Background Long non-coding RNAs (lncRNAs) have a size of more than 200 bp and are known to regulate a host of crucial cellular processes like proliferation, differentiation and apoptosis by regulating gene expression. While small noncoding RNAs (ncRNAs) such as miRNAs, siRNAs, Piwi-interacting RNAs have been extensively studied in male germ cell development, the role of lncRNAs in spermatogenesis remains largely unknown. Objective In this article, we have reviewed the biology and role of lncRNAs in spermatogenesis along with the tools available for data analysis. Results and conclusions Till date, three microarray and four RNA-seq studies have been undertaken to identify lncRNAs in mouse testes or germ cells. These studies were done on pre-natal, post-natal, adult testis, and different germ cells to identify lncRNAs regulating spermatogenesis. In case of humans, five RNA-seq studies on different germ cell populations, including two on sperm, were undertaken. We compared three studies on human germ cells to identify common lncRNAs and found 15 lncRNAs (LINC00635, LINC00521, LINC00174, LINC00654, LINC00710, LINC00226, LINC00326, LINC00494, LINC00535, LINC00616, LINC00662, LINC00668, LINC00467, LINC00608, and LINC00658) to show consistent differential expression across these studies. Some of the targets of these lncRNAs included CENPB, FAM98B, GOLGA6 family, RPGR, TPM2, GNB5, KCNQ10T1, TAZ, LIN28A, CDKN2B, CDKN2A, CDKN1A, CDKN1B, CDKN1C, EZH2, SUZ12, VEGFA genes. A lone study on human male infertility identified 9879 differentially expressed lncRNAs with three (lnc32058, lnc09522, and lnc98497) of them showing specific and high expression in immotile sperm in comparison to normal motile sperm. A few lncRNAs (Mrhl, Drm, Spga-lncRNAs, NLC1-C, HongrES2, Tsx, LncRNA-tcam1, Tug1, Tesra, AK015322, Gm2044, and LncRNA033862) have been functionally validated for their roles in spermatogenesis. Apart from rodents and humans, studies on sheep and bull have also identified lncRNAs potentially important for spermatogenesis. A number of these non-coding RNAs are strong candidates for further research on their roles in spermatogenesis.

Background In contrast with the preceding stages of the germ cells, spermatozoa are unusually rich in small non-coding RNAs in comparison to the coding RNAs. These small RNAs may have had an essential role in the process of spermatogenesis or may have critical roles in the post-fertilization development. Sporadic efforts have identified a few differentially expressed miRNAs in infertile individuals, which do not replicate in other studies. Methods In order to identify miRNAs signatures of infertility or poor sperm quality, we compared miRNA differential expression data across nine datasets, followed by their analysis by real-time PCR in a case–control study. This was followed by the validation of potential biomarkers in yet another set of cases and controls. For this, total RNA was isolated from 161 sperm samples. miRNA expression levels in infertile cases and fertile controls were measured using TaqMan real-time PCR. Meta-analyses of two miRNAs (hsa-miR-9-3p and hsa-miR-122-5p) were performed using Comprehensive Meta‐Analysis Software (version 2). All statistical analyses were performed with the help of GraphPad Prism Software (version 8). Results Literature search identified seven miRNAs (hsa-let-7a-5p, hsa-miR-9-3p, hsa-miR-22-5p, has-miR-30b-5p, hsa-miR-103-3p, hsa-miR-122-5p and hsa-miR-335-5p) showing consistent dysregulation in infertility across a minimum of four studies. In the discovery phase, six miRNAs showed strong association with infertility with four (hsa-miR-9-3p, hsa-miR-30b-5p, hsa-miR-103-3p and hsa-miR-122-5p) showing consistent differential regulation across all sub-groups. Receiver operating characteristic (ROC) curve analysis showed that the area under curve of > 0.75 was achieved by three (hsa-mir-9-3p, hsa-miR-30b-5p and hsa-miR-122-5p) miRNAs. In the validation phase, these three miRNAs showed consistent association with infertility (hsa-mir-9-3p, hsa-miR-30b-5p, and hsa-miR-122-5p). Meta-analysis on hsa-miR-122-5p showed its significant quantitative association with infertility [Hedge’s g = -2.428, p = 0.001 (Random effects)]. Conclusions Three miRNAs (hsa-miR-9-3p, hsa-miR-30b-5p and hsa-miR-122-5p) have strong linkage with infertility and a high potential as sperm quality biomarkers.

The aim of the present study was to identify RNA‐based signatures of male infertility by sperm transcriptome analysis. In this study, deep sequencing analyses of coding (mRNA) and regulatory (miRNA) transcriptomes were performed by pooling 15 oligo/oligoasthenozoospermic infertile sperm and 9 normozoospermic fertile sperm samples. Furthermore, interesting candidates were selected for validation by real‐time PCR. The comparison of miRNAs between cases and controls identified 94 differentially expressed miRNAs, of which at least 38 have known functions in spermatogenesis. In transcriptome (mRNA) data, a total of 60,505 transcripts were obtained. The comparison of coding RNAs between cases and controls revealed 11,688 differentially expressed genes. miRNA–mRNA paired analysis revealed that 94 differentially expressed miRNAs could potentially target 13,573 genes, of which 6419 transcripts were actually differentially expressed in our data. Out of these, 3303 transcripts showed inverse correlation with their corresponding regulatory miRNAs. Moreover, we found that most of the genes of miRNA–mRNA pairs were involved in male germ cell differentiation, apoptosis, meiosis, spermiogenesis and male infertility. In conclusion, we found that a number of sperm transcripts (miRNAs and mRNAs) have a very high potential of serving as infertility/sperm quality markers.

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The purpose of this study was to investigate the efficacy of hCG therapy in hypogonadotropic hypogonadic (HH) azoospermic males along with dissecting the prognostic value of Y-deletion analysis in these patients. Fifty-eight azoospermic infertile males with diminished testosterone levels (≤400 ng/dl) and hypogonadism symptoms were subjected to human chorionic gonadotropin (hCG) therapy, and Y-deletion analysis was undertaken. Post-treatment, 43% (25/58) patients showed improvement in sperm count with 8.6% (5/58) turning severe oligozoospermic, 24.14% (14/58) patients turning oligozoospermic and 10.54% (6/58) turning normozoospermic. Among responders, the mean sperm concentration was 8.47 ± 13.16 million/ml, sperm count was 17.05 ± 26.17 million, sperm motility was 52.59% ± 25.09% and sperm progressive motility was 26.91% ± 20.51%. Seventeen out of 25 (68%) responders and 11/33 (33%) nonresponders showed an improvement in libido post-therapy. A Y-deletion was observed in 8% (2/25) responders and in 39.39% (13 out of 33) nonresponders.The Y-deletions were more often found in nonresponders in comparison with the responders (Fisher's exact probability test, p = .007, one tailed). We conclude that hCG therapy in hypogonadotropic azoospermic males is effective in improving andrological parameters and sperm production and that Y-chromosome deletion analysis has prognostic significance in predicting the success of hCG therapy.