Linear Superposition and Prediction of Bacterial Promoter Activity Dynamics in Complex Conditions

Abstract

Bacteria often face complex environments. We asked how gene expression in complex conditions relates to expression in simpler conditions. To address this, we obtained accurate promoter activity dynamical measurements on 94 genes in E. coli in environments made up of all possible combinations of four nutrients and stresses. We find that the dynamics across conditions is well described by two principal component curves specific to each promoter. As a result, the promoter activity dynamics in a combination of conditions is a weighted average of the dynamics in each condition alone. The weights tend to sum up to approximately one. This weighted-average property, called linear superposition, allows predicting the promoter activity dynamics in a combination of conditions based on measurements of pairs of conditions. If these findings apply more generally, they can vastly reduce the number of experiments needed to understand how E. coli responds to the combinatorially huge space of possible environments.

Hierarchy of non-glucose sugars in Escherichia coli

“…Each measurement was done in at least two replicate wells, and repeated at least on two different days from freshly grown cells. Promoter activity was calculated as the rate of fluorescence change per OD unit as described [ 25 ]. Mean day-day relative errors of growth rate and promoter activity were 5% and 11% respectively (see Methods ).…”

Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions

“…In Escherichia coli, gene expression responses to combinations of two antibiotics, measured using fluorescent reporters for $100 genes, can be predicted by a linear or nonlinear interpolation of the responses to the individual drugs (Bollenbach and Kishony, 2011). Another study showed that even the temporal response of $100 promoters in E. coli to all possible combinations of four growth conditions could be predicted by linear superposition of temporal responses to individual conditions (Rothschild et al, 2014). Prediction of temporal expression dynamics during combination drug treatment from responses to individual drugs was also possible for 15 proteins in a human cancer cell line (Geva-Zatorsky et al, 2010).…”

Computing the functional proteome: recent progress and future prospects for genome-scale models

“…In several studies, the mass fractions of large protein subsystems and the activity of transcription factors have been shown to change linearly with growth rate or specific metabolic fluxes [66–69]. Furthermore, linear models covering several genes have been shown to capture the variation in their expression with relative accuracy [41,70]. The simplicity of these regulatory relationships (despite the complex topology and biophysical relationships [71] underlying regulatory networks) provides promise for accurate genome-scale regulatory models.…”