Association of peripheral blood DNA methylation level with Alzheimer’s disease progression

Background: Identifying biomarkers associated with Alzheimer's disease (AD) progression may enable patient enrichment and improve clinical trial designs. Epigenome-wide association studies have revealed correlations between DNA methylation at cytosine-phosphate-guanine (CpG) sites and AD pathology and diagnosis. Here, we report relationships between peripheral blood DNA methylation profiles measured using Infinium® MethylationEPIC BeadChip and AD progression in participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Results: The rate of cognitive decline from initial DNA sampling visit to subsequent visits was estimated by the slopes of the modified Preclinical Alzheimer Cognitive Composite (mPACC; mPACCdigit and mPACCtrailsB) and Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) plots using robust linear regression in cognitively normal (CN) participants and patients with mild cognitive impairment (MCI), respectively. In addition, diagnosis conversion status was assessed using a dichotomized endpoint. Two CpG sites were significantly associated with the slope of mPACC in CN participants (P < 5.79 × 10-8 [Bonferroni correction threshold]); cg00386386 was associated with the slope of mPACCdigit, and cg09422696 annotated to RP11-661A12.5 was associated with the slope of CDR-SB. No significant CpG sites associated with diagnosis conversion status were identified. Genes involved in cognition and learning were enriched. A total of 19, 13, and 5 differentially methylated regions (DMRs) associated with the slopes of mPACCtrailsB, mPACCdigit, and CDR-SB, respectively, were identified by both comb-p and DMRcate algorithms; these included DMRs annotated to HOXA4. Furthermore, 5 and 19 DMRs were associated with conversion status in CN and MCI participants, respectively. The most significant DMR was annotated to the AD-associated gene PM20D1 (chr1: 205,818,956 to 205,820,014 [13 probes], Sidak-corrected P = 7.74 × 10-24), which was associated with both the slope of CDR-SB and the MCI conversion status. Conclusion: Candidate CpG sites and regions in peripheral blood were identified as associated with the rate of cognitive decline in participants in the ADNI cohort. While we did not identify a single CpG site with sufficient clinical utility to be used by itself due to the observed effect size, a biosignature composed of DNA methylation changes may have utility as a prognostic biomarker for AD progression

Dnmt3a is essential for hematopoietic stem cell differentiation

Loss of the de novo DNA methyltransferases Dnmt3a and Dnmt3b in embryonic stem cells obstructs differentiation; however, the role of these enzymes in somatic stem cells is largely unknown. Using conditional ablation, we show that Dnmt3a loss progressively impairs hematopoietic stem cell (HSC) differentiation over serial transplantation, while simultaneously expanding HSC numbers in the bone marrow. Dnmt3a-null HSCs show both increased and decreased methylation at distinct loci, including substantial CpG island hypermethylation. Dnmt3a-null HSCs upregulate HSC multipotency genes and downregulate differentiation factors, and their progeny exhibit global hypomethylation and incomplete repression of HSC-specific genes. These data establish Dnmt3a as a critical participant in the epigenetic silencing of HSC regulatory genes, thereby enabling efficient differentiation

PINNED: identifying characteristics of druggable human proteins using an interpretable neural network

Abstract The identification of human proteins that are amenable to pharmacologic modulation without significant off-target effects remains an important unsolved challenge. Computational methods have been devised to identify features which distinguish between “druggable” and “undruggable” proteins, finding that protein sequence, tissue and cellular localization, biological role, and position in the protein–protein interaction network are all important discriminant factors. However, many prior efforts to automate the assessment of protein druggability suffer from low performance or poor interpretability. We developed a neural network-based machine learning model capable of generating druggability sub-scores based on each of four distinct categories, combining them to form an overall druggability score. The model achieves an excellent performance in separating drugged and undrugged proteins in the human proteome, with an area under the receiver operating characteristic (AUC) of 0.95. Our use of multiple sub-scores allows the assessment of potential protein targets of interest based on distinct contributors to druggability, leading to a more interpretable and holistic model to identify novel targets

PINNED: Identifying Characteristics of Druggable Human Proteins Using an Interpretable Neural Network

The identification of human proteins that are amenable to pharmacologic modulation without significant off-target effects remains an important unsolved challenge. Computational methods have been devised to identify features which distinguish between “druggable” and “undruggable” proteins, finding that protein sequence, tissue and cellular localization, biological role, and position in the protein-protein interaction network are all important discriminant factors. However, many prior efforts to automate the assessment of protein druggability suffer from low performance or poor interpretability. We developed a neural network-based machine learning model capable of generating druggability sub-scores based on each of four distinct categories, combining them to form an overall druggability score. The model achieves an excellent performance in separating drugged and undrugged proteins in the human proteome, with an area under the receiver operating characteristic (AUC) of 0.95. Our use of multiple sub-scores allows the assessment of potential protein targets of interest based on distinct contributors to druggability, leading to a more interpretable and holistic model to identify novel targets

Reduced ITPase activity and favorable IL28B genetic variant protect against ribavirin-induced anemia in interferon-free regimens

<div><p>Background</p><p>Genetic variants of inosine triphosphatase (ITPA) that confer reduced ITPase activity are associated with protection against ribavirin(RBV)-induced hemolytic anemia in peginterferon(IFN)/RBV-based treatment of hepatitis C virus (HCV). Patients with reduced ITPase activity showed improved treatment efficacy when treated with IFN/RBV. In addition, a genetic polymorphism near the IL28B gene is associated with an improved response to IFN/RBV treatment. RBV has been an important component of IFN-containing regimens, and is currently recommended in combination with several IFN-free regimens for treatment of harder to cure HCV infections.</p><p>Aim</p><p>To evaluate whether genetic variations that reduce ITPase activity impact RBV-induced anemia in IFN-free/RBV regimens</p><p>Methods</p><p>In this study, genetic analyses were conducted in the PEARL-IV trial to investigate the effect of activity-reducing ITPA variants as well as IL28B polymorphism on anemia, platelet (PLT) counts, and virologic response in HCV genotype1a-infected patients treated with the direct-acting antiviral (DAA) regimen of ombitasvir/paritaprevir/ritonavir and dasabuvir±RBV.</p><p>Results</p><p>Reduction in ITPase activity and homozygosity for the IL28Brs12979860 CC genotype protected against RBV-induced anemia. In patients receiving RBV, reduced ITPase activity was associated with reduced plasma RBV concentration and higher PLT counts. ITPase activity had no impact on response to DAA treatment, viral kinetics, or baseline IP-10 levels.</p><p>Conclusions</p><p>Our study demonstrates that genetics of ITPA and IL28B may help identify patients protected from RBV-induced anemia when treated with IFN-free regimens. Our work demonstrates for the first time that IL28B genetics may also have an impact on RBV-induced anemia. This may be of particular significance in patients with difficult-to-cure HCV infections, such as patients with decompensated cirrhosis where RBV-containing regimens likely will continue to be recommended.</p></div

DNA Methylation Dynamics of Germinal Center B Cells Are Mediated by AID

Changes in DNA methylation are required for the formation of germinal centers (GCs), but the mechanisms of such changes are poorly understood. Activation-induced cytidine deaminase (AID) has been recently implicated in DNA demethylation through its deaminase activity coupled with DNA repair. We investigated the epigenetic function of AID in vivo in germinal center B cells (GCBs) isolated from wild-type (WT) and AID-deficient (Aicda−/−) mice. We determined that the transit of B cells through the GC is associated with marked locus-specific loss of methylation and increased methylation diversity, both of which are lost in Aicda−/− animals. Differentially methylated cytosines (DMCs) between GCBs and naive B cells (NBs) are enriched in genes that are targeted for somatic hypermutation (SHM) by AID, and these genes form networks required for B cell development and proliferation. Finally, we observed significant conservation of AID-dependent epigenetic reprogramming between mouse and human B cells

GLM analysis for Hb change shows model is useful (p<0.0001, R2 = 40.2%).

<p>GLM analysis for Hb change shows model is useful (p<0.0001, R2 = 40.2%).</p