Complex diseases have multifactorial etiologies making actionable diagnostic biomarkers difficult to identify. Diagnostic research must expand beyond single or a handful of genetic or epigenetic targets for complex disease and explore a broader system of biological pathways. With the objective to develop a diagnostic tool designed to analyze a comprehensive network of epigenetic profiles in complex diseases, we used publicly available DNA methylation data from over 2,400 samples representing 20 cell types and various diseases. This tool, rather than detecting differentially methylated regions at specific genes, measures the intra-individual methylation variability within gene promoters to identify global shifts away from healthy regulatory states. To assess this new approach, we explored three distinct questions: 1) Are profiles of epigenetic variability tissue-specific? 2) Do diseased tissues exhibit altered epigenetic variability compared to normal tissue? 3) Can epigenetic variability be detected in complex disease? Unsupervised clustering established that global epigenetic variability in promoter regions is tissue-specific and promoter regions that are the most epigenetically stable in a specific tissue are associated with genes known to be essential for its function. Furthermore, analysis of epigenetic variability in these most stable regions distinguishes between diseased and normal tissue in multiple complex diseases. Finally, we demonstrate the clinical utility of this new tool in the assessment of a multifactorial condition, male infertility. We show that epigenetic variability in purified sperm is correlated with live birth outcomes in couples undergoing intrauterine insemination (IUI), a common fertility procedure. Men with the least epigenetically variable promoters were almost twice as likely to father a child than men with the greatest number of epigenetically variable promoters. Interestingly, no such difference was identified in men undergoing in vitro fertilization (IVF), another common fertility procedure, suggesting this as a treatment to overcome higher levels of epigenetic variability when trying to conceive.
Background: Human seminal cell-free deoxyribonucleic acid (cfDNA) methylation patterns have not yet been thoroughly explored; however, recent work in mouse has suggested that some cfDNA encountered in the epididymis may contaminate DNA methylation studies assessing the mature spermatozoa. Such contamination could clearly prove to be a significant confounder, for many reasons, in epigenetic studies of male factor infertility.Objectives: To explore the nature of seminal cfDNA methylation and the likelihood that it would be retained following standard semen sample processing for epigenetic analysis.
Materials and methods:We assessed 12 semen samples collected at Utah Fertility Center. For each sample, seminal cfDNA was isolated from the sperm pellet. The spermatozoa was split into three aliquots including one exposed to DNase to remove any additional cfDNA (termed "pure spermatozoa"), one not exposed to DNase, and one exposed to DNase but reintroduced to seminal cfDNA. We additionally assessed blood DNA as our benchmark for somatic cell DNA methylation patterns. DNA methylation was measured via Illumina's 850k array and assessed for differential regional methylation.Results: Forty-six thousand three hundred fifty-two differentially methylated regions (FDR > 40) were identified between pure spermatozoa and seminal cfDNA. We found at these sites that the average DNA methylation in cfDNA always fell somewhere between the average methylation in spermatozoa and blood. We also assessed each sperm treatment groups at all 46,352 regions of interest and found no significant differences at any of these sites.Discussion and conclusion: Our data suggest that seminal cfDNA is a clear mixture of both somatic and germline DNA and that cfDNA is not a contaminating feature in sperm DNA methylation studies following standard protocols in human sperm DNA extraction.
This report will take an in-depth look at a community organization’s programs throughthe analysis of the data collected from activity observations using the Out of School Time (OST)observation instrument. The Out-of-School Time Observation Tool was developed by PolicyStudies Associates, Inc. with the support of the Charles Stewart Mott Foundation (Pechman et al.2008). This report summarizes the research process, presents the main findings, and suggestshow the community organization can improve their programs. Furthermore, we explain how thisservice learning experience is explicitly connected to the norms of caring and utilitarianism.Lastly, there is a learning reflection which discusses our collective learning based on teamprocesses, ethical content, and organizational practices at the community organization.Research showed that staff was rated low in the categories of asking youth to expandupon their ideas and thoughts and employing varied teaching strategies. Based on these findings,we suggest that if staff make the necessary changes to their teaching methods, youth will becomemore engaged and have a more positive learning experience within their activities. Nevertheless,staff was rated high in the categories related to relationship building. The strong relationshipsformed between the staff and youth were apparent throughout our observations.
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