Metabolic and Immune Markers for Precise Monitoring of COVID-19 Severity and Treatment
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Abstract
Deep understanding of the SARS-CoV-2 effects on host molecular pathways is paramount for the discovery of early biomarkers of outcome of coronavirus disease 2019 (COVID-19) and the identification of novel therapeutic targets. In that light, we generated metabolomic data from COVID-19 patient blood using high-throughput targeted nuclear magnetic resonance (NMR) spectroscopy and high-dimensional flow cytometry. We find considerable changes in serum metabolome composition of COVID-19 patients associated with disease severity, and response to tocilizumab treatment. We built a clinically annotated, biologically-interpretable space for precise time-resolved disease monitoring and characterize the temporal dynamics of metabolomic change along the clinical course of COVID-19 patients and in response to therapy. Finally, we leverage joint immuno-metabolic measurements to provide a novel approach for patient stratification and early prediction of severe disease. Our results show that high-dimensional metabolomic and joint immune-metabolic readouts provide rich information content for elucidation of the host’s response to infection and empower discovery of novel metabolic-driven therapies, as well as precise and efficient clinical action.
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SciScore for 10.1101/2021.09.05.21263141: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources We also compared the results of this method to the following methods for dimensionality reduction: principal component analysis (PCA), non-negative matrix factorization (NMF), multidimensional scaling (MDS), non-linear dimensionality reduction through isometric mapping (Isomap), t-distributed stochastic neighbor embedding68 (t-SNE), uniform manifold approximation and projection (UMAP), as implemented in scikit-learn, diffusion maps (DiffMap) as implemented in Scanpy66, and minimum-distortion embedding66,69 (MDE) from the PyMDE package. scikit-learnsuggested: (scikit-learn, RRID:SCR_002577)SciScore for 10.1101/2021.09.05.21263141: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources We also compared the results of this method to the following methods for dimensionality reduction: principal component analysis (PCA), non-negative matrix factorization (NMF), multidimensional scaling (MDS), non-linear dimensionality reduction through isometric mapping (Isomap), t-distributed stochastic neighbor embedding68 (t-SNE), uniform manifold approximation and projection (UMAP), as implemented in scikit-learn, diffusion maps (DiffMap) as implemented in Scanpy66, and minimum-distortion embedding66,69 (MDE) from the PyMDE package. scikit-learnsuggested: (scikit-learn, RRID:SCR_002577)PyMDEsuggested: NoneThen, we interpolated these values across the two-dimensional latent space (scipy.interpolate.griddata). scipysuggested: (SciPy, RRID:SCR_008058)To produce a joint embedding of metabolic and immune data for each patient timepoint, we employed regularized canonical correlation analysis71 (RCCA) in the Python implementation pyrcca. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We must nonetheless acknowledge the following limitations to our study: i) our cohort is relatively small especially in comparison with large repositories such as the UK biobank; ii) our cohort is also skewed to have more patients with longitudinal follow-up for patients with severe disease - this is at least in part due to the natural dynamic severe disease having a longer recovery period; iii) our analysis of the interaction between the immune system and metabolome is purely correlational, as we can’t infer causality between the presence or activity of an immune cell type with the abundance of a metabolite. It is plausible that cytokine modulation of key metabolic enzymes or energy usage by the immune system during acute infection are a major source of the metabolic changes associated with COVID-19 progression61. It has been shown for COVID-19 specifically that in T-cells cholesterol interacts with sphingolipids in membrane rafts in a manner that is dependent on the saturation state of the fatty acids29, and more generally that lipid raft formation has a crucial role in the cytotoxic activity of CD8 T-cells62–64. The increase in triglyceride composition of lipoprotein particles and their saturation state we observed with increased disease severity could result in altered immune function. Evidence for that has been seen in the regulation of immune checkpoint proteins such as CTLA4 in MDSCs by intracellular PUFA levels in cancer models65. Another example is the shift between ...
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