A comprehensive analysis of spermatozoal RNA elements in idiopathic infertile males undergoing fertility treatment.

Current approaches to diagnosing male infertility inadequately assess the complexity of the male gamete. Beyond the paternal haploid genome, spermatozoa also deliver coding and non-coding RNAs to the oocyte. While sperm-borne RNAs have demonstrated potential involvement in embryo development, the underlying mechanisms remain unclear. In this study, 47 sperm samples from normozoospermic males undergoing fertility treatment using donor oocytes were sequenced and analyzed to evaluate associations between sperm RNA elements (exon-sized sequences) and blastocyst progression. A total of 366 RNA elements (REs) were significantly associated with blastocyst rate (padj < 0.05), some of which were linked to genes related to critical developmental processes, including mitotic spindle formation and both ectoderm and mesoderm specification. Of note, 27 RE-associated RNAs are predicted targets of our previously reported list of developmentally significant miRNAs. Inverse RE-miRNA expression patterns were consistent with miRNA-mediated down-regulation. This study provides a comprehensive set of REs which differ by the patient's ability to produce blastocysts. This knowledge can be leveraged to improve clinical screening of male infertility and ultimately reduce time to pregnancy.

The transcript integrity index (TII) provides a standard measure of sperm RNA.

Standardizing RNA quality is key to interpreting RNA-seq data as a compromised sample can mask the underlying biology. The challenge remains when evaluating RNA quality in samples with high RNA fragmentation. For example, programmed fragmentation and cytoplasmic expulsion, integral to sperm maturation, is a prime example of the complexities of interpreting RNA-seq data, given that fragmentation can be random and\or targeted. To meet this challenge, we developed an algorithm that accurately measures RNA quality in samples with high fragmentation, such as spermatozoa. The integrity of 1,000 previously identified abundant sperm transcripts were independently visualized and evaluated using the Transcript Integrity Index (TII) algorithm to identify intact transcripts. Full-length transcripts from visual and the TII algorithm were evaluated for testis preference in humans using the GTEx tissues database. Samples were then filtered by the Interquartile Range (IQR), identifying those in which the greatest number of transcripts failed to pass the visual or TII thresholds. Transcript lists were overlapped, forming the set of intact transcripts used as TII standards. Each sample was re-evaluated as a function of this TII set of intact transcripts, with poor quality samples identified as those failing in the largest number of transcripts. While ontologically enriched in roles related to spermatogenesis and/or fertilization, samples did not segregate based on birth outcome. The TII algorithm proved an effective means to identify samples of similar quality from sperm, a cell type enriched in biologically fragmented RNAs. The algorithm should facilitate other studies using samples compromised by high levels of RNA fragmentation, such as Formalin-Fixed Paraffin-Embedded samples. Requisite to assessing male health, TII provides a solution to the long-sought-after standard that identifies samples of similar quality.

COVID-19 and human reproduction: A pandemic that packs a serious punch.

The COVID-19 pandemic has led to a worldwide health emergency that has impacted 188 countries at last count. The rapid community transmission and relatively high mortality rates with COVID-19 in modern times are relatively unique features of this flu pandemic and have resulted in an unparalleled global health crisis. SARS-CoV-2, being a respiratory virus, mainly affects the lungs, but is capable of infecting other vital organs, such as brain, heart and kidney. Emerging evidence suggests that the virus also targets male and female reproductive organs that express its main receptor ACE2, although it is as yet unclear if this has any implications for human fertility. Furthermore, professional bodies have recommended discontinuing fertility services during the pandemic such that reproductive services have also been affected. Although increased safety measures have helped to mitigate the propagation of COVID-19 in a number of countries, it seems that there is no predictable timeline to containment of the virus, a goal likely to remain elusive until an effective vaccine becomes available  and widely distributed across the globe. In parallel, research on reproduction has been postponed for obvious reasons, while diagnostic tests that detect the virus or antibodies against it are of vital importance to support public health policies, such as social distancing and our obligation to wear masks in public spaces. This review aims to provide an overview of critical research and ethics issues that have been continuously emerging in the field of reproductive medicine as the COVID-19 pandemic tragically unfolds.Abbreviations: ACE2: angiotensin- converting enzyme 2; ART: Assisted reproductive technology; ASRM: American Society for Reproductive Medicine; CCR9: C-C Motif Chemokine Receptor 9; CDC: Centers for Disease Control and Prevention; COVID-19: Coronavirus disease 2019; Ct: Cycle threshold; CXCR6: C-X-C Motif Chemokine Receptor 6; ELISA: enzyme-linked immunosorbent assay; ESHRE: European Society of Human Reproduction and Embryology; ET: Embryo transfer; FSH: Follicle Stimulating Hormone; FFPE: formalin fixed paraffin embedded; FYCO1: FYVE And Coiled-Coil Domain Autophagy Adaptor 1; IFFS: International Federation of Fertility Societies; IUI: Intrauterine insemination; IVF: In vitro fertilization; LH: Luteinizing Hormone; LZTFL1: Leucine Zipper Transcription Factor Like 1; MAR: medically assisted reproduction services; MERS: Middle East Respiratory syndrome; NGS: Next Generation Sequencing; ORF: Open Reading Frame; PPE: personal protective equipment; RE: RNA Element; REDa: RNA Element Discovery algorithm; RT-PCR: Reverse=trascriptase transcriptase-polymerase chain reaction; SARS: Severe acute respiratory syndrome; SARS-CoV-2: Severe Acute Respiratory Syndrome Coronavirus 2; SLC6A20: Solute Carrier Family 6 Member 20; SMS: Single Molecule Sequencing; T: Testosterone; TMPRSS2: transmembrane serine protease 2; WHO: World Health Organization; XCR1: X-C Motif Chemokine Receptor.

What human sperm RNA-Seq tells us about the microbiome.

PURPOSE: The study was designed to assess the capacity of human sperm RNA-seq data to gauge the diversity of the associated microbiome within the ejaculate. METHODS: Semen samples were collected, and semen parameters evaluated at time of collection. Sperm RNA was isolated and subjected to RNA-seq. Microbial composition was determined by aligning sequencing reads not mapped to the human genome to the NCBI RefSeq bacterial, viral and archaeal genomes following RNA-Seq. Analysis of microbial assignments utilized phyloseq and vegan. RESULTS: Microbial composition within each sample was characterized as a function of microbial associated RNAs. Bacteria known to be associated with the male reproductive tract were present at similar levels in all samples representing 11 genera from four phyla with one exception, an outlier. Shannon diversity index (p < 0.001) and beta diversity (unweighted UniFrac distances, p = 9.99e-4; beta dispersion, p = 0.006) indicated the outlier was significantly different from all other samples. The outlier sample exhibited a dramatic increase in Streptococcus. Multiple testing indicated two operational taxonomic units, S. agalactiae and S. dysgalactiae (p = 0.009), were present. CONCLUSION: These results provide a first look at the microbiome as a component of human sperm RNA sequencing that has sufficient sensitivity to identify contamination or potential pathogenic bacterial colonization at least among the known contributors.

Human chromatin remodeler cofactor, RNA interactor, eraser and writer sperm RNAs responding to obesity.

In the United States almost 33% of adults are considered obese (BMI > 30 kg/m2). Both animal models and to a lesser extent human studies, have associated BMI, a measure of obesity, with alterations in sperm DNA methylation and RNAs. Sperm RNAs from the Assessment of Multiple Gestations from Ovarian Stimulation trial, were isolated and sequenced. A Generalized Linear Model identified 487 BMI associated human sperm RNA elements (short exon-sized sequences). They partitioned into four patterns; a continual increase with BMI, increase once obese (BMI>30 kg/m2); a steady decrease with BMI; and decrease once overweight (BMI 25 - 30 kg/m2). Gene Ontology revealed a unique relationship between BMI and transcripts associated with chromosome organization, adipogenesis, cellular stress and obesity-related inflammation. Coregulatory networks linked by Chromatin remodeler cofactors, RNA interactors, Erasers and Writers (CREWs) were uncovered to reveal a hierarchical epigenetic response pathway.