Modelling and Analysis of Central Metabolism Operating Regulatory Interactions in Salt Stress Conditions in a L-Carnitine Overproducing E. coli Strain

Based on experimental data from E. coli cultures, we have devised a mathematical model in the GMA-power law formalism that describes the central and L-carnitine metabolism in and between two steady states, non-osmotic and hyperosmotic (0.3 M NaCl). A key feature of this model is the introduction of type of kinetic order, the osmotic stress kinetic orders (gOSn), derived from the power law general formalism, which represent the effect of osmotic stress in each metabolic process of the model

Metabolomic and transcriptomic stress response of Escherichia coli

GC-MS-based analysis of the metabolic response of Escherichia coli exposed to four different stress conditions reveals reduction of energy expensive pathways.Time-resolved response of E. coli to changing environmental conditions is more specific on the metabolite as compared with the transcript level.Cease of growth during stress response as compared with stationary phase response invokes similar transcript but dissimilar metabolite responses.Condition-dependent associations between metabolites and transcripts are revealed applying co-clustering and canonical correlation analysis

Single-Cell Census of Mechanosensitive Channels in Living Bacteria

Bacteria are subjected to a host of different environmental stresses. One such insult occurs when cells encounter changes in the osmolarity of the surrounding media resulting in an osmotic shock. In recent years, a great deal has been learned about mechanosensitive (MS) channels which are thought to provide osmoprotection in these circumstances by opening emergency release valves in response to membrane tension. However, even the most elementary physiological parameters such as the number of MS channels per cell, how MS channel expression levels influence the physiological response of the cells, and how this mean number of channels varies from cell to cell remain unanswered. In this paper, we make a detailed quantitative study of the expression of the mechanosensitive channel of large conductance (MscL) in different media and at various stages in the growth history of bacterial cultures. Using both quantitative fluorescence microscopy and quantitative Western blots our study complements earlier electrophysiology-based estimates and results in the following key insights: i) the mean number of channels per cell is much higher than previously estimated, ii) measurement of the single-cell distributions of such channels reveals marked variability from cell to cell and iii) the mean number of channels varies under different environmental conditions. The regulation of MscL expression displays rich behaviors that depend strongly on culturing conditions and stress factors, which may give clues to the physiological role of MscL. The number of stress-induced MscL channels and the associated variability have far reaching implications for the in vivo response of the channels and for modeling of this response. As shown by numerous biophysical models, both the number of such channels and their variability can impact many physiological processes including osmoprotection, channel gating probability, and channel clustering

In vitro transcription profiling of the σS subunit of bacterial RNA polymerase: re-definition of the σS regulon and identification of σS-specific promoter sequence elements

Specific promoter recognition by bacterial RNA polymerase is mediated by σ subunits, which assemble with RNA polymerase core enzyme (E) during transcription initiation. However, σ70 (the housekeeping σ subunit) and σS (an alternative σ subunit mostly active during slow growth) recognize almost identical promoter sequences, thus raising the question of how promoter selectivity is achieved in the bacterial cell. To identify novel sequence determinants for selective promoter recognition, we performed run-off/microarray (ROMA) experiments with RNA polymerase saturated either with σ70 (Eσ70) or with σS (EσS) using the whole Escherichia coli genome as DNA template. We found that Eσ70, in the absence of any additional transcription factor, preferentially transcribes genes associated with fast growth (e.g. ribosomal operons). In contrast, EσS efficiently transcribes genes involved in stress responses, secondary metabolism as well as RNAs from intergenic regions with yet-unknown function. Promoter sequence comparison suggests that, in addition to different conservation of the −35 sequence and of the UP element, selective promoter recognition by either form of RNA polymerase can be affected by the A/T content in the −10/+1 region. Indeed, site-directed mutagenesis experiments confirmed that an A/T bias in the −10/+1 region could improve promoter recognition by EσS

Piecewise-Linear Models of Genetic Regulatory Networks: Theory and Example

International audienceThe experimental study of genetic regulatory networks has made tremendous progress in recent years resulting in a huge amount of data on the molecular interactions in model organisms. It is therefore not possible anymore to intuitively understand how the genes and interactions together influence the behavior of the system. In order to answer such questions, a rigorous modeling and analysis approach is necessary. In this chapter, we present a family of such models and analysis methods enabling us to better understand the dynam-ics of genetic regulatory networks. We apply such methods to the network that underlies the nutritional stress response of the bacterium E. coli. The functioning and development of living organisms is controlled by large and complex networks of genes, proteins, small molecules, and their interactions, so-called genetic regulatory networks. The study of these networks has recently taken a qualitative leap through the use of modern genomic techniques that allow for the simultaneous measurement of the expression levels of all genes of an organism. This has resulted in an ever growing description of the interactions in the studied genetic regulatory networks. However, it is necessary to go beyond the simple description of the interactions in order to understand the behavior of these networks and their relation with the actual functioning of the organism. Since the networks under study are usually very large, an intuitive approach for their understanding is out of ques-tion. In order to support this work, mathematical and computer tools are necessary: the unambiguous description of the phenomena that mathematical models provide allows for a detailed analysis of the behaviors at play, though they might not exactly represent the exact behavior of the networks. In this chapter, we will be mostly interested in the modeling of the genetic reg-ulatory networks by means of differential equations. This classical approach allows precise numerical predictions of deterministic dynamic properties of genetic regu-latory networks to be made. However, for most networks of biological interest the application of differential equations is far from straightforward. First, the biochemi-cal reaction mechanisms underlying the interactions are usually not or incompletel

Genetic Basis of Growth Adaptation of Escherichia coli after Deletion of pgi, a Major Metabolic Gene

Bacterial survival requires adaptation to different environmental perturbations such as exposure to antibiotics, changes in temperature or oxygen levels, DNA damage, and alternative nutrient sources. During adaptation, bacteria often develop beneficial mutations that confer increased fitness in the new environment. Adaptation to the loss of a major non-essential gene product that cripples growth, however, has not been studied at the whole-genome level. We investigated the ability of Escherichia coli K-12 MG1655 to overcome the loss of phosphoglucose isomerase (pgi) by adaptively evolving ten replicates of E. coli lacking pgi for 50 days in glucose M9 minimal medium and by characterizing endpoint clones through whole-genome re-sequencing and phenotype profiling. We found that 1) the growth rates for all ten endpoint clones increased approximately 3-fold over the 50-day period; 2) two to five mutations arose during adaptation, most frequently in the NADH/NADPH transhydrogenases udhA and pntAB and in the stress-associated sigma factor rpoS; and 3) despite similar growth rates, at least three distinct endpoint phenotypes developed as defined by different rates of acetate and formate secretion. These results demonstrate that E. coli can adapt to the loss of a major metabolic gene product with only a handful of mutations and that adaptation can result in multiple, alternative phenotypes

Group 3 sigma factor gene, sigJ, a key regulator of desiccation tolerance, regulates the synthesis of extracellular polysaccharide in cyanobacterium Anabaena sp. strain PCC 7120

The changes in the expression of sigma factor genes during dehydration in terrestrial Nostoc HK-01 and aquatic Anabaena PCC 7120 were determined. The expression of the sigJ gene in terrestrial Nostoc HK-01, which is homologous to sigJ (alr0277) in aquatic Anabaena PCC 7120, was significantly induced in the mid-stage of dehydration. We constructed a higher-expressing transformant of the sigJ gene (HE0277) in Anabaena PCC 7120, and the transformant acquired desiccation tolerance. The results of Anabaena oligonucleotide microarray experiments showed that a comparatively large number of genes relating to polysaccharide biosynthesis were upregulated in the HE0277 cells. The extracellular polysaccharide released into the culture medium of the HE0277 cells was as much as 3.2-fold more than that released by the control cells. This strongly suggests that the group 3 sigma factor gene sigJ is fundamental and conducive to desiccation tolerance in these cyanobacteria

Genome-Wide Screening of Genes Whose Enhanced Expression Affects Glycogen Accumulation in Escherichia coli

Using a systematic and comprehensive gene expression library (the ASKA library), we have carried out a genome-wide screening of the genes whose increased plasmid-directed expression affected glycogen metabolism in Escherichia coli. Of the 4123 clones of the collection, 28 displayed a glycogen-excess phenotype, whereas 58 displayed a glycogen-deficient phenotype. The genes whose enhanced expression affected glycogen accumulation were classified into various functional categories including carbon sensing, transport and metabolism, general stress and stringent responses, factors determining intercellular communication, aggregative and social behaviour, nitrogen metabolism and energy status. Noteworthy, one-third of them were genes about which little or nothing is known. We propose an integrated metabolic model wherein E. coli glycogen metabolism is highly interconnected with a wide variety of cellular processes and is tightly adjusted to the nutritional and energetic status of the cell. Furthermore, we provide clues about possible biological roles of genes of still unknown functions