An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health.

BACKGROUND: Diabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan. METHODS: In this study, we employed machine learning (ML) based case-control study on a diabetic cohort size of 1000 participants form Qatar Biobank to predict diabetes using clinical and bone health indicators from Dual Energy X-ray Absorptiometry (DXA) machines. ML models were utilized to distinguish diabetes groups from non-diabetes controls. Recursive feature elimination (RFE) was leveraged to identify a subset of features to improve the performance of model. SHAP based analysis was used for the importance of features and support the explainability of the proposed model. RESULTS: Ensemble based models XGboost and RF achieved over 84% accuracy for detecting diabetes. After applying RFE, we selected only 20 features which improved the model accuracy to 87.2%. From a clinical standpoint, higher HDL-Cholesterol and Neutrophil levels were observed in the diabetic group, along with lower vitamin B12 and testosterone levels. Lower sodium levels were found in diabetics, potentially stemming from clinical factors including specific medications, hormonal imbalances, unmanaged diabetes. We believe Dapagliflozin prescriptions in Qatar were associated with decreased Gamma Glutamyltransferase and Aspartate Aminotransferase enzyme levels, confirming prior research. We observed that bone area, bone mineral content, and bone mineral density were slightly lower in the Diabetes group across almost all body parts, but the difference against the control group was not statistically significant except in T12, troch and trunk area. No significant negative impact of diabetes progression on bone health was observed over a period of 5-15 yrs in the cohort. CONCLUSION: This study recommends the inclusion of ML model which combines both DXA and clinical data for the early diagnosis of diabetes.

DIAPH3 predicts survival of patients with MGMT-methylated glioblastoma.

Background: Glioblastoma is one of the most aggressive primary brain tumors, with a poor outcome despite multimodal treatment. Methylation of the MGMT promoter, which predicts the response to temozolomide, is a well-established prognostic marker for glioblastoma. However, a difference in survival can still be detected within the MGMT methylated group, with some patients exhibiting a shorter survival than others, emphasizing the need for additional predictive factors. Methods: We analyzed DIAPH3 expression in glioblastoma samples from the cancer genome atlas (TCGA). We also retrospectively analyzed one hundred seventeen histological glioblastomas from patients operated on at Saint-Luc University Hospital between May 2013 and August 2019. We analyzed the DIAPH3 expression, explored the relationship between mRNA levels and Patient's survival after the surgical resection. Finally, we assessed the methylation pattern of the DIAPH3 promoter using a targeted deep bisulfite sequencing approach. Results: We found that 36% and 1% of the TCGA glioblastoma samples exhibit copy number alterations and mutations in DIAPH3, respectively. We scrutinized the expression of DIAPH3 at single cell level and detected an overlap with MKI67 expression in glioblastoma proliferating cells, including neural progenitor-like, oligodendrocyte progenitor-like and astrocyte-like states. We quantitatively analyzed DIAPH3 expression in our cohort and uncovered a positive correlation between DIAPH3 mRNA level and patient's survival. The effect of DIAPH3 was prominent in MGMT-methylated glioblastoma. Finally, we report that the expression of DIAPH3 is at least partially regulated by the methylation of three CpG sites in the promoter region. Conclusion: We propose that combining the DIAPH3 expression with MGMT methylation could offer a better prediction of survival and more adapted postsurgical treatment for patients with MGMT-methylated glioblastoma.

DiaNet v2 deep learning based method for diabetes diagnosis using retinal images.

Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential. Current diagnostic tests like fasting plasma glucose (FPG), oral glucose tolerance tests (OGTT), random plasma glucose (RPG), and hemoglobin A1c (HbA1c) have limitations, leading to misclassifications and discomfort for patients. The aim of this study is to enhance diabetes diagnosis accuracy by developing an improved predictive model using retinal images from the Qatari population, addressing the limitations of current diagnostic methods. This study explores an alternative approach involving retinal images, building upon the DiaNet model, the first deep learning model for diabetes detection based solely on retinal images. The newly proposed DiaNet v2 model is developed using a large dataset from Qatar Biobank (QBB) and Hamad Medical Corporation (HMC) covering wide range of pathologies in the the retinal images. Utilizing the most extensive collection of retinal images from the 5545 participants (2540 diabetic patients and 3005 control), DiaNet v2 is developed for diabetes diagnosis. DiaNet v2 achieves an impressive accuracy of over 92%, 93% sensitivity, and 91% specificity in distinguishing diabetic patients from the control group. Given the high prevalence of diabetes and the limitations of existing diagnostic methods in clinical setup, this study proposes an innovative solution. By leveraging a comprehensive retinal image dataset and applying advanced deep learning techniques, DiaNet v2 demonstrates a remarkable accuracy in diabetes diagnosis. This approach has the potential to revolutionize diabetes detection, providing a more accessible, non-invasive and accurate method for early intervention and treatment planning, particularly in regions with high diabetes rates like MENA.

Epigenetic age acceleration in surviving versus deceased COVID-19 patients with acute respiratory distress syndrome following hospitalization.

BACKGROUND: Aging has been reported as a major risk factor for severe symptoms and higher mortality rates in COVID-19 patients. Molecular hallmarks such as epigenetic alterations and telomere attenuation reflect the biological process of aging. Epigenetic clocks have been shown to be valuable tools for measuring biological age in various tissues and samples. As such, these epigenetic clocks can determine accelerated biological aging and time-to-mortality across various tissues. Previous reports have shown accelerated biological aging and telomere attrition acceleration following SARS-CoV-2 infection. However, the effect of accelerated epigenetic aging on outcome (death/recovery) in COVID-19 patients with acute respiratory distress syndrome (ARDS) has not been well investigated. RESULTS: In this study, we measured DNA methylation age and telomere attrition in 87 severe COVID-19 cases with ARDS under mechanical ventilation. Furthermore, we compared dynamic changes in epigenetic aging across multiple time points until recovery or death. Epigenetic age was measured using the Horvath, Hannum, DNAm skin and blood, GrimAge, and PhenoAge clocks, whereas telomere length was calculated using the surrogate marker DNAmTL. Our analysis revealed significant accelerated epigenetic aging but no telomere attrition acceleration in severe COVID-19 cases. In addition, we observed epigenetic age deceleration at inclusion versus end of follow-up in recovered but not in deceased COVID-19 cases using certain clocks. When comparing dynamic changes in epigenetic age acceleration (EAA), we detected higher EAA using both the Horvath and PhenoAge clocks in deceased versus recovered patients. The DNAmTL measurements revealed telomere attrition acceleration in deceased COVID-19 patients between inclusion and end of follow-up and a significant change in dynamic telomere attrition acceleration when comparing patients who recovered versus those who died. CONCLUSIONS: EAA and telomere attrition acceleration were associated with treatment outcomes in hospitalized COVID-19 patients with ARDS. A better understanding of the long-term effects of EAA in COVID-19 patients and how they might contribute to long COVID symptoms in recovered individuals is urgently needed.

Effects of paternal and chronological age on BEGAIN methylation and its possible role in autism.

Children from old fathers carry an increased risk for autism spectrum (ASD) and other neurodevelopmental disorders, which may at least partially be mediated by paternal age effects on the sperm epigenome. The brain enriched guanylate kinase associated (BEGAIN) protein is involved in protein-protein interactions at and transmission across synapses. Since several epigenome-wide methylation screens reported a paternal age effect on sperm BEGAIN methylation, here we confirmed a significant negative correlation between BEGAIN promoter methylation and paternal age, using more sensitive bisulfite pyrosequencing and a larger number of sperm samples. Paternal age-associated BEGAIN hypomethylation was also observed in fetal cord blood (FCB) of male but not of female offspring. There was no comparable maternal age effect on FCB methylation. In addition, we found a significant negative correlation between BEGAIN methylation and chronological age (ranging from 1 to 70 years) in peripheral blood samples of male but not of female donors. BEGAIN hypomethylation was more pronounced in male children, adolescents and adults suffering from ASD compared to controls. Both genetic variation (CC genotype of SNP rs7141087) and epigenetic factors may contribute to BEGAIN promoter hypomethylation. The age- and sex-specific BEGAIN methylation trajectories in the male germ line and somatic tissues, in particular the brain, support a role of this gene in ASD development.

A scoping review of artificial intelligence-based methods for diabetes risk prediction.

The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.

Report on a Case with Moreno-Nishimura-Schmidt Overgrowth Syndrome: A Clinically Delineated Disease Yet of an Unknown Origin!

Introduction: Overgrowth syndromes are a heterogeneous group of genetic disorders characterized by excessive growth, often accompanied by additional clinical features, such as facial dysmorphism, hormonal imbalances, cognitive impairment, and increased risk for neoplasia. Moreno-Nishimura-Schmidt (M-N-S) overgrowth syndrome is a very rare overgrowth syndrome characterized by severe pre- and postnatal overgrowth, dysmorphic facial features, kyphoscoliosis, large hands and feet, inguinal hernia, and distinctive skeletal features. The clinical and radiological features of the disorder have been well delineated, yet its molecular pathogenesis remains unclear. Case Presentation: We report on a Lebanese boy with M-N-S syndrome, whose clinical manifestations were compared with those of previously reported 5 affected individuals. Whole-exome sequencing combined with comparative genome hybridization analysis failed to delineate the molecular basis of the phenotype. However, epigenetic studies revealed a different methylation status of several CpG sites between him and healthy controls, with methyltransferase activity showing the most significant enrichment. Conclusion: An additional case of M-N-S syndrome recapitulated the clinical and radiological manifestations described in the previous reports. The data in the epigenetic studies implicated that abnormal methylations might play an essential role in development of the disease phenotype. However, additional studies in a clinically homogeneous cohort of patients are crucial to confirm this hypothesis.

Ribosomal DNA Copy Number Variation is Coupled with DNA Methylation Changes at the 45S rDNA Locus.

The human ribosomal DNA (rDNA) copy number (CN) has been challenging to analyse, and its sequence has been excluded from reference genomes due to its highly repetitive nature. The 45S rDNA locus encodes essential components of the cell, nevertheless rDNA displays high inter-individual CN variation that could influence human health and disease. CN alterations in rDNA have been hypothesized as a possible factor in autism spectrum disorders (ASD) and were shown to be altered in Schizophrenia patients. We tested whether whole-genome bisulphite sequencing can be used to simultaneously quantify rDNA CN and measure DNA methylation at the 45S rDNA locus. Using this approach, we observed high inter-individual variation in rDNA CN, and limited intra-individual copy differences in several post-mortem tissues. Furthermore, we did not observe any significant alterations in rDNA CN or DNA methylation in Autism Spectrum Disorder (ASD) brains in 16 ASD vs 11 control samples. Similarly, no difference was detected when comparing neurons form 28 Schizophrenia (Scz) patients vs 25 controls or oligodendrocytes from 22 Scz samples vs 20 controls. However, our analysis revealed a strong positive correlation between CN and DNA methylation at the 45S rDNA locus in multiple tissues. This was observed in brain and confirmed in small intestine, adipose tissue, and gastric tissue. This should shed light on a possible dosage compensation mechanism that silences additional rDNA copies to ensure homoeostatic regulation of ribosome biogenesis.

Accelerated epigenetic aging and DNA methylation alterations in Berardinelli-Seip congenital lipodystrophy.

Berardinelli-Seip congenital lipodystrophy type 2 (CGL2) is a very rare human genetic disorder with potential significance to the understanding of the pathobiology of aging. CGL2 patients display characteristic progeroid features and suffer from type 2 diabetes, insulin resistance and fatty liver. In this study, we profiled genome-wide DNA methylation levels in CGL2 patients with BSCL2 mutations to study epigenetic age acceleration and DNA methylation alterations. This analysis revealed significant age acceleration in blood DNA of CGL2 patients using both first- and second-generation epigenetic clocks. We also observed a shortened lifespan of Caenorhabditis elegans following knockdown of the BSCL2 homolog seip-1 on a daf-16/forkhead box, class O mutant background. DNA methylation analysis revealed significant differentially methylated sites enriched for lyase activity, kinase regulator activity, protein kinase regulator activity and kinase activator activity. We could also observe significant hypomethylation in the promoter of the dual specificity phosphatase 22 gene when comparing CGL2 patients versus controls. We conclude that in line with the observed progeroid features, CGL2 patients exhibit significant epigenetic age acceleration and DNA methylation alterations that might affect pathways/genes of potential relevance to the disease.

Artificial intelligence-based methods for fusion of electronic health records and imaging data.

Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. The most important question when using multimodal data is how to fuse them-a field of growing interest among researchers. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion of these different data modalities to provide multimodal insights. To this end, in this scoping review, we focus on synthesizing and analyzing the literature that uses AI techniques to fuse multimodal medical data for different clinical applications. More specifically, we focus on studies that only fused EHR with medical imaging data to develop various AI methods for clinical applications. We present a comprehensive analysis of the various fusion strategies, the diseases and clinical outcomes for which multimodal fusion was used, the ML algorithms used to perform multimodal fusion for each clinical application, and the available multimodal medical datasets. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We searched Embase, PubMed, Scopus, and Google Scholar to retrieve relevant studies. After pre-processing and screening, we extracted data from 34 studies that fulfilled the inclusion criteria. We found that studies fusing imaging data with EHR are increasing and doubling from 2020 to 2021. In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. Specifically, early fusion was the most used technique in most applications for multimodal learning (22 out of 34 studies). We found that multimodality fusion models outperformed traditional single-modality models for the same task. Disease diagnosis and prediction were the most common clinical outcomes (reported in 20 and 10 studies, respectively) from a clinical outcome perspective. Neurological disorders were the dominant category (16 studies). From an AI perspective, conventional ML models were the most used (19 studies), followed by DL models (16 studies). Multimodal data used in the included studies were mostly from private repositories (21 studies). Through this scoping review, we offer new insights for researchers interested in knowing the current state of knowledge within this research field.

Patterns and distribution of de novo mutations in multiplex Middle Eastern families.

While de novo mutations (DNMs) are key to genetic diversity, they are also responsible for a high number of rare disorders. To date, no study has systematically examined the rate and distribution of DNMs in multiplex families in highly consanguineous populations. Leveraging WGS profiles of 645 individuals in 146 families, we implemented a combinatorial approach using 3 complementary tools for DNM discovery in 353 unique trio combinations. We found a total of 27,168 DNMs (median: 70 single-nucleotide and 6 insertion-deletions per individual). Phasing revealed around 80% of DNMs were paternal in origin. Notably, using whole-genome methylation data of spermatogonial stem cells, these DNMs were significantly more likely to occur at highly methylated CpGs (OR: 2.03; p value = 6.62 × 10-11). We then examined the effects of consanguinity and ethnicity on DNMs, and found that consanguinity does not seem to correlate with DNM rate, and special attention has to be considered while measuring such a correlation. Additionally, we found that Middle-Eastern families with Arab ancestry had fewer DNMs than African families, although not significant (p value = 0.16). Finally, for families with diseased probands, we examined the difference in DNM counts and putative impact across affected and unaffected siblings, but did not find significant differences between disease groups, likely owing to the enrichment for recessive disorders in this part of the world, or the small sample size per clinical condition. This study serves as a reference for DNM discovery in multiplex families from the globally under-represented populations of the Middle-East.

Epigenetic Risks of Medically Assisted Reproduction.

Since the birth of Louise Joy Brown, the first baby conceived via in vitro fertilization, more than 9 million children have been born worldwide using assisted reproductive technologies (ART). In vivo fertilization takes place in the maternal oviduct, where the unique physiological conditions guarantee the healthy development of the embryo. During early embryogenesis, a major wave of epigenetic reprogramming takes place that is crucial for the correct development of the embryo. Epigenetic reprogramming is susceptible to environmental changes and non-physiological conditions such as those applied during in vitro culture, including shift in pH and temperature, oxygen tension, controlled ovarian stimulation, intracytoplasmic sperm injection, as well as preimplantation embryo manipulations for genetic testing. In the last decade, concerns were raised of a possible link between ART and increased incidence of imprinting disorders, as well as epigenetic alterations in the germ cells of infertile parents that are transmitted to the offspring following ART. The aim of this review was to present evidence from the literature regarding epigenetic errors linked to assisted reproduction treatments and their consequences on the conceived children. Furthermore, we provide an overview of disease risk associated with epigenetic or imprinting alterations in children born via ART.

DNA methylation signatures in Blood DNA of Hutchinson-Gilford Progeria syndrome.

Hutchinson-Gilford Progeria Syndrome (HGPS) is an extremely rare genetic disorder caused by mutations in the LMNA gene and characterized by premature and accelerated aging beginning in childhood. In this study, we performed the first genome-wide methylation analysis on blood DNA of 15 patients with progeroid laminopathies using Infinium Methylation EPIC arrays including 8 patients with classical HGPS. We could observe DNA methylation alterations at 61 CpG sites as well as 32 significant regions following a 5 Kb tiling analysis. Differentially methylated probes were enriched for phosphatidylinositol biosynthetic process, phospholipid biosynthetic process, sarcoplasm, sarcoplasmic reticulum, phosphatase regulator activity, glycerolipid biosynthetic process, glycerophospholipid biosynthetic process, and phosphatidylinositol metabolic process. Differential methylation analysis at the level of promoters and CpG islands revealed no significant methylation changes in blood DNA of progeroid laminopathy patients. Nevertheless, we could observe significant methylation differences in classic HGPS when specifically looking at probes overlapping solo-WCGW partially methylated domains. Comparing aberrantly methylated sites in progeroid laminopathies, classic Werner syndrome, and Down syndrome revealed a common significantly hypermethylated region in close vicinity to the transcription start site of a long non-coding RNA located anti-sense to the Catenin Beta Interacting Protein 1 gene (CTNNBIP1). By characterizing epigenetically altered sites, we identify possible pathways/mechanisms that might have a role in the accelerated aging of progeroid laminopathies.

A genome-wide transcriptomic analysis of embryos fathered by obese males in a murine model of diet-induced obesity.

Paternal obesity is known to have a negative impact on the male's reproductive health as well as the health of his offspring. Although epigenetic mechanisms have been implicated in the non-genetic transmission of acquired traits, the effect of paternal obesity on gene expression in the preimplantation embryo has not been fully studied. To this end, we investigated whether paternal obesity is associated with gene expression changes in eight-cell stage embryos fathered by males on a high-fat diet. We used single embryo RNA-seq to compare the gene expression profile of embryos generated by males on a high fat (HFD) versus control (CD) diet. This analysis revealed significant upregulation of the Samd4b and Gata6 gene in embryos in response to a paternal HFD. Furthermore, we could show a significant increase in expression of both Gata6 and Samd4b during differentiation of stromal vascular cells into mature adipocytes. These findings suggest that paternal obesity may induce changes in the male germ cells which are associated with the gene expression changes in the resulting preimplantation embryos.

Increasing methylation of sperm rDNA and other repetitive elements in the aging male mammalian germline.

In somatic cells/tissues, methylation of ribosomal DNA (rDNA) increases with age and age-related pathologies, which has a direct impact on the regulation of nucleolar activity and cellular metabolism. Here, we used bisulfite pyrosequencing and show that methylation of the rDNA transcription unit including upstream control element (UCE), core promoter, 18S rDNA, and 28S rDNA in human sperm also significantly increases with donor's age. This positive correlation between sperm rDNA methylation and biological age is evolutionarily conserved among mammals with widely different life spans such as humans, marmoset, bovine, and mouse. Similar to the tandemly repeated rDNA, methylation of human α-satellite and interspersed LINE1 repeats, marmoset α-satellite, bovine alpha- and testis satellite I, mouse minor and major satellite, and LINE1-T repeats increases in the aging male germline, probably related to their sperm histone packaging. Deep bisulfite sequencing of single rDNA molecules in human sperm revealed that methylation does not only depend on donor's age, but also depend on the region and sequence context (A vs. G alleles). Both average rDNA methylation of all analyzed DNA molecules and the number of fully (>50%) methylated alleles, which are thought to be epigenetically silenced, increase with donor's age. All analyzed CpGs in the sperm rDNA transcription unit show comparable age-related methylation changes. Unlike other epigenetic aging markers, the rDNA clock appears to operate in similar ways in germline and soma in different mammalian species. We propose that sperm rDNA methylation, directly or indirectly, influences nucleolar formation and developmental potential in the early embryo.

DNA Methylation Biomarkers in Aging and Age-Related Diseases.

Recent research efforts provided compelling evidence of genome-wide DNA methylation alterations in aging and age-related disease. It is currently well established that DNA methylation biomarkers can determine biological age of any tissue across the entire human lifespan, even during development. There is growing evidence suggesting epigenetic age acceleration to be strongly linked to common diseases or occurring in response to various environmental factors. DNA methylation based clocks are proposed as biomarkers of early disease risk as well as predictors of life expectancy and mortality. In this review, we will summarize key advances in epigenetic clocks and their potential application in precision health. We will also provide an overview of progresses in epigenetic biomarker discovery in Alzheimer's, type 2 diabetes, and cardiovascular disease. Furthermore, we will highlight the importance of prospective study designs to identify and confirm epigenetic biomarkers of disease.

Methylomic profiling in trisomy 21 identifies cognition- and Alzheimer's disease-related dysregulation.

BACKGROUND: Trisomy 21 (T21) is associated with intellectual disability that ranges from mild to profound with an average intellectual quotient of around 50. Furthermore, T21 patients have a high risk of developing Alzheimer's disease (AD) early in life, characterized by the presence of senile plaques of amyloid protein and neurofibrillary tangles, leading to neuronal loss and cognitive decline. We postulate that epigenetic factors contribute to the observed variability in intellectual disability, as well as at the level of neurodegeneration seen in T21 individuals. MATERIALS AND METHODS: A genome-wide DNA methylation study was performed using Illumina Infinium® MethylationEPIC BeadChips on whole blood DNA of 3 male T21 patients with low IQ, 8 T21 patients with high IQ (4 males and 4 females), and 21 age- and sex-matched control samples (12 males and 9 females) in order to determine whether DNA methylation alterations could help explain variation in cognitive impairment between individuals with T21. In view of the increased risk of developing AD in T21 individuals, we additionally investigated the T21-associated sites in published blood DNA methylation data from the AgeCoDe cohort (German study on Ageing, Cognition, and Dementia). AgeCoDe represents a prospective longitudinal study including non-demented individuals at baseline of which a part develops AD dementia at follow-up. RESULTS: Two thousand seven hundred sixteen differentially methylated sites and regions discriminating T21 and healthy individuals were identified. In the T21 high and low IQ comparison, a single CpG located in the promoter of PELI1 was differentially methylated after multiple testing adjustment. For the same contrast, 69 differentially methylated regions were identified. Performing a targeted association analysis for the significant T21-associated CpG sites in the AgeCoDe cohort, we found that 9 showed significant methylation differences related to AD dementia, including one in the ADAM10 gene. This gene has previously been shown to play a role in the prevention of amyloid plaque formation in the brain. CONCLUSION: The differentially methylated regions may help understand the interaction between methylation alterations and cognitive function. In addition, ADAM10 might be a valuable blood-based biomarker for at least the early detection of AD.

Chromatin establishes an immature version of neuronal protocadherin selection during the naive-to-primed conversion of pluripotent stem cells.

In the mammalian genome, the clustered protocadherin (cPCDH) locus provides a paradigm for stochastic gene expression with the potential to generate a unique cPCDH combination in every neuron. Here we report a chromatin-based mechanism that emerges during the transition from the naive to the primed states of cell pluripotency and reduces, by orders of magnitude, the combinatorial potential in the human cPCDH locus. This mechanism selectively increases the frequency of stochastic selection of a small subset of cPCDH genes after neuronal differentiation in monolayers, 10-month-old cortical organoids and engrafted cells in the spinal cords of rats. Signs of these frequent selections can be observed in the brain throughout fetal development and disappear after birth, except in conditions of delayed maturation such as Down's syndrome. We therefore propose that a pattern of limited cPCDH-gene expression diversity is maintained while human neurons still retain fetal-like levels of maturation.

Genome-wide DNA methylation analysis of colorectal adenomas with and without recurrence reveals an association between cytosine-phosphate-guanine methylation and histological subtypes.

Aberrant methylation of DNA is supposed to be a major and early driver of colonic adenoma development, which may result in colorectal cancer (CRC). Although gene methylation assays are used already for CRC screening, differential epigenetic alterations of recurring and nonrecurring colorectal adenomas have yet not been systematically investigated. Here, we collected a sample set of formalin-fixed paraffin-embedded colorectal low-grade adenomas (n = 72) consisting of primary adenomas without and with recurrence (n = 59), recurrent adenomas (n = 10), and normal mucosa specimens (n = 3). We aimed to unveil differentially methylated CpG positions (DMPs) across the methylome comparing not only primary adenomas without recurrence vs primary adenomas with recurrence but also primary adenomas vs recurrent adenomas using the Illumina Human Methylation 450K BeadChip array. Unsupervised hierarchical clustering exhibited a significant association of methylation patterns with histological adenoma subtypes. No significant DMPs were identified comparing primary adenomas with and without recurrence. Despite that, a total of 5094 DMPs (false discovery rate <0.05; fold change >10%) were identified in the comparisons of recurrent adenomas vs primary adenomas with recurrence (674; 98% hypermethylated), recurrent adenomas vs primary adenomas with and without recurrence (241; 99% hypermethylated) and colorectal adenomas vs normal mucosa (4179; 46% hypermethylated). DMPs in cytosine-phosphate-guanine (CpG) islands were frequently hypermethylated, whereas open sea- and shelf-regions exhibited hypomethylation. Gene ontology analysis revealed enrichment of genes associated with the immune system, inflammatory processes, and cancer pathways. In conclusion, our methylation data could assist in establishing a more robust and reproducible histological adenoma classification, which is a prerequisite for improving surveillance guidelines.

Male obesity effects on sperm and next-generation cord blood DNA methylation.

The prevalence of metabolic disorders, in particular obesity has dramatically increased worldwide. Genetic variants explain only a minor part of the obesity epidemic induced by physical inactivity and over-nutrition. Epidemiological studies in humans and animal models indicate that epigenetic changes associated with adverse parental and/or intrauterine factors may contribute to the missing heritability of metabolic disorders. Possible adverse paternal effects are likely transmitted by sperm to the next-generation. To investigate this hypothesis, we have systematically analyzed the effects of male body mass index (BMI) on sperm epigenome and its association with next-generation fetal cord blood (FCB) DNA methylation. Methylation levels of maternally imprinted (PEG1, PEG4, PEG5, and PEG10), paternally imprinted (H19-IG DMR, IGF2-DMR0, and MEG3-IG DMR) regions, and obesity-related non-imprinted HIF3A gene were quantified by bisulphite pyrosequencing in sperm samples of 294 human donors undergoing in vitro fertilization or intracytoplasmic sperm injection, and in 113 FCBs of the resulting offspring. Multivariable regression analyses revealed that MEG3 intergenic differentially methylated region (IG DMR) showed positive correlation between sperm methylation and donor's BMI. A gender-specific correlation between paternal BMI and FCB methylation was observed for MEG3-IG DMR, HIF3A, and IGF2-DMR0. The former two genes displayed same directional nominal association (as sperm) between paternal BMI and FCB methylation in male offspring. Hypomethylation of IGF2-DMR0 with increased paternal BMI was observed in FCBs from female offsprings. Our results suggest that male obesity is nominally associated with modification of sperm DNA methylome in humans, which may affect the epigenome of the next-generation. Nevertheless, it is important to note that none of the associated p-values survived multiple testing adjustments. Future work should test the effect of associated methylation aberrations in the offspring as DNA methylation was shown to control expression and/or imprint establishment across the studied genes.