Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS

Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis. In this study, we performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). The comparison of these ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis. In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.

[1]  K. Suhre,et al.  Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS , 2022, medRxiv.

[2]  S. Rafii,et al.  Angiopoietin 2 Is Associated with Vascular Necroptosis Induction in Coronavirus Disease 2019 Acute Respiratory Distress Syndrome , 2022, The American Journal of Pathology.

[3]  M. Netea,et al.  A guide to immunotherapy for COVID-19 , 2022, Nature Medicine.

[4]  K. Suhre,et al.  maplet: an extensible R toolbox for modular and reproducible metabolomics pipelines , 2021, Bioinform..

[5]  Luang Xu,et al.  Proteomic and metabolomic profiling of urine uncovers immune responses in patients with COVID-19 , 2021, Cell Reports.

[6]  C. Agrati,et al.  Multi-omics approach to COVID-19: a domain-based literature review , 2021, Journal of translational medicine.

[7]  L. Forni,et al.  Pathophysiology of COVID-19-associated acute kidney injury , 2021, Nature Reviews Nephrology.

[8]  D. Quirk,et al.  Tofacitinib in Patients Hospitalized with Covid-19 Pneumonia , 2021, The New England journal of medicine.

[9]  H. Tun,et al.  Multi-platform omics analysis reveals molecular signature for COVID-19 pathogenesis, prognosis and drug target discovery , 2021, Signal Transduction and Targeted Therapy.

[10]  Ian J. Stewart,et al.  Urinary metabolites predict mortality or need for renal replacement therapy after combat injury , 2021, Critical Care.

[11]  Helio T. Navarro,et al.  Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia , 2020, Nature.

[12]  Hengliang Wang,et al.  Urine proteome of COVID-19 patients , 2020, URINE.

[13]  H. Karmouty-quintana,et al.  Mechanisms of Pulmonary Hypertension in Acute Respiratory Distress Syndrome (ARDS) , 2021, Frontiers in Molecular Biosciences.

[14]  Cameron R. Wolfe,et al.  Baricitinib plus Remdesivir for Hospitalized Adults with Covid-19 , 2020, The New England journal of medicine.

[15]  C. Goss,et al.  Clinical characteristics of SARS-CoV-2 infection in children with cystic fibrosis: An international observational study , 2020, Journal of Cystic Fibrosis.

[16]  G. Gao,et al.  Immune suppression in the early stage of COVID-19 disease , 2020, Nature Communications.

[17]  M. Mcphail,et al.  Mitochondrial metabolic manipulation by SARS-CoV-2 in peripheral blood mononuclear cells of patients with COVID-19 , 2020, American journal of physiology. Cell physiology.

[18]  anonymous,et al.  IMPACT OF COVID-19 ON PEOPLE'S LIV ELIHOODS, THEIR HEALTH AND OUR FOOD SYSTEMS , 2020, Saudi Medical Journal.

[19]  P. Insel,et al.  Inflammation and thrombosis in COVID-19 pathophysiology: proteinase-activated and purinergic receptors as drivers and candidate therapeutic targets , 2020, Physiological reviews.

[20]  D. Ivy,et al.  Insulin-like growth factor binding protein-2: a new circulating indicator of pulmonary arterial hypertension severity and survival , 2020, BMC Medicine.

[21]  E. Audureau,et al.  Uncontrolled Innate and Impaired Adaptive Immune Responses in Patients with COVID-19 Acute Respiratory Distress Syndrome , 2020, American journal of respiratory and critical care medicine.

[22]  L. Kontar,et al.  COVID-19– versus non–COVID-19–related Acute Respiratory Distress Syndrome: Differences and Similarities , 2020, American journal of respiratory and critical care medicine.

[23]  R. Stewart,et al.  Large-scale Multi-omic Analysis of COVID-19 Severity , 2020, medRxiv.

[24]  M. Robinson,et al.  Therapeutic targeting of metabolic alterations in acute respiratory distress syndrome , 2020, European Respiratory Review.

[25]  L. Zou,et al.  Elevated Expression of Serum Endothelial Cell Adhesion Molecules in COVID-19 Patients , 2020, The Journal of infectious diseases.

[26]  P. Horby,et al.  Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study , 2020, BMJ.

[27]  Kevin Grimes,et al.  p38 MAPK inhibition: A promising therapeutic approach for COVID-19 , 2020, Journal of Molecular and Cellular Cardiology.

[28]  H. Wang,et al.  Serum Protein Profiling Reveals a Landscape of Inflammation and Immune Signaling in Early-stage COVID-19 Infection , 2020, Molecular & Cellular Proteomics.

[29]  Mickaël Ohana,et al.  High risk of thrombosis in patients with severe SARS-CoV-2 infection: a multicenter prospective cohort study , 2020, Intensive Care Medicine.

[30]  Huanhuan Gao,et al.  Proteomic and Metabolomic Characterization of COVID-19 Patient Sera , 2020, Cell.

[31]  Y. Hu,et al.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China , 2020, The Lancet.

[32]  Sunil K. Jain,et al.  Urinary NT-proBNP as a potential noninvasive biomarker for screening of pulmonary hypertension in preterm infants: a pilot study , 2020, Journal of Perinatology.

[33]  S. Rhee,et al.  Urinary chemokine C-X-C motif ligand 16 and endostatin as predictors of tubulointerstitial fibrosis in patients with advanced diabetic kidney disease. , 2019, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[34]  Kevin C. Ma,et al.  Circulating cell death biomarker TRAIL is associated with increased organ dysfunction in sepsis. , 2019, JCI insight.

[35]  Christian Gieger,et al.  Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies , 2018, Metabolomics.

[36]  H. Parving,et al.  Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria , 2018, Cardiovascular Diabetology.

[37]  S. Ferdinandusse,et al.  Disorders of mitochondrial long-chain fatty acid oxidation and the carnitine shuttle , 2018, Reviews in Endocrine and Metabolic Disorders.

[38]  Fabian J Theis,et al.  Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway , 2017, Nature Communications.

[39]  Kieu Trinh Do,et al.  Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations , 2017, npj Systems Biology and Applications.

[40]  E. Finkelsztein,et al.  Comparison of qSOFA and SIRS for predicting adverse outcomes of patients with suspicion of sepsis outside the intensive care unit , 2017, Critical Care.

[41]  Joachim Frank,et al.  The mechanism of translation , 2017, F1000Research.

[42]  J. Levitt,et al.  Proteomic study of acute respiratory distress syndrome: current knowledge and implications for drug development , 2016, Expert review of proteomics.

[43]  M. Balaan,et al.  Acute Respiratory Distress Syndrome , 2016, Critical care nursing quarterly.

[44]  R. Bellomo,et al.  The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). , 2016, JAMA.

[45]  Guangyou Duan,et al.  A prospective observational cohort study , 2016 .

[46]  F. Domínguez,et al.  Infective endocarditis in hypertrophic A multicenter , prospective , cohort study , 2016 .

[47]  Youhe Gao,et al.  Physiological conditions can be reflected in human urine proteome and metabolome , 2015, Expert review of proteomics.

[48]  A. Cohen-Solal,et al.  Proteomics analysis reveals IGFBP2 as a candidate diagnostic biomarker for heart failure , 2015 .

[49]  R. Mallampalli,et al.  The Acute Respiratory Distress Syndrome: From Mechanism to Translation , 2015, The Journal of Immunology.

[50]  K. Shimamoto,et al.  Urinary Excretion of Fatty Acid-Binding Protein 4 is Associated with Albuminuria and Renal Dysfunction , 2014, PloS one.

[51]  S. Adams,et al.  Acylcarnitines activate proinflammatory signaling pathways. , 2014, American journal of physiology. Endocrinology and metabolism.

[52]  F. Frey,et al.  Identification of IGFBP-7 by urinary proteomics as a novel prognostic marker in early acute kidney injury. , 2014, Kidney international.

[53]  Karl Henrik Sivesind,et al.  Differences and Similarities , 2004 .

[54]  S. Anderson,et al.  IGFBP2 is a biomarker for predicting longitudinal deterioration in renal function in type 2 diabetes , 2012, Endocrine connections.

[55]  A. Khwaja KDIGO Clinical Practice Guidelines for Acute Kidney Injury , 2012, Nephron Clinical Practice.

[56]  Xianghua Liu,et al.  Urinary heme oxygenase-1 in children with congenital hydronephrosis due to ureteropelvic junction obstruction , 2012, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.

[57]  Arthur S Slutsky,et al.  Acute Respiratory Distress Syndrome The Berlin Definition , 2012 .

[58]  S. Ryter,et al.  Inflammasome-regulated cytokines are critical mediators of acute lung injury. , 2012, American journal of respiratory and critical care medicine.

[59]  Susumu Goto,et al.  KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..

[60]  F. Sánchez‐Madrid,et al.  Adhesion Molecules in Inflammatory Diseases , 1998, Drugs.

[61]  Garret A. FitzGerald,et al.  Prostaglandins and Inflammation , 2011, Arteriosclerosis, thrombosis, and vascular biology.

[62]  Fabian J. Theis,et al.  Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data , 2011, BMC Systems Biology.

[63]  L. Sorokin The impact of the extracellular matrix on inflammation , 2010, Nature Reviews Immunology.

[64]  H. Senn,et al.  Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. , 2006, Analytical chemistry.

[65]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[66]  C. Baird,et al.  The pilot study. , 2000, Orthopedic nursing.

[67]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .