Intraindividual variability of semen quality, proteome, and sncRNA profiles in a healthy cohort of young adults.

BACKGROUND: Within-subject variability of semen parameters and molecular components of ejaculates in young men remains poorly understood. OBJECTIVES: To investigate intraindividual variability (IIV) of semen parameters and molecular markers in repeated ejaculates from young men. MATERIALS AND METHODS: Semen parameters were assessed in samples collected 6-8 days apart from 164 18-19-year old participants of the Russian Children's Study, a prospective cohort. Subsets of paired samples were used for label-free quantitation and targeted mass-spectrometry of proteins in seminal plasma (SP) and seminal extracellular vesicles (EVs), and for small non-coding RNA (sncRNA) profiling in EVs and spermatozoa using RNA-seq. The mean difference between two ejaculates, within-subject variation, intraclass correlation, and concordance correlation were used to assess IIV for all parameters. Low variability with high reproducibility and high reliability was considered if CVw < 15% and ICC > 0.90, respectively. RESULTS: Analytical variability was low for all investigated parameters in technical replicates. IIV was assessed for basic semen parameters and proteins in SPs and EVs: 319 and 777 proteins, respectively, using untargeted analysis; 9 and 10 proteins using targeted quantification. We also described the IIV for sncRNA, including microRNA, piwi-interacting RNA, tRNA, and tRNA-derived small RNA (tsRNA) in EVs (409 sncRNA and 78 tsRNA) and in spermatozoa (265 sncRNA and 15 tsRNA). We identified 22 and 27 non-overlapping proteins in SP and EVs, respectively, and 46 and 9 sncRNA, including 5 and 0 tsRNA in seminal EVs and spermatozoa, respectively, with low variability. The fatty acid synthase (FAS) had the lowest IIV in both media in targeted protein quantification. DISCUSSION: We identified a number of proteins and sncRNA with low variability among 111 proteins, 176 sncRNA, and 12 tsRNA which were previously suggested as biomarkers of male fertility and reproductive outcomes: lactotransferrin, cysteine-rich secretory protein 3, alpha-1-antichymotrypsin, epididymal sperm-binding protein 1, glutathione S-transferase Mu 3, alpha-1-acid glycoprotein 2, serum amyloid P-component, aminopeptidase N, neprilysin, FAS, and miR-10b-3p, miR-122-5p, miR-205-5p, miR-222-3p, miR-34c-5p, miR-509-3-5p, miR-888-5p, miR-892a, miR-363-3p, miR-941, miR-146a-5p, miR-744-5p. CONCLUSION: These molecules have low IIV and may be promising candidate biomarkers of male fertility and reproductive health.

Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking.

Expression analysis of small noncoding RNA (sRNA), including microRNA, piwi-interacting RNA, small rRNA-derived RNA, and tRNA-derived small RNA, is a novel and quickly developing field. Despite a range of proposed approaches, selecting and adapting a particular pipeline for transcriptomic analysis of sRNA remains a challenge. This paper focuses on the identification of the optimal pipeline configurations for each step of human sRNA analysis, including reads trimming, filtering, mapping, transcript abundance quantification and differential expression analysis. Based on our study, we suggest the following parameters for the analysis of human sRNA in relation to categorical analyses with two groups of biosamples: (1) trimming with the lower length bound = 15 and the upper length bound = Read length - 40% Adapter length; (2) mapping on a reference genome with bowtie aligner with one mismatch allowed (-v 1 parameter); (3) filtering by mean threshold > 5; (4) analyzing differential expression with DESeq2 with adjusted p-value < 0.05 or limma with p-value < 0.05 if there is very little signal and few transcripts.