Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical–molecular–biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.
Protein citrullination (or deimination), an irreversible post-translational modification, has been implicated in several physiological and pathological processes, including gene expression regulation, apoptosis, rheumatoid arthritis, and Alzheimer’s disease. Several research studies have been carried out on citrullination under many conditions. However, until now, challenges in sample preparation and data analysis have made it difficult to confidently identify a citrullinated protein and assign the citrullinated site. To overcome these limitations, we generated a mouse hyper-citrullinated spectral library and set up coordinates to confidently identify and validate citrullinated sites. Using this workflow, we detect a four-fold increase in citrullinated proteome coverage across six mouse organs compared with the current state-of-the art techniques. Our data reveal that the subcellular distribution of citrullinated proteins is tissue-type-dependent and that citrullinated targets are involved in fundamental physiological processes, including the metabolic process. These data represent the first report of a hyper-citrullinated library for the mouse and serve as a central resource for exploring the role of citrullination in this organism.
Coronary artery disease remains a leading cause of death in industrialized nations, and early detection of disease is a critical intervention target to effectively treat patients and manage risk. Proteomic analysis of mixed tissue homogenates may obscure subtle protein changes that occur uniquely in underlying tissue subtypes. The unsupervised ‘convex analysis of mixtures’ (CAM) tool has previously been shown to effectively segregate cellular subtypes from mixed expression data. In this study, we hypothesized that CAM would identify proteomic information specifically informative to early atherosclerosis lesion involvement that could lead to potential markers of early disease detection. We quantified the proteome of 99 paired abdominal aorta (AA) and left anterior descending coronary artery (LAD) specimens (N = 198 specimens total) acquired during autopsy of young adults free of diagnosed cardiac disease. The CAM tool was then used to segregate protein subsets uniquely associated with different underlying tissue types, yielding markers of normal and fibrous plaque (FP) tissues in LAD and AA (N = 62 lesions markers). CAM-derived FP marker expression was validated against pathologist estimated luminal surface involvement of FP, as well as in an orthogonal cohort of “pure” fibrous plaque, fatty streak, and normal vascular specimens. A targeted mass spectrometry (MS) assay quantified 39 of 62 CAM-FP markers in plasma from women with angiographically verified coronary artery disease (CAD, N = 46) or free from apparent CAD (control, N = 40). Elastic net variable selection with logistic regression reduced this list to 10 proteins capable of classifying CAD status in this cohort with <6% misclassification error, and a mean area under the receiver operating characteristic curve of 0.992 (confidence interval 0.968–0.998) after cross validation. The proteomics-CAM workflow identified lesion-specific molecular biomarker candidates by distilling the most representative molecules from heterogeneous tissue types.
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