Literature DB >> 32394353

Understanding metabolic adaptation by using bacterial laboratory evolution and trans-omics analysis.

Takaaki Horinouchi1, Chikara Furusawa2,3.   

Abstract

Many diseases such as metabolic syndrome, cancer, inflammatory diseases, and pathological phenomena can be understood as an adaptive reconstitution of the metabolic state (metabolic adaptation). One of the effective approaches to reveal the property of metabolic networks is using model organisms such as microorganisms that are easier to experiment with than higher organisms. Using the laboratory evolution approach, we can elucidate the evolutionary dynamics in various stress environments, which provide us an understanding of the metabolic adaptation. In addition, the integration of omics data and phenotypic data enables us to clarify the genetic and phenotypic alterations during adaptation. In this review, we describe our recent studies on bacterial laboratory evolution and the omics approach to clarify the stress adaptation process. We have also obtained high-dimensional phenotypic data using our automated culture system. By combining these genomic and transcriptomic data within high-throughput phenotypic data, we can discuss the complex trans-omics network of metabolic adaptation.

Entities:  

Keywords:  Adaptive laboratory evolution; Laboratory automation; Metabolic adaptation; Omics analysis; Stress tolerance

Year:  2020        PMID: 32394353      PMCID: PMC7311587          DOI: 10.1007/s12551-020-00695-4

Source DB:  PubMed          Journal:  Biophys Rev        ISSN: 1867-2450


  40 in total

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Journal:  Int Microbiol       Date:  1998-12       Impact factor: 2.479

2.  Adaptive evolution for fast growth on glucose and the effects on the regulation of glucose transport system in Clostridium tyrobutyricum.

Authors:  Ling Jiang; Shuang Li; Yi Hu; Qing Xu; He Huang
Journal:  Biotechnol Bioeng       Date:  2011-12-20       Impact factor: 4.530

3.  Genome-wide transcriptional responses of Escherichia coli K-12 to continuous osmotic and heat stresses.

Authors:  Thusitha S Gunasekera; Laszlo N Csonka; Oleg Paliy
Journal:  J Bacteriol       Date:  2008-03-21       Impact factor: 3.490

Review 4.  Recent advances in the evolutionary engineering of industrial biocatalysts.

Authors:  James D Winkler; Katy C Kao
Journal:  Genomics       Date:  2014-09-28       Impact factor: 5.736

Review 5.  Metabolic profiling of lipids by supercritical fluid chromatography/mass spectrometry.

Authors:  Takeshi Bamba; Jae Won Lee; Atsuki Matsubara; Eiichiro Fukusaki
Journal:  J Chromatogr A       Date:  2012-05-28       Impact factor: 4.759

Review 6.  Genomics and the evolution of antibiotic resistance.

Authors:  Michael R Gillings; Ian T Paulsen; Sasha G Tetu
Journal:  Ann N Y Acad Sci       Date:  2016-10-21       Impact factor: 5.691

7.  Acid adaptation induces cross-protection against environmental stresses in Salmonella typhimurium.

Authors:  G J Leyer; E A Johnson
Journal:  Appl Environ Microbiol       Date:  1993-06       Impact factor: 4.792

8.  Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli.

Authors:  Bryson D Bennett; Elizabeth H Kimball; Melissa Gao; Robin Osterhout; Stephen J Van Dien; Joshua D Rabinowitz
Journal:  Nat Chem Biol       Date:  2009-06-28       Impact factor: 15.040

Review 9.  Microbial laboratory evolution in the era of genome-scale science.

Authors:  Tom M Conrad; Nathan E Lewis; Bernhard Ø Palsson
Journal:  Mol Syst Biol       Date:  2011-07-05       Impact factor: 11.429

10.  Highly efficient single-stranded DNA ligation technique improves low-input whole-genome bisulfite sequencing by post-bisulfite adaptor tagging.

Authors:  Fumihito Miura; Yukiko Shibata; Miki Miura; Yuhei Sangatsuda; Osamu Hisano; Hiromitsu Araki; Takashi Ito
Journal:  Nucleic Acids Res       Date:  2019-09-05       Impact factor: 16.971

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  2 in total

Review 1.  Microbial adaptive evolution.

Authors:  Aiqin Shi; Feiyu Fan; James R Broach
Journal:  J Ind Microbiol Biotechnol       Date:  2022-04-14       Impact factor: 4.258

Review 2.  Quantitative metabolic fluxes regulated by trans-omic networks.

Authors:  Satoshi Ohno; Saori Uematsu; Shinya Kuroda
Journal:  Biochem J       Date:  2022-03-31       Impact factor: 3.766

  2 in total

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