Forecasting COVID-19 Severity by Intelligent Optical Fingerprinting of Blood Samples.
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ABSTRACT:
SUBMITTER: Faria SP
PROVIDER: S-EPMC8392709 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
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2022-03-01 | E-MTAB-10970 | biostudies-arrayexpress
Project description:Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has become one of the most serious public health crises worldwide. Most infected people are asymptomatic but are still able to spread the virus. People with mild or moderate illnesses are likely to recover without hospitalization, while critically ill patients face a higher risk of organ injury or even death. In this study, we aimed to identify a novel biomarker that can predict the severity of COVID-19 patients. Clinical information and RNA-seq data of leukocytes from whole blood samples with and without a COVID-19 diagnosis (n = 100 and 26, respectively) were retrieved from the National Center for Biotechnology Information Gene Expression Omnibus database. Raw data were processed using the Transcripts Per Million (TPM) method and then transformed using log2 (TPM+1) for normalization. The CD24-CSF1R index was established. Violin plots, Kaplan-Meier curves, ROC curves, and multivariate Cox proportional hazards regression analyses were performed to evaluate the prognostic value of the established index. The CD24-CSF1R index was significantly associated with ICU admission (n = 50 ICU, 50 non-ICU) and ventilatory status (n = 42 ventilation, 58 non-ventilation) with p = 4.186e-11 and p = 1.278e-07, respectively. The ROC curve produced a relatively accurate prediction of ICU admission with an AUC of 0.8524. Additionally, patients with a high index had significantly fewer mechanical ventilation-free days than patients with a low index (p = 6.07e-07). Furthermore, the established index showed a strong prognostic ability for the risk of using a ventilator in the multivariate Cox regression model (p < 0.001). The CD24-CSF1R index was significantly associated with COVID-19 severity. The established index could have potential implications for prognosis, disease severity stratification, and clinical management.
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Project description:We investigated the effect of IL-13 neutralization on COVID-19 disease progression in mice.
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Project description:Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design and personalized medicine approaches for COVID-19.
2022-02-22 | PXD023175 | Pride
Project description:Genome-wide DNA methylation analysis of COVID-19 severity using the Illumina HumanMethylationEPIC microarray platform to analyze over 850,000 methylation sites, comparing COVID-19 patients with patients presenting with respiratory symptoms, but negative for COVID-19, using whole blood tissue.
2021-05-31 | GSE174818 | GEO
Project description:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.
2020-08-29 | GSE157103 | GEO
Project description:ObjectivesForecasting epidemics like COVID-19 is of crucial importance, it will not only help the governments but also, the medical practitioners to know the future trajectory of the spread, which might help them with the best possible treatments, precautionary measures and protections. In this study, the popular autoregressive integrated moving average (ARIMA) will be used to forecast the cumulative number of confirmed, recovered cases, and the number of deaths in Pakistan from COVID-19 spanning June 25, 2020 to July 04, 2020 (10 days ahead forecast).MethodsTo meet the desire objectives, data for this study have been taken from the Ministry of National Health Service of Pakistan's website from February 27, 2020 to June 24, 2020. Two different ARIMA models will be used to obtain the next 10 days ahead point and 95% interval forecast of the cumulative confirmed cases, recovered cases, and deaths. Statistical software, RStudio, with "forecast", "ggplot2", "tseries", and "seasonal" packages have been used for data analysis.ResultsThe forecasted cumulative confirmed cases, recovered, and the number of deaths up to July 04, 2020 are 231239 with a 95% prediction interval of (219648, 242832), 111616 with a prediction interval of (101063, 122168), and 5043 with a 95% prediction interval of (4791, 5295) respectively. Statistical measures i.e. root mean square error (RMSE) and mean absolute error (MAE) are used for model accuracy. It is evident from the analysis results that the ARIMA and seasonal ARIMA model is better than the other time series models in terms of forecasting accuracy and hence recommended to be used for forecasting epidemics like COVID-19.ConclusionIt is concluded from this study that the forecasting accuracy of ARIMA models in terms of RMSE, and MAE are better than the other time series models, and therefore could be considered a good forecasting tool in forecasting the spread, recoveries, and deaths from the current outbreak of COVID-19. Besides, this study can also help the decision-makers in developing short-term strategies with regards to the current number of disease occurrences until an appropriate medication is developed.
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