Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS
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Abstract
Acute respiratory distress syndrome (ARDS), a life-threatening condition characterized by hypoxemia and poor lung compliance, is associated with high mortality. ARDS induced by COVID-19 has similar clinical presentations and pathological manifestations as non-COVID-19 ARDS. However, COVID-19 ARDS is associated with a more protracted inflammatory respiratory failure compared to traditional ARDS. Therefore, a comprehensive molecular comparison of ARDS of different etiologies groups may pave the way for more specific clinical interventions.
Methods and findings
In this study, we compared COVID-19 ARDS (n = 43) and bacterial sepsis-induced (non-COVID-19) ARDS (n = 24) using multi-omic plasma profiles covering 663 metabolites, 1,051 lipids, and 266 proteins. To address both between- and within- ARDS group variabilities we followed two approaches. First, we identified 706 molecules differently abundant between the two ARDS etiologies, revealing more than 40 biological processes differently regulated between the two groups. From these processes, we assembled a cascade of therapeutically relevant pathways downstream of sphingosine metabolism. The analysis suggests a possible overactivation of arginine metabolism involved in long-term sequelae of ARDS and highlights the potential of JAK inhibitors to improve outcomes in bacterial sepsis-induced ARDS. The second part of our study involved the comparison of the two ARDS groups with respect to clinical manifestations. Using a data-driven multi-omic network, we identified signatures of acute kidney injury (AKI) and thrombocytosis within each ARDS group. The AKI-associated network implicated mitochondrial dysregulation which might lead to post-ARDS renal-sequalae. The thrombocytosis-associated network hinted at a synergy between prothrombotic processes, namely IL-17, MAPK, TNF signaling pathways, and cell adhesion molecules. Thus, we speculate that combination therapy targeting two or more of these processes may ameliorate thrombocytosis-mediated hypercoagulation.
Conclusion
We present a first comprehensive molecular characterization of differences between two ARDS etiologies–COVID-19 and bacterial sepsis. Further investigation into the identified pathways will lead to a better understanding of the pathophysiological processes, potentially enabling novel therapeutic interventions.
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SciScore for 10.1101/2022.05.16.22274587: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Field Sample Permit: The process for recruitment, data collection, and sample processing has been described previously [93–95]. Sex as a biological variable not detected. Randomization Experimental samples were randomized across the platform run with QC samples spaced evenly among the injections. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources For metabolite identification, raw data was extracted, peak-identified and QC processed using Metabolon’s hardware and software. Metabolon’ssuggested: None4.9 Pathway annotation and filtering: Metabolites were annotated using Metabolon’s ‘sub-pathway’ groups, lipids were annotated by lipid classes, … SciScore for 10.1101/2022.05.16.22274587: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Field Sample Permit: The process for recruitment, data collection, and sample processing has been described previously [93–95]. Sex as a biological variable not detected. Randomization Experimental samples were randomized across the platform run with QC samples spaced evenly among the injections. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources For metabolite identification, raw data was extracted, peak-identified and QC processed using Metabolon’s hardware and software. Metabolon’ssuggested: None4.9 Pathway annotation and filtering: Metabolites were annotated using Metabolon’s ‘sub-pathway’ groups, lipids were annotated by lipid classes, and proteins were annotated using signaling pathways from Kyoto Encyclopedia of Genes and Genomes (KEGG) [17]. KEGGsuggested: (KEGG, RRID:SCR_012773)4.10 Multi-Omic network inference: A partial correlation-based Gaussian graphical model (GGM) was computed using the GeneNet R package [106] to infer a multi-omic network. GeneNetsuggested: (GeneNet, RRID:SCR_007678)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our findings are based on a study design with several limitations. (1) Samples in each ARDS group were collected years apart, which may cause variation in the molecular profiles due to differences in sample collection protocols and duration of storage. (2) The number of samples in each ARDS group is limited and imbalanced, with 43 COVID-19 samples and 24 bacterial sepsis samples, which reduces statistical power. (3) Our findings are based on molecules measured in plasma; thus, the measurements may not be representative of the site of ARDS, i.e., the lungs. (4) Our results are based on statistical associations, and further experiments are needed for validation as well as mechanistic and causal insights. In summary, we presented a first report on the molecular comparison between two ARDS etiologies – COVID-19 and bacterial sepsis. Our study is a step toward solving two pertinent clinical challenges associated with ARDS: the identification of novel therapeutic options, and the delineation of heterogeneous pathophysiological manifestations within the ARDS [38]. Even though for COVID-19 ARDS, a few partially effective immunotherapeutic options have been identified in anti-IL-6 therapy and JAK inhibitors, treatment remains a challenge for bacterial sepsis-induced ARDS. Using an inter-ARDS comparison, we highlighted therapeutically relevant signaling and metabolic pathways for ARDS of different etiologies. Using an intra-ARDS analysis, we identified molecular signatures characterizi...
Results from TrialIdentifier: We found the following clinical trial numbers in your paper:
Identifier Status Title NCT04280588 Withdrawn Fingolimod in COVID-19 NCT04467840 Completed Opaganib, a Sphingosine Kinase-2 (SK2) Inhibitor in COVID-19… NCT04414618 Completed A Study of Opaganib in Coronavirus Disease 2019 Pneumonia Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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