1
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Liou GG, Chao Kaberdina A, Wang WS, Kaberdin VR, Lin-Chao S. Combined Transcriptomic and Proteomic Profiling of E. coli under Microaerobic versus Aerobic Conditions: The Multifaceted Roles of Noncoding Small RNAs and Oxygen-Dependent Sensing in Global Gene Expression Control. Int J Mol Sci 2022; 23:ijms23052570. [PMID: 35269716 PMCID: PMC8910356 DOI: 10.3390/ijms23052570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/17/2022] [Accepted: 02/21/2022] [Indexed: 11/16/2022] Open
Abstract
Adaptive mechanisms that facilitate intestinal colonization by the human microbiota, including Escherichia coli, may be better understood by analyzing the physiology and gene expression of bacteria in low-oxygen environments. We used high-throughput transcriptomics and proteomics to compare the expression profiles of E. coli grown under aerobic versus microaerobic conditions. Clustering of high-abundance transcripts under microaerobiosis highlighted genes controlling acid-stress adaptation (gadAXW, gadAB, hdeAB-yhiD and hdeD operons), cell adhesion/biofilm formation (pgaABCD and csgDEFG operons), electron transport (cydAB), oligopeptide transport (oppABCDF), and anaerobic respiration/fermentation (hyaABCDEF and hycABCDEFGHI operons). In contrast, downregulated genes were involved in iron transport (fhuABCD, feoABC and fepA-entD operons), iron-sulfur cluster assembly (iscRSUA and sufABCDSE operons), aerobic respiration (sdhDAB and sucABCDSE operons), and de novo nucleotide synthesis (nrdHIEF). Additionally, quantitative proteomics showed that the products (proteins) of these high- or low-abundance transcripts were expressed consistently. Our findings highlight interrelationships among energy production, carbon metabolism, and iron homeostasis. Moreover, we have identified and validated a subset of differentially expressed noncoding small RNAs (i.e., CsrC, RyhB, RprA and GcvB), and we discuss their regulatory functions during microaerobic growth. Collectively, we reveal key changes in gene expression at the transcriptional and post-transcriptional levels that sustain E. coli growth when oxygen levels are low.
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Affiliation(s)
- Gunn-Guang Liou
- Institute of Molecular Biology, Academia Sinica, Taipei 11529, Taiwan; (G.-G.L.); (A.C.K.); (W.-S.W.)
| | - Anna Chao Kaberdina
- Institute of Molecular Biology, Academia Sinica, Taipei 11529, Taiwan; (G.-G.L.); (A.C.K.); (W.-S.W.)
| | - Wei-Syuan Wang
- Institute of Molecular Biology, Academia Sinica, Taipei 11529, Taiwan; (G.-G.L.); (A.C.K.); (W.-S.W.)
- Molecular and Cell Biology, Taiwan International Graduate Program, Institute of Molecular Biology, Academia Sinica and Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 11490, Taiwan
| | - Vladimir R. Kaberdin
- Department of Immunology, Microbiology and Parasitology, University of the Basque Country UPV/EHU, 48940 Leioa, Spain
- Basque Foundation for Science, IKERBASQUE, Maria Diaz de Haro 3, 48013 Bilbao, Spain
- Research Centre for Experimental Marine Biology and Biotechnology (PIE-UPV/EHU), 48620 Plentzia, Spain
- Correspondence: (V.R.K.); (S.L.-C.)
| | - Sue Lin-Chao
- Institute of Molecular Biology, Academia Sinica, Taipei 11529, Taiwan; (G.-G.L.); (A.C.K.); (W.-S.W.)
- Molecular and Cell Biology, Taiwan International Graduate Program, Institute of Molecular Biology, Academia Sinica and Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 11490, Taiwan
- Correspondence: (V.R.K.); (S.L.-C.)
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2
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Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2021. [PMID: 32978650 DOI: 10.1007/10_2020_133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
Eulerian-Lagrangian approach to investigate cellular responses in a bioreactor has become the center of attention in recent years. It was introduced to biotechnological processes about two decades ago, but within the last few years, it proved itself as a powerful tool to address scale-up and -down topics of bioprocesses. It can capture the history of a cell and reveal invaluable information for, not only, bioprocess control and design but also strain engineering. This way it will be possible to shed light on the actual environment that cell experiences throughout its lifespan. Lifelines of a microorganism in a bioreactor can serve as the missing link that encompasses the biological timescales and the physical timescales. For this purpose digitalization of bioreactors provides us with new insights that are not achievable in industrial reactors easily if at all, namely, substrate and product gradients; high-shear regions are among the most interesting factors that can be reproduced adequately with help of a digital twin. In this chapter basic principles of this method will be introduced, and later on some practical aspects of particle tracking technique will be illustrated. In the final section, some of the advantages and challenges associated with this method will be discussed.
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3
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Millard P, Enjalbert B, Uttenweiler-Joseph S, Portais JC, Létisse F. Control and regulation of acetate overflow in Escherichia coli. eLife 2021; 10:63661. [PMID: 33720011 PMCID: PMC8021400 DOI: 10.7554/elife.63661] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 03/12/2021] [Indexed: 12/21/2022] Open
Abstract
Overflow metabolism refers to the production of seemingly wasteful by-products by cells during growth on glucose even when oxygen is abundant. Two theories have been proposed to explain acetate overflow in Escherichia coli – global control of the central metabolism and local control of the acetate pathway – but neither accounts for all observations. Here, we develop a kinetic model of E. coli metabolism that quantitatively accounts for observed behaviours and successfully predicts the response of E. coli to new perturbations. We reconcile these theories and clarify the origin, control, and regulation of the acetate flux. We also find that, in turns, acetate regulates glucose metabolism by coordinating the expression of glycolytic and TCA genes. Acetate should not be considered a wasteful end-product since it is also a co-substrate and a global regulator of glucose metabolism in E. coli. This has broad implications for our understanding of overflow metabolism.
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Affiliation(s)
- Pierre Millard
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France.,MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Brice Enjalbert
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France
| | | | - Jean-Charles Portais
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France.,MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France.,RESTORE, Université de Toulouse, INSERM U1031, CNRS 5070, Université Toulouse III - Paul Sabatier, EFS, Toulouse, France
| | - Fabien Létisse
- TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France.,Université Toulouse III - Paul Sabatier, Toulouse, France
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4
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Pathway Tools Visualization of Organism-Scale Metabolic Networks. Metabolites 2021; 11:metabo11020064. [PMID: 33499002 PMCID: PMC7911265 DOI: 10.3390/metabo11020064] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/12/2021] [Accepted: 01/12/2021] [Indexed: 12/20/2022] Open
Abstract
Metabolomics, synthetic biology, and microbiome research demand information about organism-scale metabolic networks. The convergence of genome sequencing and computational inference of metabolic networks has enabled great progress toward satisfying that demand by generating metabolic reconstructions from the genomes of thousands of sequenced organisms. Visualization of whole metabolic networks is critical for aiding researchers in understanding, analyzing, and exploiting those reconstructions. We have developed bioinformatics software tools that automatically generate a full metabolic-network diagram for an organism, and that enable searching and analyses of the network. The software generates metabolic-network diagrams for unicellular organisms, for multi-cellular organisms, and for pan-genomes and organism communities. Search tools enable users to find genes, metabolites, enzymes, reactions, and pathways within a diagram. The diagrams are zoomable to enable researchers to study local neighborhoods in detail and to see the big picture. The diagrams also serve as tools for comparison of metabolic networks and for interpreting high-throughput datasets, including transcriptomics, metabolomics, and reaction fluxes computed by metabolic models. These data can be overlaid on the metabolic charts to produce animated zoomable displays of metabolic flux and metabolite abundance. The BioCyc.org website contains whole-network diagrams for more than 18,000 sequenced organisms. The ready availability of organism-specific metabolic network diagrams and associated tools for almost any sequenced organism are useful for researchers working to better understand the metabolism of their organism and to interpret high-throughput datasets in a metabolic context.
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5
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Bui TT, Lee D, Selvarajoo K. ScatLay: utilizing transcriptome-wide noise for identifying and visualizing differentially expressed genes. Sci Rep 2020; 10:17483. [PMID: 33060728 PMCID: PMC7566603 DOI: 10.1038/s41598-020-74564-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 09/28/2020] [Indexed: 01/10/2023] Open
Abstract
Differential expressed (DE) genes analysis is valuable for understanding comparative transcriptomics between cells, conditions or time evolution. However, the predominant way of identifying DE genes is to use arbitrary threshold fold or expression changes as cutoff. Here, we developed a more objective method, Scatter Overlay or ScatLay, to extract and graphically visualize DE genes across any two samples by utilizing their pair-wise scatter or transcriptome-wide noise, while factoring replicate variabilities. We tested ScatLay for 3 cell types: between time points for Escherichia coli aerobiosis and Saccharomyces cerevisiae hypoxia, and between untreated and Etomoxir treated Mus Musculus embryonic stem cell. As a result, we obtain 1194, 2061 and 2932 DE genes, respectively. Next, we compared these data with two widely used current approaches (DESeq2 and NOISeq) with typical twofold expression changes threshold, and show that ScatLay reveals significantly larger number of DE genes. Hence, our method provides a wider coverage of DE genes, and will likely pave way for finding more novel regulatory genes in future works.
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Affiliation(s)
- Thuy Tien Bui
- Singapore Institute of Food and Biotechnology Innovation, Agency for Science, Technology & Research (A*STAR), 61 Biopolis Drive, Singapore, 138673, Singapore
| | - Daniel Lee
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Kumar Selvarajoo
- Singapore Institute of Food and Biotechnology Innovation, Agency for Science, Technology & Research (A*STAR), 61 Biopolis Drive, Singapore, 138673, Singapore. .,Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore.
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6
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Wehrs M, Thompson MG, Banerjee D, Prahl JP, Morella NM, Barcelos CA, Moon J, Costello Z, Keasling JD, Shih PM, Tanjore D, Mukhopadhyay A. Investigation of Bar-seq as a method to study population dynamics of Saccharomyces cerevisiae deletion library during bioreactor cultivation. Microb Cell Fact 2020; 19:167. [PMID: 32811554 PMCID: PMC7437010 DOI: 10.1186/s12934-020-01423-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 08/11/2020] [Indexed: 12/15/2022] Open
Abstract
Background Despite the latest advancements in metabolic engineering for genome editing and characterization of host performance, the successful development of robust cell factories used for industrial bioprocesses and accurate prediction of the behavior of microbial systems, especially when shifting from laboratory-scale to industrial conditions, remains challenging. To increase the probability of success of a scale-up process, data obtained from thoroughly performed studies mirroring cellular responses to typical large-scale stimuli may be used to derive crucial information to better understand potential implications of large-scale cultivation on strain performance. This study assesses the feasibility to employ a barcoded yeast deletion library to assess genome-wide strain fitness across a simulated industrial fermentation regime and aims to understand the genetic basis of changes in strain physiology during industrial fermentation, and the corresponding roles these genes play in strain performance. Results We find that mutant population diversity is maintained through multiple seed trains, enabling large scale fermentation selective pressures to act upon the community. We identify specific deletion mutants that were enriched in all processes tested in this study, independent of the cultivation conditions, which include MCK1, RIM11, MRK1, and YGK3 that all encode homologues of mammalian glycogen synthase kinase 3 (GSK-3). Ecological analysis of beta diversity between all samples revealed significant population divergence over time and showed feed specific consequences of population structure. Further, we show that significant changes in the population diversity during fed-batch cultivations reflect the presence of significant stresses. Our observations indicate that, for this yeast deletion collection, the selection of the feeding scheme which affects the accumulation of the fermentative by-product ethanol impacts the diversity of the mutant pool to a higher degree as compared to the pH of the culture broth. The mutants that were lost during the time of most extreme population selection suggest that specific biological processes may be required to cope with these specific stresses. Conclusions Our results demonstrate the feasibility of Bar-seq to assess fermentation associated stresses in yeast populations under industrial conditions and to understand critical stages of a scale-up process where variability emerges, and selection pressure gets imposed. Overall our work highlights a promising avenue to identify genetic loci and biological stress responses required for fitness under industrial conditions.
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Affiliation(s)
- Maren Wehrs
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.,Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
| | - Mitchell G Thompson
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.,Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.,Department of Plant and Microbial Biology, University of California, Berkeley, CA, 94720, USA
| | - Deepanwita Banerjee
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.,Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
| | - Jan-Philip Prahl
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.,Advanced Biofuels and Bioproducts Process Development Unit, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
| | - Norma M Morella
- Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Carolina A Barcelos
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.,Advanced Biofuels and Bioproducts Process Development Unit, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
| | - Jadie Moon
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.,Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
| | - Zak Costello
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.,Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.,Department of Energy Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Jay D Keasling
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.,Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.,Department of Bioengineering, University of California, Berkeley, CA, 94720, USA.,Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, 94720, USA.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK 2970, Horsholm, Denmark.,Synthetic Biochemistry Center, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen, China
| | - Patrick M Shih
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.,Department of Plant Biology, University of California-Davis, Davis, CA, 95616, USA
| | - Deepti Tanjore
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. .,Advanced Biofuels and Bioproducts Process Development Unit, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.
| | - Aindrila Mukhopadhyay
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. .,Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA. .,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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7
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Attractor Concepts to Evaluate the Transcriptome-wide Dynamics Guiding Anaerobic to Aerobic State Transition in Escherichia coli. Sci Rep 2020; 10:5878. [PMID: 32246034 PMCID: PMC7125300 DOI: 10.1038/s41598-020-62804-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 03/19/2020] [Indexed: 01/14/2023] Open
Abstract
For any dynamical system, like living organisms, an attractor state is a set of variables or mechanisms that converge towards a stable system behavior despite a wide variety of initial conditions. Here, using multi-dimensional statistics, we investigate the global gene expression attractor mechanisms shaping anaerobic to aerobic state transition (AAT) of Escherichia coli in a bioreactor at early times. Out of 3,389 RNA-Seq expression changes over time, we identified 100 sharply changing genes that are key for guiding 1700 genes into the AAT attractor basin. Collectively, these genes were named as attractor genes constituting of 6 dynamic clusters. Apart from the expected anaerobic (glycolysis), aerobic (TCA cycle) and fermentation (succinate pathways) processes, sulphur metabolism, ribosome assembly and amino acid transport mechanisms together with 332 uncharacterised genes are also key for AAT. Overall, our work highlights the importance of multi-dimensional statistical analyses for revealing novel processes shaping AAT.
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8
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Fumarate dependent protein composition under aerobic and anaerobic growth conditions in Escherichia coli. J Proteomics 2020; 212:103583. [DOI: 10.1016/j.jprot.2019.103583] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 10/15/2019] [Accepted: 11/11/2019] [Indexed: 11/21/2022]
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9
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Karp PD, Midford PE, Billington R, Kothari A, Krummenacker M, Latendresse M, Ong WK, Subhraveti P, Caspi R, Fulcher C, Keseler IM, Paley SM. Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biology. Brief Bioinform 2019; 22:109-126. [PMID: 31813964 DOI: 10.1093/bib/bbz104] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 07/23/2019] [Accepted: 07/24/2019] [Indexed: 12/19/2022] Open
Abstract
MOTIVATION Biological systems function through dynamic interactions among genes and their products, regulatory circuits and metabolic networks. Our development of the Pathway Tools software was motivated by the need to construct biological knowledge resources that combine these many types of data, and that enable users to find and comprehend data of interest as quickly as possible through query and visualization tools. Further, we sought to support the development of metabolic flux models from pathway databases, and to use pathway information to leverage the interpretation of high-throughput data sets. RESULTS In the past 4 years we have enhanced the already extensive Pathway Tools software in several respects. It can now support metabolic-model execution through the Web, it provides a more accurate gap filler for metabolic models; it supports development of models for organism communities distributed across a spatial grid; and model results may be visualized graphically. Pathway Tools supports several new omics-data analysis tools including the Omics Dashboard, multi-pathway diagrams called pathway collages, a pathway-covering algorithm for metabolomics data analysis and an algorithm for generating mechanistic explanations of multi-omics data. We have also improved the core pathway/genome databases management capabilities of the software, providing new multi-organism search tools for organism communities, improved graphics rendering, faster performance and re-designed gene and metabolite pages. AVAILABILITY The software is free for academic use; a fee is required for commercial use. See http://pathwaytools.com. CONTACT pkarp@ai.sri.com. SUPPLEMENTARY INFORMATION Supplementary data are available at Briefings in Bioinformatics online.
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Affiliation(s)
- Peter D Karp
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Peter E Midford
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Richard Billington
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Anamika Kothari
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | | | - Mario Latendresse
- Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, USA
| | - Wai Kit Ong
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Pallavi Subhraveti
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Ron Caspi
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Carol Fulcher
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Ingrid M Keseler
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Suzanne M Paley
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
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10
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Kurgan G, Kurgan L, Schneider A, Onyeabor M, Rodriguez-Sanchez Y, Taylor E, Martinez R, Carbonell P, Shi X, Gu H, Wang X. Identification of major malate export systems in an engineered malate-producing Escherichia coli aided by substrate similarity search. Appl Microbiol Biotechnol 2019; 103:9001-9011. [DOI: 10.1007/s00253-019-10164-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 08/27/2019] [Accepted: 09/28/2019] [Indexed: 01/29/2023]
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11
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Wehrs M, Tanjore D, Eng T, Lievense J, Pray TR, Mukhopadhyay A. Engineering Robust Production Microbes for Large-Scale Cultivation. Trends Microbiol 2019; 27:524-537. [PMID: 30819548 DOI: 10.1016/j.tim.2019.01.006] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/11/2019] [Accepted: 01/23/2019] [Indexed: 11/27/2022]
Abstract
Systems biology and synthetic biology are increasingly used to examine and modulate complex biological systems. As such, many issues arising during scaling-up microbial production processes can be addressed using these approaches. We review differences between laboratory-scale cultures and larger-scale processes to provide a perspective on those strain characteristics that are especially important during scaling. Systems biology has been used to examine a range of microbial systems for their response in bioreactors to fluctuations in nutrients, dissolved gases, and other stresses. Synthetic biology has been used both to assess and modulate strain response, and to engineer strains to improve production. We discuss these approaches and tools in the context of their use in engineering robust microbes for applications in large-scale production.
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Affiliation(s)
- Maren Wehrs
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Institut für Genetik, Technische Universität Braunschweig, Braunschweig, Germany; Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA
| | - Deepti Tanjore
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Thomas Eng
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA
| | | | - Todd R Pray
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Aindrila Mukhopadhyay
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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12
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Zieringer J, Takors R. In Silico Prediction of Large-Scale Microbial Production Performance: Constraints for Getting Proper Data-Driven Models. Comput Struct Biotechnol J 2018; 16:246-256. [PMID: 30105090 PMCID: PMC6077756 DOI: 10.1016/j.csbj.2018.06.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 06/11/2018] [Accepted: 06/12/2018] [Indexed: 12/20/2022] Open
Abstract
Industrial bioreactors range from 10.000 to 700.000 L and characteristically show different zones of substrate availabilities, dissolved gas concentrations and pH values reflecting physical, technical and economic constraints of scale-up. Microbial producers are fluctuating inside the bioreactors thereby experiencing frequently changing micro-environmental conditions. The external stimuli induce responses on microbial metabolism and on transcriptional regulation programs. Both may deteriorate the expected microbial production performance in large scale compared to expectations deduced from ideal, well-mixed lab-scale conditions. Accordingly, predictive tools are needed to quantify large-scale impacts considering bioreactor heterogeneities. The review shows that the time is right to combine simulations of microbial kinetics with calculations of large-scale environmental conditions to predict the bioreactor performance. Accordingly, basic experimental procedures and computational tools are presented to derive proper microbial models and hydrodynamic conditions, and to link both for bioreactor modeling. Particular emphasis is laid on the identification of gene regulatory networks as the implementation of such models will surely gain momentum in future studies.
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13
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Paley S, Parker K, Spaulding A, Tomb JF, O'Maille P, Karp PD. The Omics Dashboard for interactive exploration of gene-expression data. Nucleic Acids Res 2017; 45:12113-12124. [PMID: 29040755 PMCID: PMC5716103 DOI: 10.1093/nar/gkx910] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 09/27/2017] [Indexed: 01/21/2023] Open
Abstract
The Omics Dashboard is a software tool for interactive exploration and analysis of gene-expression datasets. The Omics Dashboard is organized as a hierarchy of cellular systems. At the highest level of the hierarchy the Dashboard contains graphical panels depicting systems such as biosynthesis, energy metabolism, regulation and central dogma. Each of those panels contains a series of X–Y plots depicting expression levels of subsystems of that panel, e.g. subsystems within the central dogma panel include transcription, translation and protein maturation and folding. The Dashboard presents a visual read-out of the expression status of cellular systems to facilitate a rapid top-down user survey of how all cellular systems are responding to a given stimulus, and to enable the user to quickly view the responses of genes within specific systems of interest. Although the Dashboard is complementary to traditional statistical methods for analysis of gene-expression data, we show how it can detect changes in gene expression that statistical techniques may overlook. We present the capabilities of the Dashboard using two case studies: the analysis of lipid production for the marine alga Thalassiosira pseudonana, and an investigation of a shift from anaerobic to aerobic growth for the bacterium Escherichia coli.
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Affiliation(s)
- Suzanne Paley
- Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA
| | | | - Aaron Spaulding
- Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA
| | - Jean-Francois Tomb
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA
| | - Paul O'Maille
- Biosciences Division, SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA
| | - Peter D Karp
- Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA
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