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The human gut includes plasma cells (PCs) expressing immunoglobulin A1 (IgA1) or IgA2, two structurally distinct IgA subclasses with elusive regulation, function, and reactivity. We show here that intestinal IgA1+ and IgA2+ PCs co-emerged early in life, comparably accumulated somatic mutations, and were enriched within short-lived CD19+ and long-lived CD19- PC subsets, respectively. IgA2+ PCs were extensively clonally related to IgA1+ PCs and a subset of them presumably emerged from IgA1+ precursors. Of note, secretory IgA1 (SIgA1) and SIgA2 dually coated a large fraction of mucus-embedded bacteria, including Akkermansia muciniphila. Disruption of homeostasis by inflammatory bowel disease (IBD) was associated with an increase in actively proliferating IgA1+ plasmablasts, a depletion in long-lived IgA2+ PCs, and increased SIgA1+SIgA2+ gut microbiota. Such increase featured enhanced IgA1 reactivity to pathobionts, including Escherichia coli, combined with depletion of beneficial A. muciniphila. Thus, gut IgA1 and IgA2 emerge from clonally related PCs and show unique changes in both frequency and reactivity in IBD., (© 2024 Tejedor Vaquero et al.)

Although the microbial communities from seminal fluid were an unexplored field some decades ago, their characteristics and potential roles are gradually coming to light. Therefore, a complex and specific microbiome population with commensal niches and fluctuating species has started to be revealed. In fact, certain clusters of bacteria have been associated with fertility and health, while the outgrowth of several species is potentially correlated with infertility indicators. This constitutes a compelling reason for outlining the external elements that may induce changes in the seminal microbiome composition, like lifestyle factors, gut microbiota, pathologies, prebiotics, and probiotics. In this review, we summarize the main findings about seminal microbiome, its origins and composition, its relationship with fertility, health, and influence factors, while reminding readers of the limitations and advantages introduced from technical variabilities during the experimental procedures.

Background: Several chronic conditions have been identified as risk factors for severe COVID-19 infection, yet the implications of multimorbidity need to be explored. The objective of this study was to establish multimorbidity clusters from a cohort of COVID-19 patients and assess their relationship with infection severity/mortality., Methods: The MRisk-COVID Big Data study included 14 286 COVID-19 patients of the first wave in a Spanish region. The cohort was stratified by age and sex. Multimorbid individuals were subjected to a fuzzy c-means cluster analysis in order to identify multimorbidity clusters within each stratum. Bivariate analyses were performed to assess the relationship between severity/mortality and age, sex, and multimorbidity clusters., Results: Severe infection was reported in 9.5% (95% CI: 9.0-9.9) of the patients, and death occurred in 3.9% (95% CI: 3.6-4.2). We identified multimorbidity clusters related to severity/mortality in most age groups from 21 to 65 years. In males, the cluster with highest percentage of severity/mortality was Heart-liver-gastrointestinal (81-90 years, 34.1% severity, 29.5% mortality). In females, the clusters with the highest percentage of severity/mortality were Diabetes-cardiovascular (81-95 years, 22.5% severity) and Psychogeriatric (81-95 years, 16.0% mortality)., Conclusion: This study characterized several multimorbidity clusters in COVID-19 patients based on sex and age, some of which were found to be associated with higher rates of infection severity/mortality, particularly in younger individuals. Further research is encouraged to ascertain the role of specific multimorbidity patterns on infection prognosis and identify the most vulnerable morbidity profiles in the community., Trial Registration: NCT04981249. Registered 4 August 2021 (retrospectively registered)., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Lleal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The purpose of this study is to provide novel information through Next Generation Sequencing (NGS) for the characterization of viral and bacterial RNA cargo of human sperm cells from healthy fertile donors. For this, RNA-seq raw data of poly(A) RNA from 12 sperm samples from fertile donors were aligned to microbiome databases using the GAIA software. Species of viruses and bacteria were quantified in Operational Taxonomic Units (OTU) and filtered by minimal expression level (>1% OTU in at least one sample). Mean expression values (and their standard deviation) of each species were estimated. A Hierarchical Cluster Analysis (HCA) and a Principal Component Analysis (PCA) were performed to detect common microbiome patterns among samples. Sixteen microbiome species, families, domains, and orders surpassed the established expression threshold. Of the 16 categories, nine corresponded to viruses (23.07% OTU) and seven to bacteria (2.77% OTU), among which the Herperviriales order and Escherichia coli were the most abundant, respectively. HCA and PCA displayed four clusters of samples with a differentiated microbiome fingerprint. This work represents a pilot study into the viruses and bacteria that make up the human sperm microbiome. Despite the high variability observed, some patterns of similarity among individuals were identified. Further NGS studies under standardized methodological procedures are necessary to achieve a deep knowledge of the semen microbiome and its implications in male fertility.

Objective: To identify candidates of fertility biomarkers among pairs of human sperm microRNAs., Design: Expression data of 736 sperm microRNAs from fertile and infertile individuals characterized in previous published studies by means of TaqMan quantitative polymerase chain reaction (PCR) were reexamined. A set of microRNA pairs with the best biomarker potential were selected and validated by means of quantitative real-time (qRT) PCR in an independent cohort., Setting: University laboratory., Patient(s): Semen samples were obtained from fertile (n = 10) and infertile (asthenozoospermia, n = 10; teratozoospermia, n = 10; oligozoospermia, n = 10; unexplained male infertility [UMI], n = 8) individuals. The validation cohort included 9 fertile donors and 14 infertile patients with different seminal alterations., Intervention(s): None., Main Outcome Measure(s): Spearman test was used to select microRNA pairs with a correlated expression in fertile individuals and a noncorrelated expression in each infertile group. The biomarker potential of these pairs was determined with the use of receiver operating characteristic curves. The differential relative expression of each pair in fertile and infertile populations was verified (Mann-Whitney test). Those pairs with best results were validated by qRT-PCR., Result(s): Forty-eight pairs showed significant correlations in the fertile group. The pairs that were uncorrelated in the infertile populations and displayed the best biomarker potential were hsa-miR-942-5p/hsa-miR-1208 (asthenozoospermia), hsa-miR-296-5p/hsa-miR-328-3p (teratozoospermia), hsa-miR-139-5p/hsa-miR-1260a (oligozoospermia), and hsa-miR-34b-3p/hsa-miR-93-3p (UMI). The hsa-miR-942-5p/hsa-miR-1208 pair showed the greatest potential for detecting seminal alterations in the validation cohort (85.71% true positives)., Conclusion(s): The pairs hsa-miR-942-5p/hsa-miR-1208 and hsa-miR-34b-3p/hsa-miR-93-3p have the potential to become new molecular biomarkers that could help to diagnose male infertility, especially in cases of UMI or when seminal parameters are close to the threshold values., (Copyright © 2019 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.)

Study Question: What is the most reliable normalization strategy for sperm microRNA (miRNA) quantitative Reverse Transcription Polymerase Chain Reactions (qRT-PCR) using singleplex assays?, Summary Answer: The use of the average expression of hsa-miR-100-5p and hsa-miR-30a-5p as sperm miRNA qRT-PCR data normalizer is suggested as an optimal strategy., What Is Known Already: Mean-centering methods are the most reliable normalization strategies for miRNA high-throughput expression analyses. Nevertheless, specific trustworthy reference controls must be established in singleplex sperm miRNA qRT-PCRs., Study Design, Size Duration: Cycle threshold (Ct) values from previously published sperm miRNA expression profiles were normalized using four approaches: (i) Mean-Centering Restricted (MCR) method (taken as the reference strategy); (ii) expression of the small nuclear RNA RNU6B; (iii) expression of four miRNAs selected by the Concordance Correlation Restricted (CCR) algorithm: hsa-miR-100-5p, hsa-miR-146b-5p, hsa-miR-92a-3p and hsa-miR-30a-5p; (iv) the combination of two of these miRNAs that achieved the highest proximity to MCR., Participants/materials, Setting, Methods: Expression profile data from 736 sperm miRNAs were taken from previously published studies performed in fertile donors (n = 10) and infertile patients (n = 38). For each tested normalizer molecule, expression ubiquity and uniformity across the different samples and populations were assessed as indispensable requirements for being considered as valid candidates. The reliability of the different normalizing strategies was compared to MCR based on the set of differentially expressed miRNAs (DE-miRNAs) detected between populations, the corresponding predicted targets and the associated enriched biological processes., Main Results and the Role of Chance: All tested normalizers were found to be ubiquitous and non-differentially expressed between populations. RNU6B was the least uniformly expressed candidate across samples. Data normalization through RNU6B led to dramatically misguided results when compared to MCR outputs, with a null prediction of target genes and enriched biological processes. Hsa-miR-146b-5p and hsa-miR-92a-3p were more uniformly expressed than RNU6B, but their results still showed scant proximity to the reference method. The highest resemblance to MCR was achieved by hsa-miR-100-5p and hsa-miR-30a-5p. Normalization against the combination of both miRNAs reached the best proximity rank regarding the detected DE-miRNAs (Area Under the Curve = 0.8). This combination also exhibited the best performance in terms of the target genes predicted (72.3% of True Positives) and their corresponding enriched biological processes (70.4% of True Positives)., Large Scale Data: Not applicable., Limitations, Reasons for Caution: This study is focused on sperm miRNA qRT-PCR analysis. The use of the selected normalizers in other cell types or tissues would still require confirmation., Wider Implications of the Findings: The search for new fertility biomarkers based on sperm miRNA expression using high-throughput assays is one of the upcoming challenges in the field of reproductive genetics. In this context, validation of the results using singleplex assays would be mandatory. The normalizer strategy suggested in this study would provide a universal option in this area, allowing for normalization of the validated data without causing meaningful variations of the results. Instead, qRT-PCR data normalization by RNU6B should be discarded in sperm-miRNA expression studies., Study Funding/competing Interests: This work was supported by the 2014/SGR00524 project (Agència de Gestió d'Ajuts Universitaris i de Recerca, Generalitat de Catalunya, Spain) and UAB CF-180034 grant (Universitat Autònoma de Barcelona). Celia Corral-Vazquez is a recipient of a Personal Investigador en Formació grant UAB/PIF2015 (Universitat Autònoma de Barcelona). The authors report no conflict of interest., (© The Author 2016. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)