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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.