Leveraging publicly available coronavirus data to identify new therapeutic targets for COVID-19.
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ABSTRACT:
SUBMITTER: Sell SL
PROVIDER: S-EPMC8480897 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Obesity and type 2 diabetes (T2D) are two major conditions that are related to metabolic disorders and affect a large population. Although there have been significant efforts to identify their therapeutic targets, few benefits have come from comprehensive molecular profiling. This limited availability of comprehensive molecular profiling of obesity and T2D may be due to multiple challenges, as these conditions involve multiple organs and collecting tissue samples from subjects is more difficult in obesity and T2D than in other diseases, where surgical treatments are popular choices. While there is no repository of comprehensive molecular profiling data for obesity and T2D, multiple existing data resources can be utilized to cover various aspects of these conditions. This review presents studies with available genomic data resources for obesity and T2D and discusses genome-wide association studies (GWAS), a knockout (KO)-based phenotyping study, and gene expression profiles. These studies, based on their assessed coverage and characteristics, can provide insights into how such data can be utilized to identify therapeutic targets for obesity and T2D.
| S-EPMC5938056 | biostudies-literature
Project description:Transcriptome wide association studies (TWAS) can be used as a powerful method to identify and interpret the underlying biological mechanisms behind GWAS by mapping gene expression levels with phenotypes. In TWAS, gene expression is often imputed from individual-level genotypes of regulatory variants identified from external resources, such as Genotype-Tissue Expression (GTEx) Project. In this setting, a straightforward approach to impute expression levels of a specific tissue is to use the model trained from the same tissue type. When multiple tissues are available for the same subjects, it has been demonstrated that training imputation models from multiple tissue types improves the accuracy because of shared eQTLs between the tissues and increase in effective sample size. However, existing joint-tissue methods require access of genotype and expression data across all tissues. Moreover, they cannot leverage the abundance of various expression datasets across various tissues for non-overlapping individuals. Here, we explore the optimal way to combine imputed levels across training models from multiple tissues and datasets in a flexible manner using summary-level data. Our proposed method (SWAM) combines arbitrary number of transcriptome imputation models to linearly optimize the imputation accuracy given a target tissue. By integrating models across tissues and/or individuals, SWAM can improve the accuracy of transcriptome imputation or to improve power to TWAS while only requiring individual-level data from a single reference cohort. To evaluate the accuracy of SWAM, we combined 49 tissue-specific gene expression imputation models from the GTEx Project as well as from a large eQTL study of Depression Susceptibility Genes and Networks (DGN) Project and tested imputation accuracy in GEUVADIS lymphoblastoid cell lines samples. We also extend our meta-imputation method to meta-TWAS to leverage multiple tissues in TWAS analysis with summary-level statistics. Our results capitalize on the importance of integrating multiple tissues to unravel regulatory impacts of genetic variants on complex traits.
| S-EPMC8830793 | biostudies-literature
Project description:Therapeutic interventions that lower LDL-cholesterol effectively reduce the risk of coronary artery disease (CAD). However, statins, the most widely prescribed LDL-cholesterol lowering drugs, increase diabetes risk. We used genome-wide association study (GWAS) data in the public domain to investigate the relationship of LDL-C and diabetes and identify loci encoding potential drug targets for LDL-cholesterol modification without causing dysglycemia. We obtained summary-level GWAS data for LDL-C from GLGC, glycemic traits from MAGIC, diabetes from DIAGRAM and CAD from CARDIoGRAMplusC4D consortia. Mendelian randomization analyses identified a one standard deviation (SD) increase in LDL-C caused an increased risk of CAD (odds ratio [OR] 1.63 (95 % confidence interval [CI] 1.55, 1.71), which was not influenced by removing SNPs associated with diabetes. LDL-C/CAD-associated SNPs showed consistent effect directions (binomial P = 6.85 × 10(-5)). Conversely, a 1-SD increase in LDL-C was causally protective of diabetes (OR 0.86; 95 % CI 0.81, 0.91), however LDL-cholesterol/diabetes-associated SNPs did not show consistent effect directions (binomial P = 0.15). HMGCR, our positive control, associated with LDL-C, CAD and a glycemic composite (derived from GWAS meta-analysis of four glycemic traits and diabetes). In contrast, PCSK9, APOB, LPA, CETP, PLG, NPC1L1 and ALDH2 were identified as "druggable" loci that alter LDL-C and risk of CAD without displaying associations with dysglycemia. In conclusion, LDL-C increases the risk of CAD and the relationship is independent of any association of LDL-C with diabetes. Loci that encode targets of emerging LDL-C lowering drugs do not associate with dysglycemia, and this provides provisional evidence that new LDL-C lowering drugs (such as PCSK9 inhibitors) may not influence risk of diabetes.
| S-EPMC4835528 | biostudies-literature
Project description:The first case of COVID-19 was confirmed in Israel on February 21, 2020. Within approximately 30 days, the total number of confirmed cases climbed up to 1, 000, accompanied by a doubling period of less than 3 days. About one week later, after this number exceeded 4, 000 cases, and following some extreme lockdown measures taken by the Israeli government, the daily infection rate started a sharp decrease from the peak value of 1, 131 down to slightly more than 100 new confirmed cases on April 30. Motivated by this encouraging data, similar to the trends observed in many other countries, along with the growing economic pressures, the Israeli government has quickly lifted most of its emergency regulations. Throughout May, the daily number of new cases stayed at a very low level of 20-40 until at the end of May it started a steady increase, exceeding 1, 000 by the end of June and 2, 000 on July 22. As suggested by some experts and popular media, this disturbing trend may be even a part of a "second wave". This article attempts to analyze the data available on Israel at the end of July 2020, compared to three European countries (Greece, Italy, and Sweden), in order to understand the local dynamics of COVID-19, assess the effect of the implemented intervention measures, and discuss some plausible scenarios for the foreseeable future.
| S-EPMC7595440 | biostudies-literature
Project description:We describe a method to estimate individual risks of hospitalization and death attributable to non-household and household transmission of SARS-CoV-2 using available public data on confirmed-case incidence data along with estimates of the clinical fraction, timing of transmission, isolation adherence, secondary infection risks, contact rates, and case-hospitalization and case-fatality ratios. Using the method, we estimate that risks for a 90-day period at the median daily summertime U.S. county confirmed COVID-19 case incidence of 10.8 per 100,000 and pre-pandemic contact rates range from 0.4 to 8.9 per 100,000 for the four deciles of age between 20 and 60 years. The corresponding 90-day period risk of hospitalization ranges from 13.7 to 69.2 per 100,000. Assuming a non-household secondary infection risk of 4% and pre-pandemic contact rates, the share of transmissions attributable to household settings ranges from 73% to 78%. These estimates are sensitive to the parameter assumptions; nevertheless, they are comparable to the COVID-19 hospitalization and fatality rates observed over the time period. We conclude that individual risk of hospitalization and death from SARS-CoV-2 infection is calculable from publicly available data sources. Access to publicly reported infection incidence data by setting and other exposure characteristics along with setting specific estimates of secondary infection risk would allow for more precise individual risk estimation.
| S-EPMC7721133 | biostudies-literature
Project description:Adverse drug reactions (ADRs) can have severe consequences, and therefore the ability to predict ADRs prior to market introduction of a drug is desirable. Computational approaches applied to preclinical data could be one way to inform drug labeling and marketing with respect to potential ADRs. Based on the premise that some of the molecular actors of ADRs involve interactions that are detectable in large, and increasingly public, compound screening campaigns, we generated logistic regression models that correlate postmarketing ADRs with screening data from the PubChem BioAssay database. These models analyze ADRs at the level of organ systems, using the system organ classes (SOCs). Of the 19 SOCs under consideration, nine were found to be significantly correlated with preclinical screening data. With regard to six of the eight established drugs for which we could retropredict SOC-specific ADRs, prior knowledge was found that supports these predictions. We conclude this paper by predicting that SOC-specific ADRs will be associated with three unapproved or recently introduced drugs.
| S-EPMC3464971 | biostudies-literature
Project description:Covid-19 is an infectious respiratory disease due to a coronavirus named SARS-CoV-2. A critical step of the infection cycle is the binding of the virus spike S protein to the cellular ACE-2 receptor. This interaction involves a receptor binding domain (RBD) located at the center of the S trimer, whereas the lateral N-terminal domain (NTD) displays a flat ganglioside binding site that enables the virus to bind to lipid rafts of the plasma membrane, where the ACE-2 receptor resides. S protein binding to lipid rafts can be blocked by hydroxychloroquine, which binds to gangliosides, and by azithromycin, which binds to the NTD. Based on these data, we identified the NTD of SARS-CoV-2 as a promising target for both therapeutic and vaccine strategies, a notion later supported by the discovery, in convalescent Covid-19 patients, of a neutralizing antibody (4A8) that selectively binds to the NTD. The 4A8 epitope overlaps the ganglioside binding domain, denying any access of the virus to lipid rafts when the antibody is bound to the S protein. Thus, our data explain why antibody binding to the tip of the NTD results in SARS-CoV-2 neutralization. The high level of conservation of the ganglioside binding domain of SARS-CoV-2 (100% identity in 584 of 600 isolates analyzed worldwide) offers unique opportunities for innovative vaccine/therapeutic strategies.
| S-EPMC7547605 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic has changed public health policies and human and community behaviors through lockdowns and mandates. Governments are rapidly evolving policies to increase hospital capacity and supply personal protective equipment and other equipment to mitigate disease spread in affected regions. Current models that predict COVID-19 case counts and spread are complex by nature and offer limited explainability and generalizability. This has highlighted the need for accurate and robust outbreak prediction models that balance model parsimony and performance.ObjectiveWe sought to leverage readily accessible data sets extracted from multiple states to train and evaluate a parsimonious predictive model capable of identifying county-level risk of COVID-19 outbreaks on a day-to-day basis.MethodsOur modeling approach leveraged the following data inputs: COVID-19 case counts per county per day and county populations. We developed an outbreak gold standard across California, Indiana, and Iowa. The model utilized a per capita running 7-day sum of the case counts per county per day and the mean cumulative case count to develop baseline values. The model was trained with data recorded between March 1 and August 31, 2020, and tested on data recorded between September 1 and October 31, 2020.ResultsThe model reported sensitivities of 81%, 92%, and 90% for California, Indiana, and Iowa, respectively. The precision in each state was above 85% while specificity and accuracy scores were generally >95%.ConclusionsOur parsimonious model provides a generalizable and simple alternative approach to outbreak prediction. This methodology can be applied to diverse regions to help state officials and hospitals with resource allocation and to guide risk management, community education, and mitigation strategies.
| S-EPMC8315156 | biostudies-literature
Project description:Each year, publicly available databases are updated with new compounds from different research institutions. Positive experimental outcomes are more likely to be reported; therefore, they account for a considerable fraction of these entries. Established publicly available databases such as ChEMBL allow researchers to use information without constrictions and create predictive tools for a broad spectrum of applications in the field of toxicology. Therefore, we investigated the distribution of positive and nonpositive entries within ChEMBL for a set of off-targets and its impact on the performance of classification models when applied to pharmaceutical industry data sets. Results indicate that models trained on publicly available data tend to overpredict positives, and models based on industry data sets predict negatives more often than those built using publicly available data sets. This is strengthened even further by the visualization of the prediction space for a set of 10,000 compounds, which makes it possible to identify regions in the chemical space where predictions converge. Finally, we highlight the utilization of these models for consensus modeling for potential adverse events prediction.
| S-EPMC10445286 | biostudies-literature
Project description:RNA sequencing (RNA-seq) is the leading technology for genome-wide transcript quantification. However, publicly available RNA-seq data is currently provided mostly in raw form, a significant barrier for global and integrative retrospective analyses. ARCHS4 is a web resource that makes the majority of published RNA-seq data from human and mouse available at the gene and transcript levels. For developing ARCHS4, available FASTQ files from RNA-seq experiments from the Gene Expression Omnibus (GEO) were aligned using a cloud-based infrastructure. In total 187,946 samples are accessible through ARCHS4 with 103,083 mouse and 84,863 human. Additionally, the ARCHS4 web interface provides intuitive exploration of the processed data through querying tools, interactive visualization, and gene pages that provide average expression across cell lines and tissues, top co-expressed genes for each gene, and predicted biological functions and protein-protein interactions for each gene based on prior knowledge combined with co-expression.
| S-EPMC5893633 | biostudies-other