Omics and Male Infertility: Highlighting the Application of Transcriptomic Data

Area of Science:

• Genetics and Reproductive Biology
• Bioinformatics and Computational Biology
• Genomics and Transcriptomics

Background:

• Male infertility affects 50% of infertile couples, with 30% of cases remaining idiopathic despite conventional diagnostic methods.
• Genetic factors are increasingly recognized in male infertility, necessitating advanced approaches beyond traditional semen analysis and hormonal evaluations.
• The
• omics
• perspective offers a comprehensive analysis of biological functions at molecular, transcript, protein, and metabolite levels.

Purpose of the Study:

• To explore the role of different
• omics
• branches, particularly transcriptomics, in understanding male infertility.
• To identify common genetic alterations associated with various forms of male infertility by analyzing publicly available transcriptomic data.
• To elucidate the potential of identified genes as biomarkers for diagnosing male infertility, especially idiopathic cases.

Main Methods:

• Systematic retrieval and analysis of publicly available transcriptomic datasets related to male infertility.
• Classification of 10 selected datasets into distinct etiological groups: non-obstructive azoospermia (NOA), obstructive azoospermia (OA), spermatogenic dysfunction, sperm dysfunction, and Y chromosome microdeletion.
• Bioinformatic analysis to identify commonly differentially expressed genes across all disease groups and specific subgroups.

Main Results:

• Eight genes (LDHC, PDHA2, TNP1, TNP2, ODF1, ODF2, SPINK2, PCDHB3) were found to be differentially expressed in all studied male infertility groups.
• Fifty-six additional genes were commonly identified between non-obstructive azoospermia and combined non-obstructive/obstructive azoospermia groups.
• The identified genes are critically involved in germ cell development, spermatogenesis, and metabolic processes, with aberrant expression linked to infertility.

Conclusions:

• Transcriptomic data analysis successfully identified core differentially expressed genes associated with male infertility.
• The identified genes, particularly the common eight, hold potential as diagnostic biomarkers for male infertility.
• Leveraging
• omics
• data offers a promising avenue for unraveling the genetic basis of idiopathic male infertility and developing targeted diagnostic tools.