Dataset Information

Proteomic landscape of human spermatozoa in patients with different spermatogenic impairments

ABSTRACT: Infertility is a widespread problem, affecting around 15% of couples worldwide, and is defined as the inability to achieve pregnancy within one year despite unprotected intercourse 1. Infertility can be caused by either male or female reproductive issues. Various medical conditions including malignancies, infections, urogenital conditions, or genetic causes can contribute to male infertility. However, 30-40% of men in their reproductive age are affected by idiopathic infertility, according to the guidelines of European Association of Urology (EAU) 1. Towards a better understanding of male infertility, it is mandatory to achieve a comprehensive understanding of involved genes, their RNA transcripts, and regulatory factors, including miRNAs, which influence the expression level of proteins. Therefore, such proteins need to be identified to investigate their role in spermatogenesis and male infertility. Although there are numerous studies on RNAs, including miRNAs related to male infertility 2-9, there are few studies aiming to cover the whole proteome of human sperm 10. The sperm transcriptome comprises a total of 60,505 transcripts including 11,688 differentially expressed transcripts in infertile and fertile men, as reported by Joshi et al. (2022)11. The entire sperm proteome encompasses 6871 proteins, as summarized by Castillo et al. (2018)10. Nevertheless, there is still a lack of high-throughput studies aiming to identify dysregulated proteins in sperm from subfertile men. Only few studies focused on comparisons of the sperm proteome in men with asthenozoospermia and there is virtually no proteomic studies of oligoasthenozoospermic men 12. Some identified proteins in sperm have functions in maintaining sperm motility and enabling fertilization and are involved in structural composition and/or energy metabolism 12-14 and others are not yet functionally characterized. In this study, we employed Mass spectrometry (MS) technology that is still rarely used in the field of human reproductive research to investigate the proteomic landscape of human sperm and their differential expression patterns in men with subfertility.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (human) Escherichia Coli

TISSUE(S): Cell Culture

SUBMITTER: Markus Räschle

LAB HEAD: Markus Räschle

PROVIDER: PXD039703 | Pride | 2023-05-10

REPOSITORIES: Pride

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Publications

Proteomic Landscape of Human Sperm in Patients with Different Spermatogenic Impairments.

Becker Lea Simone LS Al Smadi Mohammad A MA Raeschle Markus M Rishik Shusruto S Abdul-Khaliq Hashim H Meese Eckart E Abu-Halima Masood M

Cells 20230326 7

Although the proteome of sperm has been characterized, there is still a lack of high-throughput studies on dysregulated proteins in sperm from subfertile men, with only a few studies on the sperm proteome in asthenozoospermic and oligoasthenozoospermic men. Using liquid chromatography-mass spectrometry (LC-MS/MS) along with bioinformatics analyses, we investigated the proteomic landscape of sperm collected from subfertile men (<i>n</i> = 22), i.e., asthenozoospermic men (<i>n</i> = 13), oligoast ...[more]

PMID: 37048090

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Project description:BACKGROUND: In previous studies using candidate gene approaches, low sperm count (oligospermia) has been associated with altered sperm mRNA content and DNA methylation in both imprinted and non-imprinted genes. We performed a genome-wide analysis of sperm DNA methylation and mRNA content to test for associations with sperm function. METHODS AND RESULTS: Sperm DNA and mRNA were isolated from 21 men with a range of semen parameters presenting to a tertiary male reproductive health clinic. DNA methylation was measured with the Illumina Infinium array at 27,000 CpG loci. Unsupervised clustering of methylation data differentiated the 21 sperm samples by their motility values. Recursively partitioned mixture modeling (RPMM) of methylation data resulted in four distinct methylation profiles that were significantly associated with sperm motility (P=0.01). Linear models of microarray analysis (LIMMA) was performed based on motility and identified 9,189 CpG loci with significantly altered methylation (Q<0.05) in the low motility samples, with many loci located in genes associated with subfertility and epigenetic regulation. In the low motility samples, the majority of disrupted CpG loci (80%) were hypomethylated. Of the aberrantly methylated CpGs, 194 were associated with imprinted genes almost equally distributed into hypermethylated (predominantly paternally expressed) and hypomethylated (predominantly maternally expressed) groups. Sperm mRNA was measured with the Human Gene 1.0 ST Affymetrix GeneChip Array. LIMMA analysis based on motility identified 20 candidate transcripts as differentially expressed in low motility sperm, including HDAC1 (NCBI 3065), SIRT3 (NCBI 23410), and DNMT3A (NCBI 1788). Altered expression of these epigenetic regulatory genes was associated with RPMM DNA methylation class. CONCLUSIONS: Using integrative genome-wide approaches to study epigenetic and gene expression patterns in human sperm we identified CpG methylation profiles and mRNA alterations associated with low sperm motility, and that low motility sperm may have aberrant genome-wide hypomethylation due to excess HDAC1 activity. See "summary" above

Dataset Information

Proteomic landscape of human spermatozoa in patients with different spermatogenic impairments

Publications

Proteomic Landscape of Human Sperm in Patients with Different Spermatogenic Impairments.

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