Genome Scale-Differential Flux Analysis reveals deregulation of lung cell metabolism on SARS-CoV-2 infection

The COVID-19 pandemic is posing an unprecedented threat to the whole world. In this regard, it is absolutely imperative to understand the mechanism of metabolic reprogramming of host human cells by SARS-CoV-2. A better understanding of the metabolic alterations would aid in design of better therapeutics to deal with COVID-19 pandemic. We developed an integrated genome-scale metabolic model of normal human bronchial epithelial cells (NHBE) infected with SARS-CoV-2 using gene-expression and macromolecular make-up of the virus. The reconstructed model predicts growth rates of the virus in high agreement with the experimental measured values. Furthermore, we report a method for conducting genome-scale differential flux analysis (GS-DFA) in context-specific metabolic models. We apply the method to the context-specific model and identify severely affected metabolic modules predominantly comprising of lipid metabolism. We conduct an integrated analysis of the flux-altered reactions, host-virus protein-protein interaction network and phospho-proteomics data to understand the mechanism of flux alteration in host cells. We show that several enzymes driving the altered reactions inferred by our method to be directly interacting with viral proteins and also undergoing differential phosphorylation under diseased state. In case of SARS-CoV-2 infection, lipid metabolism particularly fatty acid oxidation, cholesterol biosynthesis and beta-oxidation cycle along with arachidonic acid metabolism are predicted to be most affected which confirms with clinical metabolomics studies. GS-DFA can be applied to existing repertoire of high-throughput proteomic or transcriptomic data in diseased condition to understand metabolic deregulation at the level of flux.


Supplementary Information Text Extended Methods:
Estimating spike protein stoichiometry from electron microscopy images The calculation of absolute protein numbers for SARS Cov2 is entirely based on the number of spike protein subunits on the virus. In recent discussions on the chemical composition of the virus(1), the spike protein subunit counts on SARS Cov2 has been assumed to be the same as that of SARS Cov. While this doesn't entirely defy logic, it would be imperative to validate the protein counts per virus in the SARS Cov2. For this purpose, we leveraged the electron micrographs of the virus taken by various research organizations. We used a combination of image analysis and mathematical modeling to derive the number of spike protein per virus.
Briefly, the electron micrographs were converted to 16 bit images. It could be observed that in several electron micrographs, the spike protein appear as the protrusions around the virus. We measured the intensities of multiple-points along the circumference of the virus. The number of spike proteins will be roughly equal to the number of positions along the circumference where we see high intensity values. We used ImageJ (FIJI) to estimate the intensity along the circumference of the virus at roughly uniformly placed points in order. It is to be noted that each intact spike protein comprises of 3 subunits. The plot between the intensity of various points (y-axis) and the angular position (x-axis) would give us intensity distribution at various angular positions. The number of peaks (corresponding to high intensity spots) would be roughly equal to the number of spike proteins (trimers) on the surface. We used custom codes written in MATLAB and leveraged 'findpeaks' functions to get the number of such peaks. This gave us the distribution of spike proteins (spike protein counts per circumference) in 2 dimensional cross section of the virus.
In order to estimate the distribution of spike proteins in the 3D surface of the virus, we assumed a uniform distribution of the spike protein on the surface. The following derivation was used to calculate the spike protein distribution (spike protein count per virus) on the surface: Total number of spike protein on the elemental disk = dn = Spike proteins along the circumference of the disk x Spike protein along the width of the disk The integration of dn while x increases from 0 to R will give us the distribution of spike proteins in one hemisphere of the circle.
Therefore, Let N total = Total number of spike protein on the surface Figure S1 shows the number of spike proteins calculated from the electron micrographs (N). The average N i.e. <N> ~ 20. Substituting that in (i), we get !"!#$ ~ 100. Note that !"!#$ is the total number of spike trimers, so the total number of spike subunits on the surface is ~300 which is the same reported for SARS Cov in recent discussions.
Hence, this analysis provides direct evidence of spike protein counts on the surface of the virus and validates the assumption. We can therefore use this estimated protein count for generation of biomass objective function/biomass equation for the SARS Cov2 virus.