1
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Novak JK, Gardner JG. Current models in bacterial hemicellulase-encoding gene regulation. Appl Microbiol Biotechnol 2024; 108:39. [PMID: 38175245 PMCID: PMC10766802 DOI: 10.1007/s00253-023-12977-4] [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: 12/06/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 01/05/2024]
Abstract
The discovery and characterization of bacterial carbohydrate-active enzymes is a fundamental component of biotechnology innovation, particularly for renewable fuels and chemicals; however, these studies have increasingly transitioned to exploring the complex regulation required for recalcitrant polysaccharide utilization. This pivot is largely due to the current need to engineer and optimize enzymes for maximal degradation in industrial or biomedical applications. Given the structural simplicity of a single cellulose polymer, and the relatively few enzyme classes required for complete bioconversion, the regulation of cellulases in bacteria has been thoroughly discussed in the literature. However, the diversity of hemicelluloses found in plant biomass and the multitude of carbohydrate-active enzymes required for their deconstruction has resulted in a less comprehensive understanding of bacterial hemicellulase-encoding gene regulation. Here we review the mechanisms of this process and common themes found in the transcriptomic response during plant biomass utilization. By comparing regulatory systems from both Gram-negative and Gram-positive bacteria, as well as drawing parallels to cellulase regulation, our goals are to highlight the shared and distinct features of bacterial hemicellulase-encoding gene regulation and provide a set of guiding questions to improve our understanding of bacterial lignocellulose utilization. KEY POINTS: • Canonical regulatory mechanisms for bacterial hemicellulase-encoding gene expression include hybrid two-component systems (HTCS), extracytoplasmic function (ECF)-σ/anti-σ systems, and carbon catabolite repression (CCR). • Current transcriptomic approaches are increasingly being used to identify hemicellulase-encoding gene regulatory patterns coupled with computational predictions for transcriptional regulators. • Future work should emphasize genetic approaches to improve systems biology tools available for model bacterial systems and emerging microbes with biotechnology potential. Specifically, optimization of Gram-positive systems will require integration of degradative and fermentative capabilities, while optimization of Gram-negative systems will require bolstering the potency of lignocellulolytic capabilities.
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Affiliation(s)
- Jessica K Novak
- Department of Biological Sciences, University of Maryland - Baltimore County, Baltimore, MD, USA
| | - Jeffrey G Gardner
- Department of Biological Sciences, University of Maryland - Baltimore County, Baltimore, MD, USA.
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2
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Patel A, McGrosso D, Hefner Y, Campeau A, Sastry AV, Maurya S, Rychel K, Gonzalez DJ, Palsson BO. Proteome allocation is linked to transcriptional regulation through a modularized transcriptome. Nat Commun 2024; 15:5234. [PMID: 38898010 PMCID: PMC11187210 DOI: 10.1038/s41467-024-49231-y] [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: 02/22/2023] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigate whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions can be modularized in the same way to reveal novel relationships between their compositions. We find that; (1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, (2) the modules in the proteome often represent combinations of modules from the transcriptome, (3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and (4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.
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Affiliation(s)
- Arjun Patel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Dominic McGrosso
- Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Ying Hefner
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Anaamika Campeau
- Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Anand V Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Svetlana Maurya
- Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - David J Gonzalez
- Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs, Lyngby, Denmark.
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3
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Wu S, Zhou H, Chen D, Lu Y, Li Y, Qiao J. Multi-omic analysis tools for microbial metabolites prediction. Brief Bioinform 2024; 25:bbae264. [PMID: 38859767 PMCID: PMC11165163 DOI: 10.1093/bib/bbae264] [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: 02/03/2024] [Revised: 05/08/2024] [Indexed: 06/12/2024] Open
Abstract
How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.
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Affiliation(s)
- Shengbo Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Haonan Zhou
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Danlei Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Yutong Lu
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Yanni Li
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
| | - Jianjun Qiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
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4
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Menon ND, Poudel S, Sastry AV, Rychel K, Szubin R, Dillon N, Tsunemoto H, Hirose Y, Nair BG, Kumar GB, Palsson BO, Nizet V. Independent component analysis reveals 49 independently modulated gene sets within the global transcriptional regulatory architecture of multidrug-resistant Acinetobacter baumannii. mSystems 2024; 9:e0060623. [PMID: 38189271 PMCID: PMC10878099 DOI: 10.1128/msystems.00606-23] [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: 06/11/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024] Open
Abstract
Acinetobacter baumannii causes severe infections in humans, resists multiple antibiotics, and survives in stressful environmental conditions due to modulations of its complex transcriptional regulatory network (TRN). Unfortunately, our global understanding of the TRN in this emerging opportunistic pathogen is limited. Here, we apply independent component analysis, an unsupervised machine learning method, to a compendium of 139 RNA-seq data sets of three multidrug-resistant A. baumannii international clonal complex I strains (AB5075, AYE, and AB0057). This analysis allows us to define 49 independently modulated gene sets, which we call iModulons. Analysis of the identified A. baumannii iModulons reveals validating parallels to previously defined biological operons/regulons and provides a framework for defining unknown regulons. By utilizing the iModulons, we uncover potential mechanisms for a RpoS-independent general stress response, define global stress-virulence trade-offs, and identify conditions that may induce plasmid-borne multidrug resistance. The iModulons provide a model of the TRN that emphasizes the importance of transcriptional regulation of virulence phenotypes in A. baumannii. Furthermore, they suggest the possibility of future interventions to guide gene expression toward diminished pathogenic potential.IMPORTANCEThe rise in hospital outbreaks of multidrug-resistant Acinetobacter baumannii infections underscores the urgent need for alternatives to traditional broad-spectrum antibiotic therapies. The success of A. baumannii as a significant nosocomial pathogen is largely attributed to its ability to resist antibiotics and survive environmental stressors. However, there is limited literature available on the global, complex regulatory circuitry that shapes these phenotypes. Computational tools that can assist in the elucidation of A. baumannii's transcriptional regulatory network architecture can provide much-needed context for a comprehensive understanding of pathogenesis and virulence, as well as for the development of targeted therapies that modulate these pathways.
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Affiliation(s)
- Nitasha D. Menon
- School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India
- Division of Host-Microbe Systems and Therapeutics, Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
| | - Saugat Poudel
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Anand V. Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Richard Szubin
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Nicholas Dillon
- Division of Host-Microbe Systems and Therapeutics, Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
- Department of Biological Sciences, University of Texas at Dallas, Dallas, Texas, USA
| | - Hannah Tsunemoto
- Division of Biological Sciences, University of California, San Diego, La Jolla, California, USA
| | - Yujiro Hirose
- Division of Host-Microbe Systems and Therapeutics, Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
- Department of Microbiology, Graduate School of Dentistry, Osaka University, Suita, Osaka, Japan
| | - Bipin G. Nair
- School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India
| | - Geetha B. Kumar
- School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Victor Nizet
- Division of Host-Microbe Systems and Therapeutics, Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA
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5
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Tan H, Guo M, Chen J, Wang J, Yu G. HetFCM: functional co-module discovery by heterogeneous network co-clustering. Nucleic Acids Res 2024; 52:e16. [PMID: 38088228 PMCID: PMC10853805 DOI: 10.1093/nar/gkad1174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/31/2023] [Accepted: 11/23/2023] [Indexed: 02/10/2024] Open
Abstract
Functional molecular module (i.e., gene-miRNA co-modules and gene-miRNA-lncRNA triple-layer modules) analysis can dissect complex regulations underlying etiology or phenotypes. However, current module detection methods lack an appropriate usage and effective model of multi-omics data and cross-layer regulations of heterogeneous molecules, causing the loss of critical genetic information and corrupting the detection performance. In this study, we propose a heterogeneous network co-clustering framework (HetFCM) to detect functional co-modules. HetFCM introduces an attributed heterogeneous network to jointly model interplays and multi-type attributes of different molecules, and applies multiple variational graph autoencoders on the network to generate cross-layer association matrices, then it performs adaptive weighted co-clustering on association matrices and attribute data to identify co-modules of heterogeneous molecules. Empirical study on Human and Maize datasets reveals that HetFCM can find out co-modules characterized with denser topology and more significant functions, which are associated with human breast cancer (subtypes) and maize phenotypes (i.e., lipid storage, drought tolerance and oil content). HetFCM is a useful tool to detect co-modules and can be applied to multi-layer functional modules, yielding novel insights for analyzing molecular mechanisms. We also developed a user-friendly module detection and analysis tool and shared it at http://www.sdu-idea.cn/FMDTool.
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Affiliation(s)
- Haojiang Tan
- School of Software, Shandong University, Jinan 250101, Shandong, China
- Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan 250101, Shandong, China
| | - Maozu Guo
- College of Electrical and Information Engineering, Beijing Uni. of Civil Eng. and Arch., Beijing 100044, China
| | - Jian Chen
- College of Agronomy & Biotechnolog, China Agricultural University, Beijing 100193, China
| | - Jun Wang
- Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan 250101, Shandong, China
| | - Guoxian Yu
- School of Software, Shandong University, Jinan 250101, Shandong, China
- Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan 250101, Shandong, China
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6
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Walls AW, Rosenthal AZ. Bacterial phenotypic heterogeneity through the lens of single-cell RNA sequencing. Transcription 2024; 15:48-62. [PMID: 38532542 DOI: 10.1080/21541264.2024.2334110] [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: 12/17/2023] [Accepted: 03/19/2024] [Indexed: 03/28/2024] Open
Abstract
Bacterial transcription is not monolithic. Microbes exist in a wide variety of cell states that help them adapt to their environment, acquire and produce essential nutrients, and engage in both competition and cooperation with their neighbors. While we typically think of bacterial adaptation as a group behavior, where all cells respond in unison, there is often a mixture of phenotypic responses within a bacterial population, where distinct cell types arise. A primary phenomenon driving these distinct cell states is transcriptional heterogeneity. Given that bacterial mRNA transcripts are extremely short-lived compared to eukaryotes, their transcriptional state is closely associated with their physiology, and thus the transcriptome of a bacterial cell acts as a snapshot of the behavior of that bacterium. Therefore, the application of single-cell transcriptomics to microbial populations will provide novel insight into cellular differentiation and bacterial ecology. In this review, we provide an overview of transcriptional heterogeneity in microbial systems, discuss the findings already provided by single-cell approaches, and plot new avenues of inquiry in transcriptional regulation, cellular biology, and mechanisms of heterogeneity that are made possible when microbial communities are analyzed at single-cell resolution.
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Affiliation(s)
- Alex W Walls
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, USA
| | - Adam Z Rosenthal
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, USA
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7
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Qiu S, Huang Y, Liang S, Zeng H, Yang A. Systematic elucidation of independently modulated genes in Lactiplantibacillus plantarum reveals a trade-off between secondary and primary metabolism. Microb Biotechnol 2024; 17:e14425. [PMID: 38393514 PMCID: PMC10886434 DOI: 10.1111/1751-7915.14425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 02/02/2024] [Indexed: 02/25/2024] Open
Abstract
Lactiplantibacillus plantarum is a probiotic bacterium widely used in food and health industries, but its gene regulatory information is limited in existing databases, which impedes the research of its physiology and its applications. To obtain a better understanding of the transcriptional regulatory network of L. plantarum, independent component analysis of its transcriptomes was used to derive 45 sets of independently modulated genes (iModulons). Those iModulons were annotated for associated transcription factors and functional pathways, and active iModulons in response to different growth conditions were identified and characterized in detail. Eventually, the analysis of iModulon activities reveals a trade-off between regulatory activities of secondary and primary metabolism in L. plantarum.
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Affiliation(s)
- Sizhe Qiu
- Department of Engineering ScienceUniversity of OxfordOxfordUK
- School of Food and HealthBeijing Technology and Business UniversityBeijingChina
| | - Yidi Huang
- School of Computer Science and EngineeringBeihang UniversityBeijingChina
| | - Shishun Liang
- Department of Life ScienceImperial College LondonLondonUK
| | - Hong Zeng
- School of Food and HealthBeijing Technology and Business UniversityBeijingChina
| | - Aidong Yang
- Department of Engineering ScienceUniversity of OxfordOxfordUK
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8
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Qiu S, Wan X, Liang Y, Lamoureux CR, Akbari A, Palsson BO, Zielinski DC. Inferred regulons are consistent with regulator binding sequences in E. coli. PLoS Comput Biol 2024; 20:e1011824. [PMID: 38252668 PMCID: PMC10833566 DOI: 10.1371/journal.pcbi.1011824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/01/2024] [Accepted: 01/12/2024] [Indexed: 01/24/2024] Open
Abstract
The transcriptional regulatory network (TRN) of E. coli consists of thousands of interactions between regulators and DNA sequences. Regulons are typically determined either from resource-intensive experimental measurement of functional binding sites, or inferred from analysis of high-throughput gene expression datasets. Recently, independent component analysis (ICA) of RNA-seq compendia has shown to be a powerful method for inferring bacterial regulons. However, it remains unclear to what extent regulons predicted by ICA structure have a biochemical basis in promoter sequences. Here, we address this question by developing machine learning models that predict inferred regulon structures in E. coli based on promoter sequence features. Models were constructed successfully (cross-validation AUROC > = 0.8) for 85% (40/47) of ICA-inferred E. coli regulons. We found that: 1) The presence of a high scoring regulator motif in the promoter region was sufficient to specify regulatory activity in 40% (19/47) of the regulons, 2) Additional features, such as DNA shape and extended motifs that can account for regulator multimeric binding, helped to specify regulon structure for the remaining 60% of regulons (28/47); 3) investigating regulons where initial machine learning models failed revealed new regulator-specific sequence features that improved model accuracy. Finally, we found that strong regulatory binding sequences underlie both the genes shared between ICA-inferred and experimental regulons as well as genes in the E. coli core pan-regulon of Fur. This work demonstrates that the structure of ICA-inferred regulons largely can be understood through the strength of regulator binding sites in promoter regions, reinforcing the utility of top-down inference for regulon discovery.
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Affiliation(s)
- Sizhe Qiu
- Department of Bioengineering, University of California San Diego, La Jolla, CA, United States of America
| | - Xinlong Wan
- Department of Bioengineering, University of California San Diego, La Jolla, CA, United States of America
| | - Yueshan Liang
- Department of Bioengineering, University of California San Diego, La Jolla, CA, United States of America
| | - Cameron R. Lamoureux
- Department of Bioengineering, University of California San Diego, La Jolla, CA, United States of America
| | - Amir Akbari
- Department of Bioengineering, University of California San Diego, La Jolla, CA, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, CA, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Daniel C. Zielinski
- Department of Bioengineering, University of California San Diego, La Jolla, CA, United States of America
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9
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Qian J, Wang Y, Hu Z, Shi T, Wang Y, Ye C, Huang H. Bacillus sp. as a microbial cell factory: Advancements and future prospects. Biotechnol Adv 2023; 69:108278. [PMID: 37898328 DOI: 10.1016/j.biotechadv.2023.108278] [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: 07/07/2023] [Revised: 09/27/2023] [Accepted: 10/25/2023] [Indexed: 10/30/2023]
Abstract
Bacillus sp. is one of the most distinctive gram-positive bacteria, able to grow efficiently using cheap carbon sources and secrete a variety of useful substances, which are widely used in food, pharmaceutical, agricultural and environmental industries. At the same time, Bacillus sp. is also recognized as a safe genus with a relatively clear genetic background, which is conducive to the industrial production of target metabolites. In this review, we discuss the reasons why Bacillus sp. has been so extensively studied and summarize its advances in systems and synthetic biology, engineering strategies to improve microbial cell properties, and industrial applications in several metabolic engineering applications. Finally, we present the current challenges and possible solutions to provide a reliable basis for Bacillus sp. as a microbial cell factory.
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Affiliation(s)
- Jinyi Qian
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China
| | - Yuzhou Wang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China
| | - Zijian Hu
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China
| | - Tianqiong Shi
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China.
| | - Yuetong Wang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China.
| | - Chao Ye
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China.
| | - He Huang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China.
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10
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Miano A, Rychel K, Lezia A, Sastry A, Palsson B, Hasty J. High-resolution temporal profiling of E. coli transcriptional response. Nat Commun 2023; 14:7606. [PMID: 37993418 PMCID: PMC10665441 DOI: 10.1038/s41467-023-43173-7] [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: 07/22/2023] [Accepted: 11/02/2023] [Indexed: 11/24/2023] Open
Abstract
Understanding how cells dynamically adapt to their environment is a primary focus of biology research. Temporal information about cellular behavior is often limited by both small numbers of data time-points and the methods used to analyze this data. Here, we apply unsupervised machine learning to a data set containing the activity of 1805 native promoters in E. coli measured every 10 minutes in a high-throughput microfluidic device via fluorescence time-lapse microscopy. Specifically, this data set reveals E. coli transcriptome dynamics when exposed to different heavy metal ions. We use a bioinformatics pipeline based on Independent Component Analysis (ICA) to generate insights and hypotheses from this data. We discovered three primary, time-dependent stages of promoter activation to heavy metal stress (fast, intermediate, and steady). Furthermore, we uncovered a global strategy E. coli uses to reallocate resources from stress-related promoters to growth-related promoters following exposure to heavy metal stress.
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Affiliation(s)
- Arianna Miano
- Department of Bioengineering, University of California San Diego, 9500 Gliman Dr, La Jolla, CA, USA.
| | - Kevin Rychel
- Department of Bioengineering, University of California San Diego, 9500 Gliman Dr, La Jolla, CA, USA
| | - Andrew Lezia
- Department of Bioengineering, University of California San Diego, 9500 Gliman Dr, La Jolla, CA, USA
| | - Anand Sastry
- Department of Bioengineering, University of California San Diego, 9500 Gliman Dr, La Jolla, CA, USA
| | - Bernhard Palsson
- Department of Bioengineering, University of California San Diego, 9500 Gliman Dr, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs, Lyngby, Denmark
| | - Jeff Hasty
- Department of Bioengineering, University of California San Diego, 9500 Gliman Dr, La Jolla, CA, USA
- Department of Molecular Biology, School of Biological Sciences, University of California San Diego, 9500 Gliman Dr, La Jolla, CA, USA
- Synthetic Biology Institute, University of California San Diego, 9500 Gliman Dr, La Jolla, CA, USA
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11
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Zhao J, Sun X, Mao Z, Zheng Y, Geng Z, Zhang Y, Ma H, Wang Z. Independent component analysis of Corynebacterium glutamicum transcriptomes reveals its transcriptional regulatory network. Microbiol Res 2023; 276:127485. [PMID: 37683565 DOI: 10.1016/j.micres.2023.127485] [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: 05/30/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023]
Abstract
Gene expression in bacteria is regulated by multiple transcription factors. Clarifying the regulation mechanism of gene expression is necessary to understand bacterial physiological activities. To further understand the structure of the transcriptional regulatory network of Corynebacterium glutamicum, we applied independent component analysis, an unsupervised machine learning algorithm, to the high-quality C. glutamicum gene expression profile which includes 263 samples from 29 independent projects. We obtained 87 robust independent regulatory modules (iModulons). These iModulons explain 76.7% of the variance in the expression profile and constitute the quantitative transcriptional regulatory network of C. glutamicum. By analyzing the constituent genes in iModulons, we identified potential targets for 20 transcription factors. We also captured the changes in iModulon activities under different growth rates and dissolved oxygen concentrations, demonstrating the ability of iModulons to comprehensively interpret transcriptional responses to environmental changes. In summary, this study provides a genome-scale quantitative transcriptional regulatory network for C. glutamicum and informs future research on complex changes in the transcriptome.
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Affiliation(s)
- Jianxiao Zhao
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China; Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China
| | - Xi Sun
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Zhitao Mao
- Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China
| | - Yangyang Zheng
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Zhouxiao Geng
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Yuhan Zhang
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Hongwu Ma
- Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China.
| | - Zhiwen Wang
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
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12
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Lamoureux CR, Decker KT, Sastry AV, Rychel K, Gao Y, McConn J, Zielinski D, Palsson BO. A multi-scale expression and regulation knowledge base for Escherichia coli. Nucleic Acids Res 2023; 51:10176-10193. [PMID: 37713610 PMCID: PMC10602906 DOI: 10.1093/nar/gkad750] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/02/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023] Open
Abstract
Transcriptomic data is accumulating rapidly; thus, scalable methods for extracting knowledge from this data are critical. Here, we assembled a top-down expression and regulation knowledge base for Escherichia coli. The expression component is a 1035-sample, high-quality RNA-seq compendium consisting of data generated in our lab using a single experimental protocol. The compendium contains diverse growth conditions, including: 9 media; 39 supplements, including antibiotics; 42 heterologous proteins; and 76 gene knockouts. Using this resource, we elucidated global expression patterns. We used machine learning to extract 201 modules that account for 86% of known regulatory interactions, creating the regulatory component. With these modules, we identified two novel regulons and quantified systems-level regulatory responses. We also integrated 1675 curated, publicly-available transcriptomes into the resource. We demonstrated workflows for analyzing new data against this knowledge base via deconstruction of regulation during aerobic transition. This resource illuminates the E. coli transcriptome at scale and provides a blueprint for top-down transcriptomic analysis of non-model organisms.
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Affiliation(s)
- Cameron R Lamoureux
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Katherine T Decker
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anand V Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ye Gao
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - John Luke McConn
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Daniel C Zielinski
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs. Lyngby, Denmark
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13
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Bajpe H, Rychel K, Lamoureux CR, Sastry AV, Palsson BO. Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infection. mSystems 2023; 8:e0043723. [PMID: 37638727 PMCID: PMC10654099 DOI: 10.1128/msystems.00437-23] [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: 05/09/2023] [Accepted: 07/19/2023] [Indexed: 08/29/2023] Open
Abstract
IMPORTANCE Pseudomonas syringae pv. tomato DC3000 is a model plant pathogen that infects tomatoes and Arabidopsis thaliana. The current understanding of global transcriptional regulation in the pathogen is limited. Here, we applied iModulon analysis to a compendium of RNA-seq data to unravel its transcriptional regulatory network. We characterize each co-regulated gene set, revealing the activity of major regulators across diverse conditions. We provide new insights on the transcriptional dynamics in interactions with the plant immune system and with other bacterial species, such as AlgU-dependent regulation of flagellar genes during plant infection and downregulation of siderophore production in the presence of a siderophore cheater. This study demonstrates the novel application of iModulons in studying temporal dynamics during host-pathogen and microbe-microbe interactions, and reveals specific insights of interest.
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Affiliation(s)
- Heera Bajpe
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Cameron R. Lamoureux
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Anand V. Sastry
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, California, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA
- Novo Nordisk Foundation Center for Biosustainability, Kongens Lyngby, Denmark
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14
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Rychel K, Tan J, Patel A, Lamoureux C, Hefner Y, Szubin R, Johnsen J, Mohamed ETT, Phaneuf PV, Anand A, Olson CA, Park JH, Sastry AV, Yang L, Feist AM, Palsson BO. Laboratory evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance. Cell Rep 2023; 42:113105. [PMID: 37713311 PMCID: PMC10591938 DOI: 10.1016/j.celrep.2023.113105] [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: 02/09/2023] [Revised: 07/09/2023] [Accepted: 08/23/2023] [Indexed: 09/17/2023] Open
Abstract
Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress-tolerance mechanisms: (1) modification of transport, (2) activation of ROS stress responses, (3) use of ROS-sensitive iron regulation, (4) motility, (5) broad transcriptional reallocation toward growth, and (6) metabolic rewiring to decrease NADH production. This work thus demonstrates the power of iModulon knowledge mapping for evolution analysis.
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Affiliation(s)
- Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Justin Tan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Arjun Patel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Cameron Lamoureux
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ying Hefner
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Richard Szubin
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Josefin Johnsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs. Lyngby, Denmark
| | - Elsayed Tharwat Tolba Mohamed
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs. Lyngby, Denmark
| | - Patrick V Phaneuf
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs. Lyngby, Denmark
| | - Amitesh Anand
- Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai, Maharashtra, India
| | - Connor A Olson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Joon Ho Park
- Department of Chemical Engineering, Massachusetts Institute of Technology, 500 Main Street, Building 76, Cambridge, MA 02139, USA
| | - Anand V Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Laurence Yang
- Department of Chemical Engineering, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Adam M Feist
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs. Lyngby, Denmark
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs. Lyngby, Denmark.
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15
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Gao ZP, Gu WC, Li J, Qiu QT, Ma BG. Independent Component Analysis Reveals the Transcriptional Regulatory Modules in Bradyrhizobium diazoefficiens USDA110. Int J Mol Sci 2023; 24:12544. [PMID: 37628727 PMCID: PMC10454721 DOI: 10.3390/ijms241612544] [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: 06/30/2023] [Revised: 07/30/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023] Open
Abstract
The dynamic adaptation of bacteria to environmental changes is achieved through the coordinated expression of many genes, which constitutes a transcriptional regulatory network (TRN). Bradyrhizobium diazoefficiens USDA110 is an important model strain for the study of symbiotic nitrogen fixation (SNF), and its SNF ability largely depends on the TRN. In this study, independent component analysis was applied to 226 high-quality gene expression profiles of B. diazoefficiens USDA110 microarray datasets, from which 64 iModulons were identified. Using these iModulons and their condition-specific activity levels, we (1) provided new insights into the connection between the FixLJ-FixK2-FixK1 regulatory cascade and quorum sensing, (2) discovered the independence of the FixLJ-FixK2-FixK1 and NifA/RpoN regulatory cascades in response to oxygen, (3) identified the FixLJ-FixK2 cascade as a mediator connecting the FixK2-2 iModulon and the Phenylalanine iModulon, (4) described the differential activation of iModulons in B. diazoefficiens USDA110 under different environmental conditions, and (5) proposed a notion of active-TRN based on the changes in iModulon activity to better illustrate the relationship between gene regulation and environmental condition. In sum, this research offered an iModulon-based TRN for B. diazoefficiens USDA110, which formed a foundation for comprehensively understanding the intricate transcriptional regulation during SNF.
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Affiliation(s)
| | | | | | | | - Bin-Guang Ma
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; (Z.-P.G.); (W.-C.G.); (J.L.); (Q.-T.Q.)
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16
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Yuan S, Sun Y, Chang W, Zhang J, Sang J, Zhao J, Song M, Qiao Y, Zhang C, Zhu M, Tang Y, Lou H. The silkworm (Bombyx mori) gut microbiota is involved in metabolic detoxification by glucosylation of plant toxins. Commun Biol 2023; 6:790. [PMID: 37516758 PMCID: PMC10387059 DOI: 10.1038/s42003-023-05150-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 07/17/2023] [Indexed: 07/31/2023] Open
Abstract
Herbivores have evolved the ability to detoxify feed components through different mechanisms. The oligophagous silkworm feeds on Cudrania tricuspidata leaves (CTLs) instead of mulberry leaves for the purpose of producing special, high-quality silk. However, CTL-fed silkworms are found to have smaller bodies, slower growth and lower silk production than those fed mulberry leaves. Here, we show that the high content of prenylated isoflavones (PIFs) that occurred in CTLs is converted into glycosylated derivatives (GPIFs) in silkworm faeces through the silkworm gut microbiota, and this biotransformation is the key process in PIFs detoxification because GPIFs are found to be much less toxic, as revealed both in vitro and in vivo. Additionally, adding Bacillus subtilis as a probiotic to remodel the gut microbiota could beneficially promote silkworm growth and development. Consequently, this study provides meaningful guidance for silk production by improving the adaptability of CTL-fed silkworms.
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Affiliation(s)
- Shuangzhi Yuan
- Department of Natural Products Chemistry, Key Laboratory of Chemical Biology of the Ministry of Education, School of Pharmaceutical Sciences, Shandong University, Jinan, 250012, P. R. China
| | - Yong Sun
- Department of Natural Products Chemistry, Key Laboratory of Chemical Biology of the Ministry of Education, School of Pharmaceutical Sciences, Shandong University, Jinan, 250012, P. R. China
| | - Wenqiang Chang
- Department of Natural Products Chemistry, Key Laboratory of Chemical Biology of the Ministry of Education, School of Pharmaceutical Sciences, Shandong University, Jinan, 250012, P. R. China
| | - Jiaozhen Zhang
- Department of Natural Products Chemistry, Key Laboratory of Chemical Biology of the Ministry of Education, School of Pharmaceutical Sciences, Shandong University, Jinan, 250012, P. R. China
| | - Jifa Sang
- Linyi University, Yishui, Linyi, 276400, P. R. China
| | - Jiachun Zhao
- Linyi University, Yishui, Linyi, 276400, P. R. China
| | - Minghui Song
- Department of Natural Products Chemistry, Key Laboratory of Chemical Biology of the Ministry of Education, School of Pharmaceutical Sciences, Shandong University, Jinan, 250012, P. R. China
| | - Yanan Qiao
- Department of Natural Products Chemistry, Key Laboratory of Chemical Biology of the Ministry of Education, School of Pharmaceutical Sciences, Shandong University, Jinan, 250012, P. R. China
| | - Chunyang Zhang
- Department of Natural Products Chemistry, Key Laboratory of Chemical Biology of the Ministry of Education, School of Pharmaceutical Sciences, Shandong University, Jinan, 250012, P. R. China
| | - Mingzhu Zhu
- Department of Natural Products Chemistry, Key Laboratory of Chemical Biology of the Ministry of Education, School of Pharmaceutical Sciences, Shandong University, Jinan, 250012, P. R. China
| | - Yajie Tang
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, 266237, P. R. China
| | - Hongxiang Lou
- Department of Natural Products Chemistry, Key Laboratory of Chemical Biology of the Ministry of Education, School of Pharmaceutical Sciences, Shandong University, Jinan, 250012, P. R. China.
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17
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Shin J, Rychel K, Palsson BO. Systems biology of competency in Vibrio natriegens is revealed by applying novel data analytics to the transcriptome. Cell Rep 2023; 42:112619. [PMID: 37285268 DOI: 10.1016/j.celrep.2023.112619] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 04/27/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023] Open
Abstract
Vibrio natriegens regulates natural competence through the TfoX and QstR transcription factors, which are involved in external DNA capture and transport. However, the extensive genetic and transcriptional regulatory basis for competency remains unknown. We used a machine-learning approach to decompose Vibrio natriegens's transcriptome into 45 groups of independently modulated sets of genes (iModulons). Our findings show that competency is associated with the repression of two housekeeping iModulons (iron metabolism and translation) and the activation of six iModulons; including TfoX and QstR, a novel iModulon of unknown function, and three housekeeping iModulons (representing motility, polycations, and reactive oxygen species [ROS] responses). Phenotypic screening of 83 gene deletion strains demonstrates that loss of iModulon function reduces or eliminates competency. This database-iModulon-discovery cycle unveils the transcriptomic basis for competency and its relationship to housekeeping functions. These results provide the genetic basis for systems biology of competency in this organism.
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Affiliation(s)
- Jongoh Shin
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
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18
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Charron-Lamoureux V, Haroune L, Pomerleau M, Hall L, Orban F, Leroux J, Rizzi A, Bourassa JS, Fontaine N, d'Astous ÉV, Dauphin-Ducharme P, Legault CY, Bellenger JP, Beauregard PB. Pulcherriminic acid modulates iron availability and protects against oxidative stress during microbial interactions. Nat Commun 2023; 14:2536. [PMID: 37137890 PMCID: PMC10156857 DOI: 10.1038/s41467-023-38222-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 04/20/2023] [Indexed: 05/05/2023] Open
Abstract
Siderophores are soluble or membrane-embedded molecules that bind the oxidized form of iron, Fe(III), and play roles in iron acquisition by microorganisms. Fe(III)-bound siderophores bind to specific receptors that allow microbes to acquire iron. However, certain soil microbes release a compound (pulcherriminic acid, PA) that, upon binding to Fe(III), forms a precipitate (pulcherrimin) that apparently functions by reducing iron availability rather than contributing to iron acquisition. Here, we use Bacillus subtilis (PA producer) and Pseudomonas protegens as a competition model to show that PA is involved in a peculiar iron-managing system. The presence of the competitor induces PA production, leading to precipitation of Fe(III) as pulcherrimin, which prevents oxidative stress in B. subtilis by restricting the Fenton reaction and deleterious ROS formation. In addition, B. subtilis uses its known siderophore bacillibactin to retrieve Fe(III) from pulcherrimin. Our findings indicate that PA plays multiple roles by modulating iron availability and conferring protection against oxidative stress during inter-species competition.
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Affiliation(s)
| | - Lounès Haroune
- Département de chimie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
- Institut de pharmacologie de Sherbrooke, Faculté de médecine, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Maude Pomerleau
- Département de biologie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Léo Hall
- Département de chimie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Frédéric Orban
- Département de chimie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Julie Leroux
- Département de biologie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Adrien Rizzi
- Département de chimie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Jean-Sébastien Bourassa
- Département de biologie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Nicolas Fontaine
- Département de chimie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Élodie V d'Astous
- Département de chimie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | | | - Claude Y Legault
- Département de chimie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Jean-Philippe Bellenger
- Département de chimie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Pascale B Beauregard
- Département de biologie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada.
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19
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Patel A, McGrosso D, Hefner Y, Campeau A, Sastry AV, Maurya S, Rychel K, Gonzalez DJ, Palsson BO. Proteome allocation is linked to transcriptional regulation through a modularized transcriptome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.20.529291. [PMID: 36865326 PMCID: PMC9980150 DOI: 10.1101/2023.02.20.529291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigated whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions could be modularized in the same way to reveal novel relationships between their compositions. We found that; 1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, 2) the modules in the proteome often represent combinations of modules from the transcriptome, 3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and 4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.
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Affiliation(s)
- Arjun Patel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Dominic McGrosso
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ying Hefner
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anaamika Campeau
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anand V. Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Svetlana Maurya
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - David J Gonzalez
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs. Lyngby, Denmark
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20
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Bi X, Cheng Y, Xu X, Lv X, Liu Y, Li J, Du G, Chen J, Ledesma-Amaro R, Liu L. etiBsu1209: A comprehensive multiscale metabolic model for Bacillus subtilis. Biotechnol Bioeng 2023; 120:1623-1639. [PMID: 36788025 DOI: 10.1002/bit.28355] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/08/2022] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
Genome-scale metabolic models (GEMs) have been widely used to guide the computational design of microbial cell factories, and to date, seven GEMs have been reported for Bacillus subtilis, a model gram-positive microorganism widely used in bioproduction of functional nutraceuticals and food ingredients. However, none of them are widely used because they often lead to erroneous predictions due to their low predictive power and lack of information on regulatory mechanisms. In this work, we constructed a new version of GEM for B. subtilis (iBsu1209), which contains 1209 genes, 1595 metabolites, and 1948 reactions. We applied machine learning to fill gaps, which formed a relatively complete metabolic network able to predict with high accuracy (89.3%) the growth of 1209 mutants under 12 different culture conditions. In addition, we developed a visualization and code-free software, Model Tool, for multiconstraints model reconstruction and analysis. We used this software to construct etiBsu1209, a multiscale model that integrates enzymatic constraints, thermodynamic constraints, and transcriptional regulatory networks. Furthermore, we used etiBsu1209 to guide a metabolic engineering strategy (knocking out fabI and yfkN genes) for the overproduction of nutraceutical menaquinone-7, and the titer increased to 153.94 mg/L, 2.2-times that of the parental strain. To the best of our knowledge, etiBsu1209 is the first comprehensive multiscale model for B. subtilis and can serve as a solid basis for rational computational design of B. subtilis cell factories for bioproduction.
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Affiliation(s)
- Xinyu Bi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China.,Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi, China
| | - Yang Cheng
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China.,Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi, China
| | - Xianhao Xu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China.,Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China.,Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China.,Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China.,Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi, China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China.,Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi, China
| | - Jian Chen
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China.,Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi, China
| | | | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China.,Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi, China
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21
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Predicting stress response and improved protein overproduction in Bacillus subtilis. NPJ Syst Biol Appl 2022; 8:50. [PMID: 36575180 PMCID: PMC9794813 DOI: 10.1038/s41540-022-00259-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 11/07/2022] [Indexed: 12/28/2022] Open
Abstract
Bacillus subtilis is a well-characterized microorganism and a model for the study of Gram-positive bacteria. The bacterium can produce proteins at high densities and yields, which has made it valuable for industrial bioproduction. Like other cell factories, metabolic modeling of B. subtilis has discovered ways to optimize its metabolism toward various applications. The first genome-scale metabolic model (M-model) of B. subtilis was published more than a decade ago and has been applied extensively to understand metabolism, to predict growth phenotypes, and served as a template to reconstruct models for other Gram-positive bacteria. However, M-models are ill-suited to simulate the production and secretion of proteins as well as their proteomic response to stress. Thus, a new generation of metabolic models, known as metabolism and gene expression models (ME-models), has been initiated. Here, we describe the reconstruction and validation of a ME model of B. subtilis, iJT964-ME. This model achieved higher performance scores on the prediction of gene essentiality as compared to the M-model. We successfully validated the model by integrating physiological and omics data associated with gene expression responses to ethanol and salt stress. The model further identified the mechanism by which tryptophan synthesis is upregulated under ethanol stress. Further, we employed iJT964-ME to predict amylase production rates under two different growth conditions. We analyzed these flux distributions and identified key metabolic pathways that permitted the increase in amylase production. Models like iJT964-ME enable the study of proteomic response to stress and the illustrate the potential for optimizing protein production in bacteria.
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22
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Coordination of CcpA and CodY Regulators in Staphylococcus aureus USA300 Strains. mSystems 2022; 7:e0048022. [PMID: 36321827 PMCID: PMC9765215 DOI: 10.1128/msystems.00480-22] [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] [Indexed: 11/07/2022] Open
Abstract
The complex cross talk between metabolism and gene regulatory networks makes it difficult to untangle individual constituents and study their precise roles and interactions. To address this issue, we modularized the transcriptional regulatory network (TRN) of the Staphylococcus aureus USA300 strain by applying independent component analysis (ICA) to 385 RNA sequencing samples. We then combined the modular TRN model with a metabolic model to study the regulation of carbon and amino acid metabolism. Our analysis showed that regulation of central carbon metabolism by CcpA and amino acid biosynthesis by CodY are closely coordinated. In general, S. aureus increases the expression of CodY-regulated genes in the presence of preferred carbon sources such as glucose. This transcriptional coordination was corroborated by metabolic model simulations that also showed increased amino acid biosynthesis in the presence of glucose. Further, we found that CodY and CcpA cooperatively regulate the expression of ribosome hibernation-promoting factor, thus linking metabolic cues with translation. In line with this hypothesis, expression of CodY-regulated genes is tightly correlated with expression of genes encoding ribosomal proteins. Together, we propose a coarse-grained model where expression of S. aureus genes encoding enzymes that control carbon flux and nitrogen flux through the system is coregulated with expression of translation machinery to modularly control protein synthesis. While this work focuses on three key regulators, the full TRN model we present contains 76 total independently modulated sets of genes, each with the potential to uncover other complex regulatory structures and interactions. IMPORTANCE Staphylococcus aureus is a versatile pathogen with an expanding antibiotic resistance profile. The biology underlying its clinical success emerges from an interplay of many systems such as metabolism and gene regulatory networks. This work brings together models for these two systems to establish fundamental principles governing the regulation of S. aureus central metabolism and protein synthesis. Studies of these fundamental biological principles are often confined to model organisms such as Escherichia coli. However, expanding these models to pathogens can provide a framework from which complex and clinically important phenotypes such as virulence and antibiotic resistance can be better understood. Additionally, the expanded gene regulatory network model presented here can deconvolute the biology underlying other important phenotypes in this pathogen.
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Rajput A, Tsunemoto H, Sastry AV, Szubin R, Rychel K, Chauhan SM, Pogliano J, Palsson BO. Advanced transcriptomic analysis reveals the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa. Nucleic Acids Res 2022; 50:9675-9688. [PMID: 36095122 PMCID: PMC9508857 DOI: 10.1093/nar/gkac743] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/06/2022] [Accepted: 09/06/2022] [Indexed: 11/14/2022] Open
Abstract
Pseudomonas aeruginosa is an opportunistic pathogen and major cause of hospital-acquired infections. The virulence of P. aeruginosa is largely determined by its transcriptional regulatory network (TRN). We used 411 transcription profiles of P. aeruginosa from diverse growth conditions to construct a quantitative TRN by identifying independently modulated sets of genes (called iModulons) and their condition-specific activity levels. The current study focused on the use of iModulons to analyze the biofilm production and antibiotic resistance of P. aeruginosa. Our analysis revealed: (i) 116 iModulons, 81 of which show strong association with known regulators; (ii) novel roles of regulators in modulating antibiotics efflux pumps; (iii) substrate-efflux pump associations; (iv) differential iModulon activity in response to beta-lactam antibiotics in bacteriological and physiological media; (v) differential activation of 'Cell Division' iModulon resulting from exposure to different beta-lactam antibiotics and (vi) a role of the PprB iModulon in the stress-induced transition from planktonic to biofilm lifestyle. In light of these results, the construction of an iModulon-based TRN provides a transcriptional regulatory basis for key aspects of P. aeruginosa infection, such as antibiotic stress responses and biofilm formation. Taken together, our results offer a novel mechanistic understanding of P. aeruginosa virulence.
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Affiliation(s)
- Akanksha Rajput
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Hannah Tsunemoto
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Anand V Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Richard Szubin
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Siddharth M Chauhan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Joe Pogliano
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.,Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.,Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kongens, Lyngby, Denmark
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24
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Adaptive laboratory evolution and independent component analysis disentangle complex vancomycin adaptation trajectories. Proc Natl Acad Sci U S A 2022; 119:e2118262119. [PMID: 35858453 PMCID: PMC9335240 DOI: 10.1073/pnas.2118262119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Infections with methicillin-resistant Staphylococcus aureus (MRSA) are associated with significant morbidity and mortality. Vancomycin is a last-line antibiotic used to treat MRSA infections; however, strains with decreased susceptibility to vancomycin (vancomycin-intermediate S. aureus [VISA]) have been spreading, and VISA infections are associated with prolonged therapeutic treatment and treatment failure. To map out the evolutionary trajectory behind VISA development, we characterized the mutational, transcriptional, and phenotypic landscape of 10 lineages of S. aureus USA300 strain JE2 that evolved in parallel to vancomycin. We demonstrate that MRSA strains adapt to vancomycin by divergent pathways leading to high or low oxacillin susceptibility characterized by mutational or transcriptional profiles. Our results point to diagnostic possibilities that may support personalized antibiotic treatment regimes. Human infections with methicillin-resistant Staphylococcus aureus (MRSA) are commonly treated with vancomycin, and strains with decreased susceptibility, designated as vancomycin-intermediate S. aureus (VISA), are associated with treatment failure. Here, we profiled the phenotypic, mutational, and transcriptional landscape of 10 VISA strains adapted by laboratory evolution from one common MRSA ancestor, the USA300 strain JE2. Using functional and independent component analysis, we found that: 1) despite the common genetic background and environmental conditions, the mutational landscape diverged between evolved strains and included mutations previously associated with vancomycin resistance (in vraT, graS, vraFG, walKR, and rpoBCD) as well as novel adaptive mutations (SAUSA300_RS04225, ssaA, pitAR, and sagB); 2) the first wave of mutations affected transcriptional regulators and the second affected genes involved in membrane biosynthesis; 3) expression profiles were predominantly strain-specific except for sceD and lukG, which were the only two genes significantly differentially expressed in all clones; 4) three independent virulence systems (φSa3, SaeR, and T7SS) featured as the most transcriptionally perturbed gene sets across clones; 5) there was a striking variation in oxacillin susceptibility across the evolved lineages (from a 10-fold increase to a 63-fold decrease) that also arose in clinical MRSA isolates exposed to vancomycin and correlated with susceptibility to teichoic acid inhibitors; and 6) constitutive expression of the VraR regulon explained cross-susceptibility, while mutations in walK were associated with cross-resistance. Our results show that adaptation to vancomycin involves a surprising breadth of mutational and transcriptional pathways that affect antibiotic susceptibility and possibly the clinical outcome of infections.
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25
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Yang H, Yang J, Liu C, Lv X, Liu L, Li J, Du G, Chen J, Liu Y. High-Level 5-Methyltetrahydrofolate Bioproduction in Bacillus subtilis by Combining Modular Engineering and Transcriptomics-Guided Global Metabolic Regulation. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:5849-5859. [PMID: 35521920 DOI: 10.1021/acs.jafc.2c01252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
5-Methyltetrahydrofolate (5-MTHF) is the predominant folate form in human plasma, which has been widely used as a nutraceutical. However, the microbial synthesis of 5-MTHF is currently inefficient, limiting green and sustainable 5-MTHF production. In this study, the Generally Regarded As Safe (GRAS) microorganism Bacillus subtilis was engineered as the 5-MTHF production host. Three precursor supply modules were first optimized by modular engineering for strengthening the supply of guanosine-5-triphosphate (GTP) and p-aminobenzoic acid (pABA). Next, the impact of genome-wide gene expression on 5-MTHF biosynthesis was evaluated using transcriptome analyses, which identified key genes for 5-MTHF production. The effects of potential genes on 5-MTHF synthesis were verified by observing the genes' up-regulated by strong promoter P566 and those down-regulated by inhibition through the clustered regularly interspaced short palindromic repeat interference (CRISPRi). Finally, a key gene for improved 5-MTHF biosynthesis, comGC, was integrated into the genome of modular engineered strain B89 for its overexpression and facilitating efficient 5-MTHF synthesis, reaching 3.41 ± 0.10 mg/L with a productivity of 0.21 mg/L/h, which was the highest level achieved by microbial synthesis. The engineered 5-MTHF-producing B. subtilis developed in this work lays the foundation of further enhancing 5-MTHF production by microbial fermentation, which can be used for isolation and purification of 5-MTHF as food and nutraceutical ingredients.
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Affiliation(s)
- Han Yang
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Jinning Yang
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Cheng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Jian Chen
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
- Qingdao Special Food Research Institute, Qingdao 266109, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
- Qingdao Special Food Research Institute, Qingdao 266109, China
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26
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Lim HG, Rychel K, Sastry AV, Bentley GJ, Mueller J, Schindel HS, Larsen PE, Laible PD, Guss AM, Niu W, Johnson CW, Beckham GT, Feist AM, Palsson BO. Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network. Metab Eng 2022; 72:297-310. [PMID: 35489688 DOI: 10.1016/j.ymben.2022.04.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/23/2022] [Accepted: 04/23/2022] [Indexed: 11/17/2022]
Abstract
Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida KT2440, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN for P. putida KT2440 and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.
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Affiliation(s)
- Hyun Gyu Lim
- Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA; Joint BioEnergy Institute, 5885 Hollis Street, 4th Floor, Emeryville, CA, 94608, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Anand V Sastry
- Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Gayle J Bentley
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA; Agile BioFoundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Emeryville, CA, 94720, USA
| | - Joshua Mueller
- Department of Chemical & Biomolecular Engineering, University of Nebraska-Lincoln, 1400 R St, Lincoln, NE, 68588, USA
| | - Heidi S Schindel
- Biosciences Division, Oak Ridge National Laboratory, 5200 Bethel Valley Rd, Oak Ridge, TN, 37830, USA
| | - Peter E Larsen
- Biosciences Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60539, USA
| | - Philip D Laible
- Biosciences Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60539, USA
| | - Adam M Guss
- Agile BioFoundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Emeryville, CA, 94720, USA; Biosciences Division, Oak Ridge National Laboratory, 5200 Bethel Valley Rd, Oak Ridge, TN, 37830, USA
| | - Wei Niu
- Department of Chemical & Biomolecular Engineering, University of Nebraska-Lincoln, 1400 R St, Lincoln, NE, 68588, USA
| | - Christopher W Johnson
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA; Agile BioFoundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Emeryville, CA, 94720, USA
| | - Gregg T Beckham
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA; Agile BioFoundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Emeryville, CA, 94720, USA
| | - Adam M Feist
- Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA; Joint BioEnergy Institute, 5885 Hollis Street, 4th Floor, Emeryville, CA, 94608, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs, Lyngby, Denmark
| | - Bernhard O Palsson
- Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA; Joint BioEnergy Institute, 5885 Hollis Street, 4th Floor, Emeryville, CA, 94608, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs, Lyngby, Denmark; Department of Pediatrics, University of California, San Diego, CA, 92093, USA.
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27
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Xavier JB, Monk JM, Poudel S, Norsigian CJ, Sastry AV, Liao C, Bento J, Suchard MA, Arrieta-Ortiz ML, Peterson EJ, Baliga NS, Stoeger T, Ruffin F, Richardson RA, Gao CA, Horvath TD, Haag AM, Wu Q, Savidge T, Yeaman MR. Mathematical models to study the biology of pathogens and the infectious diseases they cause. iScience 2022; 25:104079. [PMID: 35359802 PMCID: PMC8961237 DOI: 10.1016/j.isci.2022.104079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.
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Affiliation(s)
- Joao B. Xavier
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | | | - Saugat Poudel
- Department of Bioengineering, UC San Diego, San Diego, CA, USA
| | | | - Anand V. Sastry
- Department of Bioengineering, UC San Diego, San Diego, CA, USA
| | - Chen Liao
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Jose Bento
- Computer Science Department, Boston College, Chestnut Hill, MA, USA
| | - Marc A. Suchard
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | | | | | | | - Thomas Stoeger
- Department of Chemical and Biological Engineering; Northwestern University, Evanston, IL 60208, USA
- Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center, Northwestern University, Chicago, IL, USA
| | - Felicia Ruffin
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Reese A.K. Richardson
- Department of Chemical and Biological Engineering; Northwestern University, Evanston, IL 60208, USA
- Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center, Northwestern University, Chicago, IL, USA
| | - Catherine A. Gao
- Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center, Northwestern University, Chicago, IL, USA
- Division of Pulmonary and Critical Care, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Thomas D. Horvath
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Anthony M. Haag
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Qinglong Wu
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Tor Savidge
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Michael R. Yeaman
- David Geffen School of Medicine at UCLA & Lundquist Institute for Infection & Immunity at Harbor UCLA Medical Center, Los Angeles, CA, USA
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28
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Rajput A, Tsunemoto H, Sastry AV, Szubin R, Rychel K, Sugie J, Pogliano J, Palsson BO. Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators. Nucleic Acids Res 2022; 50:3658-3672. [PMID: 35357493 PMCID: PMC9023270 DOI: 10.1093/nar/gkac187] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/28/2022] [Accepted: 03/29/2022] [Indexed: 12/16/2022] Open
Abstract
The transcriptional regulatory network (TRN) of Pseudomonas aeruginosa coordinates cellular processes in response to stimuli. We used 364 transcriptomes (281 publicly available + 83 in-house generated) to reconstruct the TRN of P. aeruginosa using independent component analysis. We identified 104 independently modulated sets of genes (iModulons) among which 81 reflect the effects of known transcriptional regulators. We identified iModulons that (i) play an important role in defining the genomic boundaries of biosynthetic gene clusters (BGCs), (ii) show increased expression of the BGCs and associated secretion systems in nutrient conditions that are important in cystic fibrosis, (iii) show the presence of a novel ribosomally synthesized and post-translationally modified peptide (RiPP) BGC which might have a role in P. aeruginosa virulence, (iv) exhibit interplay of amino acid metabolism regulation and central metabolism across different carbon sources and (v) clustered according to their activity changes to define iron and sulfur stimulons. Finally, we compared the identified iModulons of P. aeruginosa with those previously described in Escherichia coli to observe conserved regulons across two Gram-negative species. This comprehensive TRN framework encompasses the majority of the transcriptional regulatory machinery in P. aeruginosa, and thus should prove foundational for future research into its physiological functions.
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Affiliation(s)
- Akanksha Rajput
- Department of Bioengineering, University of California, San Diego, La Jolla, USA
| | - Hannah Tsunemoto
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Anand V Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, USA
| | - Richard Szubin
- Department of Bioengineering, University of California, San Diego, La Jolla, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, USA
| | - Joseph Sugie
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Joe Pogliano
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, USA.,Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.,Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kongens, Lyngby, Denmark
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29
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Decker KT, Gao Y, Rychel K, Al Bulushi T, Chauhan S, Kim D, Cho BK, Palsson B. proChIPdb: a chromatin immunoprecipitation database for prokaryotic organisms. Nucleic Acids Res 2022; 50:D1077-D1084. [PMID: 34791440 PMCID: PMC8728212 DOI: 10.1093/nar/gkab1043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 10/05/2021] [Accepted: 10/14/2021] [Indexed: 12/03/2022] Open
Abstract
The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity. ChIP-derived data's importance and labor-intensiveness motivate its broad dissemination and reuse, which is currently an unmet need in the prokaryotic domain. To fill this gap, we present proChIPdb (prochipdb.org), an information-rich, interactive web database. This website collects public ChIP-seq/-exo data across several prokaryotes and presents them in dashboards that include curated binding sites, nucleotide-resolution genome viewers, and summary plots such as motif enrichment sequence logos. Users can search for TFs of interest or their target genes, download all data, dashboards, and visuals, and follow external links to understand regulons through biological databases and the literature. This initial release of proChIPdb covers diverse organisms, including most major TFs of Escherichia coli, and can be expanded to support regulon discovery across the prokaryotic domain.
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Affiliation(s)
- Katherine T Decker
- Department of Bioengineering, University of California, San Diego, La Jolla, CA92093, USA
| | - Ye Gao
- Department of Bioengineering, University of California, San Diego, La Jolla, CA92093, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA92093, USA
| | - Tahani Al Bulushi
- Department of Bioengineering, University of California, San Diego, La Jolla, CA92093, USA
| | - Siddharth M Chauhan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA92093, USA
| | - Donghyuk Kim
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
| | - Byung-Kwan Cho
- Department of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon34141, Republic of Korea
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA92093, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA92093, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
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30
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McConn JL, Lamoureux CR, Poudel S, Palsson BO, Sastry AV. Optimal dimensionality selection for independent component analysis of transcriptomic data. BMC Bioinformatics 2021; 22:584. [PMID: 34879815 PMCID: PMC8653613 DOI: 10.1186/s12859-021-04497-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 11/16/2021] [Indexed: 11/23/2022] Open
Abstract
Background Independent component analysis is an unsupervised machine learning algorithm that separates a set of mixed signals into a set of statistically independent source signals. Applied to high-quality gene expression datasets, independent component analysis effectively reveals both the source signals of the transcriptome as co-regulated gene sets, and the activity levels of the underlying regulators across diverse experimental conditions. Two major variables that affect the final gene sets are the diversity of the expression profiles contained in the underlying data, and the user-defined number of independent components, or dimensionality, to compute. Availability of high-quality transcriptomic datasets has grown exponentially as high-throughput technologies have advanced; however, optimal dimensionality selection remains an open question. Methods We computed independent components across a range of dimensionalities for four gene expression datasets with varying dimensions (both in terms of number of genes and number of samples). We computed the correlation between independent components across different dimensionalities to understand how the overall structure evolves as the number of user-defined components increases. We then measured how well the resulting gene clusters reflected known regulatory mechanisms, and developed a set of metrics to assess the accuracy of the decomposition at a given dimension. Results We found that over-decomposition results in many independent components dominated by a single gene, whereas under-decomposition results in independent components that poorly capture the known regulatory structure. From these results, we developed a new method, called OptICA, for finding the optimal dimensionality that controls for both over- and under-decomposition. Specifically, OptICA selects the highest dimension that produces a low number of components that are dominated by a single gene. We show that OptICA outperforms two previously proposed methods for selecting the number of independent components across four transcriptomic databases of varying sizes. Conclusions OptICA avoids both over-decomposition and under-decomposition of transcriptomic datasets resulting in the best representation of the organism’s underlying transcriptional regulatory network. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04497-7.
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Chauhan SM, Poudel S, Rychel K, Lamoureux C, Yoo R, Al Bulushi T, Yuan Y, Palsson BO, Sastry AV. Machine Learning Uncovers a Data-Driven Transcriptional Regulatory Network for the Crenarchaeal Thermoacidophile Sulfolobus acidocaldarius. Front Microbiol 2021; 12:753521. [PMID: 34777307 PMCID: PMC8578740 DOI: 10.3389/fmicb.2021.753521] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 09/30/2021] [Indexed: 01/24/2023] Open
Abstract
Dynamic cellular responses to environmental constraints are coordinated by the transcriptional regulatory network (TRN), which modulates gene expression. This network controls most fundamental cellular responses, including metabolism, motility, and stress responses. Here, we apply independent component analysis, an unsupervised machine learning approach, to 95 high-quality Sulfolobus acidocaldarius RNA-seq datasets and extract 45 independently modulated gene sets, or iModulons. Together, these iModulons contain 755 genes (32% of the genes identified on the genome) and explain over 70% of the variance in the expression compendium. We show that five modules represent the effects of known transcriptional regulators, and hypothesize that most of the remaining modules represent the effects of uncharacterized regulators. Further analysis of these gene sets results in: (1) the prediction of a DNA export system composed of five uncharacterized genes, (2) expansion of the LysM regulon, and (3) evidence for an as-yet-undiscovered global regulon. Our approach allows for a mechanistic, systems-level elucidation of an extremophile's responses to biological perturbations, which could inform research on gene-regulator interactions and facilitate regulator discovery in S. acidocaldarius. We also provide the first global TRN for S. acidocaldarius. Collectively, these results provide a roadmap toward regulatory network discovery in archaea.
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Affiliation(s)
- Siddharth M. Chauhan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Saugat Poudel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Cameron Lamoureux
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Reo Yoo
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Tahani Al Bulushi
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Yuan Yuan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Anand V. Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
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Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility. mSphere 2021; 6:e0044321. [PMID: 34431696 PMCID: PMC8386450 DOI: 10.1128/msphere.00443-21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available. IMPORTANCE Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions. Prior attempts to understand the combined effects of drugs and media on antibiotic efficacy have focused on physiological measurements but have not linked treatment outcomes to transcriptional responses on a systems level. Here, application of machine learning to transcriptomics data identified medium-dependent responses in key regulators of bacterial iron uptake and respiratory activity. The analytical workflow presented here is scalable to additional organisms and conditions and could be used to improve clinical AST by identifying the key regulatory factors dictating antibiotic susceptibility.
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33
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Production of proteins and commodity chemicals using engineered Bacillus subtilis platform strain. Essays Biochem 2021; 65:173-185. [PMID: 34028523 DOI: 10.1042/ebc20210011] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/02/2021] [Accepted: 05/06/2021] [Indexed: 12/19/2022]
Abstract
Currently, increasing demand of biochemicals produced from renewable resources has motivated researchers to seek microbial production strategies instead of traditional chemical methods. As a microbial platform, Bacillus subtilis possesses many advantages including the generally recognized safe status, clear metabolic networks, short growth cycle, mature genetic editing methods and efficient protein secretion systems. Engineered B. subtilis strains are being increasingly used in laboratory research and in industry for the production of valuable proteins and other chemicals. In this review, we first describe the recent advances of bioinformatics strategies during the research and applications of B. subtilis. Secondly, the applications of B. subtilis in enzymes and recombinant proteins production are summarized. Further, the recent progress in employing metabolic engineering and synthetic biology strategies in B. subtilis platform strain to produce commodity chemicals is systematically introduced and compared. Finally, the major limitations for the further development of B. subtilis platform strain and possible future directions for its research are also discussed.
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Sastry AV, Hu A, Heckmann D, Poudel S, Kavvas E, Palsson BO. Independent component analysis recovers consistent regulatory signals from disparate datasets. PLoS Comput Biol 2021; 17:e1008647. [PMID: 33529205 PMCID: PMC7888660 DOI: 10.1371/journal.pcbi.1008647] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 02/17/2021] [Accepted: 12/18/2020] [Indexed: 01/03/2023] Open
Abstract
The availability of bacterial transcriptomes has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E. coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent datasets, including both RNA-seq and microarray datasets. We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles and discovered that its underlying ICA-based structure was still comparable to that of the individual datasets. With this understanding, we expanded our analysis to over 3,000 E. coli expression profiles and predicted three high-impact regulons that respond to oxidative stress, anaerobiosis, and antibiotic treatment. ICA thus enables deep analysis of disparate data to uncover new insights that were not visible in the individual datasets. Cells adapt to diverse environments by regulating gene expression. Genome-wide measurements of gene expression levels have exponentially increased in recent years, but successful integration and analysis of these datasets are limited. Recently, we showed that independent component analysis (ICA), a signal deconvolution algorithm, can separate a large bacterial gene expression dataset into groups of co-regulated genes. This previous study focused on data generated by a standardized pipeline and did not address whether ICA extracts the same quantitative co-expression signals across expression profiling platforms. In this study, we show that ICA finds similar co-regulation patterns underlying multiple gene expression datasets and can be used as a tool to integrate and interpret diverse datasets. Using a dataset containing over 3,000 expression profiles, we predicted three new regulons and characterized their activities. Since large, standardized expression datasets only exist for a few bacterial strains, these results broaden the possible applications of this tool to better understand transcriptional regulation across a wide range of microbes.
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Affiliation(s)
- Anand V. Sastry
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Alyssa Hu
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - David Heckmann
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Saugat Poudel
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Erol Kavvas
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- * E-mail:
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35
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Rychel K, Decker K, Sastry AV, Phaneuf PV, Poudel S, Palsson BO. iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning. Nucleic Acids Res 2021; 49:D112-D120. [PMID: 33045728 PMCID: PMC7778901 DOI: 10.1093/nar/gkaa810] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/10/2020] [Accepted: 09/15/2020] [Indexed: 12/15/2022] Open
Abstract
Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (Escherichia coli, Staphylococcus aureus and Bacillus subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.
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Affiliation(s)
- Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Katherine Decker
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anand V Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Patrick V Phaneuf
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Saugat Poudel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
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Zielinski DC, Patel A, Palsson BO. The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale. Microorganisms 2020; 8:E2050. [PMID: 33371386 PMCID: PMC7767376 DOI: 10.3390/microorganisms8122050] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/07/2020] [Accepted: 12/16/2020] [Indexed: 02/06/2023] Open
Abstract
Microbial strains are being engineered for an increasingly diverse array of applications, from chemical production to human health. While traditional engineering disciplines are driven by predictive design tools, these tools have been difficult to build for biological design due to the complexity of biological systems and many unknowns of their quantitative behavior. However, due to many recent advances, the gap between design in biology and other engineering fields is closing. In this work, we discuss promising areas of development of computational tools for engineering microbial strains. We define five frontiers of active research: (1) Constraint-based modeling and metabolic network reconstruction, (2) Kinetics and thermodynamic modeling, (3) Protein structure analysis, (4) Genome sequence analysis, and (5) Regulatory network analysis. Experimental and machine learning drivers have enabled these methods to improve by leaps and bounds in both scope and accuracy. Modern strain design projects will require these tools to be comprehensively applied to the entire cell and efficiently integrated within a single workflow. We expect that these frontiers, enabled by the ongoing revolution of big data science, will drive forward more advanced and powerful strain engineering strategies.
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Affiliation(s)
- Daniel Craig Zielinski
- Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA; (D.C.Z.); (A.P.)
| | - Arjun Patel
- Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA; (D.C.Z.); (A.P.)
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA; (D.C.Z.); (A.P.)
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
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