Epigenetic Disruption of the Piwi Pathway in Human Spermatogenic Disorders

Epigenetic changes are involved in a wide range of common human diseases. Although DNA methylation defects are known to be associated with male infertility in mice, their impact on human deficiency of sperm production has yet to be determined. We have assessed the global genomic DNA methylation profiles in human infertile male patients with spermatogenic disorders by using the Infinium Human Methylation27 BeadChip. Three populations were studied: conserved spermatogenesis, spermatogenic failure due to germ cell maturation defects, and Sertoli cell-only syndrome samples. A disease-associated DNA methylation profile, characterized by targeting members of the PIWI-associated RNA (piRNA) processing machinery, was obtained. Bisulfite genomic sequencing and pyrosequencing in a large cohort (n = 46) of samples validated the altered DNA methylation patterns observed in piRNA-processing genes. In particular, male infertility was associated with the promoter hypermethylation-associated silencing of PIWIL2 and TDRD1. The downstream effects mediated by the epigenetic inactivation of the PIWI pathway genes were a defective production of piRNAs and a hypomethylation of the LINE-1 repetitive sequence in the affected patients. Overall, our data suggest that DNA methylation, at least that affecting PIWIL2/TDRD1, has a role in the control of gene expression in spermatogenesis and its imbalance contributes to an unsuccessful germ cell development that might explain a group of male infertility disorders

Mg–1Zn–1Ca alloy for biomedical applications. Influence of the secondary phases on the mechanical and corrosion behaviour

An as-cast Mg–1Zn–1Ca alloy has been soundly characterized to be used as a biodegradable material in biomedical applications. Ca and Zn additions have a great influence in the microstructure, mechanical properties and corrosion behaviour of Mg alloys. SEM examinations revealed that most of the Ca and Zn atoms form Mg2Ca and Ca2Mg6Zn3 precipitates, which distribute preferentially along the grain boundaries forming a continuous network of secondary phases. The results of nanoindentation tests show differences in hardness and elastic modulus between the α-Mg matrix and the secondary phases. The results of three-point bending tests shows that cracks propagate following the network formed by the intermetallic compounds at the grain boundaries (GBs). The evolved hydrogen after immersion in Hank’s solution of the alloy has been also estimated, showing a change in the corrosion mechanism after 160 h. The intermetallic compounds act as a barrier against corrosion, so that it progresses through the α-Mg matrix phase

Clinical consequences of BRCA2 hypomorphism

Breast cancer; Cancer geneticsCáncer de mama; Genética del cáncerCàncer de mama; Genètica del càncerThe tumor suppressor FANCD1/BRCA2 is crucial for DNA homologous recombination repair (HRR). BRCA2 biallelic pathogenic variants result in a severe form of Fanconi anemia (FA) syndrome, whereas monoallelic pathogenic variants cause mainly hereditary breast and ovarian cancer predisposition. For decades, the co-occurrence in trans with a clearly pathogenic variant led to assume that the other allele was benign. However, here we show a patient with biallelic BRCA2 (c.1813dup and c.7796 A > G) diagnosed at age 33 with FA after a hypertoxic reaction to chemotherapy during breast cancer treatment. After DNA damage, patient cells displayed intermediate chromosome fragility, reduced survival, cell cycle defects, and significantly decreased RAD51 foci formation. With a newly developed cell-based flow cytometric assay, we measured single BRCA2 allele contributions to HRR, and found that expression of the missense allele in a BRCA2 KO cellular background partially recovered HRR activity. Our data suggest that a hypomorphic BRCA2 allele retaining 37–54% of normal HRR function can prevent FA clinical phenotype, but not the early onset of breast cancer and severe hypersensitivity to chemotherapy

Improving tribological properties of cast Al-Si alloys through application of wear-resistant thermal spray coatings

Flame Spray Thermal Spray coatings are low-cost, high-wear surface-treatment technologies. However, little has been reported on their potential effects on cast automotive aluminum alloys. The aim of this research was to investigate the tribological properties of as-sprayed NiCrBSi and WC/12Co Flame Spray coatings applied to two cast aluminum alloys: high-copper LM24 (AlSi8Cu3Fe), and low-copper LM25 (AlSi7Mg). Potential interactions between the mechanical properties of the substrate and the deposited coatings were deemed to be significant. Microstructural, microhardness, friction, and wear (pin-on-disk, microabrasion, Taber abrasion, etc.) results are reported, and the performance differences between coatings on the different substrates were noted. The coefficient of friction was reduced from 0.69-0.72 to 0.12-0.35. Wear (pin-on-disk) was reduced by a factor of 103-104, which was related to the high surface roughness of the coatings. Microabrasion wear was dependent on coating hardness and applied load. Taber abrasion results showed a strong dependency on the substrate, coating morphology, and homogeneity

Computational tools for splicing defect prediction in breast/ovarian cancer genes: how efficient are they at predicting RNA alterations?

In silico tools for splicing defect prediction have a key role to assess the impact of variants of uncertain significance. Our aim was to evaluate the performance of a set of commonly used splicing in silico tools comparing the predictions against RNA in vitro results. This was done for natural splice sites of clinically relevant genes in hereditary breast/ovarian cancer (HBOC) and Lynch syndrome. A study divided into two stages was used to evaluate SSF-like, MaxEntScan, NNSplice, HSF, SPANR, and dbscSNV tools. A discovery dataset of 99 variants with unequivocal results of RNA in vitro studies, located in the 10 exonic and 20 intronic nucleotides adjacent to exon-intron boundaries of BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, ATM, BRIP1, CDH1, PALB2, PTEN, RAD51D, STK11, and TP53, was collected from four Spanish cancer genetic laboratories. The best stand-alone predictors or combinations were validated with a set of 346 variants in the same genes with clear splicing outcomes reported in the literature. Sensitivity, specificity, accuracy, negative predictive value (NPV) and Mathews Coefficient Correlation (MCC) scores were used to measure the performance. The discovery stage showed that HSF and SSF-like were the most accurate for variants at the donor and acceptor region, respectively. The further combination analysis revealed that HSF, HSF+SSF-like or HSF+SSF-like+MES achieved a high performance for predicting the disruption of donor sites, and SSF-like or a sequential combination of MES and SSF-like for predicting disruption of acceptor sites. The performance confirmation of these last results with the validation dataset, indicated that the highest sensitivity, accuracy, and NPV (99.44%, 99.44%, and 96.88, respectively) were attained with HSF+SSF-like or HSF+SSF-like+MES for donor sites and SSF-like (92.63%, 92.65%, and 84.44, respectively) for acceptor sites. We provide recommendations for combining algorithms to conduct in silico splicing analysis that achieved a high performance. The high NPV obtained allows to select the variants in which the study by in vitro RNA analysis is mandatory against those with a negligible probability of being spliceogenic. Our study also shows that the performance of each specific predictor varies depending on whether the natural splicing sites are donors or acceptors

Computational Tools for Splicing Defect Prediction in Breast/Ovarian Cancer Genes: How Efficient Are They at Predicting RNA Alterations?

In silico tools for splicing defect prediction have a key role to assess the impact of variants of uncertain significance. Our aim was to evaluate the performance of a set of commonly used splicing in silico tools comparing the predictions against RNA in vitro results. This was done for natural splice sites of clinically relevant genes in hereditary breast/ovarian cancer (HBOC) and Lynch syndrome. A study divided into two stages was used to evaluate SSF-like, MaxEntScan, NNSplice, HSF, SPANR, and dbscSNV tools. A discovery dataset of 99 variants with unequivocal results of RNA in vitro studies, located in the 10 exonic and 20 intronic nucleotides adjacent to exon–intron boundaries of BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, ATM, BRIP1, CDH1, PALB2, PTEN, RAD51D, STK11, and TP53, was collected from four Spanish cancer genetic laboratories. The best stand-alone predictors or combinations were validated with a set of 346 variants in the same genes with clear splicing outcomes reported in the literature. Sensitivity, specificity, accuracy, negative predictive value (NPV) and Mathews Coefficient Correlation (MCC) scores were used to measure the performance. The discovery stage showed that HSF and SSF-like were the most accurate for variants at the donor and acceptor region, respectively. The further combination analysis revealed that HSF, HSF+SSF-like or HSF+SSF-like+MES achieved a high performance for predicting the disruption of donor sites, and SSF-like or a sequential combination of MES and SSF-like for predicting disruption of acceptor sites. The performance confirmation of these last results with the validation dataset, indicated that the highest sensitivity, accuracy, and NPV (99.44%, 99.44%, and 96.88, respectively) were attained with HSF+SSF-like or HSF+SSF-like+MES for donor sites and SSF-like (92.63%, 92.65%, and 84.44, respectively) for acceptor sites.We provide recommendations for combining algorithms to conduct in silico splicing analysis that achieved a high performance. The high NPV obtained allows to select the variants in which the study by in vitro RNA analysis is mandatory against those with a negligible probability of being spliceogenic. Our study also shows that the performance of each specific predictor varies depending on whether the natural splicing sites are donors or acceptors

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

Abstract The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non-pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. This article is protected by copyright. All rights reserved.Peer reviewe

Altered gene expression signature of early stages of the germ line supports the pre-meiotic origin of human spermatogenic failure

The molecular basis of spermatogenic failure (SpF) is still largely unknown. Accumulating evidence suggests that a series of specific events such as meiosis, are determined at the early stage of spermatogenesis. This study aims to assess the expression profile of pre-meiotic genes of infertile testicular biopsies that might help to define the molecular phenotype associated with human deficiency of sperm production. An accurate quantification of testicular mRNA levels of genes expressed in spermatogonia was carried out by RT-qPCR in individuals showing SpF owing to germ cell maturation defects, Sertoli cell-only syndrome or conserved spermatogenesis. In addition, the gene expression profile of SpF was compared with that of testicular tumour, which is considered to be a severe developmental disease of germ cell differentiation. Protein expression from selected genes was evaluated by immunohistochemistry. Our results indicate that SpF is accompanied by differences in expression of certain genes associated with spermatogonia in the absence of any apparent morphological and/or numerical change in this specific cell type. In SpF testicular samples, we observed down-regulation of genes involved in cell cycle (CCNE1 and POLD1), transcription and post-transcription regulation (DAZL, RBM15 and DICER1), protein degradation (FBXO32 and TM9SF2) and homologous recombination in meiosis (MRE11A and RAD50) which suggests that the expression of these genes is critical for a proper germ cell development. Interestingly, a decrease in the CCNE1, DAZL, RBM15 and STRA8 cellular transcript levels was also observed, suggesting that the gene expression capacity of spermatogonia is altered in SpF contributing to an unsuccessful sperm production. Altogether, these data point to the spermatogenic derangement being already determined at, or arising in, the initial stages of the germ line

Sperm gene expression profile is related to pregnancy rate after insemination and is predictive of low fecundity in normozoospermic men

BACKGROUND Assessment of male fertility is traditionally based on microscopic evaluation of semen. However, the classical semen parameters do not adequately reflect sperm function, and their clinical value in predicting fertility is limited. We hypothesize that the sperm expression profile could reflect the fertilizing quality of spermatozoa and could be more informative for predicting the in vivo reproductive fitness of men with normal semen parameters. METHODS Sperm gene expression patterns of 68 normozoospermic donors (43 Phase I and 25 Phase II), used for therapeutic IUI, were analysed via TaqMan Arrays. RESULTS Significant differences in the expression of individual genes were observed between groups of donors with the lowest and highest pregnancy rates (PRs) after IUI. Additionally, we have developed a molecular means to classify the fertility status of semen donors for IUI based on the expression signature of four genes. In the Phase I study, this model had 90% sensitivity and 97% specificity for discriminating donors resulting in low PRs (cut-off value: <13.6%), far better than that obtained from the combination of sperm parameters. The translation of the model was validated in Phase II donors resulting in a sensitivity of 71.5% and a specificity of 78%. CONCLUSIONS Our findings contribute to the search for the most valuable genetic markers which are potentially useful as tools for predicting pregnancy. Our expression model could complement classical semen analysis in order to identify sperm donors with a less favourable IUI reproductive outcome despite having normal semen parameters. It may also be useful for the study of sperm function in couples with unexplained infertility