An Innovative Approach for The Integration of Proteomics and Metabolomics Data In Severe Septic Shock Patients Stratified for Mortality

Abstract

In this work, we examined plasma metabolome, proteome and clinical features in patients with severe septic shock enrolled in the multicenter ALBIOS study. The objective was to identify changes in the levels of metabolites involved in septic shock progression and to integrate this information with the variation occurring in proteins and clinical data. Mass spectrometry-based targeted metabolomics and untargeted proteomics allowed us to quantify absolute metabolites concentration and relative proteins abundance. We computed the ratio D7/D1 to take into account their variation from day 1 (D1) to day 7 (D7) after shock diagnosis. Patients were divided into two groups according to 28-day mortality. Three different elastic net logistic regression models were built: one on metabolites only, one on metabolites and proteins and one to integrate metabolomics and proteomics data with clinical parameters. Linear discriminant analysis and Partial least squares Discriminant Analysis were also implemented. All the obtained models correctly classified the observations in the testing set. By looking at the variable importance (VIP) and the selected features, the integration of metabolomics with proteomics data showed the importance of circulating lipids and coagulation cascade in septic shock progression, thus capturing a further layer of biological information complementary to metabolomics information.

Machine Learning Assisted Prediction of Prognostic Biomarkers Associated With COVID-19, Using Clinical and Proteomics Data

“…Artificial intelligence (AI) is being employed to meet new healthcare requirements, in view of the pandemic, for example, tracking the SARS-CoV-2 virus spread and quickly identifying high-risk patients (Sharma et al, 2020). Machine learning (ML) methods have been exploited to analyze various kinds of biological datasets such as proteomics data, NGS data, and metabolomics data to predict the biomarkers for classification of samples and genes associated with a particular disease state (Dumancas et al, 2017;Cambiaghi et al, 2018). The mitigation potential of AI technology has been extensively demonstrated for 1 https://covid19.who.int/ various pandemics and infectious diseases, for example, SARS, Ebola, HIV, and COVID-19 (Lalmuanawma et al, 2020;Overmyer et al, 2020).…”

Integrated Proteomics and Metabolomic Analyses of Plasma Injury Biomarkers in a Serious Brain Trauma Model in Rats

“…Thus, integration of multi “omics” analysis may help to acquire a more complete picture of the changes at the molecular level involved in diseases, provide a more precise understanding of entire biological mechanisms and may offer some novel and reliable biomarkers. Among these, the combination of metabolomic and proteomic analysis is regarded as a powerful tool and frequently applied in biomarker discovery and pathophysiological research, such as multiple sclerosis [ 10 ], severe septic shock [ 13 ], and anaplastic large cell lymphoma [ 14 ].…”

Low Neuroactive Steroids Identifies a Biological Subtype of Depression in Adults with Human Immunodeficiency Virus on Suppressive Antiretroviral Therapy

“…Additional statistical tools are described in the Supplementary Methods. Correction for multiple hypotheses testing was performed by calculating the false discovery rate (FDR), and a threshold was set at P < .05 and FDR < 0.15 [ 35 , 36 ].…”