A compendium of multi-omics data illuminating host responses to lethal human virus infections

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology

Mass spectrometric analysis of glucose-modified ribonuclease

RNase A (1 mM) was incubated with glucose (0.4 M) at 37°C for up to 14 days in phosphate buffer (0.2 M, pH 7.4), digested with trypsin and analysed by LC-MS. The major sites of fructoselysine formation were Lys1, Lys7, Lys37 and Lys41. Three of these sites (Lys7, Lys37 and Lys41) were also the major sites of Ne-(carboxymethyl)lysine formation

Proteomic analysis of the site specificity of glycation and carboxymethylation of ribonuclease

Proteomic analysis using electrospray liquid chromatography-mass spectrometry (ESI-LC-MS) has been used to compare the sites of glycation (Amadori adduct formation) and carboxymethylation of RNase and to assess the role of the Amadori adduct in the formation of the advanced glycation end-product (AGE), N-is an element of-(carboxymethyl)lysine (CIVIL). RNase (13.7 mg/mL, 1 mM) was incubated with glucose (0.4 M) at 37 degreesC for 14 days in phosphate buffer (0.2 M, pH 7.4) under air. On the basis of ESI-LC-MS of tryptic peptides, the major sites of glycation of RNase were, in order, K41, K7, K1, and K37. Three of these, in order, K41, K7, and K37 were also the major sites of CIVIL formation. In other experiments, RNase was incubated under anaerobic conditions (1 mM DTPA, N-2 purged) to form Amadori-modified protein, which was then incubated under aerobic conditions to allow AGE formation. Again, the major sites of glycation were, in order, K41, K7, K1, and K37 and the major sites of carboxymethylation were K41, K7, and K37. RNase was also incubated with 1-5 mM glyoxal, substantially more than is formed by autoxidation of glucose under experimental conditions, but there was only trace modification of lysine residues, primarily at K41. We conclude the following: (1) that the primary route to formation of CIVIL is by autoxidation of Amadori adducts on protein, rather than by glyoxal generated on autoxidation of glucose; and (2) that carboxymethylation, like glycation, is a site-specific modification of protein affected by neighboring amino acids and bound ligands, such as phosphate or phosphorylated compounds. Even when the overall extent of protein modification is low, localization of a high proportion of the modifications at a few reactive sites might have important implications for understanding losses in protein functionality in aging and diabetes and also for the design of AGE inhibitors

Integration of infant metabolite, genetic and islet autoimmunity signatures to predict type 1 diabetes by 6 years of age.

CONTEXT: Biomarkers that can accurately predict risk of type 1 diabetes (T1D) in genetically predisposed children can facilitate interventions to delay or prevent the disease. OBJECTIVE: Determine if a combination of genetic, immunologic, and metabolic features, measured at infancy, can be utilized to predict the likelihood that a child will develop T1D by the age of 6 years. DESIGN: Newborns with HLA typing enrolled in the prospective birth cohort of The Environmental Determinants of Diabetes in the Young (TEDDY). SETTING: TEDDY ascertained children in Finland, Germany, Sweden, and the United States. PATIENTS: TEDDY children were either from the general population or from families with T1D with an HLA genotype associated with T1D specific to TEDDY eligibility criteria. From the TEDDY cohort there were 702 children will all data sources measured at 3, 6 and 9 months of age, 11.4% of which progressed to T1D by the age of 6. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Diagnosis of T1D as diagnosed by American Diabetes Association criteria. RESULTS: Machine learning-based feature selection yielded classifiers based on disparate demographic, immunologic, genetic and metabolite features. The accuracy of the model utilizing all available data evaluated by the Area Under a Receiver Operating Characteristic Curve is 0.84. Reducing to only 3- and 9-month measurements did not reduce the AUC significantly. Metabolomics had the largest value when evaluating the accuracy at a low false positive rate. CONCLUSIONS: The metabolite features identified as important for progression to T1D by age 6 point to altered sugar metabolism in infancy. Integrating this information with classic risk factors improves prediction of the progression to T1D in early childhood

Quality control analysis in real-time (QC-ART): A tool for real-time quality control assessment of mass spectrometry-based proteomics data.

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics studies of large sample cohorts can easily require from months to years to complete. Acquiring consistent, high-quality data in such large-scale studies is challenging because of normal variations in instrumentation performance over time, as well as artifacts introduced by the samples themselves, such as those because of collection, storage and processing. Existing quality control methods for proteomics data primarily focus on post-hoc analysis to remove low-quality data that would degrade downstream statistics; they are not designed to evaluate the data in near real-time, which would allow for interventions as soon as deviations in data quality are detected. In addition to flagging analyses that demonstrate outlier behavior, evaluating how the data structure changes over time can aide in understanding typical instrument performance or identify issues such as a degradation in data quality because of the need for instrument cleaning and/or re-calibration. To address this gap for proteomics, we developed Quality Control Analysis in Real-Time (QC-ART), a tool for evaluating data as they are acquired to dynamically flag potential issues with instrument performance or sample quality. QC-ART has similar accuracy as standard post-hoc analysis methods with the additional benefit of real-time analysis. We demonstrate the utility and performance of QC-ART in identifying deviations in data quality because of both instrument and sample issues in near real-time for LC-MS-based plasma proteomics analyses of a sample subset of The Environmental Determinants of Diabetes in the Young cohort. We also present a case where QC-ART facilitated the identification of oxidative modifications, which are often underappreciated in proteomic experiments