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Calcium oxalate monohydrate (COM) crystals are the primary constituent of most kidney stones, but urine proteins in stone matrix are believed to be critical elements for stone formation from these crystals. Recent data have shown that hundreds of proteins appear in the stone matrix with no explanation for inclusion of so many proteins. We have proposed a stone formation model with protein stimulated COM aggregation based on polyanion-polycation aggregation, which is supported by finding that matrix is highly enriched in strongly anionic and strongly cationic proteins. Many other proteins may be drawn to such aggregates due to their limited solubility in water or charge effects. Finding similar protein enrichment in both polyarginine (pR) induced aggregates of urine proteins and COM stone matrix would support this hypothesis. Purified proteins (PP) were obtained from random urine samples of six healthy adults by ultradiafiltration. Protein aggregation was induced by adding pR to PP solutions at two concentrations; 0.25 and 0.5 μg pR /μg of PP. Samples of each fraction and the original PP mixture were lyophilized and analyzed by tandem mass spectrometry. Aggregates induced by pR addition to PP samples collected a protein mixture that mimicked the protein distribution observed in COM matrix, supporting our hypothesis. The apparently discordant behavior of certain abundant anionic proteins preferentially joining the pR aggregate, when they had demonstrated reduced abundance in COM stone matrix, suggests that this model was overdriven to aggregate. The reversal of aggregate preference of albumin at low pR addition supports this interpretation.

A challenge to understanding enhancer-gene relationships is that enhancers are not always sequentially close to the gene they regulate. Physical proximity mapping through sequencing can provide an unbiased view of the chromatin close to the proximal promoter of the renin gene ( Ren). Our objective was to determine genomic regions that physically interact with the renin proximal promoter, using two different genetic backgrounds, the Dahl salt sensitive and normotensive SS-13BN, which have been shown to have different regulation of plasma renin in vivo. The chromatin conformation capture method with sequencing focused at the Ren proximal promoter in rat-derived cardiac endothelial cells was used. Cells were fixed, chromatin close to the Ren promoter was captured, and fragments were sequenced. The clustering of mapped reads produced a genome-wide map of chromatin in contact with the Ren promoter. The largest number of contacts was found on chromosome 13, the chromosome with Ren, and contacts were found on all other chromosomes except chromosome X. These contacts were significantly enriched with genes positively correlated with Ren expression and with mapped quantitative trait loci associated with blood pressure, cardiovascular, and renal phenotypes. The results were reproducible in an independent biological replicate. The findings reported here represent the first map between a critical cardiovascular gene and physical interacting loci throughout the genome and will provide the basis for several new directions of research.

International audience; 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 Web site ( http://proteomics.mcw.edu/vipdac ).

CORRIGENDUM: InterMOD: integrated data and tools for the unification of model organism research

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.