Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity (Part 2)

ABSTRACT: There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determine disease severity. Through analysis of longitudinal samples, we confirm that the majority of these markers are directly related to disease progression and that their levels are restored to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19. Our results indicate that metabolic changes associated with COVID-19 severity can be effectively used to stratify patients and inform resource allocation during the pandemic.

ORGANISM(S): Mesocricetus Auratus Hamster

TISSUE(S): Blood

DISEASE(S): Covid-19

SUBMITTER: Gary Patti

PROVIDER: ST001853 | MetabolomicsWorkbench | Thu Jan 28 00:00:00 GMT 2021

REPOSITORIES: MetabolomicsWorkbench

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	mwtab	Other

Items per page:

1 - 1 of 1

Similar Datasets

Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity (part I)

Project description:There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determine disease severity. Through analysis of longitudinal samples, we confirm that the majority of these markers are directly related to disease progression and that their levels are restored to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19. Our results indicate that metabolic changes associated with COVID-19 severity can be effectively used to stratify patients and inform resource allocation during the pandemic.

2021-01-29 | ST001849 | MetabolomicsWorkbench

Characterisation of the blood RNA host response underpinning severity in COVID-19 patients

Project description:Infection with SARS-CoV-2 has highly variable clinical manifestations, ranging from asymptomatic infection through to life-threatening disease. Host whole blood transcriptomics can offer unique insights into the biological processes underpinning infection and disease, as well as severity. We performed whole blood RNA-Sequencing of individuals with varying degrees of COVID-19 severity. We used differential expression analysis and pathway enrichment analysis to explore how the blood transcriptome differs between individuals with mild, moderate, and severe COVID-19, performing pairwise comparisons between groups.

2022-07-19 | E-MTAB-10926 | biostudies-arrayexpress

Multiple Early Factors Anticipate Post-Acute COVID-19 Sequelae

Project description:Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk-factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data, and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific autoantibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

2022-02-01 | E-MTAB-10129 | biostudies-arrayexpress

Longitudinal single-cell epitope and RNA-sequencing reveals the immunological impact of type 1 interferon autoantibodies in critical COVID-19

Project description:Type I interferon (IFN-I) neutralizing autoantibodies have been found in some critical COVID-19 patients; however, their prevalence and longitudinal dynamics across the disease severity scale, and functional effects on circulating leukocytes remain unknown. Here, in 284 COVID-19 patients, we found IFN-I autoantibodies in 19% of critical, 6% of severe and none of the moderate cases. Longitudinal profiling of over 600,000 peripheral blood mononuclear cells using multiplexed single- cell epitope and transcriptome sequencing from 54 COVID-19 patients, 15 non-COVID-19 patients and 11 non-hospitalized healthy controls, revealed a lack of IFN-I stimulated gene (ISG-I) response in myeloid cells from critical cases, including those producing anti-IFN-I autoantibodies. Moreover, surface protein analysis showed an inverse correlation of the inhibitory receptor LAIR-1 with ISG-I expression response early in the disease course. This aberrant ISG-I response in critical patients with and without IFN-I autoantibodies, supports a unifying model for disease pathogenesis involving ISG-I suppression via convergent mechanisms.

2021-03-09 | GSE168453 | GEO

Plasma Proteomics Reveal COVID-19 Associated Network and Severity Biomarkers

Project description:Coronavirus disease 2019 (COVID-19) has been threatening public health for the last 3 years globally. So far, the pathophysiology of the disease and therapeutic strategies have not clearly known yet. In this project, performing label-free plasma proteomics analysis, we aimed at identifying severity biomarkers for COVID-19 prognosis and proposing potential drugs against the disease symptoms by building the signaling network of significantly regulated proteins and finding the corresponding virus-host interactions. A total of 38 plasma samples from 13 COVID-19 PCR positive individuals and 5 plasma samples from healthy individuals were collected for the analysis. According to the WHO criteria, the severity of our patients was categorized as moderate (n=4), severe (n=3), and critical (n=6). Also, blood samples were collected in different time points after the symptom onset: (1) 1-5 day (± 2 days); early infection, (2) 5-15 days (± 2 days); inflammatory response, and after 15 days (± 2 days); recovery which shows the first PCR negative result from a nasal swab. In summary, we found significantly regulated proteins between COVID-19 patients and uninfected individuals and proposed some critical patient-specific prognostic biomarkers, which can be used as an early predictor of the disease severity. Also, we created a COVID-19 related plasma protein network modulated by SARS-CoV2 viral proteins and indicated clinically significant targets for the disease symptoms.

2023-03-11 | PXD031813 | Pride

Temporal omics analysis in Syrian hamsters unravel cellular effector responses to moderate COVID-19

Project description:Using omics tools with the SARS-CoV-2 infected Syrian hamster as model for COVID-19, along with published datasets from COVID-19 patients, Nouailles et al. present a detailed longitudinal analysis of systemic and pulmonary quantitative and qualitative immune responses in a moderate disease setting. Targeted analysis of the alveolar and microvascular niche revealed a dominant role for monocyte-derived macrophages and endothelial cells regarding early anti-viral genes as well as pro-inflammatory and T cell recruiting chemokine expression. Recruitment of cytotoxic effector T cells coincided with viral clearance. This combined response was favorable for a moderate and self-limited disease course.

2021-06-17 | PXD025164 | Pride

Amino Acid Metabolism is Significantly Altered at the Time of Admission in Hospital for Severe COVID-19 Patients: Findings from Longitudinal Targeted Metabolomics Analysis

Project description:<p>The heterogeneity in severity and outcome of COVID-19 cases points out the urgent need for early molecular characterization of patients followed by risk-stratified care. The main objective of this study was to evaluate the fluctuations of serum metabolomic profiles of COVID-19 patients with severe illness during the different disease stages in a longitudinal manner. We demonstrate a distinct metabolomic signature in serum samples of 32 hospitalized patients at the acute phase compared to the recovery period, suggesting the tryptophan (tryptophan, kynurenine and 3-hydroxy-DL-kynurenine) and arginine (citrulline and ornithine) metabolism as contributing pathways in the immune response to SARS-CoV-2 with a potential link to the clinical severity of the disease. In addition, we suggest that glutamine deprivation may further result in inhibited M2 macrophage polarization as a complementary process, and highlight the contribution of phenylalanine and tyrosine in the molecular mechanisms underlying the severe course of the infection. In conclusion, our results provide several functional metabolic markers for disease progression and severe outcome with potential clinical application.</p>

2022-08-23 | MTBLS3852 | MetaboLights

Single-cell landscape of bronchoalveolar immune cells in COVID-19 patients

Project description:Immune characteristics associated with Coronavirus Disease-2019 (COVID-19) severity are currently unclear. We characterized bronchoalveolar lavage fluid (BALF) immune cells from patients with varying severity of COVID-19 disease and from healthy subjects using single-cell RNA-sequencing. Proinflammatory monocyte-derived macrophages were abundant in the BALF from severe COVID-9 patients. Moderate cases were characterized by the presence of highly clonally expanded tissue-resident CD8+ T cells. This atlas of the bronchoalveolar immune-microenvironment suggests potential mechanisms underlying pathogenesis and recovery in COVID-19.

2020-04-22 | GSE145926 | GEO

Methotrexate inhibition of SARS-CoV-2 entry, infection and inflammation revealed by bioinformatics approach and a hamster model

Project description:Drug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease. Here, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of in vitro (EC50 = 0.4 uM) and in vivo models, we show that methotrexate is able to inhibit COVID-19 via multiple mechanisms. Our in vitro studies illustrate that methotrexate can suppress SARS-CoV-2 entry and replication by targeting furin and DHFR of the host, respectively. Additionally, methotrexate inhibits all four SARS-CoV-2 variants of concern. In a Syrian hamster model for COVID-19, methotrexate reduced virus replication, inflammation in the infected lungs. By analysis of transcriptomic analysis of collected samples from hamster lung, we uncovered that neutrophil infiltration and the pathways of innate immune response, adaptive immune response and thrombosis are modulated in the treated animals. We demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials.

2022-12-13 | GSE179709 | GEO

Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19

Project description:Our understanding of protective vs. pathologic immune responses to SARS-CoV-2, the virus that causes Coronavirus disease 2019 (COVID-19), is limited by inadequate profiling of patients at the extremes of the disease severity spectrum. Here, we performed multi-omic single-cell immune profiling of 64 COVID-19 patients across the full range of disease severity, from outpatients with mild disease to fatal cases. Our transcriptomic, epigenomic, and proteomic analyses reveal widespread dysfunction of peripheral innate immunity in severe and fatal COVID-19, with the most profound disturbances including prominent hyperactivation signatures in neutrophils and monocytes with anti-inflammatory features. We also leverage epigenomic analysis to identify loss of accessibility at NF-kB binding sites within pro-inflammatory cytokine gene loci as a potential mechanism for the striking lack of cytokine production observed in monocytes in severe and fatal COVID-19. We further demonstrate that emergency myelopoiesis is a prominent feature of fatal COVID-19. Collectively, our results reveal disease severity-associated immune phenotypes in COVID-19 and identify pathogenesis-associated pathways that are potential targets for therapeutic intervention.

2021-05-08 | GSE174072 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data