System and transcript dynamics of cells infected with severe acute respiratory syndrome virus 2 (SARS-CoV-2)

Abstract

Statistical laws arise in many complex systems and can be explored to gain insights into their structure and behavior. Here, we investigate the dynamics of cells infected with severe acute respiratory syndrome virus 2 (SARS-CoV-2) at the system and individual gene levels; and demonstrate that the statistical frameworks used here are robust in spite of the technical noise associated with single-cell RNA sequencing (scRNA-seq) data. A biphasic fit to Taylor’s power law was observed, and it is likely associated with the larger sampling noise inherent to the measure of less expressed genes. The type of the distribution of the system, as assessed by Taylor’s parameters, varies along the course of infection in a cell type-dependent manner, but also sampling noise had a significant influence on Taylor’s parameters. At the individual gene level, we found that genes that displayed signals of punctual rank stability and/or long-range dependence behavior, as measured by Hurst exponents, were associated with translation, cellular respiration, apoptosis, protein-folding, virus processes, and immune response. Those genes were analyzed in the context of a protein-protein interaction network to find possible therapeutic targets.