Study of differential expression of ovarian duct tissue genes in the follicular phase associated with fertility in goats using transcriptomics data

Introduction: High fertility of goats is a key economic trait that has a direct impact on production efficiency in the goat industry. Many goat breeds have one or two kids per litter, and as the number of kids per litter increases, the amount of meat, milk, and other related products increases in proportion. The oviduct, as the main route of gamete transmission, plays a key role in mammalian fertility and provides a suitable environment for oocyte maturation, sperm capacitation, fertilization, and early embryo transfer. The follicular phase, which is accompanied by an increase in follicle-stimulating hormone (FSH) levels and the growth of dominant follicles, is of particular importance because it plays an important role in oocyte maturation and fertility. Among the many types of gene expression data, RNA-seq data has a great advantage over other types of data due to its high throughput and inclusion of sequences of transcribed regions and coverage of all these regions; In recent years, computer capabilities have made it possible to analyze this volume of data in a timely and cost-effective manner. One of the advantages of using RNA-seq data is the identification of new genes. In this study, our goal is to identify genes related to the oviduct in two groups of goats with low fertility (only one kid) and high fertility (giving birth to two or more kids); and ultimately, by analyzing the ontology of the index genes, metabolic pathways and molecular activities involved in the stages of pregnancy can be identified.
Materials and methods: To investigate gene expression patterns in oviduct tissue, RNA-seq data from 12 samples of 3-year-old female Yushang goats sequenced by China Agricultural University using the Illumina NovaSeq 6000 platform were used. The original SRA format files were converted to FASTQ format using SRA Toolkit v2.10.2 software. Data quality was assessed using FastQC v0.11.9 software and its graphical interface in Java environment. Based on the quality assessment results, if necessary, paired-end data were corrected and cleaned using Trimmomatic v0.39 software. After cleaning, the data were again evaluated for quality with FastQC. To prepare the reference genome for alignment, its indexing was performed using Hisat2 v2.2.1 software, and then, using the same software, the cleaned reads were aligned to the reference genome. The SAM format outputs were converted to BAM format and sorted, indexed, and prepared for the next steps using the Samtools software package. Cufflinks v2.2.1 was used to assemble the transcriptomes, and after independent assembly of each sample, the transcriptome files generated were merged with Cuffmerge software and converted into a single reference file. Finally, to display the results in the form of graphs and charts, the CummeRbund package was used in the R software environment, and gene ontology and biological pathway analyses related to differentially expressed genes were performed using the GENECARD (https://www.genecards.org) and DAVID (https://david.ncifcrf.gov/) databases.
Results and discussion: A total of 69,005 gene identifiers were detected across both groups, from which only those with a |log₂ fold change| ≥ 3 and q-value < 0.05 were selected as differentially expressed genes (DEGs), resulting in 22 candidate genes. The highest expression levels were observed in LGALS15, LGALS16, U1, and XLOC_03533. The most prominent gene in terms of expression difference between the two groups was XLOC_019343, which showed greater expression in the low-fertility group but remains uncharacterized in public databases. XLOC_03533 was the most highly expressed gene in the high-fertility group, while LGALS15, LGALS16, and U1 were dominant in the low-fertility group, with the largest expression disparities. These genes are implicated in critical reproductive processes such as endometrial remodeling, embryo implantation, placental formation, and structural and functional adaptations of the reproductive system.
Conclusions: 22 genes associated with high and low fertility were identified on the transcriptome of the samples, which played a role in processes such as pregnancy establishment, endometrial remodeling, embryo implantation and placenta formation, regulation of the pH of the fallopian tube for sperm entry, prevention of some placental diseases, pathological inflammatory response, structural and functional adaptation for reproductive processes, and regulation of the gestation period. The results of this study can provide additional information to understand the relationship between effective genes and their pathways on the effect of fertility rate in Yushang goats, which can be generalized to other goat breeds.