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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.

Background Limited understanding of the diversity of variants in the cystic fibrosis transmembrane conductance regulator ( CFTR ) gene across ancestries hampers efforts to advance molecular diagnosis of cystic fibrosis (CF). The consequences pose a risk of delayed diagnoses and subsequently worsened health outcomes for patients. Therefore, characterizing the spectrum of CFTR variants across ancestries is critical for revolutionizing molecular diagnoses of CF. Methods We analyzed 454,727 UK Biobank (UKBB) whole-exome sequences to characterize the diversity of CFTR variants across ancestries. Using the PanUKBB classification, the participants were assigned into six major groups: African (AFR), American/American Admixed (AMR), Central South Asia (CSA), East Asian (EAS), European (EUR), and Middle East (MID). We segregated ancestry-specific CFTR variants, including those that are CF-causing or clinically relevant. The ages of certain CF-causing variants were determined and analyzed for selective pressure effects, and curated phenotype analysis was performed for participants with clinically relevant CFTR genotypes. Results We detected over 4000 CFTR variants, including novel ancestry-specific variants, across six ancestries. Europeans had the most unique CFTR variants [ n  = 2212], while the American group had the least unique variants [ n  = 23]. F508del was the most prevalent CF-causing variant found in all ancestries, except in EAS, where V520F was the most prevalent. Common EAS variants such as 3600G > A, V456A, and V520, which appeared approximately 270, 215, and 338 generations ago, respectively, did not show evidence of selective pressure. Sixteen participants had two CF-causing variants, with two being diagnosed with CF. We found 154 participants harboring a CF-causing and varying clinical consequences (VCC) variant. Phenotype analysis performed for participants with multiple clinically relevant variants returned significant associations with CF and its pulmonary phenotypes [Bonferroni-adjusted p  < 0.05]. Conclusions We leveraged the UKBB database to comprehensively characterize the broad spectrum of CFTR variants across ancestries. The detection of over 4000 CFTR variants, including several ancestry-specific and uncharacterized CFTR variants, warrants the need for further characterization of their functional and clinical relevance. Overall, the presentation of classical CF phenotypes seen in non-CF diagnosed participants with more than one CF-causing variant indicates that they may benefit from current CFTR modulator therapies.

Background Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease impacting an estimated 44 million adults worldwide. The causal pathology of AD (accumulation of amyloid-beta and tau), precedes hallmark symptoms of dementia by more than a decade, necessitating development of early diagnostic markers of disease onset, particularly for new drugs that aim to modify disease processes. To evaluate differentially methylated positions (DMPs) as novel blood-based biomarkers of AD, we used a subset of 653 individuals with peripheral blood (PB) samples in the Alzheimer’s disease Neuroimaging Initiative (ADNI) consortium. The selected cohort of AD, mild cognitive impairment (MCI), and age-matched healthy controls (CN) all had imaging, genetics, transcriptomics, cerebrospinal protein markers, and comprehensive clinical records, providing a rich resource of concurrent multi-omics and phenotypic information on a well-phenotyped subset of ADNI participants. Results In this manuscript, we report cross-diagnosis differential peripheral DNA methylation in a cohort of AD, MCI, and age-matched CN individuals with longitudinal DNA methylation measurements. Epigenome-wide association studies (EWAS) were performed using a mixed model with repeated measures over time with a P value cutoff of 1 × 10 −5 to test contrasts of pairwise differential peripheral methylation in AD vs CN, AD vs MCI, and MCI vs CN. The most highly significant differentially methylated loci also tracked with Mini Mental State Examination (MMSE) scores. Differentially methylated loci were enriched near brain and neurodegeneration-related genes (e.g., BDNF, BIN1, APOC1 ) validated using the genotype tissue expression project portal (GTex). Conclusions Our work shows that peripheral differential methylation between age-matched subjects with AD relative to healthy controls will provide opportunities to further investigate and validate differential methylation as a surrogate of disease. Given the inaccessibility of brain tissue, the PB-associated methylation marks may help identify the stage of disease and progression phenotype, information that would be central to bringing forward successful drugs for AD.

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. To evaluate whether genetic variations that reduce ITPase activity impact RBV-induced anemia in IFN-free/RBV regimens. 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. 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. 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.

DNA methylation in the context of epigenetics occurs on the 5' position of cytosine, which can be further oxidized by enzymes from the Ten-eleven translocation (Tet) family, resulting in 5-hydroxymethylcytosine (5-hmC). In the context of embryonic stem cells, Tet and 5-hmC DNA act in an alternate epigenetic state that regulates epigenetic programming and stem cell differentiation. Here, the authors describe the epigenomic profiling of 5-hmC in mouse and human brain across different time periods during development and aging. DNA methylation dynamics influence brain function and are altered in neurological disorders. 5-hydroxymethylcytosine (5-hmC), a DNA base that is derived from 5-methylcytosine, accounts for ∼40% of modified cytosine in the brain and has been implicated in DNA methylation–related plasticity. We mapped 5-hmC genome-wide in mouse hippocampus and cerebellum at three different ages, which allowed us to assess its stability and dynamic regulation during postnatal neurodevelopment through adulthood. We found developmentally programmed acquisition of 5-hmC in neuronal cells. Epigenomic localization of 5-hmC–regulated regions revealed stable and dynamically modified loci during neurodevelopment and aging. By profiling 5-hmC in human cerebellum, we found conserved genomic features of 5-hmC. Finally, we found that 5-hmC levels were inversely correlated with methyl-CpG–binding protein 2 dosage, a protein encoded by a gene in which mutations cause Rett syndrome. These data suggest that 5-hmC–mediated epigenetic modification is critical in neurodevelopment and diseases.

Ross Levine, Lambert Busque and colleagues report the identification of recurrent somatic mutations in TET2 in elderly female individuals with clonal hematopoiesis. The mutations were identified in individuals without clinically apparent hematological malignancies. Aging is characterized by clonal expansion of myeloid-biased hematopoietic stem cells and by increased risk of myeloid malignancies. Exome sequencing of three elderly females with clonal hematopoiesis, demonstrated by X-inactivation analysis, identified somatic TET2 mutations. Recurrence testing identified TET2 mutations in 10 out of 182 individuals with X-inactivation skewing. TET2 mutations were specific to individuals with clonal hematopoiesis without hematological malignancies and were associated with alterations in DNA methylation.

Margaret Goodell, Wei Li and colleagues report conditional ablation of the Dnmt3a DNA methyltransferase in hematopoietic stem cells (HSCs) in mice. They show that Dnmt3a is critical for epigenetic silencing of HSC regulatory genes and for HSC 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.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Private Partners Scientific Board (PPSB) Diversity, Equity, and Inclusion Working Group (DE&I WG) was established to work with the ADNI3 Diversity Task Force to provide an industry perspective on increasing the representation of diverse participants in ADNI3 and to build precompetitive cross‐industry knowledge in engagement and recruitment of under‐represented participants (URPs). In this article, we review and highlight the role and ongoing activities within the ADNI PPSB DE&I WG and provide a cross‐industry perspective on areas where precompetitive collaboration can improve the inclusiveness in clinical trials, drawing on examples from ADNI4. Highlights New collaboration crosses boundaries to allow PPSB DE&I WG members to work together in a preproprietary way. When faced with the same challenges required by FDA combined with a growing prevalence of AD, the DE&I WG has drafted a range of initiatives that may benefit ADNI, AD patients, care partners, and respective companies involved in this work. In order to address the multifactorial problem of successfully enrolling representative populations in clinical trials, it will “take a village” to bring about sustainable changes.

Background Alzheimer’s disease (AD) is an increasing societal burden globally, with an urgent need for early screening strategies to improve disease management. Multiomics profiling represents a promising approach for identifying biomarkers and stratifying disease risk. Method We analysed data from a cohort of 391 individuals (mean age: 73.6 ± 6.7 years) enrolled in the Australian Imaging, Biomarkers, and Lifestyle (AIBL) study. Multi‐Omics Factor Analysis (MOFA) was employed to integrate and identify key axes of variation (latent factors) across three omics datasets: RNA sequencing (N = 19259), single nucleotide polymorphisms (SNPs, N = 298516), and DNA methylation (N = 154846). Latent factors were subsequently assessed with PET Amyloid (Spearman’s Rho) and time to progression (accounting for age), using Cox proportional hazards models. Time to event was derived as time for a participant to progress from MCI to AD, or time the participant remained MCI (censored). Result The unsupervised integration using MOFA showed that the principal axes of variation captured combined contributions from all three omics datasets. DNA methylation explained the largest proportion of variance, followed by RNA sequencing and SNP genotype data (31.8%, 13.9%, and 0.9% cumulative variance explained across the first 10 factors, respectively). Post hoc analyses showed that one of the factors was correlated with PET Amyloid levels (Rho = 0.20, controlling for age and APOE ε4). This factor also significantly predicted time to event, with participants exhibiting higher scores progressing to AD more quickly (Figure 2). Conclusion This study demonstrates the utility of multiomics integration for uncovering biologically relevant patterns in AD and its potential application disease risk stratification. Future research should focus on validating these patterns in larger, independent cohorts and exploring their utility in clinical applications.

Alzheimer's disease (AD) is an increasing societal burden globally, with an urgent need for early screening strategies to improve disease management. Multiomics profiling represents a promising approach for identifying biomarkers and stratifying disease risk. We analysed data from a cohort of 391 individuals (mean age: 73.6 ± 6.7 years) enrolled in the Australian Imaging, Biomarkers, and Lifestyle (AIBL) study. Multi-Omics Factor Analysis (MOFA) was employed to integrate and identify key axes of variation (latent factors) across three omics datasets: RNA sequencing (N = 19259), single nucleotide polymorphisms (SNPs, N = 298516), and DNA methylation (N = 154846). Latent factors were subsequently assessed with PET Amyloid (Spearman's Rho) and time to progression (accounting for age), using Cox proportional hazards models. Time to event was derived as time for a participant to progress from MCI to AD, or time the participant remained MCI (censored). The unsupervised integration using MOFA showed that the principal axes of variation captured combined contributions from all three omics datasets. DNA methylation explained the largest proportion of variance, followed by RNA sequencing and SNP genotype data (31.8%, 13.9%, and 0.9% cumulative variance explained across the first 10 factors, respectively). Post hoc analyses showed that one of the factors was correlated with PET Amyloid levels (Rho = 0.20, controlling for age and APOE ε4). This factor also significantly predicted time to event, with participants exhibiting higher scores progressing to AD more quickly (Figure 2). This study demonstrates the utility of multiomics integration for uncovering biologically relevant patterns in AD and its potential application disease risk stratification. Future research should focus on validating these patterns in larger, independent cohorts and exploring their utility in clinical applications.