1
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Wu L, Gao J, Zhang Y, Sui B, Wen Y, Wu Q, Liu K, He S, Bo X. A hybrid deep forest-based method for predicting synergistic drug combinations. CELL REPORTS METHODS 2023; 3:100411. [PMID: 36936075 PMCID: PMC10014304 DOI: 10.1016/j.crmeth.2023.100411] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/27/2022] [Accepted: 01/27/2023] [Indexed: 02/23/2023]
Abstract
Combination therapy is a promising approach in treating multiple complex diseases. However, the large search space of available drug combinations exacerbates challenge for experimental screening. To predict synergistic drug combinations in different cancer cell lines, we propose an improved deep forest-based method, ForSyn, and design two forest types embedded in ForSyn. ForSyn handles imbalanced and high-dimensional data in medium-/small-scale datasets, which are inherent characteristics of drug combination datasets. Compared with 12 state-of-the-art methods, ForSyn ranks first on four metrics for eight datasets with different feature combinations. We conduct a systematic analysis to identify the most appropriate configuration parameters. We validate the predictive value of ForSyn with cell-based experiments on several previously unexplored drug combinations. Finally, a systematic analysis of feature importance is performed on the top contributing features extracted by ForSyn. The resulting key genes may play key roles on corresponding cancers.
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Affiliation(s)
- Lianlian Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Jie Gao
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Yixin Zhang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Binsheng Sui
- School of Film, Xiamen University, Xiamen 361005, China
| | - Yuqi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Qingqiang Wu
- School of Film, Xiamen University, Xiamen 361005, China
| | - Kunhong Liu
- School of Film, Xiamen University, Xiamen 361005, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
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2
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Analysing and meta-analysing time-series data of microbial growth and gene expression from plate readers. PLoS Comput Biol 2022; 18:e1010138. [PMID: 35617352 PMCID: PMC9176753 DOI: 10.1371/journal.pcbi.1010138] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 06/08/2022] [Accepted: 04/26/2022] [Indexed: 11/29/2022] Open
Abstract
Responding to change is a fundamental property of life, making time-series data invaluable in biology. For microbes, plate readers are a popular, convenient means to measure growth and also gene expression using fluorescent reporters. Nevertheless, the difficulties of analysing the resulting data can be a bottleneck, particularly when combining measurements from different wells and plates. Here we present omniplate, a Python module that corrects and normalises plate-reader data, estimates growth rates and fluorescence per cell as functions of time, calculates errors, exports in different formats, and enables meta-analysis of multiple plates. The software corrects for autofluorescence, the optical density’s non-linear dependence on the number of cells, and the effects of the media. We use omniplate to measure the Monod relationship for the growth of budding yeast in raffinose, showing that raffinose is a convenient carbon source for controlling growth rates. Using fluorescent tagging, we study yeast’s glucose transport. Our results are consistent with the regulation of the hexose transporter (HXT) genes being approximately bipartite: the medium and high affinity transporters are predominately regulated by both the high affinity glucose sensor Snf3 and the kinase complex SNF1 via the repressors Mth1, Mig1, and Mig2; the low affinity transporters are predominately regulated by the low affinity sensor Rgt2 via the co-repressor Std1. We thus demonstrate that omniplate is a powerful tool for exploiting the advantages offered by time-series data in revealing biological regulation. Time series of growth and of gene expression via fluorescent reporters are rich ways to characterise the behaviours of cells. With plate readers, it is straightforward to measure 96 independent time series in a single experiment, with readings taken every 10 minutes and each time series lasting tens of hours. Analysing such data can become challenging, particularly if multiple plate-reader experiments are required to characterise a phenomenon, which then should be analysed simultaneously. Taking advantage of existing packages in Python, we have written code that automates this analysis but yet still allows users to develop custom routines. Our omniplate software corrects both measurements of optical density to become linear in the number of cells and measurements of fluorescence for autofluorescence. It estimates growth rates and fluorescence per cell as continuous functions of time and enables tens of plate-reader experiments to be analysed together. Data can be exported in text files in a format immediately suitable for public repositories. Plate readers are a convenient way to study cells; omniplate provides an equally convenient yet powerful way to analyse the resulting data.
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3
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Cantrell JM, Chung CH, Chandrasekaran S. Machine learning to design antimicrobial combination therapies: promises and pitfalls. Drug Discov Today 2022; 27:1639-1651. [DOI: 10.1016/j.drudis.2022.04.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/20/2022] [Accepted: 04/04/2022] [Indexed: 01/13/2023]
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4
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Lin W, Wu L, Zhang Y, Wen Y, Yan B, Dai C, Liu K, He S, Bo X. An enhanced cascade-based deep forest model for drug combination prediction. Brief Bioinform 2022; 23:6513435. [DOI: 10.1093/bib/bbab562] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/20/2021] [Accepted: 12/08/2021] [Indexed: 12/12/2022] Open
Abstract
Abstract
Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artificial intelligence techniques, especially machine learning methods, have become applicable for the discovery of synergistic drug combinations to significantly reduce the experimental workload. In this study, in order to predict novel synergistic drug combinations in various cancer cell lines, the cell line-specific drug-induced gene expression profile (GP) is added as a new feature type to capture the cellular response of drugs and reveal the biological mechanism of synergistic effect. Then, an enhanced cascade-based deep forest regressor (EC-DFR) is innovatively presented to apply the new small-scale drug combination dataset involving chemical, physical and biological (GP) properties of drugs and cells. Verified by the dataset, EC-DFR outperforms two state-of-the-art deep neural network-based methods and several advanced classical machine learning algorithms. Biological experimental validation performed subsequently on a set of previously untested drug combinations further confirms the performance of EC-DFR. What is more prominent is that EC-DFR can distinguish the most important features, making it more interpretable. By evaluating the contribution of each feature type, GP feature contributes 82.40%, showing the cellular responses of drugs may play crucial roles in synergism prediction. The analysis based on the top contributing genes in GP further demonstrates some potential relationships between the transcriptomic levels of key genes under drug regulation and the synergism of drug combinations.
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5
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Qi Q, Angermayr SA, Bollenbach T. Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli. Front Microbiol 2021; 12:760017. [PMID: 34745067 PMCID: PMC8564399 DOI: 10.3389/fmicb.2021.760017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 09/30/2021] [Indexed: 11/16/2022] Open
Abstract
Understanding interactions between antibiotics used in combination is an important theme in microbiology. Using the interactions between the antifolate drug trimethoprim and the ribosome-targeting antibiotic erythromycin in Escherichia coli as a model, we applied a transcriptomic approach for dissecting interactions between two antibiotics with different modes of action. When trimethoprim and erythromycin were combined, the transcriptional response of genes from the sulfate reduction pathway deviated from the dominant effect of trimethoprim on the transcriptome. We successfully altered the drug interaction from additivity to suppression by increasing the sulfate level in the growth environment and identified sulfate reduction as an important metabolic determinant that shapes the interaction between the two drugs. Our work highlights the potential of using prioritization of gene expression patterns as a tool for identifying key metabolic determinants that shape drug-drug interactions. We further demonstrated that the sigma factor-binding protein gene crl shapes the interactions between the two antibiotics, which provides a rare example of how naturally occurring variations between strains of the same bacterial species can sometimes generate very different drug interactions.
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Affiliation(s)
- Qin Qi
- Institute of Science and Technology Austria, Klosterneuburg, Austria.,Institute for Biological Physics, University of Cologne, Cologne, Germany
| | | | - Tobias Bollenbach
- Institute for Biological Physics, University of Cologne, Cologne, Germany.,Center for Data and Simulation Science, University of Cologne, Cologne, Germany
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6
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Brouwers R, Vass H, Dawson A, Squires T, Tavaddod S, Allen RJ. Stability of β-lactam antibiotics in bacterial growth media. PLoS One 2020; 15:e0236198. [PMID: 32687523 PMCID: PMC7371157 DOI: 10.1371/journal.pone.0236198] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 06/30/2020] [Indexed: 01/12/2023] Open
Abstract
Laboratory assays such as MIC tests assume that antibiotic molecules are stable in the chosen growth medium-but rapid degradation has been observed for antibiotics including β-lactams under some conditions in aqueous solution. Degradation rates in bacterial growth medium are less well known. Here, we develop a 'delay time bioassay' that provides a simple way to estimate antibiotic stability in bacterial growth media, using only a plate reader and without the need to measure the antibiotic concentration directly. We use the bioassay to measure degradation half-lives of the β-lactam antibiotics mecillinam, aztreonam and cefotaxime in widely-used bacterial growth media based on MOPS and Luria-Bertani (LB) broth. We find that mecillinam degradation can occur rapidly, with a half-life as short as 2 hours in MOPS medium at 37°C and pH 7.4, and 4-5 hours in LB, but that adjusting the pH and temperature can increase its stability to a half-life around 6 hours without excessively perturbing growth. Aztreonam and cefotaxime were found to have half-lives longer than 6 hours in MOPS medium at 37°C and pH 7.4, but still shorter than the timescale of a typical minimum inhibitory concentration (MIC) assay. Taken together, our results suggest that care is needed in interpreting MIC tests and other laboratory growth assays for β-lactam antibiotics, since there may be significant degradation of the antibiotic during the assay.
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Affiliation(s)
- Rebecca Brouwers
- SUPA, School of Physics and Astronomy, The University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Hugh Vass
- SUPA, School of Physics and Astronomy, The University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Angela Dawson
- SUPA, School of Physics and Astronomy, The University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Tracey Squires
- SUPA, School of Physics and Astronomy, The University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Sharareh Tavaddod
- SUPA, School of Physics and Astronomy, The University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Rosalind J. Allen
- SUPA, School of Physics and Astronomy, The University of Edinburgh, Edinburgh, Scotland, United Kingdom
- * E-mail:
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7
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Lukačišin M, Bollenbach T. Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions. Cell Syst 2019; 9:423-433.e3. [PMID: 31734160 DOI: 10.1016/j.cels.2019.10.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 10/03/2019] [Accepted: 10/11/2019] [Indexed: 01/10/2023]
Abstract
Effective design of combination therapies requires understanding the changes in cell physiology that result from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations. A record of this paper's transparent peer review process is included in the Supplemental Information.
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Affiliation(s)
- Martin Lukačišin
- Institute for Biological Physics, University of Cologne, 50937 Cologne, Germany; IST Austria, 3400 Klosterneuburg, Austria
| | - Tobias Bollenbach
- Institute for Biological Physics, University of Cologne, 50937 Cologne, Germany.
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8
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Campos AI, Zampieri M. Metabolomics-Driven Exploration of the Chemical Drug Space to Predict Combination Antimicrobial Therapies. Mol Cell 2019; 74:1291-1303.e6. [PMID: 31047795 PMCID: PMC6591011 DOI: 10.1016/j.molcel.2019.04.001] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 12/27/2018] [Accepted: 03/28/2019] [Indexed: 01/12/2023]
Abstract
Alternative to the conventional search for single-target, single-compound treatments, combination therapies can open entirely new opportunities to fight antibiotic resistance. However, combinatorial complexity prohibits experimental testing of drug combinations on a large scale, and methods to rationally design combination therapies are lagging behind. Here, we developed a combined experimental-computational approach to predict drug-drug interactions using high-throughput metabolomics. The approach was tested on 1,279 pharmacologically diverse drugs applied to the gram-negative bacterium Escherichia coli. Combining our metabolic profiling of drug response with previously generated metabolic and chemogenomic profiles of 3,807 single-gene deletion strains revealed an unexpectedly large space of inhibited gene functions and enabled rational design of drug combinations. This approach is applicable to other therapeutic areas and can unveil unprecedented insights into drug tolerance, side effects, and repurposing. The compendium of drug-associated metabolome profiles is available at https://zampierigroup.shinyapps.io/EcoPrestMet, providing a valuable resource for the microbiological and pharmacological communities.
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Affiliation(s)
- Adrian I Campos
- Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, 8093 Zurich, Switzerland
| | - Mattia Zampieri
- Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, 8093 Zurich, Switzerland.
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9
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Mitosch K, Rieckh G, Bollenbach T. Temporal order and precision of complex stress responses in individual bacteria. Mol Syst Biol 2019; 15:e8470. [PMID: 30765425 PMCID: PMC6375286 DOI: 10.15252/msb.20188470] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 12/28/2018] [Accepted: 01/22/2019] [Indexed: 01/27/2023] Open
Abstract
Sudden stress often triggers diverse, temporally structured gene expression responses in microbes, but it is largely unknown how variable in time such responses are and if genes respond in the same temporal order in every single cell. Here, we quantified timing variability of individual promoters responding to sublethal antibiotic stress using fluorescent reporters, microfluidics, and time-lapse microscopy. We identified lower and upper bounds that put definite constraints on timing variability, which varies strongly among promoters and conditions. Timing variability can be interpreted using results from statistical kinetics, which enable us to estimate the number of rate-limiting molecular steps underlying different responses. We found that just a few critical steps control some responses while others rely on dozens of steps. To probe connections between different stress responses, we then tracked the temporal order and response time correlations of promoter pairs in individual cells. Our results support that, when bacteria are exposed to the antibiotic nitrofurantoin, the ensuing oxidative stress and SOS responses are part of the same causal chain of molecular events. In contrast, under trimethoprim, the acid stress response and the SOS response are part of different chains of events running in parallel. Our approach reveals fundamental constraints on gene expression timing and provides new insights into the molecular events that underlie the timing of stress responses.
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Affiliation(s)
- Karin Mitosch
- IST Austria, Klosterneuburg, Austria
- EMBL Heidelberg, Heidelberg, Germany
| | - Georg Rieckh
- IST Austria, Klosterneuburg, Austria
- Division of Biological Sciences, University of California at San Diego, La Jolla, CA, USA
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10
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Gutschow MV, Mason JC, Lane KM, Maayan I, Hughey JJ, Bajar BT, Amatya DN, Valle SD, Covert MW. Combinatorial processing of bacterial and host-derived innate immune stimuli at the single-cell level. Mol Biol Cell 2018; 30:282-292. [PMID: 30462580 PMCID: PMC6589564 DOI: 10.1091/mbc.e18-07-0423] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
During the course of a bacterial infection, cells are exposed simultaneously to a range of bacterial and host factors, which converge on the central transcription factor nuclear factor (NF)-κB. How do single cells integrate and process these converging stimuli? Here we tackle the question of how cells process combinatorial signals by making quantitative single-cell measurements of the NF-κB response to combinations of bacterial lipopolysaccharide and the stress cytokine tumor necrosis factor. We found that cells encode the presence of both stimuli via the dynamics of NF-κB nuclear translocation in individual cells, suggesting the integration of NF-κB activity for these stimuli occurs at the molecular and pathway level. However, the gene expression and cytokine secretion response to combinatorial stimuli were more complex, suggesting that other factors in addition to NF-κB contribute to signal integration at downstream layers of the response. Taken together, our results support the theory that during innate immune threat assessment, a pathogen recognized as both foreign and harmful will recruit an enhanced immune response. Our work highlights the remarkable capacity of individual cells to process multiple input signals and suggests that a deeper understanding of signal integration mechanisms will facilitate efforts to control dysregulated immune responses.
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Affiliation(s)
- Miriam V Gutschow
- Department of Bioengineering, Stanford University, Stanford, CA 94305
| | - John C Mason
- Department of Bioengineering, Stanford University, Stanford, CA 94305
| | - Keara M Lane
- Department of Bioengineering, Stanford University, Stanford, CA 94305
| | - Inbal Maayan
- Department of Bioengineering, Stanford University, Stanford, CA 94305
| | - Jacob J Hughey
- Department of Bioengineering, Stanford University, Stanford, CA 94305
| | - Bryce T Bajar
- Department of Bioengineering, Stanford University, Stanford, CA 94305
| | - Debha N Amatya
- Department of Bioengineering, Stanford University, Stanford, CA 94305
| | - Sean D Valle
- Department of Bioengineering, Stanford University, Stanford, CA 94305
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305
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11
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Kochanowski K, Gerosa L, Brunner SF, Christodoulou D, Nikolaev YV, Sauer U. Few regulatory metabolites coordinate expression of central metabolic genes in Escherichia coli. Mol Syst Biol 2017; 13:903. [PMID: 28049137 PMCID: PMC5293157 DOI: 10.15252/msb.20167402] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Transcription networks consist of hundreds of transcription factors with thousands of often overlapping target genes. While we can reliably measure gene expression changes, we still understand relatively little why expression changes the way it does. How does a coordinated response emerge in such complex networks and how many input signals are necessary to achieve it? Here, we unravel the regulatory program of gene expression in Escherichia coli central carbon metabolism with more than 30 known transcription factors. Using a library of fluorescent transcriptional reporters, we comprehensively quantify the activity of central metabolic promoters in 26 environmental conditions. The expression patterns were dominated by growth rate‐dependent global regulation for most central metabolic promoters in concert with highly condition‐specific activation for only few promoters. Using an approximate mathematical description of promoter activity, we dissect the contribution of global and specific transcriptional regulation. About 70% of the total variance in promoter activity across conditions was explained by global transcriptional regulation. Correlating the remaining specific transcriptional regulation of each promoter with the cell's metabolome response across the same conditions identified potential regulatory metabolites. Remarkably, cyclic AMP, fructose‐1,6‐bisphosphate, and fructose‐1‐phosphate alone explained most of the specific transcriptional regulation through their interaction with the two major transcription factors Crp and Cra. Thus, a surprisingly simple regulatory program that relies on global transcriptional regulation and input from few intracellular metabolites appears to be sufficient to coordinate E. coli central metabolism and explain about 90% of the experimentally observed transcription changes in 100 genes.
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Affiliation(s)
- Karl Kochanowski
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland
| | - Luca Gerosa
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland
| | - Simon F Brunner
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Dimitris Christodoulou
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland
| | - Yaroslav V Nikolaev
- Institute of Molecular Biology & Biophysics, ETH Zurich, Zurich, Switzerland
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
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12
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Liu Y, Li S, Liu Z, Wang R. Bifurcation-based approach reveals synergism and optimal combinatorial perturbation. J Biol Phys 2016; 42:399-414. [PMID: 27079194 DOI: 10.1007/s10867-016-9414-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2015] [Accepted: 03/02/2016] [Indexed: 12/28/2022] Open
Abstract
Cells accomplish the process of fate decisions and form terminal lineages through a series of binary choices in which cells switch stable states from one branch to another as the interacting strengths of regulatory factors continuously vary. Various combinatorial effects may occur because almost all regulatory processes are managed in a combinatorial fashion. Combinatorial regulation is crucial for cell fate decisions because it may effectively integrate many different signaling pathways to meet the higher regulation demand during cell development. However, whether the contribution of combinatorial regulation to the state transition is better than that of a single one and if so, what the optimal combination strategy is, seem to be significant issue from the point of view of both biology and mathematics. Using the approaches of combinatorial perturbations and bifurcation analysis, we provide a general framework for the quantitative analysis of synergism in molecular networks. Different from the known methods, the bifurcation-based approach depends only on stable state responses to stimuli because the state transition induced by combinatorial perturbations occurs between stable states. More importantly, an optimal combinatorial perturbation strategy can be determined by investigating the relationship between the bifurcation curve of a synergistic perturbation pair and the level set of a specific objective function. The approach is applied to two models, i.e., a theoretical multistable decision model and a biologically realistic CREB model, to show its validity, although the approach holds for a general class of biological systems.
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Affiliation(s)
- Yanwei Liu
- Department of Mathematics, Shanghai University, Shanghai, China
| | - Shanshan Li
- Department of Mathematics, Shanghai University, Shanghai, China
| | - Zengrong Liu
- Department of Mathematics, Shanghai University, Shanghai, China
| | - Ruiqi Wang
- Department of Mathematics, Shanghai University, Shanghai, China.
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13
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Greulich P, Scott M, Evans MR, Allen RJ. Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics. Mol Syst Biol 2016; 11:796. [PMID: 26146675 PMCID: PMC4380930 DOI: 10.15252/msb.20145949] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Bacterial growth environment strongly influences the efficacy of antibiotic treatment, with slow growth often being associated with decreased susceptibility. Yet in many cases, the connection between antibiotic susceptibility and pathogen physiology remains unclear. We show that for ribosome-targeting antibiotics acting on Escherichia coli, a complex interplay exists between physiology and antibiotic action; for some antibiotics within this class, faster growth indeed increases susceptibility, but for other antibiotics, the opposite is true. Remarkably, these observations can be explained by a simple mathematical model that combines drug transport and binding with physiological constraints. Our model reveals that growth-dependent susceptibility is controlled by a single parameter characterizing the ‘reversibility’ of ribosome-targeting antibiotic transport and binding. This parameter provides a spectrum classification of antibiotic growth-dependent efficacy that appears to correspond at its extremes to existing binary classification schemes. In these limits, the model predicts universal, parameter-free limiting forms for growth inhibition curves. The model also leads to non-trivial predictions for the drug susceptibility of a translation mutant strain of E. coli, which we verify experimentally. Drug action and bacterial metabolism are mechanistically complex; nevertheless, this study illustrates how coarse-grained models can be used to integrate pathogen physiology into drug design and treatment strategies.
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Affiliation(s)
- Philip Greulich
- Cavendish Laboratory, University of CambridgeCambridge, UK
- SUPA, School of Physics and Astronomy, University of EdinburghEdinburgh, UK
| | - Matthew Scott
- Department of Applied Mathematics, University of WaterlooWaterloo, ON, Canada
| | - Martin R Evans
- SUPA, School of Physics and Astronomy, University of EdinburghEdinburgh, UK
| | - Rosalind J Allen
- SUPA, School of Physics and Astronomy, University of EdinburghEdinburgh, UK
- * Corresponding author. Tel: +44 131 6517197; E-mail:
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14
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Bollenbach T. Antimicrobial interactions: mechanisms and implications for drug discovery and resistance evolution. Curr Opin Microbiol 2015; 27:1-9. [DOI: 10.1016/j.mib.2015.05.008] [Citation(s) in RCA: 131] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 05/06/2015] [Accepted: 05/08/2015] [Indexed: 01/06/2023]
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15
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Downing T. Tackling Drug Resistant Infection Outbreaks of Global Pandemic Escherichia coli ST131 Using Evolutionary and Epidemiological Genomics. Microorganisms 2015; 3:236-67. [PMID: 27682088 PMCID: PMC5023239 DOI: 10.3390/microorganisms3020236] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Revised: 04/28/2015] [Accepted: 04/30/2015] [Indexed: 11/16/2022] Open
Abstract
High-throughput molecular screening is required to investigate the origin and diffusion of antimicrobial resistance in pathogen outbreaks. The most frequent cause of human infection is Escherichia coli, which is dominated by sequence type 131 (ST131)-a set of rapidly radiating pandemic clones. The highly infectious clades of ST131 originated firstly by a mutation enhancing conjugation and adhesion. Secondly, single-nucleotide polymorphisms occurred enabling fluoroquinolone-resistance, which is near-fixed in all ST131. Thirdly, broader resistance through beta-lactamases has been gained and lost frequently, symptomatic of conflicting environmental selective effects. This flexible approach to gene exchange is worrying and supports the proposition that ST131 will develop an even wider range of plasmid and chromosomal elements promoting antimicrobial resistance. To stop ST131, deep genome sequencing is required to understand the origin, evolution and spread of antimicrobial resistance genes. Phylogenetic methods that decipher past events can predict future patterns of virulence and transmission based on genetic signatures of adaptation and gene exchange. Both the effect of partial antimicrobial exposure and cell dormancy caused by variation in gene expression may accelerate the development of resistance. High-throughput sequencing can decode measurable evolution of cell populations within patients associated with systems-wide changes in gene expression during treatments. A multi-faceted approach can enhance assessment of antimicrobial resistance in E. coli ST131 by examining transmission dynamics between hosts to achieve a goal of pre-empting resistance before it emerges by optimising antimicrobial treatment protocols.
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Affiliation(s)
- Tim Downing
- School of Biotechnology, Faculty of Science and Health, Dublin City University, Dublin 9, Ireland.
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16
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Combinatorial code governing cellular responses to complex stimuli. Nat Commun 2015; 6:6847. [PMID: 25896517 PMCID: PMC4410637 DOI: 10.1038/ncomms7847] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Accepted: 03/04/2015] [Indexed: 01/05/2023] Open
Abstract
Cells adapt to their environment through the integration of complex signals. Multiple signals can induce synergistic or antagonistic interactions, currently considered as homogenous behaviours. Here, we use a systematic theoretical approach to enumerate the possible interaction profiles for outputs measured in the conditions 0 (control), signals X, Y, X+Y. Combinatorial analysis reveals 82 possible interaction profiles, which we biologically and mathematically grouped into five positive and five negative interaction modes. To experimentally validate their use in living cells, we apply an original computational workflow to transcriptomics data of innate immune cells integrating physiopathological signal combinations. Up to 9 of the 10 defined modes coexisted in context-dependent proportions. Each interaction mode was preferentially used in specific biological pathways, suggesting a functional role in the adaptation to multiple signals. Our work defines an exhaustive map of interaction modes for cells integrating pairs of physiopathological and pharmacological stimuli. Cells constantly integrate information from multiple stimuli. By considering every possible means by which two stimuli can interact, Cappuccio et al. define 10 interaction modes and demonstrate their preferential use by dendritic cells responding to different combinations of microbial and host inflammatory cues.
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17
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Perron GG, Inglis RF, Pennings PS, Cobey S. Fighting microbial drug resistance: a primer on the role of evolutionary biology in public health. Evol Appl 2015; 8:211-22. [PMID: 25861380 PMCID: PMC4380916 DOI: 10.1111/eva.12254] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Accepted: 02/18/2015] [Indexed: 01/03/2023] Open
Abstract
Although microbes have been evolving resistance to antimicrobials for millennia, the spread of resistance in pathogen populations calls for the development of new drugs and treatment strategies. We propose that successful, long-term resistance management requires a better understanding of how resistance evolves in the first place. This is an opportunity for evolutionary biologists to engage in public health, a collaboration that has substantial precedent. Resistance evolution has been an important tool for developing and testing evolutionary theory, especially theory related to the genetic basis of new traits and constraints on adaptation. The present era is no exception. The articles in this issue highlight the breadth of current research on resistance evolution and also its challenges. In this introduction, we review the conceptual advances that have been achieved from studying resistance evolution and describe a path forward.
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Affiliation(s)
- Gabriel G Perron
- Department of Biology, Bard College Annandale-on-Hudson, NY, USA
| | - R Fredrik Inglis
- Department of Biology, Washington University in St. Louis St. Louis, MO, USA
| | - Pleuni S Pennings
- Department of Biology, San Francisco State University San Francisco, CA, USA
| | - Sarah Cobey
- Department of Ecology and Evolution, University of Chicago Chicago, IL, USA
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18
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Mitosch K, Bollenbach T. Bacterial responses to antibiotics and their combinations. ENVIRONMENTAL MICROBIOLOGY REPORTS 2014; 6:545-557. [PMID: 25756107 DOI: 10.1111/1758-2229.12190] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Antibiotics affect bacterial cell physiology at many levels. Rather than just compensating for the direct cellular defects caused by the drug, bacteria respond to antibiotics by changing their morphology, macromolecular composition, metabolism, gene expression and possibly even their mutation rate. Inevitably, these processes affect each other, resulting in a complex response with changes in the expression of numerous genes. Genome-wide approaches can thus help in gaining a comprehensive understanding of bacterial responses to antibiotics. In addition, a combination of experimental and theoretical approaches is needed for identifying general principles that underlie these responses. Here, we review recent progress in our understanding of bacterial responses to antibiotics and their combinations, focusing on effects at the levels of growth rate and gene expression. We concentrate on studies performed in controlled laboratory conditions, which combine promising experimental techniques with quantitative data analysis and mathematical modeling. While these basic research approaches are not immediately applicable in the clinic, uncovering the principles and mechanisms underlying bacterial responses to antibiotics may, in the long term, contribute to the development of new treatment strategies to cope with and prevent the rise of resistant pathogenic bacteria.
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19
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Vega NM, Gore J. Collective antibiotic resistance: mechanisms and implications. Curr Opin Microbiol 2014; 21:28-34. [PMID: 25271119 DOI: 10.1016/j.mib.2014.09.003] [Citation(s) in RCA: 96] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2014] [Revised: 09/10/2014] [Accepted: 09/10/2014] [Indexed: 12/17/2022]
Abstract
In collective resistance, microbial communities are able to survive antibiotic exposures that would be lethal to individual cells. In this review, we explore recent advances in understanding collective resistance in bacteria. The population dynamics of 'cheating' in a system with cooperative antibiotic inactivation have been described, providing insight into the demographic factors that determine resistance allele frequency in bacteria. Extensive work has elucidated mechanisms underlying collective resistance in biofilms and addressed questions about the role of cooperation in these structures. Additionally, recent investigations of 'bet-hedging' strategies in bacteria have explored the contributions of stochasticity and regulation to bacterial phenotypic heterogeneity and examined the effects of these strategies on community survival.
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Affiliation(s)
- Nicole M Vega
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeff Gore
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA.
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20
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21
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Rothschild D, Dekel E, Hausser J, Bren A, Aidelberg G, Szekely P, Alon U. Linear superposition and prediction of bacterial promoter activity dynamics in complex conditions. PLoS Comput Biol 2014; 10:e1003602. [PMID: 24809350 PMCID: PMC4014397 DOI: 10.1371/journal.pcbi.1003602] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Accepted: 03/19/2014] [Indexed: 01/12/2023] Open
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. Bacteria face complex conditions in important settings such as our body and in biotechnological applications such as biofuel production. Understanding how bacteria respond to complex conditions is a hard problem: the number of conditions that need to be tested grows exponentially with the number of nutrients, stresses and other factors that make up the environment. To overcome this exponential explosion, we present an approach that allows computing the dynamics of gene expression in a complex condition based on measurements in simple conditions. This is based on the main discovery in this paper: using accurate promoter activity measurements, we find that promoter activity dynamics in a cocktail of media is a weighted average of the dynamics in each medium alone. The weights in the average are constant across time, and can be used to predict the dynamics in arbitrary cocktails based only on measurements on pairs of conditions. Thus, dynamics in complex conditions is, for the vast majority of genes, much simpler than it might have been; this simplicity allows new mathematical formula for accurate prediction in new conditions.
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Affiliation(s)
- Daphna Rothschild
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Erez Dekel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Jean Hausser
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Anat Bren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Guy Aidelberg
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Pablo Szekely
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Uri Alon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- * E-mail:
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22
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Wood KB, Wood KC, Nishida S, Cluzel P. Uncovering scaling laws to infer multidrug response of resistant microbes and cancer cells. Cell Rep 2014; 6:1073-1084. [PMID: 24613352 DOI: 10.1016/j.celrep.2014.02.007] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2013] [Revised: 12/30/2013] [Accepted: 02/04/2014] [Indexed: 10/25/2022] Open
Abstract
Drug resistance in bacterial infections and cancers constitutes a major threat to human health. Treatments often include several interacting drugs, but even potent therapies can become ineffective in resistant mutants. Here, we simplify the picture of drug resistance by identifying scaling laws that unify the multidrug responses of drug-sensitive and -resistant cells. On the basis of these scaling relationships, we are able to infer the two-drug response of resistant mutants in previously unsampled regions of dosage space in clinically relevant microbes such as E. coli, E. faecalis, S. aureus, and S. cerevisiae as well as human non-small-cell lung cancer, melanoma, and breast cancer stem cells. Importantly, we find that scaling relations also apply across evolutionarily close strains. Finally, scaling allows one to rapidly identify new drug combinations and predict potent dosage regimes for targeting resistant mutants without any prior mechanistic knowledge about the specific resistance mechanism.
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Affiliation(s)
- Kevin B Wood
- FAS Center for Systems Biology, Department of Molecular and Cellular Biology, School of Engineering and Applied Sciences, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA
| | - Kris C Wood
- Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142, USA
| | - Satoshi Nishida
- FAS Center for Systems Biology, Department of Molecular and Cellular Biology, School of Engineering and Applied Sciences, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA
| | - Philippe Cluzel
- FAS Center for Systems Biology, Department of Molecular and Cellular Biology, School of Engineering and Applied Sciences, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA.
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23
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Zuleta IA, Aranda-Díaz A, Li H, El-Samad H. Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry. Nat Methods 2014; 11:443-8. [PMID: 24608180 PMCID: PMC4016179 DOI: 10.1038/nmeth.2879] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2013] [Accepted: 01/16/2014] [Indexed: 01/06/2023]
Abstract
Cells adjust to changes in environmental conditions using complex regulatory programs. These cellular programs are the result of an intricate interplay between gene expression, cellular growth and protein degradation. Technologies that enable simultaneous and time-resolved measurements of these variables are necessary to dissect cellular homeostatic strategies. Here we report the development of an automated flow cytometry robotic setup that enables real-time measurement of precise and simultaneous relative growth and protein synthesis rates of multiplexed microbial populations across many conditions. These measurements generate quantitative profiles of dynamically evolving protein synthesis and degradation rates. We demonstrate this setup in the context of gene regulation of the unfolded protein response (UPR) of Saccharomyces cerevisiae and uncover a dynamic and complex landscape of gene expression, growth dynamics and proteolysis following perturbations.
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Affiliation(s)
- Ignacio A Zuleta
- 1] Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA. [2] The California Institute for Quantitative Biosciences, San Francisco, California, USA
| | - Andrés Aranda-Díaz
- 1] Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA. [2] The California Institute for Quantitative Biosciences, San Francisco, California, USA
| | - Hao Li
- 1] Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA. [2] The California Institute for Quantitative Biosciences, San Francisco, California, USA
| | - Hana El-Samad
- 1] Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA. [2] The California Institute for Quantitative Biosciences, San Francisco, California, USA
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24
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Youk H, Lim WA. Secreting and sensing the same molecule allows cells to achieve versatile social behaviors. Science 2014; 343:1242782. [PMID: 24503857 PMCID: PMC4145839 DOI: 10.1126/science.1242782] [Citation(s) in RCA: 131] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Cells that secrete and sense the same signaling molecule are ubiquitous. To uncover the functional capabilities of the core "secrete-and-sense" circuit motif shared by these cells, we engineered yeast to secrete and sense the mating pheromone. Perturbing each circuit element revealed parameters that control the degree to which the cell communicated with itself versus with its neighbors. This tunable interplay of self-communication and neighbor communication enables cells to span a diverse repertoire of cellular behaviors. These include a cell being asocial by responding only to itself and social through quorum sensing, and an isogenic population of cells splitting into social and asocial subpopulations. A mathematical model explained these behaviors. The versatility of the secrete-and-sense circuit motif may explain its recurrence across species.
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Affiliation(s)
- Hyun Youk
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA 94158, USA
- Center for Systems and Synthetic Biology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Wendell A. Lim
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA 94158, USA
- Center for Systems and Synthetic Biology, University of California San Francisco, San Francisco, CA 94158, USA
- Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA 94158, USA
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25
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Campbell EM, Chao L. A population model evaluating the consequences of the evolution of double-resistance and tradeoffs on the benefits of two-drug antibiotic treatments. PLoS One 2014; 9:e86971. [PMID: 24498003 PMCID: PMC3909004 DOI: 10.1371/journal.pone.0086971] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2013] [Accepted: 12/18/2013] [Indexed: 11/18/2022] Open
Abstract
The evolution of antibiotic resistance in microbes poses one of the greatest challenges to the management of human health. Because addressing the problem experimentally has been difficult, research on strategies to slow the evolution of resistance through the rational use of antibiotics has resorted to mathematical and computational models. However, despite many advances, several questions remain unsettled. Here we present a population model for rational antibiotic usage by adding three key features that have been overlooked: 1) the maximization of the frequency of uninfected patients in the human population rather than the minimization of antibiotic resistance in the bacterial population, 2) the use of cocktails containing antibiotic pairs, and 3) the imposition of tradeoff constraints on bacterial resistance to multiple drugs. Because of tradeoffs, bacterial resistance does not evolve directionally and the system reaches an equilibrium state. When considering the equilibrium frequency of uninfected patients, both cycling and mixing improve upon single-drug treatment strategies. Mixing outperforms optimal cycling regimens. Cocktails further improve upon aforementioned strategies. Moreover, conditions that increase the population frequency of uninfected patients also increase the recovery rate of infected individual patients. Thus, a rational strategy does not necessarily result in a tragedy of the commons because benefits to the individual patient and general public are not in conflict. Our identification of cocktails as the best strategy when tradeoffs between multiple-resistance are operating could also be extended to other host-pathogen systems. Cocktails or other multiple-drug treatments are additionally attractive because they allow re-using antibiotics whose utility has been negated by the evolution of single resistance.
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Affiliation(s)
- Ellsworth M. Campbell
- Section of Ecology, Behavior and Evolution, Division of Biological Sciences, University of California San Diego, La Jolla, California, United States of America
| | - Lin Chao
- Section of Ecology, Behavior and Evolution, Division of Biological Sciences, University of California San Diego, La Jolla, California, United States of America
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26
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Deris JB, Kim M, Zhang Z, Okano H, Hermsen R, Groisman A, Hwa T. The innate growth bistability and fitness landscapes of antibiotic-resistant bacteria. Science 2013; 342:1237435. [PMID: 24288338 DOI: 10.1126/science.1237435] [Citation(s) in RCA: 127] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
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.
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Affiliation(s)
- J Barrett Deris
- Department of Physics, University of California at San Diego, La Jolla, CA 92093-0374, USA
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27
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Transcriptional cross talk within the mar-sox-rob regulon in Escherichia coli is limited to the rob and marRAB operons. J Bacteriol 2012; 194:4867-75. [PMID: 22753060 DOI: 10.1128/jb.00680-12] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Bacteria possess multiple mechanisms to survive exposure to various chemical stresses and antimicrobial compounds. In the enteric bacterium Escherichia coli, three homologous transcription factors-MarA, SoxS, and Rob-play a central role in coordinating this response. Three separate systems are known to regulate the expression and activities of MarA, SoxS, and Rob. However, a number of studies have shown that the three do not function in isolation but rather are coregulated through transcriptional cross talk. In this work, we systematically investigated the extent of transcriptional cross talk in the mar-sox-rob regulon. While the three transcription factors were found to have the potential to regulate each other's expression when ectopically expressed, the only significant interactions observed under physiological conditions were between mar and rob systems. MarA, SoxS, and Rob all activate the marRAB promoter, more so when they are induced by their respective inducers: salicylate, paraquat, and decanoate. None of the three proteins affects the soxS promoter, though unexpectedly, it was mildly repressed by decanoate by an unknown mechanism. SoxS is the only one of the three proteins to repress the rob promoter. Surprisingly, salicylate somewhat activates transcription of rob, while decanoate represses it a bit. Rob, in turn, activates not only its downstream promoters in response to salicylate but also the marRAB promoter. These results demonstrate that the mar and rob systems function together in response to salicylate.
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28
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Churski K, Kaminski TS, Jakiela S, Kamysz W, Baranska-Rybak W, Weibel DB, Garstecki P. Rapid screening of antibiotic toxicity in an automated microdroplet system. LAB ON A CHIP 2012; 12:1629-37. [PMID: 22422170 DOI: 10.1039/c2lc21284f] [Citation(s) in RCA: 123] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
We report an automated microfluidic platform for 'digitally' screening the composition space of droplets containing cocktails of small molecules and demonstrate the features of this system by studying epistatic interactions between antibiotics and Escherichia coli ATCC 25922. This system has several key characteristics: (i) it uses small (<100 μL) samples of liquids and suspensions of bacteria that are introduced directly into the chip; (ii) it generates a sequence of droplets with compositions, including reagents and bacterial cell suspensions that are programmed by the user; (iii) it exports the sequence of droplets to an external segment of tubing that is subsequently disconnected for incubation and storage; and (iv) after incubation of bacteria in droplets, the droplets are injected into a second device equipped with an in-line fiber optic spectrophotometer that measures cell growth. The system generates and fuses droplets with precise (<1% in standard deviation) control of liquid volumes and of the concentrations of input substrates. We demonstrate the application of this technology in determining the minimum inhibitory concentration and pair-wise interactions of ampicillin, tetracycline, and chloramphenicol against E. coli. The experiments consumed small volumes of reagents and required minutes to create the droplets and several hours for their incubation and analysis.
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Affiliation(s)
- Krzysztof Churski
- Institute of Physical Chemistry, Polish Academy of Sciences, Warsaw, Poland
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29
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Shoval O, Sheftel H, Shinar G, Hart Y, Ramote O, Mayo A, Dekel E, Kavanagh K, Alon U. Evolutionary Trade-Offs, Pareto Optimality, and the Geometry of Phenotype Space. Science 2012; 336:1157-60. [DOI: 10.1126/science.1217405] [Citation(s) in RCA: 417] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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30
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Silander OK, Nikolic N, Zaslaver A, Bren A, Kikoin I, Alon U, Ackermann M. A genome-wide analysis of promoter-mediated phenotypic noise in Escherichia coli. PLoS Genet 2012; 8:e1002443. [PMID: 22275871 PMCID: PMC3261926 DOI: 10.1371/journal.pgen.1002443] [Citation(s) in RCA: 120] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2011] [Accepted: 11/16/2011] [Indexed: 11/19/2022] Open
Abstract
Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alone. Many biological processes in a cell involve small numbers of molecules and therefore fluctuate over time. As a consequence, genetically identical cells that live in the same environment differ from each other in many phenotypic traits, including the expression level of different genes. Our aim was to identify types of genes with particularly low or high levels of variation (“noise”) and to understand molecular and evolutionary factors that determine noise level. Working with the bacterium E. coli, we analyzed the expression—at the single cell level—of more than 1,500 different genes. We found particularly low levels of noise in genes that E. coli needs to live and genes that this bacterium shares with many related taxa. This suggests that cellular functions that are particularly important for this organism evolved towards low levels of variation. In contrast to previous results with yeast, we find that genes that change their expression levels in response to environmental signals do not have high levels of noise. This suggests that there may be fundamental differences in how noise is controlled in bacteria and eukaryotes.
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Affiliation(s)
- Olin K Silander
- Computational and Systems Biology, Biozentrum, University of Basel, Basel, Switzerland.
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31
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Tiziani S, Kang Y, Choi JS, Roberts W, Paternostro G. Metabolomic high-content nuclear magnetic resonance-based drug screening of a kinase inhibitor library. Nat Commun 2011; 2:545. [PMID: 22109519 DOI: 10.1038/ncomms1562] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2011] [Accepted: 10/21/2011] [Indexed: 01/13/2023] Open
Abstract
Metabolism is altered in many highly prevalent diseases and is controlled by a complex network of intracellular regulators. Monitoring cell metabolism during treatment is extremely valuable to investigate cellular response and treatment efficacy. Here we describe a nuclear magnetic resonance-based method for screening of the metabolomic response of drug-treated mammalian cells in a 96-well format. We validate the method using drugs having well-characterized targets and report the results of a screen of a kinase inhibitor library. Four hits are validated from their action on an important clinical parameter, the lactate to pyruvate ratio. An eEF-2 kinase inhibitor and an NF-kB activation inhibitor increased lactate/pyruvate ratio, whereas an MK2 inhibitor and an inhibitor of PKA, PKC and PKG induced a decrease. The method is validated in cell lines and in primary cancer cells, and may have potential applications in both drug development and personalized therapy.
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Affiliation(s)
- Stefano Tiziani
- Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, California 92037, USA
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32
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Fischbach MA. Combination therapies for combating antimicrobial resistance. Curr Opin Microbiol 2011; 14:519-23. [PMID: 21900036 DOI: 10.1016/j.mib.2011.08.003] [Citation(s) in RCA: 157] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2011] [Revised: 08/17/2011] [Accepted: 08/17/2011] [Indexed: 11/29/2022]
Abstract
New drug development strategies are needed to combat antimicrobial resistance. The object of this perspective is to highlight one such strategy: treating infections with sets of drugs rather than individual drugs. We will highlight three categories of combination therapy: those that inhibit targets in different pathways; those that inhibit distinct nodes in the same pathway; and those that inhibit the very same target in different ways. We will then consider examples of naturally occurring combination therapies produced by micro-organisms, and conclude by discussing key opportunities and challenges for making more widespread use of drug combinations.
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Affiliation(s)
- Michael A Fischbach
- Department of Bioengineering and Therapeutic Sciences and the California Institute for Quantitative Biosciences, University of California, San Francisco, CA 94158, USA.
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33
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Abstract
An elegant new study by Bollenbach and Kishony (2011) in this issue of Molecular Cell shows how bacteria resolve the apparent conflicts created when they face two signals with opposite effects on gene expression.
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Affiliation(s)
- Jonathan W. Young
- Division of Biology, California Institute of Technology, 1200 East
California, Pasadena, CA 91125, USA
| | - Michael B. Elowitz
- Division of Biology, California Institute of Technology, 1200 East
California, Pasadena, CA 91125, USA
- Howard Hughes Medical Institute, California Institute of Technology, 1200
East California, Pasadena, CA 91125, USA
- Department of Bioengineering, California Institute of Technology, 1200 East
California, Pasadena, CA 91125, USA
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