RNA‐Seq reveals the functional specificity of epididymal caput, corpus, and cauda genes of cattleyak

Xia, Lang; Adjei, Michael; Wang, Cailian; Chen, Xiaoying; Li, Chunhai; Wang, Peng; Pan, Meilan; Li, Kerui; Shahzad, Khuram; Zhao, Wangsheng

doi:10.1111/asj.13732

Cited by 6 publications

(4 citation statements)

References 80 publications

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“…We performed qPCR validation on six DE circRNAs ( Table 1 ), and although the folding rates differed, the overall trend was consistent, proving that our sequencing data was correct ( Figure 10 ). At the same time, combined with previous articles ( 29 , 30 ) on the expression of DE miRNA in yak and cattleyak epididymal tissues, the relationship chain between circRNA-miRNA-mRNA was confirmed.…”

Section: Resultssupporting

confidence: 63%

“…Based on the verified miRNA expression in our team's already published papers ( 29 , 30 ), among the 137 DE circRNAs obtained in this study, six of them (see Table 1 ) were screened to test the differential expression of circRNA by qPCR, and the relationship between circRNA, miRNA and mRNA was verified. cDNA is obtained by reverse transcription from total RNA using SuperScript™ III reverse transcriptase (Invitrogen, Carlsbad, CA, USA).…”

Section: Methodsmentioning

confidence: 99%

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Identification and analysis of differentially expressed (DE) circRNA in epididymis of yak and cattleyak

Yan²,

Pan³

et al. 2023

Front. Vet. Sci.

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Circular RNAs (circRNAs), as endogenous non-coding RNA with unique closed ring structure, is closely related to animal reproduction, and understanding the expression of circRNA in yak and cattleyak epididymal tissues is of great significance for understanding cattleyak sterility. Based on this, we screened and identified the differentially expressed circRNA in the epididymis of three yaks and two cattleyak. A total of 1,298 circRNAs were identified in the epididymis of yak and cattleyak, of which 137 differentially expressed (DE) circRNAs and the functions of some of them were elucidated in this research, as well as qPCR verification to 6 circRNAs from the 137 DE circRNAs. Gene Ontology (GO) enrichment analysis suggested that DE circRNAs were mainly related to metabolic process, development process, immune system process, reproductive process, reproduction, biological adhesion and growth. COG classification analysis showed that the DE circRNAs derived genes were mainly related to replication, recombination and repair. KEGG pathway analysis suggested that DE circRNAs were mainly involved in RNA degradation. In addition, we also screened Bta-mir-103, which is a circRNA binding miRNA related to sperm activity.

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Section: Resultssupporting

confidence: 63%

Section: Methodsmentioning

confidence: 99%

Identification and analysis of differentially expressed (DE) circRNA in epididymis of yak and cattleyak

Yan²,

Pan³

et al. 2023

Front. Vet. Sci.

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“…PAOX was considered to be related to male yellow cattle infertility. The upregulation of PAOX may be associated with toxicity and apoptosis resistance in cattleyak ( 43 , 44 ). But there is still a lack of relevant evidence on the impact of PAOX on human infertility.…”

Section: Discussionmentioning

confidence: 99%

Identifying potential biomarkers for non-obstructive azoospermia using WGCNA and machine learning algorithms

Tang,

Su,

Wei

et al. 2023

Front. Endocrinol.

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ObjectiveThe cause and mechanism of non-obstructive azoospermia (NOA) is complicated; therefore, an effective therapy strategy is yet to be developed. This study aimed to analyse the pathogenesis of NOA at the molecular biological level and to identify the core regulatory genes, which could be utilised as potential biomarkers.MethodsThree NOA microarray datasets (GSE45885, GSE108886, and GSE145467) were collected from the GEO database and merged into training sets; a further dataset (GSE45887) was then defined as the validation set. Differential gene analysis, consensus cluster analysis, and WGCNA were used to identify preliminary signature genes; then, enrichment analysis was applied to these previously screened signature genes. Next, 4 machine learning algorithms (RF, SVM, GLM, and XGB) were used to detect potential biomarkers that are most closely associated with NOA. Finally, a diagnostic model was constructed from these potential biomarkers and visualised as a nomogram. The differential expression and predictive reliability of the biomarkers were confirmed using the validation set. Furthermore, the competing endogenous RNA network was constructed to identify the regulatory mechanisms of potential biomarkers; further, the CIBERSORT algorithm was used to calculate immune infiltration status among the samples.ResultsA total of 215 differentially expressed genes (DEGs) were identified between NOA and control groups (27 upregulated and 188 downregulated genes). The WGCNA results identified 1123 genes in the MEblue module as target genes that are highly correlated with NOA positivity. The NOA samples were divided into 2 clusters using consensus clustering; further, 1027 genes in the MEblue module, which were screened by WGCNA, were considered to be target genes that are highly correlated with NOA classification. The 129 overlapping genes were then established as signature genes. The XGB algorithm that had the maximum AUC value (AUC=0.946) and the minimum residual value was used to further screen the signature genes. IL20RB, C9orf117, HILS1, PAOX, and DZIP1 were identified as potential NOA biomarkers. This 5 biomarker model had the highest AUC value, of up to 0.982, compared to other single biomarker models; additionally, the results of this biomarker model were verified in the validation set.ConclusionsAs IL20RB, C9orf117, HILS1, PAOX, and DZIP1 have been determined to possess the strongest association with NOA, these five genes could be used as potential therapeutic targets for NOA patients. Furthermore, the model constructed using these five genes, which possessed the highest diagnostic accuracy, may be an effective biomarker model that warrants further experimental validation.

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“…Rats and mice had been the most commonly used research models for studying the epididymis. In recent decades, advancements in technology, such as gene-knockout approaches ( Eddy et al, 1996 ), transcriptome sequencing ( Wu et al, 2021a ), microarray analysis ( Jervis and Robaire, 2003 ), proteomics ( Fouchécourt et al, 2000 ), cell lines ( Masuda et al, 2022 ), and single cell RNA-seq methods for transcriptome analysis ( Rinaldi et al, 2020 ; Lang et al, 2022 ), had significantly enhanced our understanding of epididymal composition. Numerous knockout mouse models have exhibited phenotypes that impact male reproductive function.…”

Section: Relevant Advancements Applied To Research Of Epididymismentioning

confidence: 99%

The significance of single-cell transcriptome analysis in epididymis research

Liu,

Feng,

et al. 2024

Front. Cell Dev. Biol.

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As a crucial component of the male reproductive system, the epididymis plays multiple roles, including sperm storage and secretion of nutritive fluids for sperm development and maturation. The acquisition of fertilization capacity by sperm occurs during their transport through the epididymis. Compared with the testis, little has been realized about the importance of the epididymis. However, with the development of molecular biology and single-cell sequencing technology, the importance of the epididymis for male fertility should be reconsidered. Recent studies have revealed that different regions of the epididymis exhibit distinct functions and cell type compositions, which are likely determined by variations in gene expression patterns. In this research, we primarily focused on elucidating the cellular composition and region-specific gene expression patterns within different segments of the epididymis and provided detailed insights into epididymal function in male fertility.

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RNA‐Seq reveals the functional specificity of epididymal caput, corpus, and cauda genes of cattleyak

Cited by 6 publications

References 80 publications

Identification and analysis of differentially expressed (DE) circRNA in epididymis of yak and cattleyak

Identification and analysis of differentially expressed (DE) circRNA in epididymis of yak and cattleyak

Identifying potential biomarkers for non-obstructive azoospermia using WGCNA and machine learning algorithms

The significance of single-cell transcriptome analysis in epididymis research

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