Novel bioinformatic analyses of somatic cell contamination in sperm samples.

The assessment of epigenetic profiles in sperm is sensitive to somatic cell contamination, which can influence methylation signals at gene promoters. This contamination is particularly problematic in the assessment of DNA methylation in samples with low sperm counts, where fractional amounts of somatic cell DNA can lead to significant shifts in measured methylation state. In this study, a new method of detecting possible somatic cell contamination is proposed through two multi-region bioinformatic models: a traditional differential methylation analysis and a machine learning logistic regression model. These models were trained on publicly available sperm (n = 489) and blood (n = 1029) DNA methylation array data and tested on a contamination set, wherein the sperm of four donors with normal sperm counts were run on a 450k methylation array with four permutations each, including pure blood, half blood and half sperm by DNA concentration, half blood and half sperm by cell count, and pure sperm (n = 16). The DMR and logistic regression model classified the contamination testing set with 100% and 94% accuracy, respectively. These new methods of detecting the effects of somatic cell contamination allow for more accurate differentiation between epigenetic profiles that contain a biological somatic-like shift and those that have somatic-like signatures because of contamination.

Detection of neuron-derived cfDNA in blood plasma: a new diagnostic approach for neurodegenerative conditions.

Neurodegenerative diseases, such as Alzheimer's disease (AD), pose significant challenges in early diagnosis, leading to irreversible brain damage and cognitive decline. In this study, we present a novel diagnostic approach that utilizes whole molecule analysis of neuron-derived cell-free DNA (cfDNA) as a biomarker for early detection of neurodegenerative diseases. By analyzing Differential Methylation Regions (DMRs) between purified cortical neurons and blood plasma samples, we identified robust biomarkers that accurately distinguish between neuronal and non-neuronal cfDNA. The use of cfDNA offers the advantage of convenient and minimally invasive sample collection compared to traditional cerebrospinal fluid or tissue biopsies, making this approach more accessible and patient friendly. Targeted sequencing at the identified DMR locus demonstrated that a conservative cutoff of 5% of neuron-derived cfDNA in blood plasma accurately identifies 100% of patients diagnosed with AD, showing promising potential for early disease detection. Additionally, this method effectively differentiated between patients with mild cognitive impairment (MCI) who later progressed to AD and those who did not, highlighting its prognostic capabilities. Importantly, the differentiation between patients with neurodegenerative diseases and healthy controls demonstrated the specificity of our approach. Furthermore, this cfDNA-based diagnostic strategy outperforms recently developed protein-based assays, which often lack accuracy and convenience. While our current approach focused on a limited set of loci, future research should explore the development of a more comprehensive model incorporating multiple loci to increase diagnostic accuracy further. Although certain limitations, such as technical variance associated with PCR amplification and bisulfite conversion, need to be addressed, this study emphasizes the potential of cfDNA analysis as a valuable tool for pre-symptomatic detection and monitoring of neurodegenerative diseases. With further development and validation, this innovative diagnostic strategy has the potential to significantly impact the field of neurodegenerative disease research and patient care, offering a promising avenue for early intervention and personalized therapeutic approaches.

Epigenetic determinants of reproductive potential augment the predictive ability of the semen analysis.

OBJECTIVE: To investigate the power of DNA methylation variability in sperm cells in assessing male fertility potential. DESIGN: Retrospective cohort. SETTING: Fertility care centers. PATIENTS: Male patients seeking infertility treatment and fertile male sperm donors. INTERVENTION: None. MAIN OUTCOME MEASURES: Sperm DNA methylation data from 43 fertile sperm donors were analyzed and compared with the data from 1344 men seeking fertility assessment or treatment. Methylation at gene promoters with the least variable methylation in fertile patients was used to create 3 categories of promoter dysregulation in the infertility treatment cohort: poor, average, and excellent sperm quality. RESULTS: After controlling for female factors, there were significant differences in intrauterine insemination pregnancy and live birth outcomes between the poor and excellent groups across a cumulative average of 2-3 cycles: 19.4% vs. 51.7% (P=.008) and 19.4% vs. 44.8% (P=.03), respectively. Live birth outcomes from in vitro fertilization, primarily with intracytoplasmic sperm injection, were not found to be significantly different among any of the 3 groups. CONCLUSION: Methylation variability in a panel of 1233 gene promoters could augment the predictive ability of semen analysis and be a reliable biomarker for assessing intrauterine insemination outcomes. In vitro fertilization with intracytoplasmic sperm injection appears to overcome high levels of epigenetic instability in sperm.

Tissue-specific DNA methylation variability and its potential clinical value.

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.

Tissue specific age acceleration patterns in the sperm of oligozoospermic men.

To determine if disease can modify aging patterns in an affected tissue without altering the aging patterns of other tissues, blood and semen of individuals with oligozoospermia (n = 10) were compared to the blood and semen of individuals with normozoospermia (n = 24). DNA methylation data was obtained via Illumina's 850 K array. The Horvath and Jenkins age calculators were then utilized to predict the epigenetic age of blood and sperm. Epigenetic age of sperm was approximated using germ-line age differential (GLAD) values. Using nonpaired t-tests, it was found that sperm of oligozoospermic men (mean GLAD score of 0.078) were predicted to be significantly older than the sperm of normozoospermic men (mean GLAD score of -0.017), returning a p-value of 0.03. However, there was not a significant epigenetic age difference between the blood of those with oligozoospermia (mean GLAD equivalent score of -0.027) and normozoospermia (mean GLAD equivalent score of 0.048), producing a p-value of 0.20. These results lead to the conclusion that tissue specific aging is occurring in sperm of oligozoospermic individuals but not in unaffected somatic tissues (in this case, blood).

Epigenetic mechanisms within the sperm epigenome and their diagnostic potential.

The sperm epigenome contains a highly unique and specialized epigenetic landscape. Insightful questions need be asked about these epigenetic signatures and their predictive potential to assess the approximately 1 in 6 couples who experience infertility. Among those couples that do experience infertility, approximately half of the cases involve a male factor. Unfortunately, there is a significant lack of effective diagnostic tools in the male infertility space and thus clinicians are left with little data upon which they can formulate data driven treatment plans. Taking together this information and the striking prevalence of male infertility it's obvious that there is a need for improved diagnostic techniques for male infertility. Many studies have identified what appear to be clinically meaningful epigenetic alterations in sperm that may add utility in the diagnoses of infertility and improvement of pregnancy outcomes. Many researchers believe that continued analysis of these various epigenetic mechanisms may provide powerful predictive insight. In fact, there is promising current data suggesting that the predictive power of DNA methylation, Nuclear Proteins, and miRNA signatures in sperm likely can improve what is currently found with traditional diagnosis of male infertility. The focus of this review is to give a brief understanding to the field of epigenetics and the potential predictive power the sperm epigenome may hold in relation to improving the treatment and diagnosis of male infertility patients.