Age-severity matched cytokine profiling reveals specific signatures in Covid-19 patients.
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ABSTRACT:
SUBMITTER: Angioni R
PROVIDER: S-EPMC7646225 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Inflammatory cytokines and chemokines (CC) drive COVID-19 pathology. Yet, patients with similar circulating CC levels present with different disease severity. Here, we determined 171 microRNAomes from 58 hospitalized COVID-19 patients (Cohort 1) and levels of 25 cytokines and chemokines (CC) in the same samples. Combining microRNA (miRNA) and CC measurements allowed for discrimination of severe cases with greater accuracy than using miRNA or CC levels alone. Severity group-specific associations between miRNAs and COVID-19-associated CC (e.g., IL6, CCL20) or clinical hallmarks of COVID-19 (e.g., neutrophilia, hypoalbuminemia) separated patients with similar CC levels but different disease severity. Analysis of an independent cohort of 108 patients from a different center (Cohort 2) demonstrated feasibility of CC/miRNA profiling in leftover hospital blood samples with similar severe disease CC and miRNA profiles, and revealed CCL20, IL6, IL10, and miR-451a as key correlates of fatal COVID-19. These findings highlight that systemic miRNA/CC networks underpin severe COVID-19.
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Project description:The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high-dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Here, we propose an interpretable deep learning method called moETM to perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder and employs multiple linear decoders to learn the multi-omics signatures. moETM demonstrates superior performance compared with six state-of-the-art methods on seven publicly available datasets. By applying moETM to the scRNA + scATAC data, we identified sequence motifs corresponding to the transcription factors regulating immune gene signatures. Applying moETM to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omics biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.
| S-EPMC10475851 | biostudies-literature
Project description:BackgroundThe SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system.MethodsIn order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings.ResultsHere, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.ConclusionsOur study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.
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Project description:SARS-CoV-2 is an emerging mosquito-borne pathogen with increasing public health significance. To characterize interconnected immune responses to SARS-CoV-2, we here examined transcriptional signatures of CD4 and CD8 T cells, B cells, NK cells, monocytes, myeloid dendritic cells (mDCs), and plasmacytoid dendritic cells (pDCs) from three individuals with naturally-acquired SARS-CoV-2 infection. Whole transcriptome analysis demonstrated that all seven immune cells among PBMCs were clustered together with disease severity regardless of cell types. mDCs and pDCs gene signatures distinct disease severity. A distinct phenotype was observed in severe patients with or without plasma viremia, demonstrating a highly impaired induction of Interferon stimulated genes (ISGs) accompanied by imbalanced type I interferon sensing and an excessive inflammatory response. Our study provides detailed insights into the systemic immune response to SARS-CoV-2 infection and reveals profound alterations in seven major immune cells in blood from different disease severe COVID-19 patients.
2022-11-11 | GSE216529 | GEO
Project description:Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1-5 days post-hospital admission) and late (6-20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs.
2022-12-18 | GSE221234 | GEO
Project description:Viral strains, age, and host factors including genetics and proteins are associated with variable immune responses against SARS-CoV-2 and disease severity. We hypothesized that unique proteins/pathways are associated with COVID-19 disease severity in Puerto Rican Hispanics. A total of 121 men and women aged 21-80 years-old were recruited in Puerto Rico. Plasma samples were collected from unvaccinated COVID-19 infected subjects during acute disease (n=39) and compared to COVID-19 negative individuals (n=56) during acute disease using proteomics and cytokine expression. Infected individuals were stratified based on symptomatology as follows: mild (n=18), moderate (n=13), and severe (n=8). Quantitative proteomics was performed in plasma samples using Tandem Mass Tag (TMT) labeling. Cytokines in plasma were quantified using a human cytokine array. Proteomics analyses revealed 56 differentially regulated proteins and the top 3 pathways that were predicted to be inhibited in severe patients including LXR/RXR signaling, Production of NO and ROS in macrophages, and Synaptogenesis signaling. Decreased cadherin-13 validated by ELISA, which participates in synaptogenesis, is a novel protein is a novel protein not previously reported in other studies of COVID-19 severity and validated by ELISA. Cytokine analyses showed that TNF⍺ levels decreased with disease severity. This study uncovers potential host predictors of COVID-19 severity and new avenues for treatment in Puerto Rican Hispanics.
2024-06-16 | PXD046103 | Pride
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).
2022-03-01 | E-MTAB-10970 | biostudies-arrayexpress
Project description:In this study, we sought to identify circulating microRNA (miRNA) signatures associated with COVID-19 severity and outcome through small RNA-sequencing of serum samples from 89 COVID-19 patients and 45 healthy controls. As results, a set of miRNAs associated with lung disease, vascular damage and inflammation were upregulated in serum of COVID-19 patients vs controls, while miRNAs that inhibit pro-inflammatory cytokines and chemokines, angiogenesis and stress response were downregulated. In addition, patients with severe COVID-19 vs mild or moderate disease had a circulating miRNA signature associated with sepsis, hearth failure, tissue fibrosis, inflammation, and impairment of type I IFN and antiviral responses. A subset of the differentially expressed miRNAs predicted ICU admission, sequelae and mortality in COVID-19 patients. Investigation of the differentially expressed circulating miRNAs in relevant human cell types in vitro showed that some of these miRNAs were modulated directly by SARS-CoV-2 infection or indirectly by type I IFN stimulation.
2022-05-03 | GSE201790 | GEO
Project description:Background metabolic changes through SARS-CoV-2 infection has been reported but not fully comprehended. This metabolic dysregulation affects multiple organs during COVID-19 and its early detection can be used as a prognosis marker of severity. Therefore, we aimed to characterize metabolic and cytokine profile at COVID-19 onset and its relationship with disease severity to identify metabolic profiles predicting disease progression. Material and Methods we performed a retrospective cross-sectional study in 123 COVID-19 patients which were stratified as asymptomatic/mild, moderate and severe according to the highest COVID-19 severity status, and a group of healthy controls. We performed an untargeted plasma metabolic profiling (gas chromatography and capillary electrophoresis-mass spectrometry (GC and CE-MS)) and cytokine evaluation. Results After data filtering and identification we observed 105 metabolites dysregulated (66 GC-MS and 40 CE-MS) which shown different expression patterns for each COVID-19 severity status. These metabolites belonged to different metabolic pathways including amino acid, energy, and nitrogen metabolism among others. Severity-specific metabolic dysregulation was observed, as an increased transformation of L-tryptophan into L-kynurenine. Thus, metabolic profiling at hospital admission differentiate between severe and moderate patients in the later phase of worse evolution. Several plasma pro-inflammatory biomarkers showed significant correlation with deregulated metabolites, specially with L-kynurenine and L-tryptophan. Finally, we describe a strong sex-related dysregulation of metabolites, cytokines and chemokines between severe and moderate patients. In conclusion, metabolic profiling of COVID-19 patients at disease onset is a powerful tool to unravel the SARS-CoV-2 molecular pathogenesis. Conclusions This technique makes it possible to identify metabolic phenoconversion that predicts disease progression and explains the pronounced pathogenesis differences between sexes.
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Project description:The determinants of severe COVID-19 in healthy adults are poorly understood, which limits the opportunity for early intervention. We present a multiomic analysis using machine learning to characterize the genomic basis of COVID-19 severity. We use single-cell multiome profiling of human lungs to link genetic signals to cell-type-specific functions. We discover >1,000 risk genes across 19 cell types, which account for 77% of the SNP-based heritability for severe disease. Genetic risk is particularly focused within natural killer (NK) cells and T cells, placing the dysfunction of these cells upstream of severe disease. Mendelian randomization and single-cell profiling of human NK cells support the role of NK cells and further localize genetic risk to CD56bright NK cells, which are key cytokine producers during the innate immune response. Rare variant analysis confirms the enrichment of severe-disease-associated genetic variation within NK-cell risk genes. Our study provides insights into the pathogenesis of severe COVID-19 with potential therapeutic targets.
| S-EPMC9163145 | biostudies-literature