ABSTRACT
The emergence of the SARS-CoV-2 virus and subsequent COVID-19 pandemic initiated intense research into the mechanisms of action for this virus. It was quickly noted that COVID-19 presents more seriously in conjunction with other human disease conditions such as hypertension, diabetes, and lung diseases. We conducted a bioinformatics analysis of COVID-19 comorbidity-associated gene sets, identifying genes and pathways shared among the comorbidities, and evaluated current knowledge about these genes and pathways as related to current information about SARS-CoV-2 infection. We performed our analysis using GeneWeaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 comorbidities. Phenotypic analysis of shared genes revealed significant enrichment for immune system phenotypes and for cardiovascular-related phenotypes, which might point to alleles and phenotypes in mouse models that could be evaluated for clues to COVID-19 severity. Through pathway analysis, we identified enriched pathways shared by comorbidity datasets and datasets associated with SARS-CoV-2 infection.
Subject(s)
COVID-19/mortality , COVID-19/pathology , Computational Biology/methods , Animals , Cardiovascular Diseases/epidemiology , Cardiovascular Diseases/genetics , Comorbidity , Cytokine Release Syndrome/mortality , Databases, Genetic , Diabetes Mellitus/epidemiology , Diabetes Mellitus/genetics , Disease Models, Animal , Hepatitis/epidemiology , Hepatitis/genetics , Humans , Kidney Diseases/epidemiology , Kidney Diseases/genetics , Lung Diseases/epidemiology , Lung Diseases/genetics , Mice , Respiratory Distress Syndrome/mortality , SARS-CoV-2 , Severity of Illness IndexABSTRACT
The emergence of the SARS-CoV-2 virus and subsequent COVID-19 pandemic initiated intense research into the mechanisms of action for this virus. It was quickly noted that COVID-19 presents more seriously in conjunction with other human disease conditions such as hypertension, diabetes, and lung diseases. We conducted a bioinformatics analysis of COVID-19 comorbidity-associated gene sets, identifying genes and pathways shared among the comorbidities, and evaluated current knowledge about these genes and pathways as related to current information about SARS-CoV-2 infection. We performed our analysis using GeneWeaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 comorbidities. Phenotypic analysis of shared genes revealed significant enrichment for immune system phenotypes and for cardiovascular-related phenotypes, which might point to alleles and phenotypes in mouse models that could be evaluated for clues to COVID-19 severity. Through pathway analysis, we identified enriched pathways shared by comorbidity datasets and datasets associated with SARS-CoV-2 infection.
ABSTRACT
BACKGROUND: Understanding mechanisms underlying specific chemotherapeutic responses in subtypes of cancer may improve identification of treatment strategies most likely to benefit particular patients. For example, triple-negative breast cancer (TNBC) patients have variable response to the chemotherapeutic agent cisplatin. Understanding the basis of treatment response in cancer subtypes will lead to more informed decisions about selection of treatment strategies. METHODS: In this study we used an integrative functional genomics approach to investigate the molecular mechanisms underlying known cisplatin-response differences among subtypes of TNBC. To identify changes in gene expression that could explain mechanisms of resistance, we examined 102 evolutionarily conserved cisplatin-associated genes, evaluating their differential expression in the cisplatin-sensitive, basal-like 1 (BL1) and basal-like 2 (BL2) subtypes, and the two cisplatin-resistant, luminal androgen receptor (LAR) and mesenchymal (M) subtypes of TNBC. RESULTS: We found 20 genes that were differentially expressed in at least one subtype. Fifteen of the 20 genes are associated with cell death and are distributed among all TNBC subtypes. The less cisplatin-responsive LAR and M TNBC subtypes show different regulation of 13 genes compared to the more sensitive BL1 and BL2 subtypes. These 13 genes identify a variety of cisplatin-resistance mechanisms including increased transport and detoxification of cisplatin, and mis-regulation of the epithelial to mesenchymal transition. CONCLUSIONS: We identified gene signatures in resistant TNBC subtypes indicative of mechanisms of cisplatin. Our results indicate that response to cisplatin in TNBC has a complex foundation based on impact of treatment on distinct cellular pathways. We find that examination of expression data in the context of heterogeneous data such as drug-gene interactions leads to a better understanding of mechanisms at work in cancer therapy response.
Subject(s)
Antineoplastic Agents/therapeutic use , Cisplatin/therapeutic use , Drug Resistance, Neoplasm/genetics , Genomics/methods , Triple Negative Breast Neoplasms/drug therapy , Animals , Biological Evolution , Cell Line, Tumor , Conserved Sequence , Epithelial-Mesenchymal Transition/genetics , Female , Gene Expression Profiling , Gene Expression Regulation, Neoplastic , Humans , Mice , Rats , Receptors, Androgen/metabolismABSTRACT
Members of the lymphocyte antigen-6 (Ly6)/urokinase-type plasminogen activator receptor (uPAR) superfamily of proteins are cysteine-rich proteins characterized by a distinct disulfide bridge pattern that creates the three-finger Ly6/uPAR (LU) domain. Although the Ly6/uPAR family proteins share a common structure, their expression patterns and functions vary. To date, 35 human and 61 mouse Ly6/uPAR family members have been identified. Based on their subcellular localization, these proteins are further classified as GPI-anchored on the cell membrane, or secreted. The genes encoding Ly6/uPAR family proteins are conserved across different species and are clustered in syntenic regions on human chromosomes 8, 19, 6 and 11, and mouse Chromosomes 15, 7, 17, and 9, respectively. Here, we review the human and mouse Ly6/uPAR family gene and protein structure and genomic organization, expression, functions, and evolution, and introduce new names for novel family members.
Subject(s)
Antigens, Ly/genetics , Multigene Family/genetics , Receptors, Urokinase Plasminogen Activator/genetics , Animals , Chromosomes/genetics , Genome, Human , Humans , Mice , Neutrophils , Protein Domains , Signal TransductionABSTRACT
The mouse genome database (MGD) is the model organism database component of the mouse genome informatics system at The Jackson Laboratory. MGD is the international data resource for the laboratory mouse and facilitates the use of mice in the study of human health and disease. Since its beginnings, MGD has included comparative genomics data with a particular focus on human-mouse orthology, an essential component of the use of mouse as a model organism. Over the past 25 years, novel algorithms and addition of orthologs from other model organisms have enriched comparative genomics in MGD data, extending the use of orthology data to support the laboratory mouse as a model of human biology. Here, we describe current comparative data in MGD and review the history and refinement of orthology representation in this resource.
