAATGAGCCCCTTCCAGAGC
R: TCAGCGCGTCTCTTTTGTCT
MSTRG.21419.11 F: ACTAGCAGTGACTGTGTGGC
R: TGACAGCCAAAGAGCCTGAG
MSTRG.9047.14 F: GAGTGGACTTACCTGTGGCC
R: CTGTGCTGCTGTAGTGGACA
MSTRG.31651.4 F: TGCGTGAGTTTGCTGTTTCG
R: AGCACTGAGAGAGACGTCCT
MSTRG.21424.1 F: CCCCCATCTTCCCTTGACAC
R: TTTTGGCACAGTCCCACCTT
MSTRG.25601.2 F: TGTGAGGCTTGTTCGGAGAC
R: ACAGGCAGAGAAGGGAAACG
GAPDH F: CATGGAGAAGGCTGGTGCTC
R: CACCCATCACGAACATGGGA
cli-miR-1662-5p F:GCGTTGACATCATCATACTTGGGAT
pbv-miR-1662-5p F: GCGCGGTGAGGTAGTAAGTTGTAT
pbv-miR-205b-5p F: CCCTTCATTCCACCGGAATCTG
oga-miR-98 F: CGGTTGACATCATCATACTTGGGA
cli-miR-1388-3p F: ATCTCAGGTTCGTCAGCCCATG
gga-miR-144-3p F: GCCGCTACAGTATAGATGATGTACTC
U6 F: CGCAAGGATGACACGCAAAT
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F, forward primer; R, reversed primer.

GAPDH (mRNA and lncRNA) and U6 (miRNA) were selected as reference genes. The reversed primer for miRNA were using Universal PCR Primer R (10 μM) (Sangon Biotech, Shanghai, China).

Target Gene Prediction and Functional Enrichment Analysis of mRNAs, lncRNAs, and miRNAs

To predict target mRNAs of lncRNA, miRanda v3.3a (Betel, et al., 2010) and Cis mode method (Kopp and Mendell, 2018) were employed, searching within 100 Kb upstream and downstream regions of each lncRNA (Supplementary Table S3-4). Additionally, default parameters of miRanda v3.3a were utilized to predict target mRNAs and target lncRNAs of DE miRNAs (Supplementary Table S3-6 and 7).

For enrichment analysis for Gene Ontology (GO) terms associated with DE mRNAs, lncRNA target genes, and miRNA target genes, the topGO v2.46.0 R package was used (Supplementary Table S4-1, 2, and 2,3). Similarly, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DE mRNAs, lncRNA target genes, and miRNA target genes was conducted on ClusterProfiler V4.1.1 (Yu, et al., 2012) (Supplementary Table S4-4, 5 and 6). GO terms and KEGG pathways with a P-value < 0.05 were considered significantly enriched.

Construction of mRNA–miRNA–lncRNA Competing Endogenous RNA Network

The competing endogenous RNA (ceRNA) networks were constructed by removing positively correlated DE lncRNA–miRNA and miRNA–mRNA pairs as described by Zhao, et al. (2022) and visualized using Cytoscape V3.10.0 (Otasek, et al., 2019).

To facilitate subsequent analysis, the ENSEMBL_GENE/TRANSCRIPT_ID was converted into OFFICIAL_GENE_SYMBOL using the DAVID Tools (https://david.ncifcrf.gov) for the supplementary information (Supplementary Table S6-1 and 2).

Statistical Analysis

Differences in SM, SV, SC, SA, fertility, BW, total testes weight (TW), seminiferous tubule diameter, seminiferous epithelium height, and sperm maturity scores between the high- and low-SMR groups were evaluated and compared using Student's t-test. Data were analyzed using SPSS Statistics 26 (SPSS Inc., Chicago, IL), with statistical significance set at P < 0.05. The expression levels of related genes were analyzed by qPCR using the 2-ΔΔCt method (Livak and Schmittgen, 2001). The qPCR for a single DE RNA was conducted in duplicate with three replicates.

RESULTS

Sperm Mobility, Fertility, and Histological Observation of Testes in Ganders of Different SMR Groups

The SM, SV, SC, SA, fertility, and testicular histological observations for the two SMR groups are presented in Table 2 and Figure 1. No significant differences were observed in SV (P = 0.794), SC (P = 0.600), or SA (P = 0.077) between the groups. However, the SM in the high-SMR group (0.43 ± 0.02) was significantly higher than that of the low-SMR group (0.10 ± 0.01; P < 0.001). Moreover, fertility rates were 89 and 81% in the high- and low-SMR flock groups, respectively. Notably, compared with the low-SMR group, the high-SMR group had significantly larger seminiferous tubule diameters (P = 0.008), seminiferous epithelium thickness (P = 0.025), and sperm maturity (P = 0.018).

Table 2.

Sperm mobility, semen quality, fertility, and histological observation of testes in Zi geese between different SMR groups (mean ± SEM).

Item H-SMR (n = 3) L-SMR (n = 3) P-value
Sperm mobility (absorbance units) 0.43 ± 0.02a 0.10 ± 0.01b 0.000
Semen volume (mL) 0.20 ± 0.06 0.18 ± 0.02 0.794
Sperm concentration (108/mL) 6.39 ± 0.94 7.00 ± 0.52 0.600
Sperm abnormality (%) 22.83 ± 3.18 34.24 ± 3.61 0.077
Fertility (%) 89%a 81%b 0.030
Body weight (kg) 4.13 ± 0.02 4.38 ± 0.19 0.251
Total testes weight (g) 11.49 ± 4.30 6.86 ± 1.55 0.240
Spermatogenic tubule diameter (μm) 332.33 ± 13.13a 228.00 ± 16.46b 0.008
Spermatogenic epithelial thickness (μm) 58.00 ± 5.46a 37.79 ± 1.83b 0.025
Sperm maturity score (Johnsen) 9.12 ± 0.35a 7.53 ± 0.21b 0.018
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H-SMR, high sperm mobility rank; L-SMR, low sperm mobility rank.

Fertility = number of fertilized eggs certain flock/number of eggs collected this flock × 100%. (Wang et al., 2023)

The software Image-pro plus 6.0 (Media Cybernetics, Inc., Rockville, MD) was used for the analysis and measurement of the testicular tissue structure.

Sperm maturity score: The maturation of spermatogenic cells in the seminiferous epithelium was classified according to the Johnsen score. (Johnsen, 1970).

a,b

Different letters in the same row indicate a significant difference, P < 0.05.

Figure 1.

Figure 1

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Geese testicular tissue from different SMR groups stained with hematoxylin and eosin (H&E). H&E staining of paraffin sections (40 ×) of the testis with high (A) and low (a) SMR. H&E staining of paraffin sections (100 ×) of the testis with high (B) and low (b) SMR. H&E staining of paraffin sections (200 ×) of the testis with high (C) and low (c) SMR. H&E staining of paraffin sections (400 ×) of the testis with high (D) and low (d) SMR. E1, E2, E3, and E4: Diameter of seminiferous tubule; F1, F2, F3, and F4: Thickness of seminiferous epithelium.

Transcriptome Sequencing Output, Alignment, and Gene Annotation

Approximately 759 million paired-end raw reads from the RNA-seq libraries were obtained from the six samples, with 46 GB data per sample (Supplementary Table S2-2). The alignment of clean reads to the reference genome was highly successful, with the majority (88.67 - 90.11%) being effectively aligned (Supplementary Table S2-4). Additionally, 15,439 genes were successfully functionally annotated for GO and KEGG analysis, establishing the background gene set for enrichment analysis (Supplementary Table S2-5).

Approximately 119 million single-end raw reads were generated from the sRNA-seq libraries across six samples, averaging 7 GB data per sample (Supplementary Table S2-3). The alignment and annotation statistics for the sRNA-seq reads are shown in Figure 2, revealing successful classification and annotation for 43.67% of the transcripts.

Figure 2.

Figure 2

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Small-RNA sequencing alignment annotation. The abscissa is the sample name, and the ordinate is the read numbers of ncRNAs in the sample. The column of different colors represents different ncRNA symbols, and the length of the column represents the read numbers of the ncRNA.

Differential Expression Analysis of mRNAs, lncRNAs, and miRNAs

A total of 15150 mRNAs, 19094 lncRNAs, and 1812 miRNAs were expressed in the two SMR groups (Supplementary Table S3-1, 2 and 3). The statistical results of the DEGs between the high- and low-SMR groups are shown in Figure 3. Compared with the low-SMR group, the high-SMR group had 49 upregulated mRNAs, 178 upregulated lncRNAs, 12 upregulated miRNAs, 27 downregulated mRNAs, 166 downregulated lncRNAs, and 5 downregulated miRNAs.

Figure 3.

Figure 3

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Differentially expressed mRNA, lncRNA, and miRNA in the 2 SMR groups. Upregulated: number of highly expressed genes in the high sperm mobility rank group compared with that in the low sperm mobility rank group. Downregulated: number of low-expression genes in the high sperm mobility rank group compared to that in the low sperm mobility rank group.

Validation of RNA and Small-RNA Sequencing using qPCR

The expression trend alignment results of qPCR and transcriptome sequencing are shown in Figure 4. For validation, 5 DE mRNAs, 6 DE lncRNAs, and 6 DE miRNAs were randomly selected for qPCR analysis. The expression trends observed in the qPCR results were consistent with those of RNA-seq and sRNA-seq analyses, confirming the reliability of the differential analysis results for further analysis.

Figure 4.

Figure 4

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Alignment of transcriptome sequencing and qPCR results for expression trends between high- and low-SMR groups. (A) mRNA, (B) lncRNA, (C) miRNA, and (D) regression equation and correlation coefficient between RT-qPCR and sequencing data. Sequencing expression fold change = log2(fold change); qPCR expression fold change = log2(2-ΔΔCt).

Target Gene Prediction and Functional Enrichment Analysis of mRNAs, lncRNAs, and miRNAs

Figure 5A illustrates the results of GO terms enrichment analysis of the DE mRNAs between high- and low-SMR groups (Supplementary Table S4-1). The enriched GO terms included those related to the extracellular environment, extracellular matrix, molecular activities, growth and development, hormone secretion, cell proliferation, neuronal function, immune response, and bone development. Examples included “extracellular region (CC),” “G protein-coupled receptor activity (MF),” and “animal organ morphogenesis (BP).” Figure 6A presents the results of the KEGG pathway enrichment analysis of the DE mRNAs between the high- and low-SMR groups (Supplementary Table S4-4). Notably, DE mRNAs were significantly enriched in two pathways (P < 0.05): “caffeine metabolism” and “apelin signaling pathway.”

Figure 5.

Figure 5

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GO function enrichment analysis between high- and low-SMR groups. (A) DE mRNA; (B) DE lncRNA; (C) DE miRNA. Abbreviations: MF: molecular function; BP: biological process; CC: cell component. Rich factor: number of enriched differential genes/number of annotated differential genes in the GO term; a larger rich factor indicated a greater degree of enrichment. FDR ranges from 0 to 1, the closer it is to zero, the more significant the enrichment. The top 20 GO term descriptions with the smallest FDR values, that is, the most significant enrichment, were selected for display. This also applies to Figure 6.

Figure 6.

Figure 6

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KEGG pathway enrichment analysis between high- and low-SMR groups. (A) DE mRNA; (B) DE lncRNA; (C) DE miRNA.

The results of the GO terms enrichment analysis of DE lncRNA target mRNAs between the high- and low-SMR groups are shown in Figure 5B (Supplementary Table S4-2). In the high- and low-SMR groups, 344 DE lncRNAs targeted and regulated 14,133 mRNAs in their adjacent positions through the Cis mode (Supplementary Table S3-4). Most target mRNAs were enriched in functions and components related to cell membranes, synapses, and molecular transmission, specifically in key components and structures involved in synaptic neurotransmission. The enriched GO terms included “plasma membrane (CC),” “purine ribonucleoside triphosphate binding (MF),” and “response to stimulus (BP).” The KEGG pathway enrichment analysis results for the DE lncRNA target mRNAs between the high- and low-SMR groups are shown in Figure 6B (Supplementary Table S4-5). The target mRNAs were significantly enriched in five pathways (P < 0.05): “Adherens junction,” “glycosaminoglycan biosynthesis-chondroitin sulfate or dermatan sulfate,” “N-Glycan biosynthesis,” “glycosaminoglycan biosynthesis-heparan sulfate/heparin,” and “neuroactive ligand-receptor interaction signaling pathway.”

The GO enrichment analysis results for the predicted target mRNAs of the 17 DE miRNAs are shown in Figure 5C (Supplementary Table S4-3). Using the miRanda database, the 17 DE miRNAs were employed to predict target genes, resulting in the identification of 2, 406 and 565 target genes and sites, respectively, across both groups (Supplementary Table S3-6). Target genes regulated by the DE miRNAs were predominantly enriched in processes associated with protein modification, regulation of cellular structures and organelles, molecular binding and catalytic activity, and cellular metabolic processes. Specifically, the enriched GO terms included “intracellular organelle (CC),” “protein binding (MF),” and “metabolic process (BP).” The results of the KEGG pathway enrichment analysis of the DE miRNAs are shown in Figure 6C (Supplementary Table S4-6). The DE miRNAs were significantly enriched in 13 pathways (P < 0.05): “regulation of actin cytoskeleton,” “other types of O-glycan biosynthesis,” “glycosphingolipid biosynthesis-ganglion series,” “Adherens junction,” “endocytosis,” “lysosome,” “Hedgehog signaling pathway,” “glycosylphosphatidy linositol (GPI)-anchor biosynthesis,” “homologous recombination,” “autophagy-animal,” “Salmonella infection,” “autophagy-other,” and “terpenoid backbone biosynthesis.”

Construction of mRNA–miRNA–lncRNA Competing Endogenous RNA Networks

The constructed ceRNA network is presented in Figure 7A (Supplementary Table S5-1), comprising 109 nodes and 252 edges. Within these nodes, there were 12 upregulated DE miRNAs, 7 downregulated DE mRNAs, and 90 upregulated DE lncRNAs. The 252 edges included 17 miRNA–mRNA pairs and 235 lncRNA–miRNA pairs. The second ceRNA network (Figure 7B) (Supplementary Table S5-2) comprised 86 nodes and 132 edges. The 86 nodes included 5 downregulated DE miRNAs, 2 upregulated DE mRNAs, and 79 upregulated DE lncRNAs. The 132 edges included 3 miRNA–mRNA pairs and 129 lncRNA–miRNA pairs.

Figure 7.

Figure 7

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mRNA–miRNA–lncRNA ceRNA interaction network construction between high- and low-SMR groups. (A) downregulated mRNAs-upregulated miRNAs-downregulated lncRNAs; (B) upregulated mRNAs-downregulated miRNAs-upregulated lncRNAs.

Most DE miRNA nodes were upregulated, whereas most mRNA nodes were downregulated in these two ceRNA networks (Supplementary Table S5-3). Furthermore, the upregulated and downregulated DE lncRNA node distributions were relatively similar. Individual miRNAs generally regulate multiple mRNAs and lncRNAs. Based on the targeted binding degree value of each factor, most miRNAs had higher binding degrees. The top three ranked factors based on binding degree were cli-miR-1388-3p, oha-miR-182-5p, and gga-miR-144-3p, which interacted with 33, 31, and 31 other factors, respectively. Among the mRNAs and lncRNAs, LOC106037574, LOC106049708, PRKAG3, MSTRG.21876.13, MSTRG.21876.16, and MSTRG.21876.17 showed the highest binding degree. We then performed KEGG pathway functional annotation on these key genes, revealing associations with pathways such as “fructose and mannose metabolism,” “adherens junction,” “glutamatergic synapse,” “neuroactive ligand-receptor interaction,” “AMPK signaling pathway,” “apelin signaling pathway,” “Wnt signaling pathway,” and “FoxO signaling pathway.”

DISCUSSION

A total of 76 DE mRNAs, 344 DE lncRNAs, and 17 DE miRNAs were screened. Enrichment analysis revealed three potential influential pathways, namely, apelin signaling, Wnt signaling, and adherens junction pathways. Based on our comprehensive analysis of the ceRNA network, we identified LOC106049708–oha-miR-182-5p–MSTRG.2479.6 and PRKAG3–ocu-miR-10b-5p–MSTRG.9047.14 as two key transcriptional regulatory pathways. To ensure the uniformity and stability of the SM traits in the high- and low SMR groups, we selected three ganders from each SMR group for transcriptomic analysis (Liu et al., 2017; Wang et al., 2021).

mRNA, lncRNA, and miRNA Associated With Sperm Mobility

LOC106049708 encodes the tumor necrosis factor apoptosis ligand receptor 1 (DR4/TRAILR1/TNFRSF10A) that contains a death domain with a high binding degree and is negatively regulated by oha-miR-182-5p, oga-miR-182 and ocu-miR-96-5p in the ceRNA network. Grataroli et al. (2004) found that TRAILR1 can bind tumor necrosis factor-α-related apoptosis-inducing ligand (TRAIL), inducing germ cell apoptosis in the testis and playing an important role in maintaining testicular germ cell homeostasis and functional spermatogenesis. Lin and Richburg (2014) reported significant reductions in testes weight, germ cell counts, spermatid head counts, and other parameters in Trail-deficient mice. Interestingly, the expression of LOC106049708 in the low-SMR group was significantly higher than that in the high-SMR group. This may have been caused by an apoptotic program being initiated to limit the number of germinal cells in the testis when the testicular supporting capacity was insufficient (Moreno, et al., 2006; Rodriguez, et al., 1997). These results suggested that LOC106049708 affects SM by participating in the regulation of spermatogenesis and apoptosis. This notion was indirectly supported by the morphological observation of the testes in the different SMR groups; abnormal testicular tissue development and function may be a major contributing factor to the lower SM (Figure 1). These findings were consistent with those of previous reports (Sun, et al., 2019; Li, et al., 2020).

Ding et al. (2020) found a positive correlation between the expression level of miR-182-5p and normal sperm morphology numbers in individuals with teratozoospermia (TZ), suggesting its potential use as a biomarker for TZ. Martinez et al. (2022) and Curry et al. (2011) reported that sperm with high miR-182-5p expression maintain high motility and integrity. In our study, we observed a significant upregulation of miR-182-5p expression in the high-SMR group that was positively correlated with SM. These findings are consistent with the results reported by Ding et al. (2020) and Martinez et al. (2022). A possible explanation is that miR-182-5p can reportedly inhibit inflammatory responses and regulate physiological apoptosis during spermatogenesis in the testis (Qin, et al., 2018; Zhang, et al., 2018; Fei, et al., 2021;Wang, et al., 2024).

X-prolyl aminopeptidase 3 (XPNPEP3), encoded by the XPNPEP3, is a crucial metalloectopeptidase in the protein transport pathway to mitochondria (Prasai, 2017). Wachoski-Dark et al (2022) reported that XPNPEP3 maintains mitochondrial proteostasis, ensuring mitochondrial energy and ATP production (Gakh, et al., 2002; Singh, et al., 2017). Biochemical studies have demonstrated that the functional loss of XPNPEP3 disrupts the Hedgehog and Wnt signaling pathways, potentially affecting spermatozoa motility (Baala, et al., 2007; Böttinger, 2010; Tong, et al., 2023). Based on sequencing results, we found a significant enrichment of XPNPEP in the energy metabolism pathway, identifying it as a key gene influencing SM. Furthermore, our findings revealed that XPNPEP is targeted and positively regulated by MSTRG.21876.14; notably, both were highly expressed in the high-SMR group. This suggests a potential regulatory role for the XPNPEP-MSTRG.21876.14 pairs in SM.

Both our research and that of Wu et al. (2023) found that ADCY family members were differentially expressed between the high and low SM groups, and DE genes were significantly enriched in the eukaryotic cytoskeleton GO term. These findings highlight the critical roles of energy metabolism and cytoskeletal stability in sperm mobility. However, our study did not identify DE miRNA similar to those of Wu's findings. This disparity may have stemmed from the species factor. The Zi geese originated from the swan geese (Anser cygnoides), while the Yili geese originated from the greylag geese (Anser anser) (Wen, et al., 2023).

Signaling Pathway Related to Sperm Mobility

The apelin signaling pathway emerged as a pivotal pathway significantly enriched in our study, positioned upstream of the AC/Cyclic AMP (cAMP), cAMP/protein kinase A (PKA), and AMP-activated protein kinase (AMPK) pathways. Apelin activates the apelin receptor (APJ) to bind the G protein family member and inhibits AC from participating in cAMP production (Pozdniakova and Ladilov, 2018). Within the Apelin signaling pathway, 3 genes—GNB3, ADCY8, and PRKAG3—were identified and found to be upregulated in the high-SMR group. Notably, PRKAG3 encodes a γ subunit of AMPK, which binds to AMP and enhances AMPK activity. As a cellular energy sensor, AMPK is involved in regulating cellular carbohydrate and lipid metabolism (Weyrich, et al., 2007). Hence, apelin signaling may have an important role in maintaining spermatogenic homeostasis in testes.

The Wnt signaling pathway was another key pathway enriched in the KEGG pathway annotation analysis. Specifically, WNT7A and WIF1 were enriched in the Wnt signaling pathway and upregulated in the high-SMR group. Wnt signaling is closely related to sperm motility (Xue, et al., 2021). Yin et al.(2021) screened six DE miRNAs in the testes with high and low sperm motility of domestic pigeons, whose target mRNAs were related to the Wnt signaling pathway. Studies in male Wnt7a-/- mice have demonstrated abnormal Müllerian duct development and infertility (Parr and McMahon, 1998). Our findings indicated that WNT7A expression was lower in the low-SMR group than that of the high-SMR group. Additionally, we observed narrower seminiferous epithelial thickness and a lower quantity of mature sperm in the testes of the low-SMR group. We speculate that the Wnt signaling pathway may be a crucial regulatory pathway impacting SM and testicular development.

The KEGG pathway enrichment analysis revealed enrichment of the adherens junction pathway among upregulated mRNAs, lncRNAs, and miRNAs. Previous studies have demonstrated that interfering with the expression of genes associated with the adherens junction pathway can induce the disassembly of the apical protein complex and microtubule-based cytoskeleton in the seminiferous epithelium (Wen, et al., 2019). This disruption can lead to structural and functional impairments in the seminiferous tubules and the blood-testis barrier (Mandai, et al., 2015; Takai, et al., 2008), potentially affecting normal sperm production and maturation and resulting in male infertility (Lee and Cheng, 2008).

Competing Endogenous RNA Networks of mRNA-miRNA-lncRNA

Grataroli et al. (2004) reported that LOC106049708 (TRAILR1) is expressed primarily in Sertoli cells and post-meiotic germ cells, where it aids in removing damaged germ cells and regulating the number of developing germ cells to match the testicular Sertoli cell capacity. MiR-182-5p is involved in regulating apoptotic pathways during spermatogenesis and can enhance the stability of the testicular microenvironment and sperm quality by targeting the MREs of LOC106049708 (Qin, et al., 2018). While the biological functions of MSTRG.2479.6 remain unclear, we speculate that it regulates SM in geese. Indeed, the ceRNA pathway LOC106049708–oha-miR-182-5p–MSTRG.2479.6 is crucial in the regulation of normal sperm formation and SM in the testes.

PRKAG3 participates in AMPK activity regulation, playing an important role in the regulation of cellular energy metabolism, pH, and glycogen metabolism in the testicular microenvironment (Milan, et al., 2000; Roux, et al., 2006). Meanwhile, miR-10b-5p contributes to PI3K/AKT signaling regulation, with an important role in maintaining ATP and glucose metabolism homeostasis (Quan, et al., 2022; Li, et al., 2024). Ma et al. (2021) observed significantly upregulated expression of miR-10b-5p in infertile mice exhibiting low sperm motility. Consistently, in our study, the expression level of ocu-miR-10b-5p was higher in the low-SMR group. While the biological functions of MSTRG.9047.14 remain unclear, we speculate that it participates in the regulation of SM in geese. The PRKAG3–ocu-miR-10b-5p–MSTRG.9047.14 pathway (Figure 7B) can affect AMPK activation and modulate sperm energy metabolism balance to regulate SM.

In summary, numerous genes and complex signaling pathways influence SM traits. However, the maturation of sperm and the realization of its motility function are affected by the synergistic action of the testis and epididymis; thus, further studies are needed to systematically and comprehensively analyze the SM regulation process of the testis and epididymis and validate the impact of these key genes on SM.

CONCLUSIONS

A total of 76 DE mRNAs, 344 DE lncRNAs, and 17 DE miRNAs were identified between the two SMR groups. Among these, LOC106049708, XPNPEP3, GNB3, ADCY8, PRKAG3, oha-miR-182-5p, and ocu-miR-10b-5p emerged as pivotal contributors to SM. Notably, two ceRNA network pathways, namely, LOC106049708–oha-miR-182-5p–MSTRG.2479.6 and PRKAG3–ocu-miR-10b-5p–MSTRG.9047.14, were identified as pivotal regulators of SM. However, further studies are required to confirm the involvement of these key genes in the development of testes and sperm mobility. Overall, our findings offer crucial insights into the identification of key genes and ceRNA pathways influencing sperm mobility in geese.

DISCLOSURES

The authors declare no conflicts of interest.

ACKNOWLEDGMENTS

This work was supported by Bio-breeding industry innovation and development project of Heilongjiang province (2024), Heilongjiang Province Double First-class Characteristic Discipline Platform Project (No.HLJ2022TSXK).

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psj.2024.103895.

Appendix. Supplementary materials

mmc1.pdf (5.1MB, pdf)
mmc2.xlsx (18KB, xlsx)
mmc3.xlsx (12.8MB, xlsx)
mmc4.xlsx (21.6MB, xlsx)
mmc5.xlsx (1.6MB, xlsx)
mmc6.xlsx (26.7KB, xlsx)
mmc7.xlsx (745.7KB, xlsx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.pdf (5.1MB, pdf)
mmc2.xlsx (18KB, xlsx)
mmc3.xlsx (12.8MB, xlsx)
mmc4.xlsx (21.6MB, xlsx)
mmc5.xlsx (1.6MB, xlsx)
mmc6.xlsx (26.7KB, xlsx)
mmc7.xlsx (745.7KB, xlsx)

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