Cell responses only partially shape cell-to-cell variations in protein abundances in Escherichia coli chemotaxis

TL;DR

This study uses a maximum entropy approach to explore how cell-to-cell protein abundance variations in E. coli chemotaxis are shaped by chemotactic function and other cellular processes, revealing that chemotaxis alone does not fully determine these variations.

Contribution

The paper introduces a MaxEnt-based method to analyze constraints on protein abundance variations imposed by cellular functions, applied here to E. coli chemotaxis.

Findings

Cell-to-cell protein variation is partly determined by chemotaxis.

Other cellular processes also influence protein abundance variations.

MaxEnt effectively uncovers relationships between cell form and function.

Abstract

Cell-to-cell variations in protein abundance in clonal cell populations are ubiquitous in living systems. Since protein composition determines responses in individual cells, it stands to reason that the variations themselves are subject to selective pressures. But the functional role of these cell-to-cell differences is not well understood. One way to tackle questions regarding relationships between form and function is to perturb the form (e.g., change the protein abundances) and observe the resulting changes in some function. Here we take on the form-function relationship from the inverse perspective, asking instead what specific constraints on cell-to-cell variations in protein abundance are imposed by a given functional phenotype. We develop a maximum entropy (MaxEnt) based approach to posing questions of this type, and illustrate the method by application to the well characterized chemotactic response in Escherichia coli (E. coli). We find that full determination of observed cell-to-cell variations in protein abundances is not inherent in chemotaxis itself, but in fact appears to be jointly imposed by the chemotaxis program in conjunction with other factors, e.g., the protein synthesis machinery and/or additional non-chemotactic cell functions such as cell metabolism. These results illustrate the power of MaxEnt as a tool for the investigation of relationships between biological form and function.