Cellular health and growth requires protein synthesis to be both efficient to ensure sufficient production, and accurate to avoid producing defective or unstable proteins. The background of misreading error frequency by individual tRNAs is as low as 2 × 10−6 per codon but is codon-specific with some error frequencies above 10−3 per codon. Here we test the effect on error frequency of blocking post-transcriptional modifications of the anticodon loops of four tRNAs in Escherichia coli. We find two types of responses to removing modification. Blocking modification of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm tRNA}_{{\rm UUC}}^{{\rm Glu}}$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm tRNA}^{\rm Asp}_{\rm QUC}$\end{document} increases errors, suggesting that the modifications act at least in part to maintain accuracy. Blocking even identical modifications of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm tRNA}^{\rm Lys}_{\rm UUU}$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm tRNA}^{\rm Tyr}_{\rm QUA}$\end{document} has the opposite effect of decreasing errors. One explanation could be that the modifications play opposite roles in modulating misreading by the two classes of tRNAs. Given available evidence that modifications help preorder the anticodon to allow it to recognize the codons, however, the simpler explanation is that unmodified ‘weak’ tRNAs decode too inefficiently to compete against cognate tRNAs that normally decode target codons, which would reduce the frequency of misreading.

Protein synthesis must rapidly and repeatedly discriminate between a single correct and many incorrect aminoacyl-tRNAs. We have attempted to measure the frequencies of all possible missense errors by tRNA

Proinsulin folding within the endoplasmic reticulum (ER) remains incompletely understood, but it is clear that in mutant INS gene–induced diabetes of youth (MIDY), progression of the (three) native disulfide bonds of proinsulin becomes derailed, causing insulin deficiency, β-cell ER stress, and onset of diabetes. Herein, we have undertaken a molecular dissection of proinsulin disulfide bond formation, using bioengineered proinsulins that can form only two (or even only one) of the native proinsulin disulfide bonds. In the absence of preexisting proinsulin disulfide pairing, Cys(B19)-Cys(A20) (a major determinant of ER stress response activation and proinsulin stability) preferentially initiates B-A chain disulfide bond formation, whereas Cys(B7)-Cys(A7) can initiate only under oxidizing conditions beyond that existing within the ER of β-cells. Interestingly, formation of these two “interchain” disulfide bonds demonstrates cooperativity, and together, they are sufficient to confer intracellular transport competence to proinsulin. The three most common proinsulin disulfide mispairings in the ER appear to involve Cys(A11)-Cys(A20), Cys(A7)-Cys(A20), and Cys(B19)-Cys(A11), each disrupting the critical Cys(B19)-Cys(A20) pairing. MIDY mutations inhibit Cys(B19)-Cys(A20) formation, but treatment to force oxidation of this disulfide bond improves folding and results in a small but detectable increase of proinsulin export. These data suggest possible therapeutic avenues to ameliorate ER stress and diabetes.

Protein disulfide isomerase acts as a reductase to reduce a mutant proinsulin called Akita, priming it for retrotranslocation across the endoplasmic reticulum (ER) membrane by using the Sel1L-Hrd1-p97 ER-associated degradation machinery.

Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell–specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 33,862 nuclei and identify seven cell types ranging in abundance from 59.6% to 1.0% of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site–distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome-wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3.5% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, transcription factor motifs, and target genes for type 2 diabetes signals. These chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for nominating causal GWAS SNPs and cell types.

AbstractBackgroundSkeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases, mobility, and quality of life. It is composed of several different cell and muscle fiber types.ResultsHere, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 30,531 nuclei, representing 11 libraries, profiled in this study, and identify seven distinct cell types ranging in abundance from 63% (type II fibers) to 0.9% (muscle satellite cells) of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site-distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, and transcription factor motifs for creatinine levels and type 2 diabetes signals.ConclusionsThese chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for investigating specific cell types and nominating causal GWAS SNPs and cell types.

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by the loss of pancreatic islet beta cells. The mechanisms of T1D genetic risk remain poorly understood. Here, we present a multi-omic integrative study of single-cell/nucleus molecular profiles of gene expression and chromatin accessibility in the same biological samples from healthy and beta-cell autoantibody+ (AAB+) human pancreatic islets to characterize mechanisms of islet-mediated T1D genetic risk. We additionally performed single-cell/nucleus multi-omic profiling of healthy islets under two stimulatory conditions used as in vitro models of T1D (cytokine cocktail and CVB4 infection) to evaluate how environmental exposures recapitulate multi-omic signatures of T1D. In total, we analyzed 121,272 cells/nuclei across 34 libraries, identifying 10 distinct cell types. We identified cell-type-specific and disease-associated cis-regulatory elements and nominated likely target genes. We provide evidence that T1D genetic risk is mediated through multiple pancreatic cell populations, including islet endocrine cells (beta, alpha, gamma, and delta), exocrine acinar and ductal cells, and immune cells. Finally, we identified three independent T1D risk variants acting through pancreatic islet endocrine cells at the TOX, RASGRP1, and DLK1/MEG3 loci. Together, this work improves our understanding of how non-coding genetic variants encode T1D risk through a complex interplay of different cell types in the pancreas.