Slow protein fluctuations explain the emergence of growth phenotypes and persistence in clonal bacterial populations

One of the most challenging problems in microbiology is to understand how a small fraction of microbes that resists killing by antibiotics can emerge in a population of genetically identical cells, the phenomenon known as persistence or drug tolerance. Its characteristic signature is the biphasic kill curve, whereby microbes exposed to a bactericidal agent are initially killed very rapidly but then much more slowly. Here we relate this problem to the more general problem of understanding the emergence of distinct growth phenotypes in clonal populations. We address the problem mathematically by adopting the framework of the phenomenon of so-called weak ergodicity breaking, well known in dynamical physical systems, which we extend to the biological context. We show analytically and by direct stochastic simulations that distinct growth phenotypes can emerge as a consequence of slow-down of stochastic fluctuations in the expression of a gene controlling growth rate. In the regime of fast gene transcription, the system is ergodic, the growth rate distribution is unimodal, and accounts for one phenotype only. In contrast, at slow transcription and fast translation, weakly non-ergodic components emerge, the population distribution of growth rates becomes bimodal, and two distinct growth phenotypes are identified. When coupled to the well-established growth rate dependence of antibiotic killing, this model describes the observed fast and slow killing phases, and reproduces much of the phenomenology of bacterial persistence. The model has major implications for efforts to develop control strategies for persistent infections.Comment: 26 pages, 7 figure

ClgR regulation of chaperone and protease systems is essential for Mycobacterium tuberculosis parasitism of the macrophage

Chaperone and protease systems play essential roles in cellular homeostasis and have vital functions in controlling the abundance of specific cellular proteins involved in processes such as transcription, replication, metabolism and virulence. Bacteria have evolved accurate regulatory systems to control the expression and function of chaperones and potentially destructive proteases. Here, we have used a combination of transcriptomics, proteomics and targeted mutagenesis to reveal that the clp gene regulator (ClgR) of Mycobacterium tuberculosis activates the transcription of at least ten genes, including four that encode protease systems (ClpP1/C, ClpP2/C, PtrB and HtrA-like protease Rv1043c) and three that encode chaperones (Acr2, ClpB and the chaperonin Rv3269). Thus, M. tuberculosis ClgR controls a larger network of protein homeostatic and regulatory systems than ClgR in any other bacterium studied to date. We demonstrate that ClgR-regulated transcriptional activation of these systems is essential for M. tuberculosis to replicate in macrophages. Furthermore, we observe that this defect is manifest early in infection, as M. tuberculosis lacking ClgR is deficient in the ability to control phagosome pH 1 h post-phagocytosis

Interrogation of global mutagenesis data with a genome scale model of Neisseria meningitidis to assess gene fitness in vitro and in sera

BACKGROUND: Neisseria meningitidis is an important human commensal and pathogen that causes several thousand deaths each year, mostly in young children. How the pathogen replicates and causes disease in the host is largely unknown, particularly the role of metabolism in colonization and disease. Completed genome sequences are available for several strains but our understanding of how these data relate to phenotype remains limited. RESULTS: To investigate the metabolism of N. meningitidis we generated and selected a representative Tn5 library on rich medium, a minimal defined medium and in human serum to identify genes essential for growth under these conditions. To relate these data to a systems-wide understanding of the pathogen's biology we constructed a genome-scale metabolic network: Nmb_iTM560. This model was able to distinguish essential and non-essential genes as predicted by the global mutagenesis. These essentiality data, the library and the Nmb_iTM560 model are powerful and widely applicable resources for the study of meningococcal metabolism and physiology. We demonstrate the utility of these resources by predicting and demonstrating metabolic requirements on minimal medium such as a requirement for PEP carboxylase, and by describing the nutritional and biochemical status of N. meningitidis when grown in serum, including a requirement for both the synthesis and transport of amino acids. CONCLUSIONS: This study describes the application of a genome scale transposon library combined with an experimentally validated genome-scale metabolic network of N. meningitidis to identify essential genes and provide novel insight to the pathogen's metabolism both in vitro and during infection

Modeling and simulation of the main metabolism in Escherichia coli and its several single-gene knockout mutants with experimental verification

Peer reviewedPublisher PD

Isoenergetic penta- and hexanucleotide microarray probing and chemical mapping provide a secondary structure model for an RNA element orchestrating R2 retrotransposon protein function

LNA (locked nucleic acids, i.e. oligonucleotides with a methyl bridge between the 2′ oxygen and 4′ carbon of ribose) and 2,6-diaminopurine were incorporated into 2′-O-methyl RNA pentamer and hexamer probes to make a microarray that binds unpaired RNA approximately isoenergetically. That is, binding is roughly independent of target sequence if target is unfolded. The isoenergetic binding and short probe length simplify interpretation of binding to a structured RNA to provide insight into target RNA secondary structure. Microarray binding and chemical mapping were used to probe the secondary structure of a 323 nt segment of the 5′ coding region of the R2 retrotransposon from Bombyx mori (R2Bm 5′ RNA). This R2Bm 5′ RNA orchestrates functioning of the R2 protein responsible for cleaving the second strand of DNA during insertion of the R2 sequence into the genome. The experimental results were used as constraints in a free energy minimization algorithm to provide an initial model for the secondary structure of the R2Bm 5′ RNA

The Genetic Requirements for Fast and Slow Growth in Mycobacteria

Mycobacterium tuberculosis infects a third of the world's population. Primary tuberculosis involving active fast bacterial replication is often followed by asymptomatic latent tuberculosis, which is characterised by slow or non-replicating bacteria. Reactivation of the latent infection involving a switch back to active bacterial replication can lead to post-primary transmissible tuberculosis. Mycobacterial mechanisms involved in slow growth or switching growth rate provide rational targets for the development of new drugs against persistent mycobacterial infection. Using chemostat culture to control growth rate, we screened a transposon mutant library by Transposon site hybridization (TraSH) selection to define the genetic requirements for slow and fast growth of Mycobacterium bovis (BCG) and for the requirements of switching growth rate. We identified 84 genes that are exclusively required for slow growth (69 hours doubling time) and 256 genes required for switching from slow to fast growth. To validate these findings we performed experiments using individual M. tuberculosis and M. bovis BCG knock out mutants. We have demonstrated that growth rate control is a carefully orchestrated process which requires a distinct set of genes encoding several virulence determinants, gene regulators, and metabolic enzymes. The mce1 locus appears to be a component of the switch to slow growth rate, which is consistent with the proposed role in virulence of M. tuberculosis. These results suggest novel perspectives for unravelling the mechanisms involved in the switch between acute and persistent TB infections and provide a means to study aspects of this important phenomenon in vitro

Electrochemical evidence that pyranopterin redox chemistry controls the catalysis of YedY, a mononuclear Mo enzyme

A long-standing contradiction in the field of mononuclear Mo enzyme research is that small-molecule chemistry on active-site mimic compounds predicts ligand participation in the electron transfer reactions, but biochemical measurements only suggest metal-centered catalytic electron transfer. With the simultaneous measurement of substrate turnover and reversible electron transfer that is provided by Fourier-transformed alternating-current voltammetry, we show that Escherichia coli YedY is a mononuclear Mo enzyme that reconciles this conflict. In YedY, addition of three protons and three electrons to the well-characterized "as-isolated" Mo(V) oxidation state is needed to initiate the catalytic reduction of either dimethyl sulfoxide or trimethylamine N-oxide. Based on comparison with earlier studies and our UV-vis redox titration data, we assign the reversible one-proton and one-electron reduction process centered around +174 mV vs. standard hydrogen electrode at pH 7 to a Mo(V)-to-Mo(IV) conversion but ascribe the two-proton and two-electron transition occurring at negative potential to the organic pyranopterin ligand system. We predict that a dihydro-to-tetrahydro transition is needed to generate the catalytically active state of the enzyme. This is a previously unidentified mechanism, suggested by the structural simplicity of YedY, a protein in which Mo is the only metal site

Strong negative self regulation of Prokaryotic transcription factors increases the intrinsic noise of protein expression

Background Many prokaryotic transcription factors repress their own transcription. It is often asserted that such regulation enables a cell to homeostatically maintain protein abundance. We explore the role of negative self regulation of transcription in regulating the variability of protein abundance using a variety of stochastic modeling techniques. Results We undertake a novel analysis of a classic model for negative self regulation. We demonstrate that, with standard approximations, protein variance relative to its mean should be independent of repressor strength in a physiological range. Consequently, in that range, the coefficient of variation would increase with repressor strength. However, stochastic computer simulations demonstrate that there is a greater increase in noise associated with strong repressors than predicted by theory. The discrepancies between the mathematical analysis and computer simulations arise because with strong repressors the approximation that leads to Michaelis-Menten-like hyperbolic repression terms ceases to be valid. Because we observe that strong negative feedback increases variability and so is unlikely to be a mechanism for noise control, we suggest instead that negative feedback is evolutionarily favoured because it allows the cell to minimize mRNA usage. To test this, we used in silico evolution to demonstrate that while negative feedback can achieve only a modest improvement in protein noise reduction compared with the unregulated system, it can achieve good improvement in protein response times and very substantial improvement in reducing mRNA levels. Conclusions Strong negative self regulation of transcription may not always be a mechanism for homeostatic control of protein abundance, but instead might be evolutionarily favoured as a mechanism to limit the use of mRNA. The use of hyperbolic terms derived from quasi-steady-state approximation should also be avoided in the analysis of stochastic models with strong repressors