STUDY QUESTION:Does vasectomy alter the seminal microbiome, and what proportions of seminal microorganisms originate from the urinary and upper reproductive tract? SUMMARY ANSWER:Vasectomy is associated with modest shifts in the seminal microbial community structure, where inter-individual variability prevails, and the semen shares over 60% of bacterial communities with urine, suggesting an influence of the urinary tract and upstream genitourinary compartments on the seminal microenvironment. WHAT IS KNOWN ALREADY:The semen harbours a polymicrobial community whose origin is not fully understood. Also, the effect of vasectomy, a common sterilization procedure, on seminal microenvironment is not clear. Recent studies with a limited sample size suggest that part of the seminal microbiome may derive from the upper genital tract, and that vasectomy may alter seminal microbial composition, potentially revealing testicular or epididymal microbial contributions. STUDY DESIGN SIZE DURATION:This prospective cohort study including 82 men undergoing vasectomy, with paired semen and urine samples collected before and 3 months after the procedure. PARTICIPANTS/MATERIALS SETTING METHODS:Paired semen and urine samples were collected pre- and post-vasectomy. The seminal and urine microbiome was analysed by sequencing the V4 hypervariable region of the 16S rRNA gene. Amplicon sequence variants were decontaminated using rigorous negative-control-based methods. Microbial diversity, taxonomic composition, and differential abundance were assessed, and functional profiles were predicted using PICRUSt2. MAIN RESULTS AND THE ROLE OF CHANCE:More than 60% of seminal bacterial genera were also present in urine, indicating substantial overlap. Vasectomy significantly altered β-diversity; however, the effect was small, and several predicted functional pathways were detected, including lipid metabolism. Although some genera differed between pre- and post-vasectomy samples, these did not remain significant after false discovery rate (FDR) correction. These findings should be interpreted considering the prevailing inter-individual variability and shared urinary-seminal taxa. LARGE SCALE DATA:The 16S rRNA gene sequencing data have been uploaded to the SRA database under BioProject ID PRJNA1355064. LIMITATIONS REASONS FOR CAUTION:The main limitations of the study are the use of 16S rRNA gene sequencing, which limits species-level resolution, the lack of repetitive sampling, and the absence of absolute bacterial load quantification. Also, the functional pathway analyses present predictive estimates from the sequencing data rather than from direct measurements of the microbial metabolic activity. WIDER IMPLICATIONS OF THE FINDINGS:Vasectomy may induce subtle changes in the seminal microenvironment by modifying microbial composition and metabolic functions, as indicated by these exploratory analyses. Nevertheless, our study findings should not be interpreted as evidence against vasectomy, rather as novel information to better understand the dynamics of the seminal microbiome. Whether the microbial changes have any effect on the male urogenital health requires further research. STUDY FUNDING/COMPETING INTERESTS:This work was supported by projects ENDORE (SAF2017-87526-R); Endo-Map (PID2021-12728OB-I00), and ROSY (CNS2022-135999), funded by MICIU/AEI/10.13039/501100011033 and by FEDER, EU; I.L.-B. and C.M.T. are supported by the FPU22/03045 grant and FPU23/01576, awarded by MCIN/AEI/10.13039/501100011033, respectively; additionally, S.A. obtained a mobility grant for senior researchers to do a research stay abroad funded by the Spanish Ministry of Science, Innovation and Universities (ref. PRX24/00372); I.P.-P. and A.S.-L. were supported by Becas Fundación Ramón Areces para Estudios Postdoctorales-Convocatorias XXXVI-XXXV para Ampliación de Estudios en el Extranjero en Ciencias de la Vida y de la Materia; A.S.-L. is supported by the Estonian Research Council (grant no. PSG1082); funding for open access charge: Universidad de Granada / CBUA. The authors declare no competing interests.

RESEARCH QUESTION:Do semen quality parameters differ between 'high' and 'low' levels of physical fitness in a cohort of Spanish men? DESIGN:Semen samples donated by 324 Spanish men without known reproductive disorders were analysed to determine sperm count, concentration and progressive motility. Overall fitness, cardiorespiratory fitness and muscular strength were self-reported using the International Fitness Scale. Additionally, muscular strength was assessed objectively using a handgrip dynamometer. Analyses of covariance and adjusted linear regression analyses were performed to explore the association between the components of physical fitness and semen quality parameters. RESULTS:Men with higher self-reported cardiorespiratory fitness and muscular strength presented higher sperm counts compared with men with lower values [adjusted raw mean difference 44.315 × 106 (95% CI 7.699-80.931) spermatozoa/ejaculate and 42.766 × 106 (95% CI 6.215-79.316) spermatozoa/ejaculate; adjusted mean Z-score difference 0.232 (95% CI 0.014-0.449) SDs and 0.236 (95% CI 0.019-0.453) SDs; P = 0.037 and 0.033, respectively]. Objectively measured handgrip muscular strength was not associated with any semen quality parameter. CONCLUSIONS:This cross-sectional study suggests that self-reported cardiorespiratory fitness and muscular strength are positively associated with semen quality parameters, while objectively measured handgrip strength shows no association. These results may provide insight into modifiable lifestyle factors potentially related to semen quality, and help guide future randomized controlled trials to clarify causal pathways.

Abstract Background Emerging evidence suggests microbial dysbiosis may contribute to gynaecological pathologies, including endometriosis and recurrent implantation failure (RIF). The vaginal microbiome is well-characterised, with Lactobacillus -dominance as a hallmark of health, yet interactions between vaginal, cervical, and endometrial microbiomes remain inconclusive. The adjacent sites to the vagina, rectal and urinary environments in infertile women are scarcely studied. Methods This cross-sectional study included 136 reproductive-aged women, enrolled at the Reproductive Unit of the University Hospital Virgen de las Nieves (Granada, Spain) between March 2019 and March 2024. Each participant provided five samples during the mid-secretory phase: vaginal and cervical swabs, endometrial brushing, urine, and rectal swabs. The microbiome was analysed using 16S rRNA gene sequencing (V4 region) with functional profiles inferred using PICRUSt2. Diversity and bacterial abundance were compared between endometriosis, RIF, and controls, adjusting for age, body mass index, and antimicrobial use. Results In total, 520 samples from 104 women were analysed and revealed shared microbial profiles across the vagina, cervix, endometrium, and urine. The rectal microbiome differed significantly from urogenital sites (Global PERMANOVA, adj p -value = 0.001, R 2 = 0.259). A Lactobacillus gradient in the reproductive tract was observed, with dominance > 98% in the lower tract, 69.72% in urine, and 46.25% in the endometrium. Moreover, Lactobacillus negatively correlated with all other genera within the reproductive tract. Significant differential abundance was detected between body sites: 15 bacteria between vagina-cervix, 166 vagina-uterus and 172 cervix-uterus (all adj p -values < 0.05). Diversity comparisons between the condition groups (endometriosis and RIF) and controls at each anatomical sites revealed no significant differences in microbial communities and functional pathways. However, four bacterial genera showed a significantly different abundance between the endometriosis and controls in the vagina. Conclusions Our study results provide knowledge about the microbial composition throughout the female urogenital tract and rectum, highlighting the interindividual variability rather than the site-specificity. The vaginal bacteria that associated with endometriosis should be investigated further for clarifying their potential as non-invasive biomarkers of the disease. Microbiomes of other urogenital sites do not seem to associate with endometriosis, and microbiomes of the urogenital-rectal axis do not seem to correlate with RIF. Trial registration N/A.

Non-obstructive azoospermia, a severe form of male infertility caused by spermatogenic failure (SPGF), has a largely unknown genetic basis across ancestries. To our knowledge, this is the first trans-ethnic meta-analysis of genome-wide association studies on SPGF, involving 2255 men with idiopathic SPGF and 3608 controls from European and Asian populations. Using logistic regression and inverse variance methods, we identify two significant genetic associations with Sertoli cell-only (SCO) syndrome, the most extreme SPGF phenotype. The G allele of rs34915133, in the major histocompatibility complex class II region, significantly increases SCO risk (P = 5.25E-10, OR = 1.57), supporting a potential immune-related cause. Additionally, the rs10842262 variant in the SOX5 gene region is also a genetic marker of SCO (P = 5.29E-09, OR = 0.72), highlighting the key role of this gene in the male reproductive function. Our findings reveal shared genetic factors in male infertility across ancestries and provide insights into the molecular mechanisms underlying SCO.

Our study was aimed to identify subtype-specific immune cells in the testis of patients with non-obstructive azoospermia (NOA) showing Sertolli cell-only (SCO) phenotype. A distinctive composition of testis immune cell populations, such as macrophages and T cells, was observed in SCO patients compared to non-SCO patients. The testis maintains a delicate balance of immune cell populations due to its immune-privileged condition. However, histological and transcriptomic evidence highlights heightened immune system activity in patients with NOA. Specifically, in SCO, the most severe form of NOA, genetic findings suggest a role for antigen presentation, particularly involving class II HLA molecules, indicating an immune-mediated component in this phenotype. Therefore, immune dysregulation may play a key role in the pathogenesis of this extreme form of male infertility that may help predict success in artificial reproductive treatments. Testicular biopsies from fourteen patients with NOA were collected and cryopreserved as single cell suspensions. All samples were obtained at the time of testicular biopsy for assisted reproductive technologies. The proportions and expression profiles of germ, somatic non-immune and somatic immune cells were compared between SCO and non-SCO patients to identify SCO-specific cell clusters and differentially expressed genes. Patients were classified into SCO (n = 5) or non-SCO (n = 8) groups after histopathological tests and testicular sperm extraction (TESE) success was recorded for all of them. Then, single cell RNA sequencing (scRNA-seq) libraries were generated using the Next GEM technology by 10x genomics. More than 100.000 single cell transcriptomes passed QC and were included in unsupervised clustering and differential gene expression analyses using the methods implemented in Scanpy. Initially, we defined more than 20 clusters encompassing all germinal and somatic cell types, including known immune cells in the human testis. Additionally, we identified sets of immune cells not previously characterized and several subtypes of somatic cells in this tissue. Notably, we observed a higher representation of immune cells in SCO patients compared to non-SCO cases. Specifically, the proportions of T cell subtypes and dendritic cells, along with the profiles of resident macrophages, suggest increased infiltration and heightened immune system activity in SCO patients. This immune imbalance is characterized by an overrepresentation of pro-inflammatory and effector immune cells, including macrophages and proinflammatory T cells. These findings further support the hypothesis that the immune microenvironment plays a crucial role in the pathogenesis of male infertility, and specially in SCO, contributing to the distinct immunological profile observed in this phenotype of NOA. Despite the concordance between the different SCO and non-SCO patients, sample size remains a limitation of the current study and larger cohorts should be tested to validate the results. Additionally, functional characterization is often required to confirm the biological relevance of the identified immune cell profiles in SCO pathogenesis. The testis remains a relatively underexplored tissue in terms of its immune and non-immune somatic cellular composition. Given the increasing prevalence of male infertility, understanding the cellular and molecular mechanisms underlying this condition is becoming compelling, especially in extreme patterns as SCO. No

Which assisted reproductive technology (ART) procedures affect the sex ratio at birth (secondary sex ratio; SSR)? Classic IVF insemination, blastocyst transfer, PGT, and fresh embryo transfer are the procedures that increase the SSR. The SSR resulting from ART pregnancies is lower than that observed in natural conceptions. Several factors have been proposed to explain these differences, some related to patients (such as older maternal age or obstetric history) and others related to ART (insemination technique, transfer day, PGT). There is no consensus across studies, with many attributing these differences to country-specific characteristics (geographical, legal, social). Spain is one of the European countries with the highest number of ART cycles, including egg donation and PGT, making it an ideal setting to analyze the influence of these factors. Data collection was conducted by the National Register of Assisted Reproductive Technologies from the Ministry of Health, in collaboration with the Spanish Fertility Society. The data for births resulting from treatments performed between, 2015-2022, in Spain were provided compulsory by clinics in aggregate form. Data provided by the 15% of participants center was monitored each year. Data on SSR from births resulting from natural conception were obtained from Spanish National Institute of Statistics. 269816 births were included, involving 16897 with IUI using husband/partner’s semen (IUI-H) and 17001 using donor semen (IUI-D), 52158 with fresh own eggs, 30363 with fresh donor eggs, 64432 with frozen embryo replacement from own eggs (FERO), and 33432 from donor eggs (FERD), 22259 with preimplantation genetic testing, 2702 frozen own eggs (FORO), and 30888 donor eggs (FORD). Subgroup analyses and univariable and logistic regression with weighted data by frequencies were performed. The SSR mean in natural reproduction in Spain is 51.45%, remaining stable between 2015 and 2022. However, the SSR for ART-born children was 50.5%, ranging from 50,0% in 2015-2017 to 51.1% in 2020-2022 (p < 0.001). The only variable directly associated with this trend towards an increased SSR in ART-born children was the increase in the percentage of blastocyst transfers (32,7% in 2015-2017 vs 44,9% in 2020-2022; p < 0.001). Neither IUI-H, IUI-D, nor FOR were associated with the sex ratio in univariable analysis. Multivariate analysis showed that the risk of male birth was higher for births following PGT and fresh embryo transfer (OR 1.04 [1.01-1.07] and 1.03 [1.02-1.05], respectively). In the subgroup of fresh embryo transfer, male birth was related to conventional IVF insemination (OR: 1.11 (1.06-1.16). No relationship was observed when the oocytes were from women under 35 years old (100% donor cycles) compared with own egg cycles (in which 65% of the women were >35 years old). The biases and limitations inherent in retrospective and aggregated data collection. The lack of data regarding the insemination technique in the FOR group prevented an analysis of its influence on the SSR. Therefore, the results should be interpreted with caution. The lower SSR observed in ART may be compensated by conventional IVF, fresh transfer, blastocyst transfer, and PGT. Nevertheless, further research with disaggregated cycle-specific data is necessary to determine the role of other factors in the SSR in ART and to confirm and expand upon the findings of this study. No

Can artificial intelligence predict the absence of sperm in testicular biopsies of infertile men diagnosed with non-obstructive azoospermia (NOA) using non-invasive biomarker detection? A machine learning model based on blood biomarkers and genetic determinants was highly efficient in predicting testicular sperm extraction (TESE) outcome in NOA patients (AUC=0.65). Male infertility due to severe spermatogenic failure (SPGF) can lead to NOA, characterized by absence of sperm in the ejaculate. While some azoospermic men may father biological children via TESE, this procedure is unsuccessful in those exhibiting the Sertoli cell-only (SCO) phenotype. An immune-mediated component has been revealed for SCO, based on the association of the MHC class II region. This genetic region is crucial in many autoimmune diseases, including celiac disease, which has been associated with reproductive disorders. This study aims to develop a non-invasive diagnostic method to predict TESE outcome in NOA patients. A study including a total of 293 infertile men diagnosed with SPGF was conducted, comprising 143 SCO patients and 150 infertile men due to SPGF but without SCO. Different machine learning models were evaluated to predict the phenotype (SCO or non-SCO) and, consequently, the TESE failure, in patients suffering from idiopathic male infertility due to SPGF. Three machine learning models (artificial neural networks, random forest, and logistic regression) were evaluated and compared based on standard metrics. Four parameters (genetic and hormonal) obtained through non-invasive procedures were included in the models: 1) polygenic risk scores (PRS) based on association data of celiac disease, 2) presence of specific amino acid residues in the MHC class II protein HLA-DRβ1, 3) values for follicle-stimulating hormone (FSH), and 4) FSH to luteinizing hormone ratio (FSH/LH). The best performing machine learning model, an artificial neural network, achieved the following results on the testing dataset: AUC=0.65, accuracy=0.61, sensitivity=0.62 and specificity=0.61. The presence of specific HLA-DRβ1 residues was the most discriminative variable for distinguishing between SCO and non-SCO phenotypes, supporting the hypothesis of an immune-mediated component in most SCO patients. This finding aligns with the HLA-DRB1 locus being the main genome-wide association with SCO. Additionally, the PRSs calculated for each infertile man based on genome-wide association data for celiac disease were significantly higher in SCO patients compared to non-SCO patients (PRS model P-value=2.48E-02), highlighting its potential as a predictive parameter. Moreover, FSH levels and their ratio to LH contributed to SCO prediction, consistent with previous associations between elevated levels of FSH and this phenotype. While the model demonstrated a promising AUC in diagnosing SCO patients and predicting unsuccessful TESE, further refinement is needed through incorporating of additional informative biomarkers. The performance of the model was limited by small sample size, which constrained the training and testing datasets. Therefore, replication in larger cohorts is warranted. Machine learning models predicting TESE outcomes could provide infertile men due to SPGF a very valuable probability estimate of success based on a pre-biopsy histological phenotype diagnosis. While further improvements to the reported model are needed, it has the potential to help SCO patients avoid unnecessary invasive procedures. No

Optical genome mapping (OGM) is a next-generation cytogenetic technique that may be beneficial for detecting subtle structural chromosomal alterations that can go unnoticed with conventional studies in couples with recurrent pregnancy loss. We report the case of a couple referred to our assisted reproduction unit due to a history of recurrent pregnancy loss. Initially, conventional cytogenetic studies were performed to identify a possible genetic cause. To this end, the karyotypes of both members of the couple were determined. The fetal tissue from the third miscarriage was analyzed using comparative genome hybridization (CGH) array. Subsequently, the cytogenetic analysis of the couple was extended with the OGM technique. Basic infertility studies revealed normal results, and the karyotypes of both partners were initially reported as normal with respect to structural abnormalities. Following the third miscarriage, an array CGH analysis of the abortive tissue detected a deletion-duplication on chromosomes 1 and 10, respectively. Moreover, OGM revealed a balanced translocation between chromosomes 1 and 10 in the male which had not been detected through conventional karyotyping. A retrospective review of the karyotype by an expert cytogeneticist identified an apparent translocation that had previously gone unrecognized. Structural chromosomal abnormalities may be underestimated in couples experiencing multiple miscarriages because they are not always accurately recognized by conventional cytogenetic techniques. OGM offers a valuable complement to these traditional methods by identifying chromosomal alterations that may have been overlooked by karyotyping, precisely characterizing the nature of the structural rearrangements. While OGM cannot currently replace karyotyping due to limitations such as the inability to detect certain translocations (e.g., Robertsonian translocations), it can enhance diagnostic accuracy and provide additional insights into the genetic causes of repeated pregnancy loss. Therefore, OGM may serve as a useful supplementary tool for improving diagnosis and management in affected couples.

RESEARCH QUESTION:Should seminal metabolites be analysed from fresh ejaculate or after liquefaction to establish a protocol for biomarker discovery? DESIGN:Semen samples were collected from 15 healthy donors, with two aliquots obtained for each donor, one before and one after the liquefaction process, resulting in total of 30 samples for analysis. Non-targeted metabolomics analysis was conducted using liquid chromatography-high-resolution mass spectrometry on these paired samples. Data quality was assessed using MarkerView software. Metabolites were identified using the 2021 NIST Mass Spectral Library, PeakView, CEU Mass Mediator and Sirius software. RESULTS:A total of 1664 mass-to-charge ratio values were detected and 76 metabolites were identified, including amino acids, lipids, carbohydrates and compounds related to oxidative stress and sperm function. Principal component analysis did not reveal any statistically significant differences between the pre- and post-liquefaction samples. However, univariate statistical testing detected subtle changes in metabolite levels, most (1611) having similar or increased intensities in post-liquefaction samples, along with notable interindividual variability. CONCLUSIONS:The semen liquefaction process does not seem to affect the overall metabolic profile, allowing flexibility in sample analysis without compromising data integrity. This supports the robustness of metabolomics for semen analysis and its potential for identifying new fertility biomarkers.

How does the PIWIL4-rs508485 genetic variant influence the risk of extreme phenotypes of male infertility? The T allele of rs508485 increases the risk of developing severe male infertility patterns by altering specific miRNA binding and post-transcriptional regulation of PIWIL4. PIWI proteins, particularly PIWIL4, are essential in spermatogenesis by processing PIWI-interacting RNAs (piRNAs) to maintain genome stability in germ cells. The PIWIL4-rs508485 genetic variant has been previously associated with non-obstructive azoospermia (NOA) in European and Asian populations. NOA is characterized by the absence of sperm in the ejaculate and represents one of the most severe forms of male infertility. However, the precise role of this genetic variation in the severe spermatogenic failure (SPGF) remains unclear. A case-control association study was conducted on 1,516 infertile men diagnosed with SPGF and 2,451 fertile controls from a European population. Logistic regression models were used to determine the relationship between rs508485 polymorphism with severe male infertility phenotypes and functional luciferase assays were performed to evaluate its impact on PIWIL4 function. Genotyping of the PIWIL4-rs508485 polymorphism was performed in all participants, followed by rigorous quality control. Logistic regression analysis assessed associations between this genetic variant and specific infertility phenotypes, including Sertoli cell-only (SCO) syndrome (complete absence of germ cells in the testes) and testicular sperm extraction (TESE) outcome. Bioinformatic tools were used to predict the potential changes in miRNA binding affinity, and subsequent luciferase reporter assays were performed to validate them. We observed that the presence of the T allele of PIWIL4-rs508485 significantly increased the risk of the SCO phenotype (P = 2.69E-03, OR = 1.34) and unsuccessful sperm retrieval via TESE (P = 1.09E-03, OR = 1.54), thus suggesting that this variant could serve as a predictive biomarker for both SCO and TESE outcome. Bioinformatic analyses revealed that the risk allele may disrupt miRNA binding to the 3’UTR of PIWIL4, which was further confirmed by luciferase reporter assays, potentially leading to post-transcriptional silencing and influencing gene regulation. This regulatory effect may contribute to impaired spermatogenesis, explaining the observed genetic associations. Although the study provides strong statistical evidence, replication in diverse populations is needed. Additional studies are required to confirm these findings and explore other genetic factors influencing male infertility. This study supports the key role of PIWIL4 in spermatogenesis, highlighting the importance of miRNA-mediated regulation in severe male infertility by modulating gene expression. Identifying genetic factors underlying spermatogenesis could contribute to the development of personalized treatments and therapeutic approaches for spermatogenic failure. No

STUDY QUESTION:Do the genetic determinants of idiopathic severe spermatogenic failure (SPGF) differ between generations? SUMMARY ANSWER:Our data support that the genetic component of idiopathic SPGF is impacted by dynamic changes in environmental exposures over decades. WHAT IS KNOWN ALREADY:The idiopathic form of SPGF has a multifactorial etiology wherein an interaction between genetic, epigenetic, and environmental factors leads to the disease onset and progression. At the genetic level, genome-wide association studies (GWASs) allow the analysis of millions of genetic variants across the genome in a hypothesis-free manner, as a valuable tool for identifying susceptibility risk loci. However, little is known about the specific role of non-genetic factors and their influence on the genetic determinants in this type of conditions. STUDY DESIGN, SIZE, DURATION:Case-control genetic association analyses were performed including a total of 912 SPGF cases and 1360 unaffected controls. PARTICIPANTS/MATERIALS, SETTING, METHODS:All participants had European ancestry (Iberian and German). SPGF cases were diagnosed during the last decade either with idiopathic non-obstructive azoospermia (n = 547) or with idiopathic non-obstructive oligozoospermia (n = 365). Case-control genetic association analyses were performed by logistic regression models considering the generation as a covariate and by in silico functional characterization of the susceptibility genomic regions. MAIN RESULTS AND THE ROLE OF CHANCE:This analysis revealed 13 novel genetic association signals with SPGF, with eight of them being independent. The observed associations were mostly explained by the interaction between each lead variant and the age-group. Additionally, we established links between these loci and diverse non-genetic factors, such as toxic or dietary habits, respiratory disorders, and autoimmune diseases, which might potentially influence the genetic architecture of idiopathic SPGF. LARGE SCALE DATA:GWAS data are available from the authors upon reasonable request. LIMITATIONS, REASONS FOR CAUTION:Additional independent studies involving large cohorts in ethnically diverse populations are warranted to confirm our findings. WIDER IMPLICATIONS OF THE FINDINGS:Overall, this study proposes an innovative strategy to achieve a more precise understanding of conditions such as SPGF by considering the interactions between a variable exposome through different generations and genetic predisposition to complex diseases. STUDY FUNDING/COMPETING INTEREST(S):This work was supported by the "Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020)" (ref. PY20_00212, P20_00583), the Spanish Ministry of Economy and Competitiveness through the Spanish National Plan for Scientific and Technical Research and Innovation (ref. PID2020-120157RB-I00 funded by MCIN/ AEI/10.13039/501100011033), and the 'Proyectos I+D+i del Programa Operativo FEDER 2020' (ref. B-CTS-584-UGR20). ToxOmics-Centre for Toxicogenomics and Human Health, Genetics, Oncology and Human Toxicology, is also partially supported by the Portuguese Foundation for Science and Technology (Projects: UIDB/00009/2020; UIDP/00009/2020). The authors declare no competing interests. TRIAL REGISTRATION NUMBER:N/A.

Abstract Study question Do the genetic factors associated with idiopathic severe spermatogenic failure (SPGF) differ across age groups due to evolving environmental exposures over generations? Summary answer Dynamic changes in environmental exposures have the potential to reshape the genetic predisposition to idiopathic SPGF. What is known already Accumulating evidence suggests that the idiopathic form of SPGF may represent a complex trait with a multifactorial aetiology, wherein environmental factors influence the predisposition and development of the disease through their interaction with genetic polymorphisms. In a recent genome-wide association study (GWAS) of SPGF conducted in a European population, we identified genetic variants associated with this condition in immune and spermatogenesis-related genes. However, while GWASs serve as valuable tools for identifying susceptibility risk loci, our understanding of the influence of environmental factors and their specific impact on the genetic architecture of these conditions remains limited. Study design, size, duration Genome-wide genotype data from 912 SPGF cases and 1,360 unaffected controls from the Iberian Peninsula and Germany were analysed considering age as a proxy to assess exposure to varying environmental factors across generations. An in silico functional prioritisation of associated genomic regions was also performed to comprehensively evaluate the gene-environment relationships influencing the disease. Participants/materials, setting, methods The study cohort was stratified into two age groups: individuals aged 40 or older in 2023 (452 cases and 965 controls) and those under 40 in 2023 (460 cases and 424 controls). SPGF comprised individuals with non-obstructive azoospermia (n = 547) or non-obstructive oligozoospermia (n = 365). Logistic regression models were used for the case-control analysis, with the generation included as a covariate. Bioinformatic approaches involved the examination of functional annotations of the genome sourced from different public databases. Main results and the role of chance Thirteen genetic loci were found to be associated with idiopathic SPGF, assuming generation-dependent effects. Five of them showed a correlation with an increased susceptibility to SPGF in the youngest age group (rs72818509: P = 2.19E−05, rs57701320: P = 5.20E−05, rs79733930: P = 7.81E−05, rs77137582: P = 1.04E−04, rs75938373: P = 3.56E−05), while the remaining eight were specifically associated with the disease in the oldest age group (rs144324356: P = 2.37E−05, rs3763812: P = 8.61E−05, rs61997636: P = 1.00E−04, rs111364930: P = 9.63E−05, rs115408081: P = 5.77E-05, rs142908940: P = 1.76E−04, rs144031067: P = 1.76E-04, rs74514513: P = 1.76E-04). These associations were primarily attributed to the interaction between each lead variant and the age group. Additionally, associations were established between the identified genomic risk loci and various environmental factors, including toxic habits (such as smoking or alcohol consumption), diet-related traits (such as cholesterol levels, type 2 diabetes, and body mass index), respiratory disorders (such as asthma or COVID-19), autoimmune diseases (including systemic lupus erythematosus), and educational attainment. Our findings suggest that changes in environmental factors across generations could alter the genetic basis of idiopathic SPGF over time. Limitations, reasons for caution Despite the overall statistical power of our study cohort being suitable for detecting the expected effects, further independent studies comprising extensive cohorts across diverse ethnic populations are needed to validate the outcomes of our research. Wider implications of the findings Using a novel approach assess the potential impact of varying environmental factors on the genetic susceptibility to complex diseases, we gained insights into the intricate mechanisms underlying onset and progression of idiopathic SPGF. Understanding the etiological causes of multifactorial human disorders would help in developing personalized healthcare strategies. Trial registration number Not applicable

Abstract Study question Does the analysis of the endometrial microbiome provide the same information when using DNA or RNA sequencing-based techniques? Summary answer DNA vs. RNA-based microbiome analysis techniques demonstrate significant microbial compositional differences, meaning that the previous endometrial 16S rRNA gene-based microbiome analysis results might be overestimated. What is known already In recent years, high-throughput sequencing technologies have revolutionised the field of reproductive microbiome research. Our understanding of the composition of endometrial microbiome predominantly relies on DNA-based 16S rRNA gene profiling method. Regardless of its effectiveness and low cost, its underestimation of microbial diversity, abundance, or functionality and lack of detection precision on species level have been highlighted. The meta-transcriptome analysis, RNA-based method, overcomes these limitations and identifies functional genes that are actively expressed by the alive microbes. This study aims to elucidate for the first time the endometrial microbiome using a dual approach of 16S rRNA gene-method and meta-transcriptomic analyses. Study design, size, duration In total forty-seven women with infertility at ages 27-42 years old were enrolled in the present study. Paired endometrial samples, endometrial brushing and Pipelle biopsy were collected from each participant at the mid-secretory menstrual phase (LH + 7/9). Participants/materials, setting, methods Endometrial samples were collected using Tao Brush for 16S rRNA analysis (DNA analysis) and Pipelle endometrial curette for meta-transcriptomic analysis (total RNA analysis). QIAamp UCP Pathogen Mini Kit was used for DNA extraction and 16S rRNA gene V3-V4 regions were sequenced on MiSeq. RNA was extracted using miRNeasy Micro kit, with rRNA removal by RiboZero kit and Libraries were generated using Stranded Total RNA Prep. Taxonomy was assigned by using Kraken2 (v2.2.1), Bracken (v2.7). Main results and the role of chance To the best of our knowledge, this is the first study to compare endometrial microbial identification using both metagenomics (16S rRNA sequencing) and meta-transcriptomics (meta-RNA sequencing) within the same cohort. Analysis of the composition of the microorganisms by bacterial 16S rRNA gene sequencing revealed that the most abundant bacterial genus in the endometrium was Lactobacillus. In the meta-transcriptome analysis, no microorganisms belonging to the Lactobacillus genus were detected in high abundance. This indicates that the relative abundance of this genus at the DNA level does not necessarily imply microbial activity or the presence of viable Lactobacillus in the endometrium. The meta-transcriptomics analysis detected microbial taxa such as Staphylococcus, Bacillus, Streptococcus, and Burkholderia as the most dominant. The results suggest that DNA-based detection of microorganisms in the endometrium may be overestimated due to the presence of naked DNA sequences or microorganisms from the vagina and cervix. Furthermore, RNA-based analysis demonstrates a population of active microorganisms different from the image shown by DNA analysis. Altogether, our study results indicate that the uterine microenvironment, as previously identified through DNA sequencing, may partly reflect the vaginocervical microenvironment, with the uterine tissue being a low biomass site not dominated by lactobacilli. Limitations, reasons for caution The endometrial samples were paired; however, DNA was analysed in the endometrial brushing sample while RNA in the tissue biopsy, which might result in some differences in microbial composition. Wider implications of the findings Contrary to the general belief of the Lactobacillus dominance in the human endometrium, our study suggests that the endometrial microenvironment may be harbouring DNA fragments and/or cells of lactobacilli originating from the lower reproductive tract. Our study results call out to re-consider/re-analyse the endometrial microbiome in health and disease. Trial registration number not applicable

Abstract Study question Does semen harbour functionally active microorganisms, and whether the microbial composition associate with semen quality parameters? Summary answer Seminal fluid harbours functionally alive microorganisms including bacteria, viruses, archaea, viroids and fungi whose composition and metabolic functions associate with semen quality parameters. What is known already Seminal fluid has been shown to possess different microbes, where different bacteria have been associated with spermatogenesis, seminal parameters and infertility. Nevertheless, previous results are inconclusive and the core microorganismal composition in semen is not determined and whether the identified bacterial DNA sequences refer to alive/functionally active microbes is not clear. Further, there is limited knowledge of the potential host-microbe interactions. Study design, size, duration In total 78 men with age between 18 and 45 years donated semen for the study, 59 men with good quality semen parameters and 19 men with low semen quality and diagnosed asthenospermia, oligoasthenozoospermia or oligoasthenoteratozoospermia comprised control and case groups, respectively. Participants/materials, setting, methods Semen quality parameters were analysed following WHO, 2021 guidelines. Seminal fluid underwent NextSeq total RNA sequencing, separating human and non-human sequences with Hisat2 aligner. Taxonomic classification of microorganisms was obtained using Kraken2. Microbial species and human RNA-Seq analyses, comparing control and infertility groups, utilized metagenomeSeq and edgeR R packages. Human and microbial pathways were annotated using MetaCyc, followed by GSEA analysis. The integration of metabolic pathways between host and microbes was investigated. Main results and the role of chance With our total RNAseq analysis, we mapped the entire alive microbiota composing of 6453 microorganisms within the seminal fluid samples. Microbes such as bacteria, fungi, viruses, viroids and archea were identified. When analysing microbiota associations with seminal parameters, 404 microbial species (Lactobacillus, Gardnerella, Prevotella, Atopobium, Pseudomonas among others) were significantly differently present among infertile men vs. good quality semen group. The host transcriptomic analysis identified 2569 differentially expressed genes in infertile vs. good quality semen groups, involved in sperm differentiation, spermatid development, reproduction, cilium movement and meiosis. Metabolic pathway analysis detected possible metabolic activity in the host-microbiota crosstalk in FAO-PWY: fatty acid & beta-oxidation, LIPASYN-PWY: phospholipases, PWY66-429: fatty acid biosynthesis, PWY-3781: aerobic respiration I pathways. Limitations, reasons for caution Bigger sample size of infertile men would be needed to validate the study findings. Wider implications of the findings We confirm the presence of active microbes in semen with implications in seminal quality parameters such as motility, sperm count and morphology. Our results contribute to better understanding of seminal microbiota dysregulation in infertility with broader implications for reproductive health, suggesting functional links between seminal microbiota and sperm parameters. Trial registration number NA

Abstract Study question Does seminal microbiome differ between men with good and poor sperm quality parameters? Summary answer Men with poor sperm quality values presented a higher microbial richness, while after rigorous contamination controlling no specific bacteria associated with sperm quality parameters. What is known already Infertility represents a major health concern affecting 1 in every 6 couples. Approximately 50% of infertility cases are attributed to male factor, where seminal quality is crucial. Recent studies have brought to light the role of the microbiome in sperm quality and function. Changes in microbial communities can impact reproductive functions, as these microorganisms may affect spermatozoa by releasing specific molecules or influencing direct adhesion. Since current evidence in the field is inconclusive, as the studies are performed on small sample sizes and lack proper positive and negative controls, there is a need for rigorous studies on bigger cohorts. Study design, size, duration This cross-sectional study included 115 men (age= 33.6±9.6 years; BMI= 25.0±3.5). Semen samples assessment was performed between March 2019 and July 2021 at the sperm biobank and University Hospital. The study was approved by the regional Ethics Committee. Participants/materials, setting, methods The study population was categorized into two seminal quality groups based on WHO reference values. Accordingly, 66 men presented good sperm quality while 49 poor sperm quality. Sperm concentration and progressive motility parameters were assessed in semen. Seminal microbiome was analyzed using 16 rRNA gene sequencing. For rigorous contamination controlling, negative controls together with in silico decontamination (MicroDecon package) were applied. Microbial diversity and composition were assessed including age and BMI as confounders. Main results and the role of chance After rigorous decontamination analysis including negative controls, 774 microorganisms were identified in the semen. Ninety-one bacteria were detected as contaminants. Next, a filter of 30% prevalence was applied to identify the core seminal microbiome, which resulted in a total of 36 bacterial genera in the semen in our cohort of men. Bacteria such as Actinomyces, Finegoldia, Campylobacter, Anaerococcus and Peptoniphilus were the the most prevalent among seminal samples. Men with poor sperm quality presented a richer seminal microbiome compared to good sperm quality group (α-diversity p-value=0.022), while β-diversity did not show a significant microbial dissimilarity between groups (R2=0.012, p-value>0.05). Differential abundance analysis on the core microbiome detected increased abundances of Veillonella (logFC=1.836), Gemella (logFC=1.612) and a member of Pasteurellaceae family (logFC=1.628) in men with lower sperm quality (all p-values<0.05). However, differences did not remain significant after multiple comparison correction (FDR p-values>0.05). Limitations, reasons for caution This study reported specific microbial alterations in men with poor sperm quality parameters, which should be investigated further considering different life-style factors such as smoking and diet. Wider implications of the findings With our rigorous study protocol, we see that although seminal microbiome could have some potential implications on seminal quality, the core microbiome does not seem to associate with seminal parameters. More research is needed, but it could be there has been previously overinterpretation of the microbiome associations with seminal parameters. Trial registration number not applicable

Can genome-wide genotyping data be analysed in a hypothesis-driven fashion to better understand the genetic component of male infertility due to severe spermatogenic failure (SPGF)? We revealed that both common and rare genetic variants in genomic regions involved in spermatogenesis contribute to the development of idiopathic SPGF. Spermatogenesis is a tightly regulated process involving the controlled expression of over 2,000 genes. Mutations in spermatogenesis-related genes have been associated with SPGF development. Next-generation sequencing methods are useful for identifying rare mutations that explain monogenic forms of this condition. Additionally, genome-wide association studies (GWAS) have provided valuable insights into the role of common polymorphisms in complex forms of SPGF. Interestingly, novel methods have shown that GWAS datasets can be used to infer rare coding variants that are causal for male infertility phenotypes, but this approach has never been applied to characterise the genetic component of a whole case-control cohort. We conducted a thorough examination of both common (minor allele frequency, MAF > 0.01) and rare (MAF < 0.01) genetic variation within a set of 1,797 spermatogenesis genes. This gene panel was meticulously curated through an exhaustive search in the literature and in different databases of male infertility genetics. The genotype data were obtained from a previously published GWAS in Europeans, encompassing over 6 million polymorphisms. We analysed a cohort that included 1,274 SPGF patients and 1,951 unaffected controls with European ancestry. Three independent approaches were follow: (1) variant-wise analysis using logistic regression models followed by a meta-analysis of the study cohorts; (2) gene-wise analysis using a Combined Multivariate and Collapsing (CMC) burden test; and (3) identification and characterisation of highly damaging rare coding variants in homozygosity. Multiple testing correction was applied to determine statistical significance Our results suggested a potential association between SPGF and the rs12347237*T variant of the SHOC1 gene in the GWAS population (P = 3.17E-05, OR = 2.94). This association was subsequently validated in an independent Iberian cohort (Pcombined = 6.992E-06, OR = 2.61). Furthermore, our gene-wise analysis showed putative associations through the cumulative effect of some rare and low-frequency variants in SHOC1 (P = 4.9E-03), PCSK4 (P = 1E-04), AP3B1 (P = 5E-04), and DLK1 (P = 5E-04). Remarkably, the rare and common variants identified in SHOC1 are part of the same haplotype block, impacting both coding and non-coding regions. Additionally, we identified 35 individuals carrying 32 very rare and potentially pathogenic variants in homozygosity within the coding regions of the selected genes. The analysis of low-frequency variants presents challenges in achieving sufficient statistical power to detect genetic associations. Consequently, independent studies involving large sample sets are essential to replicate our findings. In addition, the specific role of the identified variants in the pathogenic mechanisms of SPGF should be assessed using functional experiments. The discovery of novel genetic risk factors for SPGF and the elucidation of the genetic causes underlying both monogenic and complex forms of SPGF provide new perspectives for personalized medicine and reproductive counselling. not applicable

STUDY QUESTION: Can genome-wide genotyping data be analysed using a hypothesis-driven approach to enhance the understanding of the genetic basis of severe spermatogenic failure (SPGF) in male infertility? SUMMARY ANSWER: Our findings revealed a significant association between SPGF and the SHOC1 gene and identified three novel genes (PCSK4, AP3B1, and DLK1) along with 32 potentially pathogenic rare variants in 30 genes that contribute to this condition. WHAT IS KNOWN ALREADY SPGF: is a major cause of male infertility, often with an unknown aetiology. SPGF can be due to either multifactorial causes, including both common genetic variants in multiple genes and environmental factors, or highly damaging rare variants. Next-generation sequencing methods are useful for identifying rare mutations that explain monogenic forms of SPGF. Genome-wide association studies (GWASs) have become essential approaches for deciphering the intricate genetic landscape of complex diseases, offering a cost-effective and rapid means to genotype millions of genetic variants. Novel methods have demonstrated that GWAS datasets can be used to infer rare coding variants that are causal for male infertility phenotypes. However, this approach has not been previously applied to characterize the genetic component of a whole case-control cohort. STUDY DESIGN, SIZE, DURATION: We employed a hypothesis-driven approach focusing on all genetic variation identified, using a GWAS platform and subsequent genotype imputation, encompassing over 20 million polymorphisms and a total of 1571 SPGF patients and 2431 controls. Both common (minor allele frequency, MAF > 0.01) and rare (MAF < 0.01) variants were investigated within a total of 1797 loci with a reported role in spermatogenesis. This gene panel was meticulously assembled through comprehensive searches in the literature and various databases focused on male infertility genetics. PARTICIPANTS/MATERIALS, SETTING, METHODS: This study involved a European cohort using previously and newly generated data. Our analysis consisted of three independent methods: (i) variant-wise association analyses using logistic regression models, (ii) gene-wise association analyses using combined multivariate and collapsing burden tests, and (iii) identification and characterisation of highly damaging rare coding variants showing homozygosity only in SPGF patients. MAIN RESULTS AND THE ROLE OF CHANCE: The variant-wise analyses revealed an association between SPGF and SHOC1-rs12347237 (P = 4.15E-06, odds ratio = 2.66), which was likely explained by an altered binding affinity of key transcription factors in regulatory regions and the disruptive effect of coding variants within the gene. Three additional genes (PCSK4, AP3B1, and DLK1) were identified as novel relevant players in human male infertility using the gene-wise burden test approach (P < 5.56E-04). Furthermore, we linked a total of 32 potentially pathogenic and recessive coding variants of the selected genes to 35 different cases. LARGE SCALE DATA Publicly available via GWAS catalog (accession number: GCST90239721). LIMITATIONS, REASONS FOR CAUTION: The analysis of low-frequency variants presents challenges in achieving sufficient statistical power to detect genetic associations. Consequently, independent studies with larger sample sizes are essential to replicate our results. Additionally, the specific roles of the identified variants in the pathogenic mechanisms of SPGF should be assessed through functional experiments. WIDER IMPLICATIONS OF THE FINDINGS Our findings highlight the benefit of using GWAS genotyping to screen for both common and rare variants potentially implicated in idiopathic cases of SPGF, whether due to complex or monogenic causes. The discovery of novel genetic risk factors for SPGF and the elucidation of the underlying genetic causes provide new perspectives for personalized medicine and reproductive counselling. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the Spanish Ministry of Science and Innovation through the Spanish National Plan for Scientific and Technical Research and Innovation (PID2020-120157RB-I00) and the Andalusian Government through the research projects of 'Plan Andaluz de Investigaci & oacute;n, Desarrollo e Innovacion (PAIDI 2020)' (ref. PY20_00212) and 'Proyectos de Investigaci & oacute;n aplicada FEDER-UGR 2023' (ref. C-CTS-273-UGR23). S.G.-M. was funded by the previously mentioned projects (ref. PY20_00212 and PID2020-120157RB-I00). A.G.-J. was funded by MCIN/AEI/10.13039/501100011033 and FSE 'El FSE invierte en tu futuro' (grant ref. FPU20/02926). IPATIMUP integrates the i3S Research Unit, which is partially supported by the Portuguese Foundation for Science and Technology (FCT), financed by the European Social Funds (COMPETE-FEDER) and National Funds (projects PEstC/SAU/LA0003/2013 and POCI-01-0145-FEDER-007274). S.S. is supported by FCT funds (10.54499/DL57/2016/CP1363/CT0019), ToxOmics-Centre for Toxicogenomics and Human Health, Genetics, Oncology and Human Toxicology, and is also partially supported by the Portuguese Foundation for Science and Technology (UIDP/00009/2020 and UIDB/00009/2020). S. Larriba received support from Instituto de Salud Carlos III (grant: DTS18/00101), co-funded by FEDER funds/European Regional Development Fund (ERDF)-a way to build Europe) and from 'Generalitat de Catalunya' (grant 2021SGR052). S. Larriba is also sponsored by the 'Researchers Consolidation Program' from the SNS-Dpt. Salut Generalitat de Catalunya (Exp. CES09/020). All authors declare no conflict of interest related to this study.