Multiscale Embedded Correlation Networks to Uncover Pathologically Relevant Lipid Modules
Co-regulated genes often display similar patterns of gene expression, which translates to strong correlations between their gene expression levels (Williams, 2015). Under the same analogy, strong correlations between lipid levels can imply that these lipids lie along a common metabolic pathway and are co-regulated, and changing correlation patterns between lipid-pairs in disease compared to healthy states can potentially indicate pathologically relevant metabolic dysregulation. We had previously shown in a cohort of antecedent diabetes that such a systems approach to interrogate lipidomics data based on differential correlations can sieve out pathway aberrations even before actual changes in metabolite levels set in (Lu et al., 2019). Thus, in order to decipher lipid pathway dysregulation at early stages of infection, we then looked for pathologically relevant lipid modules in mild COVID-19 relative to healthy controls using MEGENA R to construct networks from differentially correlated lipid pairs calculated via the R package DGCA. Only differential correlations with empirical p < 0.05 were displayed (Figure 4 ). Four notable modules in the global network were circled and enlarged for emphasized discussion.
Figure 4 Differential Correlation Analyses of Plasma Lipids in Mild COVID-19 Relative to Healthy Controls
Multiscale embedded correlation network analysis illustrates the differential correlation of lipids in mild COVID-19 relative to healthy controls to reveal changes in lipid metabolic pathways upon early stage of viral infection. Only lipid pairs with significant differential correlations (empirical p < 0.05) were included. Sign/sign indicates direction and strength of correlation in control/mild COVID-19, and number that follows indicates number of lipid pairs in the global networks exhibiting this pattern of change. For instance, red line +/++ 1 in the upper legend of the global networks indicates that correlation between two connected lipid pairs was positive (+) in controls, and the correlation became even more strongly positive (++) in mild COVID-19 patients, as defined by statistically significant (p < 0.05) increase in correlation coefficients between the lipid pair across the two conditions. A total of 1 lipid pair connected by red lines in the global network displayed this pattern of change (+/++). Blue line +/− : positive in controls → negative in mild COVID-19. Teal line +/0: positive in controls → insignificant in mild COVID-19. Gold line ++/+: strongly positive in controls → weaker positive in mild COVID-19. Purple line 0/−: insignificant correlation in controls → negative correlation in mild COVID-19. Gray line 0/+: insignificant correlation in controls → positive correlation in mild COVID-19. Four modules (I–IV) of biological interest were circled and expanded for better visual clarity. (I) Module with hub PS 34:1 connected to numerous PEs by teal lines, indicating PS-PE positive correlations in healthy controls were lost in mild COVID-19. (II) Module with hub BMP 38:5(18:1/20:4) connected to CEs by blue lines, indicating BMP-CE correlations became negative in mild COVID-19. (III) Module with GM3 d18:0/25:0 as hub connected to several PSs by blue and purple lines, indicating GM3-PS correlations became negative in mild COVID-19. (IV) Module with LysoPC 16:1 as the hub connected to numerous PUFA-PEs by blue lines, indicating lysoPC-PUFA-PE correlations changed from positive in healthy controls to negative in mild COVID-19. PS, phosphatidylserines; PE, phosphatidylethanolamines; BMP, bis(monoacylglycero)phosphates; CE, cholesteryl esters; GM3, monosiaolodihexosyl gangliosides; PUFA-PE, polyunsaturated PEs.