Type 2 diabetes mellitus (T2D) is one of the most prevalent diseases in the world and presents a major health and economic burden, a notable proportion of which could be alleviated with improved early prediction and intervention. While standard risk factors including age, obesity, and hypertension have shown good predictive performance, we show that the use of CpG DNA methylation information leads to a significant improvement in the prediction of 10-year T2D incidence risk.Whilst previous studies have been largely constrained by linear assumptions and the use of CpGs one-at-the-time, we have adopted a more flexible approach based on a range of linear and tree-ensemble models for classification and time-to-event prediction. Using the Generation Scotland cohort (n=9,537) our best performing model (Area Under the Curve (AUC)=0.880, Precision Recall AUC (PRAUC)=0.539, McFadden’s R2=0.316) used a LASSO Cox proportional-hazards predictor and showed notable improvement in onset prediction, above and beyond standard risk factors (AUC=0.860, PRAUC=0.444 R2=0.261). Replication of the main finding was observed in an external test dataset (the German-based KORA study, p=3.7×10−4). Tree-ensemble methods provided comparable performance and future improvements to these models are discussed.Finally, we introduce MethylPipeR, an R package with accompanying user interface, for systematic and reproducible development of complex trait and incident disease predictors. While MethylPipeR was applied to incident T2D prediction with DNA methylation in our experiments, the package is designed for generalised development of predictive models and is applicable to a wide range of omics data and target traits.
DNAm can provide for 10-year T2D risk and the applicability of linear and tree-ensemble survival models.In this study, we use one of the world's largest studies with paired genome-wide DNAm and data linkage to electronic health records, Generation Scotland (n = 14,613, n = 626 incident T2D cases over 15 years of follow-up), to develop and validate epigenetic scores for T2D. We show the added contribution of these epigenetic scores to prediction over and above standard risk factors, for example, age, sex and BMI and externally validate these results in the KORA S4 cohort.
We describe construction of the 660 kilobase synthetic yeast chromosome XI (synXI) and reveal how synthetic redesign of non-coding DNA elements impact the cell. To aid construction from synthesized 5 to 10 kilobase DNA fragments, we implemented CRISPR-based methods for synthetic crossovers in vivo and used these methods in an extensive process of bug discovery, redesign and chromosome repair, including for the precise removal of 200 kilobases of unexpected repeated sequence. In synXI, the underlying causes of several fitness defects were identified as modifications to non-coding DNA, including defects related to centromere function and mitochondrial activity that were subsequently corrected. As part of synthetic yeast chromosome design, loxPsym sequences for Cre-mediated recombination are inserted between most genes. Using the GAP1 locus from chromosome XI, we show here that targeted insertion of these sites can be used to create extrachromosomal circular DNA on demand, allowing direct study of the effects and propagation of these important molecules. Construction and characterization of synXI has uncovered effects of non-coding and extrachromosomal circular DNA, contributing to better understanding of these elements and informing future synthetic genome design.
Enzymatic deficiencies cause the accumulation of toxic levels of substrates in a cell and are associated with life-threatening pathologies. Restoring physiological enzymes levels by injecting a recombinant version of the defective enzyme could provide a viable therapeutic option. However, these enzyme replacement therapies have had limited success, as the recombinant enzymes are less catalytically active, cause immune response and are difficult to manufacture. Moreover, the vast sequence design space makes finding enzymes with desired therapeutic properties extremely challenging. Here, we present a new enzyme engineering framework, which builds on recent advances in deep learning, variational calculus and natural language processing, to design variants of human enzymes with biochemical features comparable to the wild type protein as a way to rapidly build targeted libraries for downstream screening. We applied our method to design variants of human Sphyngosine-1-phosphate lyase (HsS1PL) as potential therapeutic treatments for nephrotic syndrome type 14 (NPHS14), and characterized their biochemical properties through extensive sequence and molecular dynamics analyses.
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