Project description:Targeted mass spectrometric assays for the 22 proteins in a second test cohort containing 19 COVID-19 patients<ul><li>Dataset imported into MassIVE from <a href="https://www.iprox.org/page/project.html?id=IPX0002171000">https://www.iprox.org/page/project.html?id=IPX0002171000</a> on 05/30/20</li></ul>
Project description:Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using ten independent patients, seven of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 new COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.<ul><li>Dataset imported into MassIVE from <a href="https://www.iprox.org/page/project.html?id=IPX0002106000">https://www.iprox.org/page/project.html?id=IPX0002106000</a> on 05/29/20</li></ul>
Project description:A Comparative Study of Human Testes and Epididymis through the Proteomics and RNA-seq Methods
<ul><li>Dataset imported into MassIVE from <a href="https://www.iprox.cn/page/project.html?id=IPX0003098000">https://www.iprox.cn/page/project.html?id=IPX0003098000</a> on 12/10/21</li></ul>
Project description:The causative organism, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exhibits a wide spectrum of clinical manifestations in disease-ridden patients. Differences in the severity of COVID-19 ranges from asymptomatic infections and mild cases to the severe form, leading to acute respiratory distress syndrome (ARDS) and multiorgan failure with poor survival. MiRNAs can regulate various cellular processes, including proliferation, apoptosis, and differentiation, by binding to the 3′UTR of target mRNAs inducing their degradation, thus serving a fundamental role in post-transcriptional repression. Alterations of miRNA levels in the blood have been described in multiple inflammatory and infectious diseases, including SARS-related coronaviruses. We used microarrays to delineate the miRNAs and snoRNAs signature in the peripheral blood of severe COVID-19 cases (n=9), as compared to mild (n=10) and asymptomatic (n=10) patients, and identified differentially expressed transcripts in severe versus asymptomatic, and others in severe versus mild COVID-19 cases. A cohort of 29 male age-matched patients were selected. All patients were previously diagnosed with COVID-19 using TaqPath COVID-19 Combo Kit (Thermo Fisher Scientific, Waltham, Massachusetts), or Cobas SARS-CoV-2 Test (Roche Diagnostics, Rotkreuz, Switzerland), with a CT value < 30. Additional criterion for selection was age between 35 and 75 years. Participants were grouped into severe, mild and asymptomatic. Classifying severe cases was based on requirement of high-flow oxygen support and ICU admission (n=9). Whereas mild patients were identified based on symptoms and positive radiographic findings with pulmonary involvement (n=10). Patients with no clinical presentation were labelled as asymptomatic cases (n=10).
Project description:To analyse gene expression pattern in different disease state of COVID-19 patients. Experimental workflow: 1) rRNA was removed by using RNase H method, 2) QAIseq FastSelect RNA Removal Kit was used to remove the Globin RNA, 3) The purified fragmented cDNA was combined with End Repair Mix, then add A-Tailing Mix, mix well by pipetting, incubation, 4) PCR amplification, 5) Library quality control and pooling cyclization, 6) The RNA library was sequenced by MGI2000 PE100 platform with 100bp paired-end reads. Analysis steps: 1) RNA-seq raw sequencing reads were filtered by SOAPnuke (Li et al., 2008) to remove reads with sequencing adapter, with low-quality base ratio (base quality < 5) > 20%, and with unknown base (’N’ base) ratio > 5%. 2) Reads aligned to rRNA by Bowtie2 (v2.2.5) (Langmead and Salzberg, 2012) were removed. 3) The clean reads were mapped to the reference genome using HISAT2 (Kim et al., 2015). Bowtie2 (v2.2.5) was applied to align the clean reads to the transcriptome. 4)Then the gene expression level (FPKM) was determined by RSEM (Li and Dewey, 2011). Genes with FPKM > 0.1 in at least one sample were retained.
Project description:To reveal genetic determinants of susceptibility to COVID-19 severity in the population and further explore potential immune-related factors, we performed a genome-wide association study on 284 confirmed COVID-19 patients (cases) and 95 healthy individuals (controls). We compared cases and controls of European (EUR) ancestry and African American (AFR) ancestry separately. To further exploring the linkage between HLA and COVID-19 severity, we applied fine-mapping analysis to dissect the HLA association with mild and severe cases.
Project description:The COVID-19 is a mild to moderate respiratory tract infection in the majority, but also can cause life-threatening respiratory failure or persistent debilitating symptoms in a subset of patients. However, the mechanism of protective immunity in mild cases and the pathogenesis of severe COVID-19 remain unclear. On the other hand, it has been proposed that the potent anti-inflammatory effects of corticosteroids are beneficial to decrease the fatality rate in severe COVID-19 patients but its specific mechanism is still in debate.
Project description:Although most SARS-CoV-2-infected individuals experience mild COVID-19, some patients suffer from severe COVID-19, which is accompanied by acute respiratory distress syndrome and systemic inflammation. To identify factors driving severe progression of COVID-19, we performed single-cell RNA-seq using peripheral blood mononuclear cells (PBMCs) obtained from healthy donors, patients with mild or severe COVID-19, and patients with severe influenza. Patients with COVID-19 exhibited hyper-inflammatory signatures across all types of cells among PBMCs, particularly upregulation of the TNF/IL-1beta-driven inflammatory response as compared to severe influenza. In classical monocytes from patients with severe COVID-19, type I IFN response co-existed with the TNF/IL-1beta-driven inflammation, and this was not seen in patients with milder COVID-19 infection. Based on this, we propose that the type I IFN response exacerbates inflammation in patients with severe COVID-19 infection.
Project description:<p>The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing. TCGA is a joint effort of the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), which are both part of the National Institutes of Health, U.S. Department of Health and Human Services.</p> <p>TCGA projects are organized by cancer type or subtype. Click <a href="http://cancergenome.nih.gov/cancersselected" target="_blank">here</a> for a current list of cancer types selected for study in TCGA.</p> <p>Data from TCGA (e.g., gene expression, copy number variation and clinical information), are available via the <a href="https://tcga-data.nci.nih.gov/tcga/" target="_blank">TCGA Data Portal</a>, EXCEPT for the genomic sequence data (.bam files), which are hosted at the <a href="https://cghub.ucsc.edu/" target="_blank">Cancer Genomics Hub (CGHub)</a>.</p> <p>Data from TCGA projects are organized into two tiers: <b>Open Access and Controlled Access</b>. <ul> <li>Open Access data tier contains data that cannot be attributed to an individual research participant. The Open Access data tier does not require user certification. Data in Open Access tier are available in the TCGA Data Portal.</li> <li>Controlled Access data tier contains individual-level genotype data that are unique to an individual. Access to data in the Controlled Access data tier requires user certification through <a href="https://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?login=&page=login" target="_blank">dbGaP Authorized Access</a>.</li> <li>Controlled Access data types consist of the following: <ul> <li>Individual germline variant data (SNP .cel files)</li> <li>Primary sequence data (.bam files), which are available at CGHub</li> <li>Clinical free text fields</li> <li>Exon Array files (for Glioblastoma and Ovarian projects only)</li> </ul> </li> </ul> </p> <p><b>NOTE: TCGA strives to release most data in the open access tier. Individual genotype or sequence files are prominent exceptions. Commonly requested files such as descriptions of somatic mutations or clinical data are open access.</b></p> <p><b>The TCGA study is utilized in the following dbGaP substudies.</b> To view genotypes and other molecular data collected in these substudies, please click on the following substudies below or in the "Substudies" box located on the right hand side of this top-level study page phs000178 TCGA study. <ul> <li><a href="./study.cgi?study_id=phs000441">phs000441</a> Integrated Genomic Analyses of Ovarian Carcinoma (OV)</li> <li><a href="./study.cgi?study_id=phs000489">phs000489</a> Comprehensive Genomic Characterization Defines Human Glioblastoma Genes and Core Pathways</li> </ul> </p>