A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA685117 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:To elucidate key pathways in the host transcriptome of patients infected with SARS-CoV-2, we used RNA sequencing (RNA Seq) to analyze nasopharyngeal (NP) swab and whole blood (WB) samples from 333 COVID-19 patients and controls, including patients with other viral and bacterial infections. Analyses of differentially expressed genes (DEGs) and pathways was performed relative to other infections (e.g. influenza, other seasonal coronaviruses, bacterial sepsis) in both NP swabs and WB. Comparative COVID-19 host responses between NP swabs and WB were examined. Both hospitalized patients and outpatients exhibited upregulation of interferon-associated pathways, although heightened and more robust inflammatory and immune responses were observed in hospitalized patients with more clinically severe disease. A two-layer machine learning-based classifier, run on an independent test set of NP swab samples, was able to discriminate between COVID-19 and non-COVID-19 infectious or non-infectious acute respiratory illness using complete (>1,000 genes), medium (<100) and small (<20) gene biomarker panels with 85.1%-86.5% accuracy, respectively. These findings demonstrate that SARS-CoV-2 infection has a distinct biosignature that differs between NP swabs and WB and can be leveraged for differential diagnosis of COVID-19 disease.
2020-12-15 | GSE163151 | GEO
Project description:Detection of SARS-CoV-2 using RT–PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT–PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples.
2020-09-11 | PXD021388 | Pride
Project description:Recent studies suggest that chronic inflammation and immune dysregulation in the local tissues and systemically play a critical role in the pathophysiology of long COVID. Here, we report a role for nasal epithelium in mediating inflammation in a subset of long COVID patients. We demonstrate impaired barrier function of nasal epithelial cells, inadequate wound healing potential, and nasal cell hypersensitivity along with a persistent inflammatory state in long COVID. These inflamed structural cells activate type 1 innate lymphoid cells in the blood of long COVID patients and propagate systemic inflammation. We demonstrated the importance of understanding the immunological mechanisms driving long COVID to develop effective treatments. We highlight the critical role of the nasal epithelial barrier and the interplay between epithelial cells and ILCs in maintaining mucosal homeostasis and contributing to chronic inflammation in long COVID.
2025-01-31 | GSE286429 | GEO
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease-19 (COVID-19), has emerged as the cause of a global pandemic. We used RNA sequencing to analyze 286 nasopharyngeal (NP) swab and 53 whole-blood (WB) samples from 333 patients with COVID-19 and controls. Overall, a muted immune response was observed in COVID-19 relative to other infections (influenza, other seasonal coronaviruses, and bacterial sepsis), with paradoxical down-regulation of several key differentially expressed genes. Hospitalized patients and outpatients exhibited up-regulation of interferon-associated pathways, although heightened and more robust inflammatory responses were observed in hospitalized patients with more clinically severe illness. Two-layer machine learning-based host classifiers consisting of complete (>1000 genes), medium (<100), and small (<20) gene biomarker panels identified COVID-19 disease with 85.1-86.5% accuracy when benchmarked using an independent test set. SARS-CoV-2 infection has a distinct biosignature that differs between NP swabs and WB and can be leveraged for COVID-19 diagnosis.
| S-EPMC7857687 | biostudies-literature
Project description:Bovine nasal swabs viral metagenome Raw sequence reads
| PRJNA634678 | ENA
Project description:We used total RNA of nasopharyngeal swabs from COVID-19 patients to identify their gene expression profile. Multiple biological process were significantly enriched in either asymptomatic or mildly symptomatic patients. These significantly expressed genes were suggested to contribute to the severity of the disease. We also performed metagenomics analysis to identify differences in the microbiome profile of the two groups of patients.
2022-06-29 | GSE186651 | GEO
Project description:Nasal swabs and co-cultured samples Raw sequence reads
| PRJNA550035 | ENA
Project description:Cattle nasal and rectal swabs samples metagenome raw sequence reads
| PRJNA1120236 | ENA
Project description:Detection of SARS-CoV-2 using RT-PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT-PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples.
| MSV000086175 | MassIVE