SUMMARY Sirtuins are NAD+-dependent protein deacylases that regulate several aspects of metabolism and aging. In contrast to the other mammalian sirtuins, the primary enzymatic activity of mitochondrial sirtuin 4 (SIRT4) and its overall role in metabolic control has remained enigmatic. Using a combination of phylogenetics, structural biology, and enzymology, we show that SIRT4 removes three acyl moieties from lysine residues – methylglutaryl(MG)-, hydroxymethylglutaryl(HMG)-, and 3-methylglutaconyl(MGc)-lysine. The metabolites leading to these post-translational modifications are intermediates in leucine oxidation, and we show a primary role for SIRT4 in controlling this pathway in mice. Furthermore, we find that dysregulated leucine metabolism in SIRT4KO mice leads to elevated basal and stimulated insulin secretion, which progressively develops into glucose intolerance and insulin resistance. These findings identify a robust enzymatic activity for SIRT4, uncover a mechanism controlling branched-chain amino acid flux, and position SIRT4 as a crucial player maintaining insulin secretion and glucose homeostasis during aging.

Background - There is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment. Methods - Using the UK Biobank (UKB) resource, we developed our own polygenic risk score (PRS) for coronary artery disease (CAD). We used an additional 60,000 UKB individuals to develop an integrated risk tool (IRT) that combined our PRS with established risk tools (either the American Heart Association/American College of Cardiology's Pooled Cohort Equations (PCE) or UK's QRISK3), and we tested our IRT in an additional, independent, set of 186,451 UKB individuals. Results - The novel CAD PRS shows superior predictive power for CAD events, compared to other published PRSs and is largely uncorrelated with PCE and QRISK3. When combined with PCE into an integrated risk tool, it has superior predictive accuracy. Overall, 10.4% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, compared to 4.4% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.9% (95% CI 4.7-7.0). When individuals were stratified into age-by-sex subgroups the improvement was larger for all subgroups (range 8.3%-15.4%), with best performance in 40-54yo men (15.4%, 95% CI 11.6-19.3). Comparable results were found using a different risk tool (QRISK3), and also a broader definition of cardiovascular disease. Use of the IRT is estimated to avoid up to 12,000 deaths in the USA over a 5-year period. Conclusions - An integrated risk tool that includes polygenic risk outperforms current risk stratification tools and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person's polygenic risk.

Nucleotide excision repair (NER) is essential for removing many types of DNA lesions from the genome, yet the mechanisms of NER in humans remain poorly understood. This review summarizes our current understanding of the structure, biochemistry, interaction partners, mechanisms, and disease-associated mutations of one of the critical NER proteins, XPA.

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The spatial distribution of genetic variation within proteins is shaped by evolutionary constraint and provides insight into the functional importance of protein regions and the potential pathogenicity of protein alterations. Here, we comprehensively evaluate the 3D spatial patterns of human germline and somatic variation in 6,604 experimentally derived protein structures and 33,144 computationally derived homology models covering 77% of all human proteins. Using a systematic approach, we quantify differences in the spatial distributions of neutral germline variants, disease-causing germline variants, and recurrent somatic variants. Neutral missense variants exhibit a general trend toward spatial dispersion, which is driven by constraint on core residues. In contrast, germline disease-causing variants are generally clustered in protein structures and form clusters more frequently than recurrent somatic variants identified from tumor sequencing. In total, we identify 215 proteins with significant spatial constraints on the distribution of disease-causing missense variants in experimentally derived protein structures, only 65 (30%) of which have been previously reported. This analysis identifies many clusters not detectable from sequence information alone; only 12% of proteins with significant clustering in 3D were identified from similar analyses of linear protein sequence. Furthermore, spatial analyses of mutations in homology-based structural models are highly correlated with those from experimentally derived structures, supporting the use of computationally derived models. Our approach highlights significant differences in the spatial constraints on different classes of mutations in protein structure and identifies regions of potential function within individual proteins.