1
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Trivellin C, Torello Pianale L, Olsson L. Robustness quantification of a mutant library screen revealed key genetic markers in yeast. Microb Cell Fact 2024; 23:218. [PMID: 39098937 PMCID: PMC11298085 DOI: 10.1186/s12934-024-02490-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 07/24/2024] [Indexed: 08/06/2024] Open
Abstract
BACKGROUND Microbial robustness is crucial for developing cell factories that maintain consistent performance in a challenging environment such as large-scale bioreactors. Although tools exist to assess and understand robustness at a phenotypic level, the underlying metabolic and genetic mechanisms are not well defined, which limits our ability to engineer more strains with robust functions. RESULTS This study encompassed four steps. (I) Fitness and robustness were analyzed from a published dataset of yeast mutants grown in multiple environments. (II) Genes and metabolic processes affecting robustness or fitness were identified, and 14 of these genes were deleted in Saccharomyces cerevisiae CEN.PK113-7D. (III) The mutants bearing gene deletions were cultivated in three perturbation spaces mimicking typical industrial processes. (IV) Fitness and robustness were determined for each mutant in each perturbation space. We report that robustness varied according to the perturbation space. We identified genes associated with increased robustness such as MET28, linked to sulfur metabolism; as well as genes associated with decreased robustness, including TIR3 and WWM1, both involved in stress response and apoptosis. CONCLUSION The present study demonstrates how phenomics datasets can be analyzed to reveal the relationship between phenotypic response and associated genes. Specifically, robustness analysis makes it possible to study the influence of single genes and metabolic processes on stable microbial performance in different perturbation spaces. Ultimately, this information can be used to enhance robustness in targeted strains.
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Affiliation(s)
- Cecilia Trivellin
- Department of Life Sciences, Division of Industrial Biotechnology, Chalmers University of Technology, 412 96, Gothenburg, Sweden
| | - Luca Torello Pianale
- Department of Life Sciences, Division of Industrial Biotechnology, Chalmers University of Technology, 412 96, Gothenburg, Sweden
| | - Lisbeth Olsson
- Department of Life Sciences, Division of Industrial Biotechnology, Chalmers University of Technology, 412 96, Gothenburg, Sweden.
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2
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French S, Guo ABY, Ellis MJ, Deisinger JP, Johnson JW, Rachwalski K, Piquette ZA, Lluka T, Zary M, Gamage S, Magolan J, Brown ED. A platform for predicting mechanism of action based on bacterial transcriptional responses identifies an unusual DNA gyrase inhibitor. Cell Rep 2024; 43:114053. [PMID: 38578824 DOI: 10.1016/j.celrep.2024.114053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 02/02/2024] [Accepted: 03/19/2024] [Indexed: 04/07/2024] Open
Abstract
In the search for much-needed new antibacterial chemical matter, a myriad of compounds have been reported in academic and pharmaceutical screening endeavors. Only a small fraction of these, however, are characterized with respect to mechanism of action (MOA). Here, we describe a pipeline that categorizes transcriptional responses to antibiotics and provides hypotheses for MOA. 3D-printed imaging hardware PFIboxes) profiles responses of Escherichia coli promoter-GFP fusions to more than 100 antibiotics. Notably, metergoline, a semi-synthetic ergot alkaloid, mimics a DNA replication inhibitor. In vitro supercoiling assays confirm this prediction, and a potent analog thereof (MLEB-1934) inhibits growth at 0.25 μg/mL and is highly active against quinolone-resistant strains of methicillin-resistant Staphylococcus aureus. Spontaneous suppressor mutants map to a seldom explored allosteric binding pocket, suggesting a mechanism distinct from DNA gyrase inhibitors used in the clinic. In all, the work highlights the potential of this platform to rapidly assess MOA of new antibacterial compounds.
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Affiliation(s)
- Shawn French
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Amelia Bing Ya Guo
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Michael J Ellis
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Julia P Deisinger
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Jarrod W Johnson
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Kenneth Rachwalski
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Zoë A Piquette
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Telmah Lluka
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Miranda Zary
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Sineli Gamage
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Jakob Magolan
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Eric D Brown
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada.
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3
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Hartmann FSF, Weiß T, Kastberg LLB, Workman CT, Seibold GM. Precise and versatile microplate reader-based analyses of biosensor signals from arrayed microbial colonies. Front Microbiol 2023; 14:1187228. [PMID: 37389345 PMCID: PMC10303141 DOI: 10.3389/fmicb.2023.1187228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/23/2023] [Indexed: 07/01/2023] Open
Abstract
Genetically encoded fluorescent biosensors have emerged as a powerful tool to support phenotypic screenings of microbes. Optical analyses of fluorescent sensor signals from colonies grown on solid media can be challenging as imaging devices need to be equipped with appropriate filters matching the properties of fluorescent biosensors. Toward versatile fluorescence analyses of different types of biosensor signals derived from arrayed colonies, we investigate here the use of monochromator equipped microplate readers as an alternative to imaging approaches. Indeed, for analyses of the LacI-controlled expression of the reporter mCherry in Corynebacterium glutamicum, or promoter activity using GFP as reporter in Saccharomyces cerevisiae, an improved sensitivity and dynamic range was observed for a microplate reader-based analyses compared to their analyses via imaging. The microplate reader allowed us to capture signals of ratiometric fluorescent reporter proteins (FRPs) with a high sensitivity and thereby to further improve the analysis of internal pH via the pH-sensitive FRP mCherryEA in Escherichia coli colonies. Applicability of this novel technique was further demonstrated by assessing redox states in C. glutamicum colonies using the FRP Mrx1-roGFP2. By the use of a microplate reader, oxidative redox shifts were measured in a mutant strain lacking the non-enzymatic antioxidant mycothiol (MSH), indicating its major role for maintaining a reduced redox state also in colonies on agar plates. Taken together, analyses of biosensor signals from microbial colonies using a microplate reader allows comprehensive phenotypic screenings and thus facilitates further development of new strains for metabolic engineering and systems biology.
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4
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Bak SK, Seong W, Rha E, Lee H, Kim SK, Kwon KK, Kim H, Lee SG. Novel High-Throughput DNA Part Characterization Technique for Synthetic Biology. J Microbiol Biotechnol 2022; 32:1026-1033. [PMID: 35879270 PMCID: PMC9628936 DOI: 10.4014/jmb.2207.07013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 07/19/2022] [Accepted: 07/19/2022] [Indexed: 12/15/2022]
Abstract
This study presents a novel DNA part characterization technique that increases throughput by combinatorial DNA part assembly, solid plate-based quantitative fluorescence assay for phenotyping, and barcode tagging-based long-read sequencing for genotyping. We confirmed that the fluorescence intensities of colonies on plates were comparable to fluorescence at the single-cell level from a high-end, flow-cytometry device and developed a high-throughput image analysis pipeline. The barcode tagging-based long-read sequencing technique enabled rapid identification of all DNA parts and their combinations with a single sequencing experiment. Using our techniques, forty-four DNA parts (21 promoters and 23 RBSs) were successfully characterized in 72 h without any automated equipment. We anticipate that this high-throughput and easy-to-use part characterization technique will contribute to increasing part diversity and be useful for building genetic circuits and metabolic pathways in synthetic biology.
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Affiliation(s)
- Seong-Kun Bak
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea,Biosystems and Bioengineering Program, University of Science and Technology, Daejeon 34141, Republic of Korea
| | - Wonjae Seong
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Eugene Rha
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Hyewon Lee
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Seong Keun Kim
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Kil Koang Kwon
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Haseong Kim
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea,Biosystems and Bioengineering Program, University of Science and Technology, Daejeon 34141, Republic of Korea,Corresponding authors H.S. Kim Phone: +82-42-860-4372 Fax: +82-42-860-4489 E-mail:
| | - Seung-Goo Lee
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea,Biosystems and Bioengineering Program, University of Science and Technology, Daejeon 34141, Republic of Korea,
S.G. Lee Phone: +82-42-860-4373 E-mail:
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5
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Expanding the search for small-molecule antibacterials by multidimensional profiling. Nat Chem Biol 2022; 18:584-595. [PMID: 35606559 DOI: 10.1038/s41589-022-01040-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 04/15/2022] [Indexed: 11/08/2022]
Abstract
New techniques for systematic profiling of small-molecule effects can enhance traditional growth inhibition screens for antibiotic discovery and change how we search for new antibacterial agents. Computational models that integrate physicochemical compound properties with their phenotypic and molecular downstream effects can not only predict efficacy of molecules yet to be tested, but also reveal unprecedented insights on compound modes of action (MoAs). The unbiased characterization of compounds that themselves are not growth inhibitory but exhibit diverse MoAs, can expand antibacterial strategies beyond direct inhibition of core essential functions. Early and systematic functional annotation of compound libraries thus paves the way to new models in the selection of lead antimicrobial compounds. In this Review, we discuss how multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms.
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6
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Visualizing the pH in Escherichia coli Colonies via the Sensor Protein mCherryEA Allows High-Throughput Screening of Mutant Libraries. mSystems 2022; 7:e0021922. [PMID: 35430898 PMCID: PMC9238402 DOI: 10.1128/msystems.00219-22] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Cytoplasmic pH in bacteria is tightly regulated by diverse active mechanisms and interconnected regulatory processes. Many processes and regulators underlying pH homeostasis have been identified via phenotypic screening of strain libraries for nongrowth at low or high pH values. Direct screens with respect to changes of the internal pH in mutant strain collections are limited by laborious methods, which include fluorescent dyes and radioactive probes. Genetically encoded biosensors equip single organisms or strain libraries with an internal sensor molecule during the generation of the strain. Here, we used the pH-sensitive mCherry variant mCherryEA as a ratiometric pH biosensor. We visualized the internal pH of Escherichia coli colonies on agar plates by the use of a GelDoc imaging system. Combining this imaging technology with robot-assisted colony picking and spotting allowed us to screen and select mutants with altered internal pH values from a small transposon mutagenesis-derived E. coli library. Identification of the transposon (Tn) insertion sites in strains with altered internal pH levels revealed that the transposon was inserted into trkH (encoding a transmembrane protein of the potassium uptake system) or rssB (encoding the adaptor protein RssB, which mediates the proteolytic degradation of the general stress response regulator RpoS), two genes known to be associated with pH homeostasis and pH stress adaptation. This successful screening approach demonstrates that the pH sensor-based analysis of arrayed colonies on agar plates is a sensitive approach for the rapid identification of genes involved in pH homeostasis or pH stress adaptation in E. coli. IMPORTANCE Phenotypic screening of strain libraries on agar plates has become a versatile tool to understand gene functions and to optimize biotechnological platform organisms. Screening is supported by genetically encoded biosensors that allow to easily measure intracellular processes. For this purpose, transcription factor-based biosensors have emerged as the sensor type of choice. Here, the target stimulus initiates the activation of a response gene (e.g., a fluorescent protein), followed by transcription, translation, and maturation. Due to this mechanistic principle, biosensor readouts are delayed and cannot report the actual intracellular state of the cell in real time. To capture rapid intracellular processes adequately, fluorescent reporter proteins are extensively applied. However, these sensor types have not previously been used for phenotypic screenings. To take advantage of their properties, we established here an imaging method that allows application of a rapid ratiometric sensor protein for assessing the internal pH of colonies in a high-throughput manner.
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7
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Combining CRISPRi and metabolomics for functional annotation of compound libraries. Nat Chem Biol 2022; 18:482-491. [PMID: 35194207 PMCID: PMC7612681 DOI: 10.1038/s41589-022-00970-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 01/05/2022] [Indexed: 02/06/2023]
Abstract
Molecular profiling of small-molecules offers invaluable insights into the function of compounds and allows for hypothesis generation about small molecule direct targets and secondary effects. However, current profiling methods are either limited in the number of measurable parameters or throughput. Here, we developed a multiplexed, unbiased framework that, by linking genetic to drug-induced changes in nearly a thousand metabolites, allows for high-throughput functional annotation of compound libraries in Escherichia coli. First, we generated a reference map of metabolic changes from (CRISPR) interference with 352 genes in all major essential biological processes. Next, based on the comparison of genetic with 1342 drug-induced metabolic changes we made de novo predictions of compound functionality and revealed antibacterials with unconventional Modes of Action. We show that our framework, combining dynamic gene silencing with metabolomics, can be adapted as a general strategy for comprehensive high-throughput analysis of compound functionality, from bacteria to human cell lines.
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8
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Affiliation(s)
- Moritz D. Lürig
- Department of Biology Lund University Lund Sweden
- Department of Fish Ecology and Evolution Eawag Kastanienbaum Switzerland
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9
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Tsai NC, Hsu TS, Kuo SC, Kao CT, Hung TH, Lin DG, Yeh CS, Chu CC, Lin JS, Lin HH, Ko CY, Chang TH, Su JC, Lin YCJ. Large-scale data analysis for robotic yeast one-hybrid platforms and multi-disciplinary studies using GateMultiplex. BMC Biol 2021; 19:214. [PMID: 34560855 PMCID: PMC8461970 DOI: 10.1186/s12915-021-01140-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 09/03/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Yeast one-hybrid (Y1H) is a common technique for identifying DNA-protein interactions, and robotic platforms have been developed for high-throughput analyses to unravel the gene regulatory networks in many organisms. Use of these high-throughput techniques has led to the generation of increasingly large datasets, and several software packages have been developed to analyze such data. We previously established the currently most efficient Y1H system, meiosis-directed Y1H; however, the available software tools were not designed for processing the additional parameters suggested by meiosis-directed Y1H to avoid false positives and required programming skills for operation. RESULTS We developed a new tool named GateMultiplex with high computing performance using C++. GateMultiplex incorporated a graphical user interface (GUI), which allows the operation without any programming skills. Flexible parameter options were designed for multiple experimental purposes to enable the application of GateMultiplex even beyond Y1H platforms. We further demonstrated the data analysis from other three fields using GateMultiplex, the identification of lead compounds in preclinical cancer drug discovery, the crop line selection in precision agriculture, and the ocean pollution detection from deep-sea fishery. CONCLUSIONS The user-friendly GUI, fast C++ computing speed, flexible parameter setting, and applicability of GateMultiplex facilitate the feasibility of large-scale data analysis in life science fields.
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Affiliation(s)
- Ni-Chiao Tsai
- Department of Life Science and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan
| | - Tzu-Shu Hsu
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - Shang-Che Kuo
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, 10617, Taiwan
| | - Chung-Ting Kao
- Department of Life Science and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan
| | - Tzu-Huan Hung
- Biotechnology Division, Taiwan Agricultural Research Institute, Taichung, 41362, Taiwan
| | - Da-Gin Lin
- Biotechnology Division, Taiwan Agricultural Research Institute, Taichung, 41362, Taiwan
| | - Chung-Shu Yeh
- Genomics Research Center, Academia Sinica, Taipei, 11529, Taiwan
| | - Chia-Chen Chu
- Department of Life Science and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan
| | - Jeng-Shane Lin
- Department of Life Sciences, National Chung Hsing University, Taichung, 40227, Taiwan
| | - Hsin-Hung Lin
- Department of Horticulture and Biotechnology, Chinese Culture University, Taipei, 11114, Taiwan
| | - Chia-Ying Ko
- Department of Life Sciences and Institute of Fisheries Science, National Taiwan University, Taipei, 10617, Taiwan
| | - Tien-Hsien Chang
- Genomics Research Center, Academia Sinica, Taipei, 11529, Taiwan.
| | - Jung-Chen Su
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan.
| | - Ying-Chung Jimmy Lin
- Department of Life Science and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan.
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, 10617, Taiwan.
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10
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Lürig MD, Donoughe S, Svensson EI, Porto A, Tsuboi M. Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.642774] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic diversity, population dynamics, mechanisms of divergence and adaptation, and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics – the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, provides the opportunity to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV as an efficient and comprehensive method to collect phenomic data in ecological and evolutionary research. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can effectively capture phenomic-level data by taking pictures and analyzing them using CV. Next we describe the primary types of image-based data, review CV approaches for extracting them (including techniques that entail machine learning and others that do not), and identify the most common hurdles and pitfalls. Finally, we highlight recent successful implementations and promising future applications of CV in the study of phenotypes. In anticipation that CV will become a basic component of the biologist’s toolkit, our review is intended as an entry point for ecologists and evolutionary biologists that are interested in extracting phenotypic information from digital images.
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11
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Farha MA, French S, Brown ED. Systems-Level Chemical Biology to Accelerate Antibiotic Drug Discovery. Acc Chem Res 2021; 54:1909-1920. [PMID: 33787225 DOI: 10.1021/acs.accounts.1c00011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Drug-resistant bacterial infections pose an imminent and growing threat to public health. The discovery and development of new antibiotics of novel chemical class and mode of action that are unsusceptible to existing resistance mechanisms is imperative for tackling this threat. Modern industrial drug discovery, however, has failed to provide new drugs of this description, as it is dependent largely on a reductionist genes-to-drugs research paradigm. We posit that the lack of success in new antibiotic drug discovery is due in part to a lack of understanding of the bacterial cell system as whole. A fundamental understanding of the architecture and function of bacterial systems has been elusive but is of critical importance to design strategies to tackle drug-resistant bacterial pathogens.Increasingly, systems-level approaches are rewriting our understanding of the cell, defining a dense network of redundant and interacting components that resist perturbations of all kinds, including by antibiotics. Understanding the network properties of bacterial cells requires integrative, systematic, and genome-scale approaches. These methods strive to understand how the phenotypic behavior of bacteria emerges from the many interactions of individual molecular components that constitute the system. With the ability to examine genomic, transcriptomic, proteomic, and metabolomic consequences of, for example, genetic or chemical perturbations, researchers are increasingly moving away from one-gene-at-a-time studies to consider the system-wide response of the cell. Such measurements are demonstrating promise as quantitative tools, powerful discovery engines, and robust hypothesis generators with great value to antibiotic drug discovery.In this Account, we describe our thinking and findings using systems-level studies aimed at understanding bacterial physiology broadly and in uncovering new antibacterial chemical matter of novel mechanism. We share our systems-level toolkit and detail recent technological developments that have enabled unprecedented acquisition of genome-wide interaction data. We focus on three types of interactions: gene-gene, chemical-gene, and chemical-chemical. We provide examples of their use in understanding cell networks and how these insights might be harnessed for new antibiotic discovery. By example, we show the application of these principles in mapping genetic networks that underpin phenotypes of interest, characterizing genes of unknown function, validating small-molecule screening platforms, uncovering novel chemical probes and antibacterial leads, and delineating the mode of action of antibacterial chemicals. We also discuss the importance of computation to these approaches and its probable dominance as a tool for systems approaches in the future. In all, we advocate for the use of systems-based approaches as discovery engines in antibacterial research, both as powerful tools and to stimulate innovation.
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Affiliation(s)
- Maya A. Farha
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario L8N 3Z5, Canada
- Michael G. DeGroote Institute of Infectious Disease Research, McMaster University, Hamilton, Ontario L8N 3Z5, Canada
| | - Shawn French
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario L8N 3Z5, Canada
- Michael G. DeGroote Institute of Infectious Disease Research, McMaster University, Hamilton, Ontario L8N 3Z5, Canada
| | - Eric D. Brown
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario L8N 3Z5, Canada
- Michael G. DeGroote Institute of Infectious Disease Research, McMaster University, Hamilton, Ontario L8N 3Z5, Canada
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12
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Yáñez Feliú G, Earle Gómez B, Codoceo Berrocal V, Muñoz Silva M, Nuñez IN, Matute TF, Arce Medina A, Vidal G, Vitalis C, Dahlin J, Federici F, Rudge TJ. Flapjack: Data Management and Analysis for Genetic Circuit Characterization. ACS Synth Biol 2021; 10:183-191. [PMID: 33382586 DOI: 10.1021/acssynbio.0c00554] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Characterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different laboratories and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parametrize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting, and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated with circuit part composition via SBOL (Synthetic Biology Open Language). We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.
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Affiliation(s)
- Guillermo Yáñez Feliú
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Benjamín Earle Gómez
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Verner Codoceo Berrocal
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Macarena Muñoz Silva
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Isaac N Nuñez
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Tamara F Matute
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Anibal Arce Medina
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Gonzalo Vidal
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Carlos Vitalis
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Jonathan Dahlin
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Fernán Federici
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
- FONDAP, Center for Genome Regulation, Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Timothy J Rudge
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
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13
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Schäfer AB, Wenzel M. A How-To Guide for Mode of Action Analysis of Antimicrobial Peptides. Front Cell Infect Microbiol 2020; 10:540898. [PMID: 33194788 PMCID: PMC7604286 DOI: 10.3389/fcimb.2020.540898] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 09/18/2020] [Indexed: 12/11/2022] Open
Abstract
Antimicrobial peptides (AMPs) are a promising alternative to classical antibiotics in the fight against multi-resistant bacteria. They are produced by organisms from all domains of life and constitute a nearly universal defense mechanism against infectious agents. No drug can be approved without information about its mechanism of action. In order to use them in a clinical setting, it is pivotal to understand how AMPs work. While many pore-forming AMPs are well-characterized in model membrane systems, non-pore-forming peptides are often poorly understood. Moreover, there is evidence that pore formation may not happen or not play a role in vivo. It is therefore imperative to study how AMPs interact with their targets in vivo and consequently kill microorganisms. This has been difficult in the past, since established methods did not provide much mechanistic detail. Especially, methods to study membrane-active compounds have been scarce. Recent advances, in particular in microscopy technology and cell biological labeling techniques, now allow studying mechanisms of AMPs in unprecedented detail. This review gives an overview of available in vivo methods to investigate the antibacterial mechanisms of AMPs. In addition to classical mode of action classification assays, we discuss global profiling techniques, such as genomic and proteomic approaches, as well as bacterial cytological profiling and other cell biological assays. We cover approaches to determine the effects of AMPs on cell morphology, outer membrane, cell wall, and inner membrane properties, cellular macromolecules, and protein targets. We particularly expand on methods to examine cytoplasmic membrane parameters, such as composition, thickness, organization, fluidity, potential, and the functionality of membrane-associated processes. This review aims to provide a guide for researchers, who seek a broad overview of the available methodology to study the mechanisms of AMPs in living bacteria.
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Affiliation(s)
| | - Michaela Wenzel
- Division of Chemical Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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14
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French S, Guo ABY, Brown ED. A comprehensive guide to dynamic analysis of microbial gene expression using the 3D-printed PFIbox and a fluorescent reporter library. Nat Protoc 2020; 15:575-603. [DOI: 10.1038/s41596-019-0257-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Accepted: 10/11/2019] [Indexed: 01/25/2023]
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15
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Yang JH, Wright SN, Hamblin M, McCloskey D, Alcantar MA, Schrübbers L, Lopatkin AJ, Satish S, Nili A, Palsson BO, Walker GC, Collins JJ. A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action. Cell 2019; 177:1649-1661.e9. [PMID: 31080069 PMCID: PMC6545570 DOI: 10.1016/j.cell.2019.04.016] [Citation(s) in RCA: 181] [Impact Index Per Article: 36.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 03/19/2019] [Accepted: 04/08/2019] [Indexed: 12/13/2022]
Abstract
Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.
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Affiliation(s)
- Jason H Yang
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sarah N Wright
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Meagan Hamblin
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Douglas McCloskey
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Miguel A Alcantar
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Lars Schrübbers
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Allison J Lopatkin
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Sangeeta Satish
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Amir Nili
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Bernhard O Palsson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Graham C Walker
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - James J Collins
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
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