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Cellular Ecophysiology of Microbe pp 1–9Cite as

Bioinformatic, Molecular, and Genetic Tools for Exploring Genome-Wide Responses to Hydrocarbons

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Part of the book series: Handbook of Hydrocarbon and Lipid Microbiology ((HHLM))

Abstract

The response profiles of bacteria to hydrocarbons in the wild can be directly assessed by high-throughput cDNA sequencing of metagenomes, tracking the fate or metabolism of labeled cells in the microbial community or can be indirectly inferred from the screening of mutant libraries for key genetic determinants. Transcriptome, proteome, and metabolome data are collected from homogeneous bacterial populations that are exposed to hydrocarbons under strictly controlled culturing conditions.

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References

  • Aylward FO, Eppley JM, Smith JM, Chavez FP, Scholin CA, DeLong EF (2015) Microbial community transcriptional networks are conserved in three domains at ocean basin scales. Proc Natl Acad Sci U S A 112:5443–5448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bailey TL, Johnson J, Grant CE, Noble WS (2015) The MEME suite. Nucleic Acids Res 43(W1):W39–W49

    Article  PubMed  PubMed Central  Google Scholar 

  • Bargiela R, Herbst FA, Martínez-Martínez M, Seifert J, Rojo D, Cappello S, Genovese M, Crisafi F, Denaro R, Chernikova TN, Barbas C, von Bergen M, Yakimov MM, Ferrer M, Golyshin PN (2015) Metaproteomics and metabolomics analyses of chronically petroleum-polluted sites reveal the importance of general anaerobic processes uncoupled with degradation. Proteomics 15:3508–3520

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bezuidt O, Lima-Mendez G, Reva ON (2009) SeqWord Gene Island Sniffer: a program to study the lateral genetic exchange among bacteria. World Acad Sci Eng Technol 58:1169–1174

    Google Scholar 

  • Bock C, Farlik M, Sheffield NC (2016) Multi-omics of single cells: strategies and applications. Trends Biotechnol 34:605–608

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chang A, Schomburg I, Placzek S, Jeske L, Ulbrich M, Xiao M, Sensen CW, Schomburg D (2015) BRENDA in 2015: exciting developments in its 25th year of existence. Nucleic Acids Res 43(Database issue):D439–D446

    Article  PubMed  Google Scholar 

  • Creecy JP, Conway T (2015) Quantitative bacterial transcriptomics with RNA-seq. Curr Opin Microbiol 23:133–140

    Article  CAS  PubMed  Google Scholar 

  • Daims H, Wagner M (2007) Quantification of uncultured microorganisms by fluorescence microscopy and digital image analysis. Appl Microbiol Biotechnol 75:237–248

    Article  CAS  PubMed  Google Scholar 

  • Dharmadi Y, Gonzalez R (2004) DNA microarrays: experimental issues, data analysis, and application to bacterial systems. Biotechnol Prog 20:1309–1324

    Article  CAS  PubMed  Google Scholar 

  • Fislage R (1998) Differential display approach to quantitation of environmental stimuli on bacterial gene expression. Electrophoresis 19:613–616

    Article  CAS  PubMed  Google Scholar 

  • Galagan J, Lyubetskaya A, Gomes A (2013) ChIP-Seq and the complexity of bacterial transcriptional regulation. Curr Top Microbiol Immunol 363:43–68

    CAS  PubMed  Google Scholar 

  • Garza DR, Dutilh BE (2015) From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems. Cell Mol Life Sci 72:4287–4308

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Graham JE, Clark-Curtiss JE (1999) Identification of Mycobacterium tuberculosis RNAs synthesized in response to phagocytosis by human macrophages by selective capture of transcribed sequences (SCOTS). Proc Natl Acad Sci U S A 96:11554–11559

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hrdlickova R, Toloue M, Tian B (2016) RNA-Seq methods for transcriptome analysis. Wiley Interdiscip Rev RNA. doi:10.1002/wrna.1364

    PubMed  Google Scholar 

  • Huson DH, Beier S, Flade I, Górska A, El-Hadidi M, Mitra S, Ruscheweyh HJ, Tappu R (2016) MEGAN community edition – interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput Biol 12:e1004957

    Article  PubMed  PubMed Central  Google Scholar 

  • Ito T, Sakaki Y (1997) Fluorescent differential display. Methods Mol Biol 85:37–44

    CAS  PubMed  Google Scholar 

  • Kodzius R, Gojobori T (2016) Single-cell technologies in environmental omics. Gene 576:701–707

    Article  CAS  PubMed  Google Scholar 

  • Liu XS (2007) Getting started in tiling microarray analysis. PLoS Comput Biol 3:1842–1844

    CAS  PubMed  Google Scholar 

  • Losada PM, Chouvarine P, Dorda M, Hedtfeld S, Mielke S, Schulz A, Wiehlmann L, Tümmler B (2016) The cystic fibrosis lower airways microbial metagenome. ERJ Open Res 2:00096–02015

    Google Scholar 

  • Maaß S, Becher D (2016) Methods and applications of absolute protein quantification in microbial systems. J Proteome 136:222–233

    Article  Google Scholar 

  • Mazurkiewicz P, Tang CM, Boone C, Holden DW (2006) Signature-tagged mutagenesis: barcoding mutants for genome-wide screens. Nat Rev Genet 7:929–939

    Article  CAS  PubMed  Google Scholar 

  • Myers KS, Park DM, Beauchene NA, Kiley PJ (2015) Defining bacterial regulons using ChIP-seq. Methods 86:80–88

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ness SA (2007) Microarray analysis: basic strategies for successful experiments. Mol Biotechnol 36:205–219

    Article  CAS  PubMed  Google Scholar 

  • Neufeld JD, Wagner M, Murrell JC (2007) Who eats what, where and when? Isotope-labelling experiments are coming of age. ISME J 1:103–110

    Article  CAS  PubMed  Google Scholar 

  • Otto A, van Dijl JM, Hecker M, Becher D (2014) The Staphylococcus aureus proteome. Int J Med Microbiol 304:110–120

    Article  CAS  PubMed  Google Scholar 

  • Pierneef R, Cronje L, Bezuidt O, Reva ON (2015) Pre_GI: a global map of ontological links between horizontally transferred genomic islands in bacterial and archaeal genomes. Database 2015:bav058

    PubMed  Google Scholar 

  • Putri SP, Nakayama Y, Matsuda F, Uchikata T, Kobayashi S, Matsubara A, Fukusaki E (2013) Current metabolomics: practical applications. J Biosci Bioeng 115:579–589

    Article  CAS  PubMed  Google Scholar 

  • Rediers H, Rainey PB, Vanderleyden J, De Mot R (2005) Unraveling the secret lives of bacteria: use of in vivo expression technology and differential fluorescence induction promoter traps as tools for exploring niche-specific gene expression. Microbiol Mol Biol Rev 69:217–261

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Reva ON, Hallin PF, Willenbrock H, Sicheritz-Ponten T, Tümmler B, Ussery DW (2008) Global features of the Alcanivorax borkumensis SK2 genome. Environ Microbiol 10:614–625

    Article  CAS  PubMed  Google Scholar 

  • Sabirova JS, Ferrer M, Regenhardt D, Timmis KN, Golyshin PN (2006) Proteomic insights into metabolic adaptations in Alcanivorax borkumensis induced by alkane utilization. J Bacteriol 188:3763–3773

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Segata N, Boernigen D, Tickle TL, Morgan XC, Garrett WS, Huttenhower C (2013) Computational meta’omics for microbial community studies. Mol Syst Biol 9:666

    Article  PubMed  PubMed Central  Google Scholar 

  • Soufi B, Macek B (2015) Global analysis of bacterial membrane proteins and their modifications. Int J Med Microbiol 305:203–208

    Article  CAS  PubMed  Google Scholar 

  • van Beilen JB, Panke S, Lucchini S, Franchini AG, Röthlisberger M, Witholt B (2001) Analysis of Pseudomonas putida alkane-degradation gene clusters and flanking insertion sequences: evolution and regulation of the alk genes. Microbiology 147:1621–1630

    Article  PubMed  Google Scholar 

  • Van Oudenhove L, Devreese B (2013) A review on recent developments in mass spectrometry instrumentation and quantitative tools advancing bacterial proteomics. Appl Microbiol Biotechnol 97:4749–4762

    Article  PubMed  Google Scholar 

  • Vasilakou E, Machado D, Theorell A, Rocha I, Nöh K, Oldiges M, Wahl SA (2016) Current state and challenges for dynamic metabolic modeling. Curr Opin Microbiol 33:97–104

    Article  CAS  PubMed  Google Scholar 

  • Yakimov MM, Timmis KN, Golyshin PN (2007) Obligate oil-degrading marine bacteria. Curr Opin Biotechnol 18:257–266

    Article  CAS  PubMed  Google Scholar 

  • Yarza P, Yilmaz P, Pruesse E, Glöckner FO, Ludwig W, Schleifer KH, Whitman WB, Euzéby J, Amann R, Rosselló-Móra R (2014) Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol 12:635–645

    Article  CAS  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Centre for Bioinformatics and Computational Biology, Department of Biochemistry, University of Pretoria, Hillcrest, Pretoria, South Africa

    O. N. Reva & R. E. Pierneef

  2. Klinische Forschergruppe, Medizinische Hochschule Hannover, OE 6710, Hannover, Germany

    B. Tümmler

Authors
  1. O. N. Reva
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  2. R. E. Pierneef
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  3. B. Tümmler
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Corresponding author

Correspondence to O. N. Reva .

Editor information

Editors and Affiliations

  1. Consejo Superior de Investigaciones, Estacion Experimental del Zaidin, Granada, Spain

    Tino Krell

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Reva, O.N., Pierneef, R.E., Tümmler, B. (2017). Bioinformatic, Molecular, and Genetic Tools for Exploring Genome-Wide Responses to Hydrocarbons. In: Krell, T. (eds) Cellular Ecophysiology of Microbe. Handbook of Hydrocarbon and Lipid Microbiology . Springer, Cham. https://doi.org/10.1007/978-3-319-20796-4_33-1

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  • DOI: https://doi.org/10.1007/978-3-319-20796-4_33-1

  • Received:

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20796-4

  • Online ISBN: 978-3-319-20796-4

  • eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences

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