The E. coli molecular phenotype under different growth conditions

Abstract

Modern systems biology requires extensive, carefully curated measurements of cellular components in response to different environmental conditions. While high-throughput methods have made transcriptomics and proteomics datasets widely accessible and relatively economical to generate, systematic measurements of both mRNA and protein abundances under a wide range of different conditions are still relatively rare. Here we present a detailed, genome-wide transcriptomics and proteomics dataset of E. coli grown under 34 different conditions. Additionally, we provide measurements of doubling times and in-vivo metabolic fluxes through the central carbon metabolism. We manipulate concentrations of sodium and magnesium in the growth media, and we consider four different carbon sources glucose, gluconate, lactate, and glycerol. Moreover, samples are taken both in exponential and stationary phase, and we include two extensive time-courses, with multiple samples taken between 3 hours and 2 weeks. We find that exponential-phase samples systematically differ from stationary-phase samples, in particular at the level of mRNA. Regulatory responses to different carbon sources or salt stresses are more moderate, but we find numerous differentially expressed genes for growth on gluconate and under salt and magnesium stress. Our data set provides a rich resource for future computational modeling of E. coli gene regulation, transcription, and translation.

Predicting bacterial growth conditions from mRNA and protein abundances

“…coli (strain REL606) mRNA and protein abundances, measured under 34 different conditions [35,36]. This dataset consists of a total of 155 samples, for which mRNA abundances are available for 152 and protein abundances for 105 (Fig 1).…”

“…For the remainder of this work (unless otherwise noted) we use the term “growth condition” to refer to the four-dimensional vector of categorical variables defining: i) growth phase (exponential, stationary, late stationary), ii) carbon source (glucose, glycerol, gluconate, lactate), iii) Mg 2+ concentration (low, base, high), and iv) Na + concentration (base, high). While we note that growth phase is not strictly an environmental feature, we suspected that this indicator of cellular state would be an important feature to consider since prior research has shown that the macromolecular composition of cells varies substantially between exponentially growing and stationary phase cells [35,36]. With these data and features, the question we set out to answer is: to what extent are machine learning models capable of discriminating between the known growth parameters given only knowledge of gene expression levels?…”

“…coli samples previously described [35,36]. Throughout this study, we used different subsets of these samples in different parts of the analysis.…”

“…Our study leverages a large dataset of transcriptomic and proteomic measurements of E . coli growth under multiple distinct but closely-related conditions [35]. We use mRNA and protein composition data to train several distinct machine learning models and find that highly similar environmental conditions can be discriminated with a high degree of accuracy.…”

Influence of cultivation conditions on the proteomic profile of Mycobacterium tuberculosis H37RV

“…Принимая во внимание, что состав питательной среды может влиять не только на качественный состав белков, но и на их количественную представленность [3,27,28], нами проведен сравнительный количественный анализ 2000 белков микобактерий, культивируемых на разных питательных средах. При этом достоверные различия были получены только для одного белка, фумаратредуктазы (Rv1553/FrdB) (p<0,05).…”