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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Phaneuf PV, Kim SH, Rychel K, Rode C, Beulig F, Palsson BO, Yang L. Meta-analysis Driven Strain Design for Mitigating Oxidative Stresses Important in Biomanufacturing. ACS Synth Biol 2024. [PMID: 38934464 DOI: 10.1021/acssynbio.3c00572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
As the availability of data sets increases, meta-analysis leveraging aggregated and interoperable data types is proving valuable. This study leveraged a meta-analysis workflow to identify mutations that could improve robustness to reactive oxygen species (ROS) stresses using an industrially important melatonin production strain as an example. ROS stresses often occur during cultivation and negatively affect strain performance. Cellular response to ROS is also linked to the SOS response and resistance to pH fluctuations, which is important to strain robustness in large-scale biomanufacturing. This work integrated more than 7000 E. coli adaptive laboratory evolution (ALE) mutations across 59 experiments to statistically associate mutated genes to 2 ROS tolerance ALE conditions from 72 unique conditions. Mutant oxyR, fur, iscR, and ygfZ were significantly associated and hypothesized to contribute fitness in ROS stress. Across these genes, 259 total mutations were inspected in conjunction with transcriptomics from 46 iModulon experiments. Ten mutations were chosen for reintroduction based on mutation clustering and coinciding transcriptional changes as evidence of fitness impact. Strains with mutations reintroduced into oxyR, fur, iscR, and ygfZ exhibited increased tolerance to H2O2 and acid stress and reduced SOS response, all of which are related to ROS. Additionally, new evidence was generated toward understanding the function of ygfZ, an uncharacterized gene. This meta-analysis approach utilized aggregated and interoperable multiomics data sets to identify mutations conferring industrially relevant phenotypes with the least drawbacks, describing an approach for data-driven strain engineering to optimize microbial cell factories.
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Affiliation(s)
- P V Phaneuf
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220. Kongens Lyngby 2800, Denmark
| | - S H Kim
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220. Kongens Lyngby 2800, Denmark
| | - K Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla ,California92093-0412 ,United States
| | - C Rode
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220. Kongens Lyngby 2800, Denmark
| | - F Beulig
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220. Kongens Lyngby 2800, Denmark
| | - B O Palsson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220. Kongens Lyngby 2800, Denmark
- Department of Bioengineering, University of California, San Diego, La Jolla ,California92093-0412 ,United States
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla ,California92093-0021, United States
- Department of Pediatrics, University of California, San Diego, La Jolla ,California 92093-0412, United States
| | - L Yang
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220. Kongens Lyngby 2800, Denmark
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3
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Bleem AC, Kuatsjah E, Johnsen J, Mohamed ET, Alexander WG, Kellermyer ZA, Carroll AL, Rossi R, Schlander IB, Peabody V GL, Guss AM, Feist AM, Beckham GT. Evolution and engineering of pathways for aromatic O-demethylation in Pseudomonas putida KT2440. Metab Eng 2024:S1096-7176(24)00082-X. [PMID: 38936762 DOI: 10.1016/j.ymben.2024.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 06/17/2024] [Accepted: 06/24/2024] [Indexed: 06/29/2024]
Abstract
Biological conversion of lignin from biomass offers a promising strategy for sustainable production of fuels and chemicals. However, aromatic compounds derived from lignin commonly contain methoxy groups, and O-demethylation of these substrates is often a rate-limiting reaction that influences catabolic efficiency. Several enzyme families catalyze aromatic O-demethylation, but they are rarely compared in vivo to determine an optimal biocatalytic strategy. Here, two pathways for aromatic O-demethylation were compared in Pseudomonas putida KT2440. The native Rieske non-heme iron monooxygenase (VanAB) and, separately, a heterologous tetrahydrofolate-dependent demethylase (LigM) were constitutively expressed in P. putida, and the strains were optimized via adaptive laboratory evolution (ALE) with vanillate as a model substrate. All evolved strains displayed improved growth phenotypes, with the evolved strains harboring the native VanAB pathway exhibiting growth rates ∼1.8x faster than those harboring the heterologous LigM pathway. Enzyme kinetics and transcriptomics studies investigated the contribution of selected mutations toward enhanced utilization of vanillate. The VanAB-overexpressing strains contained the most impactful mutations, including those in VanB, the reductase for vanillate O-demethylase, PP_3494, a global regulator of vanillate catabolism, and fghA, involved in formaldehyde detoxification. These three mutations were combined into a single strain, which exhibited approximately 5x faster vanillate consumption than the wild-type strain in the first 8 h of cultivation. Overall, this study illuminates the details of vanillate catabolism in the context of two distinct enzymatic mechanisms, yielding a platform strain for efficient O-demethylation of lignin-related aromatic compounds to value-added products.
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Affiliation(s)
- Alissa C Bleem
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Eugene Kuatsjah
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Josefin Johnsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Elsayed T Mohamed
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - William G Alexander
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA; Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN, USA
| | - Zoe A Kellermyer
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Austin L Carroll
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA; Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN, USA
| | - Riccardo Rossi
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark; Department of Bioengineering, University of California, San Diego, CA, USA
| | - Ian B Schlander
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
| | - George L Peabody V
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN, USA
| | - Adam M Guss
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA; Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN, USA
| | - Adam M Feist
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark; Joint BioEnergy Institute, Emeryville, CA, USA; Department of Bioengineering, University of California, San Diego, CA, USA.
| | - Gregg T Beckham
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
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4
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Joshi SHN, Jenkins C, Ulaeto D, Gorochowski TE. Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0037. [PMID: 38919711 PMCID: PMC11197468 DOI: 10.34133/bdr.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/23/2024] [Indexed: 06/27/2024] Open
Abstract
Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.
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Affiliation(s)
| | - Christopher Jenkins
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - David Ulaeto
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - Thomas E. Gorochowski
- School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK
- BrisEngBio,
School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
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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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6
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Shin J, Zielinski DC, Palsson BO. Deciphering nutritional stress responses via knowledge-enriched transcriptomics for microbial engineering. Metab Eng 2024; 84:34-47. [PMID: 38825177 DOI: 10.1016/j.ymben.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/27/2024] [Accepted: 05/28/2024] [Indexed: 06/04/2024]
Abstract
Understanding diverse bacterial nutritional requirements and responses is foundational in microbial research and biotechnology. In this study, we employed knowledge-enriched transcriptomic analytics to decipher complex stress responses of Vibrio natriegens to supplied nutrients, aiming to enhance microbial engineering efforts. We computed 64 independently modulated gene sets that comprise a quantitative basis for transcriptome dynamics across a comprehensive transcriptomics dataset containing a broad array of nutrient conditions. Our approach led to the i) identification of novel transporter systems for diverse substrates, ii) a detailed understanding of how trace elements affect metabolism and growth, and iii) extensive characterization of nutrient-induced stress responses, including osmotic stress, low glycolytic flux, proteostasis, and altered protein expression. By clarifying the relationship between the acetate-associated regulon and glycolytic flux status of various nutrients, we have showcased its vital role in directing optimal carbon source selection. Our findings offer deep insights into the transcriptional landscape of bacterial nutrition and underscore its significance in tailoring strain engineering strategies, thereby facilitating the development of more efficient and robust microbial systems for biotechnological applications.
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Affiliation(s)
- Jongoh Shin
- 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, Lyngby, 2800, Denmark; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
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7
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Lu M, Sha Y, Kumar V, Xu Z, Zhai R, Jin M. Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis. Biotechnol Adv 2024; 72:108339. [PMID: 38508427 DOI: 10.1016/j.biotechadv.2024.108339] [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: 11/23/2023] [Revised: 02/07/2024] [Accepted: 02/18/2024] [Indexed: 03/22/2024]
Abstract
As a sustainable and renewable alternative to petroleum fuels, advanced biofuels shoulder the responsibility of energy saving, emission reduction and environmental protection. Traditional engineering of cell factories for production of advanced biofuels lacks efficient high-throughput screening tools and regulating systems, impeding the improvement of cellular productivity and yield. Transcription factor-based biosensors have been widely applied to monitor and regulate microbial cell factory products due to the advantages of fast detection and in-situ screening. This review updates the design and application of transcription factor-based biosensors tailored for advanced biofuels and related intermediates. The construction and genetic parts selection principle of biosensors are discussed. Strategies to enhance the performance of biosensor, including regulating promoter strength and RBS strength, optimizing plasmid copy number, implementing genetic amplifier, and modulating the structure of transcription factor, have also been summarized. We further review the application of biosensors in high-throughput screening of new metabolic engineering targets, evolution engineering, confirmation of protein function, and dynamic regulation of metabolic flux for higher production of advanced biofuels. At last, we discuss the current limitations and future trends of transcription factor-based biosensors.
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Affiliation(s)
- Minrui Lu
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Biorefinery Research Institution, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Yuanyuan Sha
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Biorefinery Research Institution, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Vinod Kumar
- School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, United Kingdom
| | - Zhaoxian Xu
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Biorefinery Research Institution, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Rui Zhai
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Biorefinery Research Institution, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Mingjie Jin
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Biorefinery Research Institution, Nanjing University of Science and Technology, Nanjing 210094, China.
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8
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Borchert AJ, Bleem AC, Lim HG, Rychel K, Dooley KD, Kellermyer ZA, Hodges TL, Palsson BO, Beckham GT. Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440. mSystems 2024; 9:e0094223. [PMID: 38323821 PMCID: PMC10949508 DOI: 10.1128/msystems.00942-23] [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: 09/14/2023] [Accepted: 01/07/2024] [Indexed: 02/08/2024] Open
Abstract
There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules ("functional modules"), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets. IMPORTANCE This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.
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Affiliation(s)
- Andrew J. Borchert
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Alissa C. Bleem
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
- Agile BioFoundry, Emeryville, California, USA
| | - Hyun Gyu Lim
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
- Joint BioEnergy Institute, Emeryville, California, USA
- Department of Biological Engineering, Inha University, Incheon, Korea
| | - Kevin Rychel
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Keven D. Dooley
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado, USA
- Agile BioFoundry, Emeryville, California, USA
| | - Zoe A. Kellermyer
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Tracy L. Hodges
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado, USA
- Agile BioFoundry, Emeryville, California, USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
- Joint BioEnergy Institute, Emeryville, California, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- Department of Pediatrics, University of California, San Diego, California, USA
| | - Gregg T. Beckham
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
- Agile BioFoundry, Emeryville, California, USA
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9
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Choe D, Olson CA, Szubin R, Yang H, Sung J, Feist AM, Palsson BO. Advancing the scale of synthetic biology via cross-species transfer of cellular functions enabled by iModulon engraftment. Nat Commun 2024; 15:2356. [PMID: 38490991 PMCID: PMC10943186 DOI: 10.1038/s41467-024-46486-3] [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: 12/30/2023] [Accepted: 02/29/2024] [Indexed: 03/18/2024] Open
Abstract
Machine learning applied to large compendia of transcriptomic data has enabled the decomposition of bacterial transcriptomes to identify independently modulated sets of genes, such iModulons represent specific cellular functions. The identification of iModulons enables accurate identification of genes necessary and sufficient for cross-species transfer of cellular functions. We demonstrate cross-species transfer of: 1) the biotransformation of vanillate to protocatechuate, 2) a malonate catabolic pathway, 3) a catabolic pathway for 2,3-butanediol, and 4) an antimicrobial resistance to ampicillin found in multiple Pseudomonas species to Escherichia coli. iModulon-based engineering is a transformative strategy as it includes all genes comprising the transferred cellular function, including genes without functional annotation. Adaptive laboratory evolution was deployed to optimize the cellular function transferred, revealing mutations in the host. Combining big data analytics and laboratory evolution thus enhances the level of understanding of systems biology, and synthetic biology for strain design and development.
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Affiliation(s)
- Donghui Choe
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Connor A Olson
- 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
| | - Hannah Yang
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jaemin Sung
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - 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, Copenhagen, 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, Copenhagen, Denmark.
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10
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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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11
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Dodge AG, Thoma CJ, O’Connor MR, Wackett LP. Recombinant Pseudomonas growing on non-natural fluorinated substrates shows stress but overall tolerance to cytoplasmically released fluoride anion. mBio 2024; 15:e0278523. [PMID: 38063407 PMCID: PMC10790756 DOI: 10.1128/mbio.02785-23] [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: 10/13/2023] [Accepted: 10/23/2023] [Indexed: 01/17/2024] Open
Abstract
IMPORTANCE Society uses thousands of organofluorine compounds, sometimes denoted per- and polyfluoroalkyl substances (PFAS), in hundreds of products, but recent studies have shown some to manifest human and environmental health effects. As a class, they are recalcitrant to biodegradation, partly due to the paucity of fluorinated natural products to which microbes have been exposed. Another limit to PFAS biodegradation is the intracellular toxicity of fluoride anion generated from C-F bond cleavage. The present study identified a broader substrate specificity in an enzyme originally studied for its activity on the natural product fluoroacetate. A recombinant Pseudomonas expressing this enzyme was used here as a model system to better understand the limits and effects of a high level of intracellular fluoride generation. A fluoride stress response has evolved in bacteria and has been described in Pseudomonas spp. The present study is highly relevant to organofluorine compound degradation or engineered biosynthesis in which fluoride anion is a substrate.
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Affiliation(s)
- Anthony G. Dodge
- Department of Biochemistry, Molecular Biology and Biophysics and Biotechnology Institute, University of Minnesota, Twin Cities, Minnesota, USA
| | - Calvin J. Thoma
- Department of Biochemistry, Molecular Biology and Biophysics and Biotechnology Institute, University of Minnesota, Twin Cities, Minnesota, USA
| | - Madeline R. O’Connor
- Department of Biochemistry, Molecular Biology and Biophysics and Biotechnology Institute, University of Minnesota, Twin Cities, Minnesota, USA
| | - Lawrence P. Wackett
- Department of Biochemistry, Molecular Biology and Biophysics and Biotechnology Institute, University of Minnesota, Twin Cities, Minnesota, USA
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12
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Kulakowski S, Banerjee D, Scown CD, Mukhopadhyay A. Improving microbial bioproduction under low-oxygen conditions. Curr Opin Biotechnol 2023; 84:103016. [PMID: 37924688 DOI: 10.1016/j.copbio.2023.103016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/17/2023] [Accepted: 10/07/2023] [Indexed: 11/06/2023]
Abstract
Microbial bioconversion provides access to a wide range of sustainably produced chemicals and commodities. However, industrial-scale bioproduction process operations are preferred to be anaerobic due to the cost associated with oxygen transfer. Anaerobic bioconversion generally offers limited substrate utilization profiles, lower product yields, and reduced final product diversity compared with aerobic processes. Bioproduction under conditions of reduced oxygen can overcome the limitations of fully aerobic and anaerobic bioprocesses, but many microbial hosts are not developed for low-oxygen bioproduction. Here, we describe advances in microbial strain engineering involving the use of redox cofactor engineering, genome-scale metabolic modeling, and functional genomics to enable improved bioproduction processes under low oxygen and provide a viable path for scaling these bioproduction systems to industrial scales.
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Affiliation(s)
- Shawn Kulakowski
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Deepanwita Banerjee
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Corinne D Scown
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Energy Analysis and Environmental Impacts Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Environmental Genomics & Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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13
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Hanke P, Parrello B, Vasieva O, Akins C, Chlenski P, Babnigg G, Henry C, Foflonker F, Brettin T, Antonopoulos D, Stevens R, Fonstein M. Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning. Metab Eng Commun 2023; 17:e00225. [PMID: 37435441 PMCID: PMC10331477 DOI: 10.1016/j.mec.2023.e00225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 06/02/2023] [Accepted: 06/03/2023] [Indexed: 07/13/2023] Open
Abstract
The goal of this study is to develop a general strategy for bacterial engineering using an integrated synthetic biology and machine learning (ML) approach. This strategy was developed in the context of increasing L-threonine production in Escherichia coli ATCC 21277. A set of 16 genes was initially selected based on metabolic pathway relevance to threonine biosynthesis and used for combinatorial cloning to construct a set of 385 strains to generate training data (i.e., a range of L-threonine titers linked to each of the specific gene combinations). Hybrid (regression/classification) deep learning (DL) models were developed and used to predict additional gene combinations in subsequent rounds of combinatorial cloning for increased L-threonine production based on the training data. As a result, E. coli strains built after just three rounds of iterative combinatorial cloning and model prediction generated higher L-threonine titers (from 2.7 g/L to 8.4 g/L) than those of patented L-threonine strains being used as controls (4-5 g/L). Interesting combinations of genes in L-threonine production included deletions of the tdh, metL, dapA, and dhaM genes as well as overexpression of the pntAB, ppc, and aspC genes. Mechanistic analysis of the metabolic system constraints for the best performing constructs offers ways to improve the models by adjusting weights for specific gene combinations. Graph theory analysis of pairwise gene modifications and corresponding levels of L-threonine production also suggests additional rules that can be incorporated into future ML models.
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Affiliation(s)
- Paul Hanke
- Argonne National Laboratory, 9700 S. Cass Ave, Argonne, IL, 60439, USA
| | - Bruce Parrello
- University of Chicago, 5801 S. Ellis Ave, Chicago, IL, 60637, USA
| | - Olga Vasieva
- BSMI, 1818 Skokie Blvd., #201, Northbrook, IL, 60062, USA
| | - Chase Akins
- Argonne National Laboratory, 9700 S. Cass Ave, Argonne, IL, 60439, USA
| | - Philippe Chlenski
- Department of Computer Science, Columbia University, New York, NY, 10027, USA
| | - Gyorgy Babnigg
- Argonne National Laboratory, 9700 S. Cass Ave, Argonne, IL, 60439, USA
| | - Chris Henry
- Argonne National Laboratory, 9700 S. Cass Ave, Argonne, IL, 60439, USA
| | - Fatima Foflonker
- Argonne National Laboratory, 9700 S. Cass Ave, Argonne, IL, 60439, USA
| | - Thomas Brettin
- Argonne National Laboratory, 9700 S. Cass Ave, Argonne, IL, 60439, USA
| | | | - Rick Stevens
- Argonne National Laboratory, 9700 S. Cass Ave, Argonne, IL, 60439, USA
- University of Chicago, 5801 S. Ellis Ave, Chicago, IL, 60637, USA
| | - Michael Fonstein
- Argonne National Laboratory, 9700 S. Cass Ave, Argonne, IL, 60439, USA
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14
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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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15
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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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16
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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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17
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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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18
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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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19
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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: 0] [Impact Index Per Article: 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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20
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Rodionova IA, Lim HG, Rodionov DA, Hutchison Y, Dalldorf C, Gao Y, Monk J, Palsson BO. CyuR is a Dual Regulator for L-Cysteine Dependent Antimicrobial Resistance in Escherichia coli. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541025. [PMID: 37292663 PMCID: PMC10245726 DOI: 10.1101/2023.05.16.541025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Hydrogen sulfide (H 2 S), mainly produced from L-cysteine (Cys), renders bacteria highly resistant to oxidative stress. This mitigation of oxidative stress was suggested to be an important survival mechanism to achieve antimicrobial resistance (AMR) in many pathogenic bacteria. CyuR (known as DecR or YbaO) is a recently characterized Cys-dependent transcription regulator, responsible for the activation of the cyuAP operon and generation of hydrogen sulfide from Cys. Despite its potential importance, the regulatory network of CyuR remains poorly understood. In this study, we investigated the roles of the CyuR regulon in a Cys-dependent AMR mechanism in E. coli strains. We found: 1) Cys metabolism has a significant role in AMR and its effect is conserved in many E. coli strains, including clinical isolates; 2) CyuR negatively controls the expression of mdlAB encoding a transporter that exports antibiotics such as cefazolin and vancomycin; 3) CyuR binds to a DNA sequence motif 'GAAwAAATTGTxGxxATTTsyCC' in the absence of Cys, confirmed by an in vitro binding assay; and 4) CyuR may regulate 25 additional genes as suggested by in silico motif scanning and transcriptome sequencing. Collectively, our findings expanded the understanding of the biological roles of CyuR relevant to antibiotic resistance associated with Cys.
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21
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Borchert AJ, Bleem A, Beckham GT. RB-TnSeq identifies genetic targets for improved tolerance of Pseudomonas putida towards compounds relevant to lignin conversion. Metab Eng 2023; 77:208-218. [PMID: 37059293 DOI: 10.1016/j.ymben.2023.04.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 03/21/2023] [Accepted: 04/12/2023] [Indexed: 04/16/2023]
Abstract
Lignin-derived mixtures intended for bioconversion commonly contain high concentrations of aromatic acids, aliphatic acids, and salts. The inherent toxicity of these chemicals places a significant bottleneck upon the effective use of microbial systems for the valorization of these mixtures. Pseudomonas putida KT2440 can tolerate stressful quantities of several lignin-related compounds, making this bacterium a promising host for converting these chemicals to valuable bioproducts. Nonetheless, further increasing P. putida tolerance to chemicals in lignin-rich substrates has the potential to improve bioprocess performance. Accordingly, we employed random barcoded transposon insertion sequencing (RB-TnSeq) to reveal genetic determinants in P. putida KT2440 that influence stress outcomes during exposure to representative constituents found in lignin-rich process streams. The fitness information obtained from the RB-TnSeq experiments informed engineering of strains via deletion or constitutive expression of several genes. Namely, ΔgacAS, ΔfleQ, ΔlapAB, ΔttgR::Ptac:ttgABC, Ptac:PP_1150:PP_1152, ΔrelA, and ΔPP_1430 mutants showed growth improvement in the presence of single compounds, and some also exhibited greater tolerance when grown using a complex chemical mixture representative of a lignin-rich chemical stream. Overall, this work demonstrates the successful implementation of a genome-scale screening tool for the identification of genes influencing stress tolerance against notable compounds within lignin-enriched chemical streams, and the genetic targets identified herein offer promising engineering targets for improving feedstock tolerance in lignin valorization strains of P. putida KT2440.
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Affiliation(s)
- Andrew J Borchert
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Alissa Bleem
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Gregg T Beckham
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
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22
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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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23
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Chen JW, Shrestha L, Green G, Leier A, Marquez-Lago TT. The hitchhikers' guide to RNA sequencing and functional analysis. Brief Bioinform 2023; 24:bbac529. [PMID: 36617463 PMCID: PMC9851315 DOI: 10.1093/bib/bbac529] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/18/2022] [Accepted: 11/07/2022] [Indexed: 01/10/2023] Open
Abstract
DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.
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Affiliation(s)
- Jiung-Wen Chen
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Lisa Shrestha
- Department of Genetics, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
| | - George Green
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - André Leier
- Department of Genetics, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
| | - Tatiana T Marquez-Lago
- Department of Genetics, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
- Department of Microbiology, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
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24
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Yu W, Xu X, Jin K, Liu Y, Li J, Du G, Lv X, Liu L. Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications. Biotechnol Adv 2023; 62:108077. [PMID: 36502964 DOI: 10.1016/j.biotechadv.2022.108077] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/06/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022]
Abstract
Genetically encoded biosensors are the vital components of synthetic biology and metabolic engineering, as they are regarded as powerful devices for the dynamic control of genotype metabolism and evolution/screening of desirable phenotypes. This review summarized the recent advances in the construction and applications of different genetically encoded biosensors, including fluorescent protein-based biosensors, nucleic acid-based biosensors, allosteric transcription factor-based biosensors and two-component system-based biosensors. First, the construction frameworks of these biosensors were outlined. Then, the recent progress of biosensor applications in creating versatile microbial cell factories for the bioproduction of high-value chemicals was summarized. Finally, the challenges and prospects for constructing robust and sophisticated biosensors were discussed. This review provided theoretical guidance for constructing genetically encoded biosensors to create desirable microbial cell factories for sustainable bioproduction.
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Affiliation(s)
- Wenwen Yu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Xianhao Xu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Ke Jin
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China.
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25
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Escorcia-Rodríguez JM, Gaytan-Nuñez E, Hernandez-Benitez EM, Zorro-Aranda A, Tello-Palencia MA, Freyre-González JA. Improving gene regulatory network inference and assessment: The importance of using network structure. Front Genet 2023; 14:1143382. [PMID: 36926589 PMCID: PMC10012345 DOI: 10.3389/fgene.2023.1143382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 02/20/2023] [Indexed: 03/03/2023] Open
Abstract
Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that methods inferring co-expression networks should no longer be assessed equally with those inferring regulatory interactions. While methods inferring regulatory interactions perform better in global regulatory network inference than co-expression-based methods, the latter is better suited to infer function-specific regulons and co-regulation networks. When merging expression data, the size increase should outweigh the noise inclusion and graph structure should be considered when integrating the inferences. We conclude with guidelines to take advantage of inference methods and their assessment based on the applications and available expression datasets.
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Affiliation(s)
- Juan M Escorcia-Rodríguez
- Regulatory Systems Biology Research Group, Program of Systems Biology, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Estefani Gaytan-Nuñez
- Regulatory Systems Biology Research Group, Program of Systems Biology, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico.,Undergraduate Program in Genomic Sciences, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Ericka M Hernandez-Benitez
- Regulatory Systems Biology Research Group, Program of Systems Biology, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico.,Undergraduate Program in Genomic Sciences, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Andrea Zorro-Aranda
- Regulatory Systems Biology Research Group, Program of Systems Biology, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico.,Department of Chemical Engineering, Universidad de Antioquia, Medellín, Colombia
| | - Marco A Tello-Palencia
- Regulatory Systems Biology Research Group, Program of Systems Biology, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico.,Undergraduate Program in Genomic Sciences, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Julio A Freyre-González
- Regulatory Systems Biology Research Group, Program of Systems Biology, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
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26
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Abstract
Pseudomonas putida KT2440 is an emerging microbial chassis for biobased chemical production from renewable feedstocks and environmental bioremediation. However, tools for studying, engineering, and modulating protein complexes and biosynthetic enzymes in this organism are largely underdeveloped. Genetic code expansion for the incorporation of unnatural amino acids (unAAs) into proteins can advance such efforts and, furthermore, enable additional controls of biological processes of the strain. In this work, we established the orthogonality of two widely used archaeal tRNA synthetase and tRNA pairs in KT2440. Following the optimization of decoding systems, four unAAs were incorporated into proteins in response to a UAG stop codon at 34.6-78% efficiency. In addition, we demonstrated the utility of genetic code expansion through the incorporation of a photocross-linking amino acid, p-benzoyl-l-phenylalanine (pBpa), into glutathione S-transferase (GstA) and a chemosensory response regulator (CheY) for protein-protein interaction studies in KT2440. This work reported the successful genetic code expansion in KT2440 for the first time. Given the diverse structure and functions of unAAs that have been added to protein syntheses using the archaeal systems, our research lays down a solid foundation for future work to study and enhance the biological functions of KT2440.
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Affiliation(s)
- Xinyuan He
- Department of Chemical & Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, United States
| | - Tianyu Gao
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, United States
| | - Yan Chen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, United States
| | - Kun Liu
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, United States
| | - Jiantao Guo
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, United States
- The Nebraska Center for Integrated Biomolecular Communication (NCIBC), University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, United States
| | - Wei Niu
- Department of Chemical & Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, United States
- The Nebraska Center for Integrated Biomolecular Communication (NCIBC), University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, United States
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