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  • 1
    Language: English
    In: Proceedings of the National Academy of Sciences of the United States of America, 15 October 2013, Vol.110(42), pp.16754-9
    Description: Bacterial growth is crucially dependent on protein synthesis and thus on the cellular abundance of ribosomes and related proteins. Here, we show that the slow diffusion of the bulky tRNA complexes in the crowded cytoplasm imposes a physical limit on the speed of translation, which ultimately limits the rate of cell growth. To study the required allocation of ancillary translational proteins to alleviate the effect of molecular crowding, we develop a model for cell growth based on a coarse-grained partitioning of the proteome. We find that coregulation of ribosome- and tRNA-affiliated proteins is consistent with measured growth-rate dependencies and results in near-optimal allocation over a broad range of growth rates. The analysis further resolves a long-standing controversy in bacterial growth physiology concerning the growth-rate dependence of translation speed and serves as a caution against premature identification of phenomenological parameters with mechanistic processes.
    Keywords: Models, Biological ; Escherichia Coli -- Growth & Development ; Escherichia Coli Proteins -- Biosynthesis ; Protein Biosynthesis -- Physiology ; Proteome -- Biosynthesis ; RNA, Bacterial -- Metabolism ; RNA, Transfer -- Metabolism
    ISSN: 00278424
    E-ISSN: 1091-6490
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  • 2
    Language: English
    In: Proceedings of the National Academy of Sciences of the United States of America, 03 July 2012, Vol.109(27), pp.10775-80
    Description: The rapid emergence of bacterial strains resistant to multiple antibiotics is posing a growing public health risk. The mechanisms underlying the rapid evolution of drug resistance are, however, poorly understood. The heterogeneity of the environments in which bacteria encounter antibiotic drugs could play an important role. E.g., in the highly compartmentalized human body, drug levels can vary substantially between different organs and tissues. It has been proposed that this could facilitate the selection of resistant mutants, and recent experiments support this. To study the role of spatial heterogeneity in the evolution of drug resistance, we present a quantitative model describing an environment subdivided into relatively isolated compartments with various antibiotic concentrations, in which bacteria evolve under the stochastic processes of proliferation, migration, mutation and death. Analytical and numerical results demonstrate that concentration gradients can foster a mode of adaptation that is impossible in uniform environments. It allows resistant mutants to evade competition and circumvent the slow process of fixation by invading compartments with higher drug concentrations, where less resistant strains cannot subsist. The speed of this process increases sharply with the sensitivity of the growth rate to the antibiotic concentration, which we argue to be generic. Comparable adaptation rates in uniform environments would require a high selection coefficient (s 〉 0.1) for each forward mutation. Similar processes can occur if the heterogeneity is more complex than just a linear gradient. The model may also be applicable to other adaptive processes involving environmental heterogeneity and range expansion.
    Keywords: Bacterial Infections ; Biological Evolution ; Models, Biological ; Anti-Bacterial Agents -- Pharmacokinetics ; Bacteria -- Drug Effects ; Drug Resistance, Bacterial -- Physiology
    ISSN: 00278424
    E-ISSN: 1091-6490
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  • 3
    Language: English
    In: Science, Nov 29, 2013, Vol.342(6162), p.1068(1)
    Description: Introduction: Understanding how bacteria harboring antibiotic resistance grow in the presence of antibiotics is critical for predicting the spread and evolution of drug resistance. Because drugs inhibit cell growth and a cell's growth state globally influences its gene expression, the expression of drug resistance is subject to an innate, growth-mediated feedback, leading to complex behaviors that affect both the characterization and the prevention of antibiotic resistance. We characterized the consequences of this feedback for the growth of antibiotic-resistant bacteria. Methods: We studied the growth of Escherichia coli strains expressing resistance to translation-inhibiting antibiotics, by using both bulk and single-cell techniques. The growth of each strain was quantified in a broad range of drug concentrations by using time-lapse microscopy (to track the responses of individual cells to antibiotics inside a microfluidic chemostat) and by the enrichment of batch cultures for nongrowing cells. We formulated a quantitative phenomenotogical model to predict the growth rates of drug-resistant strains in the presence of drugs, based on the weft-characterized biochemistry of drug and drug-resistance interactions and on bacterial growth laws that dictate relations between cell growth and gene expression. We tested the model predictions for various drugs and resistance mechanisms by constructing strains that constitutively express varying degrees of drug resistance. Results: In strains expressing a moderate degree of drug resistance, growth rates dropped abruptly above a critical drug concentration, the minimum inhibitory concentration (MIC), whose value increased linearly with the basal level of resistance expression (see figure below, panel A). Cells exhibited growth bistability over a broad range of drug concentrations below the MIC: Isogenic cells expressing drug resistance coexisted in growing and nongrowing states in a homogeneous environment (panel B). Our model accurately predicted the range of drug concentrations in which growth histability occurred, as well as the growth rates of the growing subpopulation, without any ad hoc fitting parameters. These findings reveal a plateau-like fitness landscape (panel A), which can be used to study the evolution of drug resistance in environments with varying drug concentrations. Discussion: The broad occurrence of growth bistability in drug-resistant bacteria challenges the common notions and measures of drug efficacy and resistance. And because growth bistability can arise without complex regulation when gene expression is coupled to the state of cell growth, similar physiological links may underlie the growth bistability implicated in causing bacterial persistence. The availability of quantitative, predictive models will facilitate the formulation of strategies to limit the efficacy and evolvability of drug resistance. DOI: 10.1126/science.1237435
    Keywords: Escherichia Coli -- Genetic Aspects ; Gene Expression -- Research ; Microbial Drug Resistance -- Research
    ISSN: 0036-8075
    Source: Cengage Learning, Inc.
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  • 4
    Language: English
    In: Science (New York, N.Y.), 29 November 2013, Vol.342(6162), pp.1237435
    Description: To predict the emergence of antibiotic resistance, quantitative relations must be established between the fitness of drug-resistant organisms and the molecular mechanisms conferring resistance. These relations are often unknown and may depend on the state of bacterial growth. To bridge this gap, we have investigated Escherichia coli strains expressing resistance to translation-inhibiting antibiotics. We show that resistance expression and drug inhibition are linked in a positive feedback loop arising from an innate, global effect of drug-inhibited growth on gene expression. A quantitative model of bacterial growth based on this innate feedback accurately predicts the rich phenomena observed: a plateau-shaped fitness landscape, with an abrupt drop in the growth rates of cultures at a threshold drug concentration, and the coexistence of growing and nongrowing populations, that is, growth bistability, below the threshold.
    Keywords: Adaptation, Physiological ; Drug Resistance, Bacterial ; Genetic Fitness ; Escherichia Coli -- Drug Effects ; Protein Synthesis Inhibitors -- Pharmacology
    ISSN: 00368075
    E-ISSN: 1095-9203
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  • 5
    Language: English
    In: Proceedings of the National Academy of Sciences of the United States of America, 20 June 2017, Vol.114(25), pp.6438-6443
    Description: The human gut harbors a dynamic microbial community whose composition bears great importance for the health of the host. Here, we investigate how colonic physiology impacts bacterial growth, which ultimately dictates microbiota composition. Combining measurements of bacterial physiology with analysis of published data on human physiology into a quantitative, comprehensive modeling framework, we show how water flow in the colon, in concert with other physiological factors, determine the abundances of the major bacterial phyla. Mechanistically, our model shows that local pH values in the lumen, which differentially affect the growth of different bacteria, drive changes in microbiota composition. It identifies key factors influencing the delicate regulation of colonic pH, including epithelial water absorption, nutrient inflow, and luminal buffering capacity, and generates testable predictions on their effects. Our findings show that a predictive and mechanistic understanding of microbial ecology in the gut is possible. Such predictive understanding is needed for the rational design of intervention strategies to actively control the microbiota.
    Keywords: Colon Physiology ; Colonic Ph ; Gut Microbiota ; Stool Consistency ; Water Absorption ; Bacteria -- Growth & Development ; Colon -- Microbiology ; Gastrointestinal Microbiome -- Physiology ; Microbiota -- Physiology
    ISSN: 00278424
    E-ISSN: 1091-6490
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  • 6
    Language: English
    In: Proceedings of the National Academy of Sciences of the United States of America, 2012, Vol.109(26), pp.10340-10345
    Description: We introduce a theoretical framework that exploits the ever-increasing genomic sequence information for protein structure prediction. Structure-based models are modified to incorporate constraints by a large number of non-local contacts estimated from direct coupling analysis (DCA) of co-evolving genomic sequences. A simple hybrid method, called DCA-fold, integrating DCA contacts with an accurate knowledge of local information (e.g., the local secondary structure) is sufficient to fold proteins in the range of 1–3 Å resolution. ; p. 10340-10345.
    Keywords: Models ; Protein Structure ; Nucleotide Sequences ; Prediction ; Proteins ; Methodology
    ISSN: 0027-8424
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  • 7
    In: Nature, 2015, Vol.528(7580), p.99
    Description: Overflow metabolism refers to the seemingly wasteful strategy in which cells use fermentation instead of the more efficient respiration to generate energy, despite the availability of oxygen. Known as the Warburg effect in the context of cancer growth, this phenomenon occurs ubiquitously for fast-growing cells, including bacteria, fungi and mammalian cells, but its origin has remained unclear despite decades of research. Here we study metabolic overflow in Escherichia coli, and show that it is a global physiological response used to cope with changing proteomic demands of energy biogenesis and biomass synthesis under different growth conditions. A simple model of proteomic resource allocation can quantitatively account for all of the observed behaviours, and accurately predict responses to new perturbations. The key hypothesis of the model, that the proteome cost of energy biogenesis by respiration exceeds that by fermentation, is quantitatively confirmed by direct measurement of protein abundances via quantitative mass spectrometry.
    Keywords: E Coli ; Metabolism ; Cells ; Bioenergetics ; Proteins;
    ISSN: 0028-0836
    E-ISSN: 14764687
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  • 8
    Language: English
    In: Science (New York, N.Y.), 19 November 2010, Vol.330(6007), pp.1099-102
    Description: In bacteria, the rate of cell proliferation and the level of gene expression are intimately intertwined. Elucidating these relations is important both for understanding the physiological functions of endogenous genetic circuits and for designing robust synthetic systems. We describe a phenomenological study that reveals intrinsic constraints governing the allocation of resources toward protein synthesis and other aspects of cell growth. A theory incorporating these constraints can accurately predict how cell proliferation and gene expression affect one another, quantitatively accounting for the effect of translation-inhibiting antibiotics on gene expression and the effect of gratuitous protein expression on cell growth. The use of such empirical relations, analogous to phenomenological laws, may facilitate our understanding and manipulation of complex biological systems before underlying regulatory circuits are elucidated.
    Keywords: Cell Proliferation ; Escherichia Coli K12 -- Genetics ; Gene Expression -- Physiology
    ISSN: 00368075
    E-ISSN: 1095-9203
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  • 9
    Language: English
    In: Proceedings of the National Academy of Sciences of the United States of America, 17 December 2013, Vol.110(51), pp.20533-8
    Description: A long-standing problem in molecular biology is the determination of a complete functional conformational landscape of proteins. This includes not only proteins' native structures, but also all their respective functional states, including functionally important intermediates. Here, we reveal a signature of functionally important states in several protein families, using direct coupling analysis, which detects residue pair coevolution of protein sequence composition. This signature is exploited in a protein structure-based model to uncover conformational diversity, including hidden functional configurations. We uncovered, with high resolution (mean ~1.9 Å rmsd for nonapo structures), different functional structural states for medium to large proteins (200-450 aa) belonging to several distinct families. The combination of direct coupling analysis and the structure-based model also predicts several intermediates or hidden states that are of functional importance. This enhanced sampling is broadly applicable and has direct implications in protein structure determination and the design of ligands or drugs to trap intermediate states.
    Keywords: Conformational Plasticity ; Covariation ; Molecular Dynamics ; Statistical Inference ; Models, Molecular ; Proteins -- Chemistry
    ISSN: 00278424
    E-ISSN: 1091-6490
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  • 10
    Language: English
    In: Proceedings of the National Academy of Sciences of the United States of America, 2011, Vol.108(30), pp.12473-12478
    Description: Sequence-function relations for small RNA (sRNA)-mediated gene silencing were quantified for the sRNA RyhB and some of its mRNA targets in Escherichia coli. Numerous mutants of RyhB and its targets were generated and their in vivo functions characterized at various levels of target and RyhB expression. Although a core complementary region is required for repression by RyhB, variations in the complementary sequences of the core region gave rise to a continuum of repression strengths, correlated exponentially with the computed free energy of RyhB-target duplex formation. Moreover, sequence variations in the linker region known to interact with the RNA chaperone Hfq also gave rise to a continuum of repression strengths, correlated exponentially with the computed energy cost of keeping the linker region open. These results support the applicability of the thermodynamic model in predicting sRNA-mRNA interaction and suggest that sequences at these locations may be used to fine-tune the degree of repression. Surprisingly, a truncated RyhB without the Hfq-binding region is found to repress multiple targets of the wild-type RyhB effectively, both in the presence and absence of Hfq, even though the former is required for the activity of wild-type RyhB itself. These findings challenge the commonly accepted model concerning the function of Hfq in gene silencing--both in providing stability to the sRNAs and in catalyzing the target mRNAs to take on active conformations--and raise the intriguing question of why many endogenous sRNAs subject their functions to Hfq-dependences. ; p. 12473-12478.
    Keywords: Models ; Messenger Rna ; Energy Costs ; Escherichia Coli ; Prediction ; Gene Silencing ; Energy ; Thermodynamics ; Mutants
    ISSN: 0027-8424
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