Coincident with the tsunami of COVID-19–related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

One of the major difficulties for many laboratories setting up proteomics programs has been obtaining and maintaining the computational infrastructure required for the analysis of the large flow of proteomics data. We describe a system that combines distributed cloud computing and open source software to allow laboratories to set up scalable virtual proteomics analysis clusters without the investment in computational hardware or software licensing fees. Additionally, the pricing structure of distributed computing providers, such as Amazon Web Services, allows laboratories or even individuals to have large-scale computational resources at their disposal at a very low cost per run. We provide detailed step by step instructions on how to implement the virtual proteomics analysis clusters as well as a list of current available preconfigured Amazon machine images containing the OMSSA and X!Tandem search algorithms and sequence databases on the Medical College of Wisconsin Proteomics Center website (http://proteomics.mcw.edu/vipdac).

Model organisms are widely used for understanding basic biology, and have significantly contributed to the study of human disease. In recent years, genomic analysis has provided extensive evidence of widespread conservation of gene sequence and function amongst eukaryotes, allowing insights from model organisms to help decipher gene function in a wider range of species. The InterMOD consortium is developing an infrastructure based around the InterMine data warehouse system to integrate genomic and functional data from a number of key model organisms, leading the way to improved cross-species research. So far including budding yeast, nematode worm, fruit fly, zebrafish, rat and mouse, the project has set up data warehouses, synchronized data models, and created analysis tools and links between data from different species. The project unites a number of major model organism databases, improving both the consistency and accessibility of comparative research, to the benefit of the wider scientific community.

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Hyperglycemia is a critical factor in the development of vascular endothelial dysfunction in type 2 diabetes mellitus (T2DM). While hyperglycemic states are an independent risk factor for endothelial dysfunction, a pro‐inflammatory environment during diabetes is demonstrated to serve as a priming factor for hyperglycemia‐induced vascular damage. Whether hyperglycemic states result in a disruption of similar cellular pathways under both diabetic and non‐diabetic states, remains largely unknown. This study aimed to address this gap in knowledge through molecular and functional characterization of primary cardiac microvascular endothelial cells (RCMVECs) derived from the spontaneously T2DM Goto‐Kakizaki (GK) rat model in comparison to the control Wistar Kyoto (WKY) rat model under normal and hyperglycemic conditions. GK and WKY RCMVECs were cultured under both normal (NG; 4.5 mM) and high glucose (HG; 25 mM) conditions for two weeks, followed by tandem mass spectrometry (MS/MS), qPCR, tube formation assay (TFA), microplate based fluorimetry for reactive oxygen species (ROS), and mitochondrial respiration analyses. Following enrichment and pathway analyses of the tandem MS/MS and qPCR datasets, several molecular targets involved in angiogenic, redox and metabolic functions were significantly altered (FC>2; p<0.05) in the GK RCMVEC response to HG, but not in the WKY RCMVECs. Glycolytic enzymes were markedly reduced, and PMA‐induced superoxide production was enhanced in GK RCMVECs (HG vs NG; p<0.05). Additionally, insulin resistance genes were markedly altered in GK RCMVECs under HG conditions (p<0.05). While HG caused reduction in TFA parameters in WKY RCMVECs, GK RCMVECs exhibited fractured tubes under baseline conditions regardless of glycemic condition. Through the integration of high‐throughput tandem MS/MS analysis, bioinformatics analyses, transcriptomic analysis and in vitro functional assays, we identified a wide spectrum of molecular derangements in endothelial cells that were triggered by environmental (hyperglycemic) and/or genetic (diabetic permissive) determinants. We infer that the consequence of an incapacitated glycolytic machinery could well result in a greater reliance on the OXPHOS pathway in GK HG RCMVECs. This may lead to a significant superoxide burden and a spike in the levels of pro‐atherogenic factors. In conclusion, a hyperglycemia microenvironment exhibited distinct changes in the diabetic endothelial response as compared to those observed in healthy states. Support or Funding Information Support for this project has been provided by the National Institutes of Health National Institute of Diabetes and Digestive and Kidney Diseases (K01‐DK105043 to BRH) and the Department of Biomedical Engineering at the Medical College of Wisconsin and Marquette University, Milwaukee, WI, USA. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.