Combinatorial Analysis of Phenotypic and Clinical Risk Factors Associated With Hospitalized COVID-19 Patients.

Characterization of the risk factors associated with variability in the clinical outcomes of COVID-19 is important. Our previous study using genomic data identified a potential role of calcium and lipid homeostasis in severe COVID-19. This study aimed to identify similar combinations of features (disease signatures) associated with severe disease in a separate patient population with purely clinical and phenotypic data. The PrecisionLife combinatorial analytics platform was used to analyze features derived from de-identified health records in the UnitedHealth Group COVID-19 Data Suite. The platform identified and analyzed 836 disease signatures in two cohorts associated with an increased risk of COVID-19 hospitalization. Cohort 1 was formed of cases hospitalized with COVID-19 and a set of controls who developed mild symptoms. Cohort 2 included Cohort 1 individuals for whom additional laboratory test data was available. We found several disease signatures where lower levels of lipids were found co-occurring with lower levels of serum calcium and leukocytes. Many of the low lipid signatures were independent of statin use and 50% of cases with hypocalcemia signatures were reported with vitamin D deficiency. These signatures may be attributed to similar mechanisms linking calcium and lipid signaling where changes in cellular lipid levels during inflammation and infection affect calcium signaling in host cells. This study and our previous genomics analysis demonstrate that combinatorial analysis can identify disease signatures associated with the risk of developing severe COVID-19 separately from genomic or clinical data in different populations. Both studies suggest associations between calcium and lipid signaling in severe COVID-19.

A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization.

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

Epigenetic profiling at mouse imprinted gene clusters reveals novel epigenetic and genetic features at differentially methylated regions.

Genomic imprinting arises from allele-specific epigenetic modifications that are established during gametogenesis and that are maintained throughout somatic development. These parental-specific modifications include DNA methylation and post-translational modifications to histones, which create allele-specific active and repressive domains at imprinted regions. Through the use of a high-density genomic tiling array, we generated DNA and histone methylation profiles at 11 imprinted gene clusters in the mouse from DNA and from chromatin immunoprecipitated from sperm, heart, and cerebellum. Our analysis revealed that despite high levels of differential DNA methylation at non-CpG islands within these regions, imprinting control regions (ICRs) and secondary differentially methylated regions (DMRs) were identified by an overlapping pattern of H3K4 trimethylation (active chromatin) and H3K9 trimethylation (repressive chromatin) modifications in somatic tissue, and a sperm differentially methylated region (sDMR; sperm not equal somatic tissue). Using these features as a common signature of DMRs, we identified 11 unique regions that mapped to known imprinted genes, to uncharacterized genes, and to intergenic regions flanking known imprinted genes. A common feature among these regions was the presence of a CpG island and an array of tandem repeats. Collectively, this study provides a comprehensive analysis of DNA methylation and histone H3K4me3 and H3K9me3 modifications at imprinted gene clusters, and identifies common epigenetic and genetic features of regions regulating genomic imprinting.

Towards a mutant map of the mouse--new models of neurological, behavioural, deafness, bone, renal and blood disorders.

With the completion of the first draft of the human genome sequence, the next major challenge is assigning function to genes. One approach is genome-wide random chemical mutagenesis, followed by screening for mutant phenotypes of interest and subsequent mapping and identification of the mutated genes in question. We (a consortium made up of GlaxoSmithKline, the MRC Mammalian Genetics Unit and Mouse Genome Centre, Harwell, Imperial College, London, and the Royal London Hospital) have used ENU mutagenesis in the mouse for the rapid generation of novel mutant phenotypes for use as animal models of human disease and for gene function assignment (Nolan et al., 2000). As of 2003, 35,000 mice have been produced to date in a genome-wide screen for dominant mutations and screened using a variety of screening protocols. Nearly 200 mutants have been confirmed as heritable and added to the mouse mutant catalogue and, overall, we can extrapolate that we have recovered over 700 mutants from the screening programme. For further information on the project and details of the data, see http://www.mgu.har.mrc.ac.uk/mutabase.

Visualizing the laboratory mouse: capturing phenotype information.

A concerted effort to develop myriad new phenotypic alleles through mutagenesis programs presents new challenges for the biomedical community and for the informatics infrastructure needed to support this work. To handle and co-ordinate large programs of treatment, breeding, and sequential or longitudinal testing for a variety of obvious and subtle traits requires sophisticated data management software. Further, trait analyses, heritability testing, and animal availability and status must be captured and disseminated to the wider community. The Mouse Genome Database (MGD) will serve as the central integration point for the various mutagenesis programs, registering new alleles, providing accession identifiers, and capturing phenotypic descriptions. In addition, MGD will provide public access to unified searches over all alleles with links to the centres of origin for detailed testing data.

Organization and evolution of a gene-rich region of the mouse genome: a 12.7-Mb region deleted in the Del(13)Svea36H mouse.

Del(13)Svea36H (Del36H) is a deletion of approximately 20% of mouse chromosome 13 showing conserved synteny with human chromosome 6p22.1-6p22.3/6p25. The human region is lost in some deletion syndromes and is the site of several disease loci. Heterozygous Del36H mice show numerous phenotypes and may model aspects of human genetic disease. We describe 12.7 Mb of finished, annotated sequence from Del36H. Del36H has a higher gene density than the draft mouse genome, reflecting high local densities of three gene families (vomeronasal receptors, serpins, and prolactins) which are greatly expanded relative to human. Transposable elements are concentrated near these gene families. We therefore suggest that their neighborhoods are gene factories, regions of frequent recombination in which gene duplication is more frequent. The gene families show different proportions of pseudogenes, likely reflecting different strengths of purifying selection and/or gene conversion. They are also associated with relatively low simple sequence concentrations, which vary across the region with a periodicity of approximately 5 Mb. Del36H contains numerous evolutionarily conserved regions (ECRs). Many lie in noncoding regions, are detectable in species as distant as Ciona intestinalis, and therefore are candidate regulatory sequences. This analysis will facilitate functional genomic analysis of Del36H and provides insights into mouse genome evolution.

LIMaS: the JAVA-based application and database for microarray experiment tracking.

Microarrays allow monitoring of gene expression for tens of thousands of genes in parallel and are being used routinely to generate huge amounts of valuable data. Handling and analysis of such data are becoming major bottlenecks in the utilization of the technology. To enable the researcher to interpret the results postanalysis, we have developed a laboratory information management system for microarrays (LIMaS) with an n-tier Java front-end and relational database to record and manage large-scale expression data preanalysis. This system enables the laboratory to replace the paper trail with an efficient and fully customizable interface giving it the ability to adapt to any working practice, e.g., handling many resources used to form many products (chaining of resources). The ability to define sets of activities, resources, and workflows makes LIMaS MIAME-supportive.

Novel phenotypes identified by plasma biochemical screening in the mouse.

We used ENU mutagenesis in the mouse for the rapid generation of novel mutant phenotypes for both gene function studies and use as new animal models of human disease (Nolan et al. 2000b). One focus of the program was the development of a blood biochemistry screen. At 8-12 weeks of age, approximately 300 ml of blood was collected from F1 offspring of ENU mutagenized male mice. This yielded approximately 125 ml of plasma, used to perform a profile of 17 standard biochemical tests on an Olympus analyzer. Cohorts of F1 mice were also aged and then retested to detect late onset phenotypes. In total, 1,961 F1s were screened. Outliers were identified by running means and standard deviations. Of 70 mice showing consistent abnormalities in plasma biochemistry, 29 were entered into inheritance testing. Of these, 9 phenotypes were confirmed as inherited, 10 found not to be inherited, and 10 are still being tested. Inherited mutant phenotypes include abnormal lipid profiles (low total and HDL cholesterol, high triglycerides); abnormalities in bone and liver metabolism (low ALP, high ALP, high ALT, and AST); abnormal plasma electrolyte levels (high sodium and chloride); as well as phenotypes of interest for the study of diabetes (high glucose). The gene loci bearing the mutations are currently being mapped and further characterized. Our results have validated our biochemical screen, which is applicable to other mutagenesis projects, and we have produced a new set of mutants with defined metabolic phenotypes.