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 publicly available 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.

Figure 1

Systemic drivers of COVID-19 associated inflammation. COVID-19 begins as a respiratory tract infection that targets the lung epithelium, but in many severe cases, as the disease progresses, clinical manifestations can span the entire body as a result of systemic inflammation. This includes multisystem abnormalities in biological processes and metabolic functions that may exacerbate the inflammatory response observed in severe cases. An integrated analysis of proteomics and metabolomics data collected from a cohort presenting a range of COVID-19-related health outcomes led to the identification of potential biomarkers within the lungs, liver, gastrointestinal tract, kidneys, and peripheral blood. This analysis provides a deeper resolution of the possible molecular determinants of COVID-19-associated inflammation that are worthy of further investigation. This figure was created with BioRender.com.

Figure 2

Distribution of study participants by age and health outcome. (a) colors in all subplots reflect health status of groups as provided in the legend of subplot a. The sample population is not uniform for age ranges, with a higher proportion of older participants falling into the severe group. (b) age has a strong association with health outcomes (p-value = 0.001). (c) BMI is not associated with health outcomes (p-value = 0.1488). For assessing the association of age and gender with the health outcome status, we performed the Kruskal–Wallis test. (d) time between disease onset and sample collection for metabolites varies among health status. (e) metabolite and protein samples have been collected at different times within individuals and for groups with different health statuses. (f) ordination plot using proteins and (g) ordination plot using metabolite profiles reveal overall structure among individuals colored by health status. However, the signal is stronger using metabolite profiles measured by omeClust enrichment score (metabolite enrichment score = 0.26 and protein enrichment score = 0.08) (SFig. 1).

Figure 3

metabolite changes in COVID-19. (a) 20 most significant metabolites with lowest q-value (FDR) in comparison of severe group vs. healthy group are shown. Then, the corresponding changes in non-severe and non-COVID-19 for the same metabolites are shown. (b,c,d,e) show different patterns we observed among these associations. For example, Cytosine has a higher level in COVID-19 groups vs. non-COVID-19 and has been shown that it can play a biomarker for COVID-19 diagnostics.

Figure 4

protein changes in COVID-19. (a) 20 most significant metabolites with lowest q-value (FDR) in comparison of severe group vs. healthy group are shown. Then, we show the corresponding changes in non-severe and non-COVID-19. (b,c,d,e) show different patterns we observed among these associations.

Figure 5

Enrichment pathway between severe group and healthy group. Pathway enrichment analysis was performed for metabolite profiles and protein profiles separately. Each metabolite (protein) assigned a rank based on coefficient from testing severe group vs. healthy group using generalized linear models. We applied our omePath tool with a Wilcoxon signed rank test and HMDB database as the reference for metabolite pathways and the Reactome pathway database with Physical Entity (PE) class for Uniprot to all levels of the pathway hierarchy mapping file. (a) Regulation of Complement cascade pathway using protein data was significantly enriched in the COVID-19 positive patients, with non-severe health outcome compared to the healthy group. (b) Purine Metabolism pathway using metabolite data was significantly enriched in COVID-19 positive patients with the severe health outcome compared to the healthy group. (c) Keratinization pathway using protein data was significantly enriched in COVID-19 positive patients with the non-sever health outcome compared to the healthy group.

Figure 6

Precision, Recall, and F-1 Score for various ML techniques used for the various groups. The precision of DNN outperforms other methods since it can detect severe-COVID-19 better than the rest of the methods. We also see the same trend in recall since it correctly detects the severe-COVID-19 and COVID-19 with a higher degree of certainty. A model with a low false positive case is better when used for prediction.