OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations.

BACKGROUND: Modern genome sequencing leads to an ever-growing collection of genomic annotations. Combining these elements with a set of input regions (e.g. genes) would yield new insights in genomic associations, such as those involved in gene regulation. The required data are scattered across different databases making a manual approach tiresome, unpractical, and prone to error. Semi-automatic approaches require programming skills in data parsing, processing, overlap calculation, and visualization, which most biomedical researchers lack. Our aim was to develop an automated tool providing all necessary algorithms, benefiting both bioinformaticians and researchers without bioinformatic training. RESULTS: We developed overlapping annotated genomic regions (OGRE) as a comprehensive tool to associate and visualize input regions with genomic annotations. It does so by parsing regions of interest, mining publicly available annotations, and calculating possible overlaps between them. The user can thus identify location, type, and number of associated regulatory elements. Results are presented as easy to understand visualizations and result tables. We applied OGRE to recent studies and could show high reproducibility and potential new insights. To demonstrate OGRE's performance in terms of running time and output, we have conducted a benchmark and compared its features with similar tools. CONCLUSIONS: OGRE's functions and built-in annotations can be applied as a downstream overlap association step, which is compatible with most genomic sequencing outputs, and can thus enrich pre-existing analyses pipelines. Compared to similar tools, OGRE shows competitive performance, offers additional features, and has been successfully applied to two recent studies. Overall, OGRE addresses the lack of tools for automatic analysis, local genomic overlap calculation, and visualization by providing an easy to use, end-to-end solution for both biologists and computational scientists.

Shaping the future of male reproductive health: fostering talent at the 14th Network of Young Researchers in Andrology meeting.

The 14th Network of Young Researchers in Andrology (NYRA) meeting was a 2-day conference held at the University of East Anglia in Norwich, UK, organized by the NYRA. The meeting brought together researchers and experts to discuss and exchange ideas on male infertility and spermatogenesis. The meeting covered a wide range of topics related to male germline research, including the impact of mutations in the male germline on future generations, the use of innovative sequencing tools for the study of male infertility, and the intricate germline epigenome. The impact of aging on spermatogenesis was also discussed, with a focus on the increased DNA fragmentation rates, changes in DNA methylation patterns, and longer telomeres associated with aging sperm. Additionally, progress on fertility preservation options for children undergoing gonadotoxic cancer treatments was presented. The meeting also featured workshops on leadership and career development strategies in science, providing a valuable opportunity for young researchers to learn from experts in the field and exchange ideas with their peers. Overall, the meeting provided a platform for researchers to discuss the latest developments in male germline research, highlighting the importance of empowering young researchers to tackle male reproductive health.

A germ cell-specific ageing pattern in otherwise healthy men.

Life-long sperm production leads to the assumption that male fecundity remains unchanged throughout life. However, recently it was shown that paternal age has profound consequences for male fertility and offspring health. Paternal age effects are caused by an accumulation of germ cell mutations over time, causing severe congenital diseases. Apart from these well-described cases, molecular patterns of ageing in germ cells and their impact on DNA integrity have not been studied in detail. In this study, we aimed to assess the effects of 'pure' ageing on male reproductive health and germ cell quality. We assembled a cohort of 198 healthy men (18-84 years) for which end points such as semen and hormone profiles, sexual health and well-being, and sperm DNA parameters were evaluated. Sperm production and hormonal profiles were maintained at physiological levels over a period of six decades. In contrast, we identified a germ cell-specific ageing pattern characterized by a steady increase of telomere length in sperm and a sharp increase in sperm DNA instability, particularly after the sixth decade. Importantly, we found sperm DNA methylation changes in 236 regions, mostly nearby genes associated with neuronal development. By in silico analysis, we found that 10 of these regions are located in loci which can potentially escape the first wave of genome-wide demethylation after fertilization. In conclusion, human male germ cells present a unique germline-specific ageing process, which likely results in diminished fecundity in elderly men and poorer health prognosis for their offspring.

Does the FSHB c.-211G>T polymorphism impact Sertoli cell number and the spermatogenic potential in infertile patients?

BACKGROUND: A genetic variant within the FSHB gene can deviate FSH action on spermatogenesis. The c.-211G>T FSHB single nucleotide polymorphism impacts FSHB transcription and biosynthesis due to interference with the LHX3 transcription factor binding. This SNP was previously shown to be strongly associated with lowered testicular volume, reduced sperm counts, and decreased FSH levels in patients carrying one or two T-alleles. OBJECTIVE: To determine the impact of the SNP FSHB c.-211G>T on Sertoli cell (SC) number, Sertoli cell workload (SCWL) and thereby spermatogenic potential. MATERIAL AND METHODS: Testicular biopsies of 31 azoospermic, homozygous T patients (26 non-obstructive azoospermia (NOA), and five obstructive azoospermia (OA)) were matched to patients with GG genotype. Marker proteins for SC (SOX9), spermatogonia (MAGE A4), and round spermatids (CREM) were used for semi-automatical quantification by immunofluorescence. SCWL (number of germ cells served by one SC) was determined and an unbiased clustering on the patient groups performed. RESULTS: Quantification of SC number in NOA patients did not yield significant differences when stratified by FSHB genotype. SC numbers are also not significantly different between FSHB genotypes for the OA patient group and between NOA and OA groups. SCWL in the NOA patient cohort is significantly reduced when compared to the OA control patients; however, in neither group an effect of the genotype could be observed. The cluster analysis of the whole study cohort yielded two groups only, namely NOA and OA, and no clustering according to the FSHB genotype. DISCUSSION AND CONCLUSION: The FSHB c.-211G>T polymorphism does not affect SC numbers or SCWL, thereby in principle maintaining the spermatogenic potential. The previously observed clinical phenotype for the FSHB genotype might therefore be caused by a hypo-stimulated spermatogenesis and not due to a decreased SC number.

Gathering insights on disease etiology from gene expression profiles of healthy tissues.

MOTIVATION: Gene expression profiles have been widely used to study disease states. It may be possible, however, to gather insights into human diseases by comparing gene expression profiles of healthy organs with different disease incidence or severity. We tested this hypothesis and developed an approach to identify candidate genes associated with disease development by focusing on cancer incidence since it varies greatly across human organs. RESULTS: We normalized organ-specific cancer incidence by organ weight and found that reproductive organs tend to have a higher mass-normalized cancer incidence, which could be due to evolutionary trade-offs. Next, we performed a genome-wide scan to identify genes whose expression across healthy organs correlates with organ-specific cancer incidence. We identified a large number of genes, including genes previously associated with tumorigenesis and new candidate genes. Most genes exhibiting a positive correlation with cancer incidence were related to ribosomal and transcriptional activity, translation and protein synthesis. Organs with enhanced transcriptional and translational activation may have higher cell proliferation and therefore be more likely to develop cancer. Furthermore, we found that organs with lower cancer incidence tend to express lower levels of known cancer-associated genes. Overall, these results demonstrate how genes and processes that predispose organs to specific diseases can be identified using gene expression profiles from healthy tissues. Our approach can be applied to other diseases and serve as foundation for further oncogenomic analyses. CONTACT: jp@senescence.info SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.