Metabolite, Protein, And Tissue Dysfunction Associated With COVID-19 Disease Severity

Abstract

Proteins are direct products of the genome and metabolites are functional products of interactions between the host and other factors such as environment, disease state, clinical information, etc. Omics data, including proteins and metabolites, are useful in characterizing biological processes underlying COVID-19 along with patient data and clinical information, yet few methods are available to effectively analyze such diverse and unstructured data. Using an integrated approach that combines proteomics and metabolomics data, we investigated the changes in metabolites and proteins in relation to patient characteristics (e.g., age, gender, and health outcome) and clinical information (e.g., metabolic panel and complete blood count test results). We found significant enrichment of biological indicators of lung, liver, and gastrointestinal dysfunction associated with disease severity using metabolite and protein profiles. Our analyses specifically identified enriched proteins that play a critical role in responses to injury or infection within these anatomical sites, but may contribute to excessive systemic inflammation within the context of COVID-19. Furthermore, we have used this information in conjunction with machine learning algorithms to predict the health status of patients presenting symptoms of COVID-19. This work provides a roadmap for understanding the biochemical pathways and molecular mechanisms that drive disease severity, progression, and treatment of COVID-19.

Hospital Antimicrobial Stewardship: Non-Bacterial Saliva Microbiome Emerges as a Biomarker of COVID-19 Severity

“…Several clinical features were found to be significantly different between critical and moderate groups, among them C-reactive protein (CRP) values being lower for the moderate group when compared to the critical group (Mann-Whitney U test, U = 562.5, p = 0.030), which has been confirmed previously using proteomics 10 . A similar trend was observed statistically for lactose dehydrogenase (LDH) being lower in the moderate group (Mann-Whitney U test, U = 470.5, p = 0.026), and the ratio of neutrophils to lymphocyte (NLR) also being lower in the moderate group (Mann-Whitney U test, U = 741.0, p = 0.037).…”

Untangling the link between the human gut microbiota composition and the severity of the symptoms of the COVID‐19 infection

“…This result is in accordance with the above taxonomical analysis and the increase of species related to putrefactive dysbiosis. Remarkably, several public studies highlighted the possible correlation between COVID‐19 disease severity and the alteration of systemic amino acid metabolism (Masoodi et al, 2022; Paez‐Franco et al, 2021; Rahnavard et al, 2022). In particular, the enrichment of specific amino acids, such as arginine and proline, could contribute to excessive systemic inflammation and a consequent increase in the severity of the disease (Rahnavard et al, 2022).…”

“…Remarkably, several public studies highlighted the possible correlation between COVID‐19 disease severity and the alteration of systemic amino acid metabolism (Masoodi et al, 2022; Paez‐Franco et al, 2021; Rahnavard et al, 2022). In particular, the enrichment of specific amino acids, such as arginine and proline, could contribute to excessive systemic inflammation and a consequent increase in the severity of the disease (Rahnavard et al, 2022). Therefore, the increased protein metabolic capability of the intestinal microbiome observed in this study could suggest the intestinal bacterial communities' contribution to systemic inflammation.…”

“…In fact, the reduction of these short-chain fatty acid-producing bacteria could increase oxidative stress and ROS production that affect the regulation of the intestinal barrier and contribute to the systemic inflammation (Wang et al, 2021). These observations were mainly obtained through the 16S rRNA gene sequencing analysis (Chen et al, 2022;Mazzarelli et al, 2021;Romani et al, 2022;Wang et al, 2021;Xu et al, 2021), whose main limit is to allow a taxonomic profiling of the microbial communities down to genus level while being inaccurate at the species level and preventing the reconstruction of functional impact of the microbiome.…”

Mass spectrometry and machine learning in the identification of COVID-19 biomarkers

“…However, the technique's potential has already been demonstrated. A study developed by Rahnavard et al (2022) obtained the proteomic and metabolomic profile of 28 patients with severe COVID-19 and compared it to a control group composed of 28 healthy patients, 25 non-COVID-19 patients with similar clinical symptoms and 25 non-severe COVID-19 patients. They evaluated the performance of three machine learning models -KNN (k-nearest neighbors), RF (random forest) and LR (linear regression) and a deep neural network (DNN) for COVID-19 prognosis.…”