Functional proteomic analysis of seminal plasma proteins in men with various semen parameters

Abstract

Background: Alterations at the molecular level in spermatozoa and seminal plasma can affect male fertility. The objective of this study was to determine if analysis of differential expression of proteins in varying semen parameters can serve as potential biomarkers for male infertility.

Methods: The differential expression of proteins in the seminal plasma of men based on sperm count and morphology were examined utilizing proteomic tools. Subjects were categorized based on sperm concentration and morphology into 4 groups: 1) normal sperm count and normal morphology (NN); 2) normal sperm count and abnormal morphology (NA); 3) oligozoospermia and normal morphology (ON); and 4) oligozoospermia and abnormal morphology (OA). Proteomic analysis was performed by LC-MS/MS followed by functional bioinformatics analysis. Protein distribution in the NA, ON and OA groups was compared with that of the NN group.

Results: Twenty proteins were differentially expressed among the 4 groups. Among the unique proteins identified, 3 were downregulated in the NA group, 1 in the ON group and 1 in the OA group while 2 were upregulated in the ON and OA groups. The functional analysis 1) identified biological regulation as the major processes affected and 2) determined that most of the identified proteins were of extracellular origin.

Conclusions: We have identified proteins that are over-or underexpressed in the seminal plasma of men with poor sperm quality. The distinct presence of some of the proteins may serve as potential biomarkers and provide insight into the mechanistic role played by these proteins in male infertility. Further studies using Western Blot analysis are required to validate these findings.

Figure 1

Broad categorical analysis of proteomics data. Differentially expressed proteins list encompasses proteins that overlap with other categories (common, unique, low abundant and significant) of proteins.

Figure 2

Venn diagram showing distribution of 20 differentially expressed proteins. This was based on the NSC ratio cut-off >2 across 3 samples, NA, OA, and ON in comparison to the baseline NN sample.

Figure 3

Functional annotations from consolidated findings using publicly available software tools (GO term mapper, GO term finder, UniProtKB, STRAP, BioGPS) and proprietary pathway software packages (Ingenuity Pathway Analysis and Metacore™) showing cellular distribution of significant proteins in NN, NA, OA, and ON groups.

Figure 4

Functional annotations from consolidated findings using publicly available software tools (GO term mapper, GO term finder, UniProtKB, STRAP, BioGPS) and proprietary pathway software packages (Ingenuity Pathway Analysis and Metacore™) showing biological processes of significant proteins in NN, NA, OA and ON group.

Figure 5

Functional annotations from consolidated findings using publicly available software tools (GO term mapper, GO term finder, UniProtKB, STRAP, BioGPS) and proprietary pathway software packages (Ingenuity Pathway Analysis and Metacore™) showing comparison of cellular distribution amongst the proteins in Common, DEP, and Low abundant category.

Figure 6

Functional annotations from consolidated findings using publicly available software tools (GO term mapper, GO term finder, UniProtKB, STRAP, BioGPS) and proprietary pathway software packages (Ingenuity Pathway Analysis and Metacore™) showing Comparison of Biological Processes amongst the proteins in Common, DEP, and Low abundant category.