Advertisement

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

  • O. N. Reva
  • R. E. Pierneef
  • B. Tümmler
Reference work entry
Part of the Handbook of Hydrocarbon and Lipid Microbiology book series (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.

References

  1. 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 112:5443–5448CrossRefPubMedPubMedCentralGoogle Scholar
  2. Bailey TL, Johnson J, Grant CE, Noble WS (2015) The MEME suite. Nucleic Acids Res 43(W1):W39–W49CrossRefPubMedPubMedCentralGoogle Scholar
  3. 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 15:3508–3520CrossRefPubMedPubMedCentralGoogle Scholar
  4. 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 58:1169–1174Google Scholar
  5. Bock C, Farlik M, Sheffield NC (2016) Multi-omics of single cells: strategies and applications. Trends Biotechnol 34:605–608CrossRefPubMedPubMedCentralGoogle Scholar
  6. 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–D446CrossRefPubMedGoogle Scholar
  7. Creecy JP, Conway T (2015) Quantitative bacterial transcriptomics with RNA-seq. Curr Opin Microbiol 23:133–140CrossRefPubMedGoogle Scholar
  8. Daims H, Wagner M (2007) Quantification of uncultured microorganisms by fluorescence microscopy and digital image analysis. Appl Microbiol Biotechnol 75:237–248CrossRefPubMedGoogle Scholar
  9. Dharmadi Y, Gonzalez R (2004) DNA microarrays: experimental issues, data analysis, and application to bacterial systems. Biotechnol Prog 20:1309–1324CrossRefPubMedGoogle Scholar
  10. Fislage R (1998) Differential display approach to quantitation of environmental stimuli on bacterial gene expression. Electrophoresis 19:613–616CrossRefPubMedGoogle Scholar
  11. Galagan J, Lyubetskaya A, Gomes A (2013) ChIP-Seq and the complexity of bacterial transcriptional regulation. Curr Top Microbiol Immunol 363:43–68PubMedGoogle Scholar
  12. Garza DR, Dutilh BE (2015) From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems. Cell Mol Life Sci 72:4287–4308CrossRefPubMedPubMedCentralGoogle Scholar
  13. 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 96:11554–11559CrossRefPubMedPubMedCentralGoogle Scholar
  14. Hrdlickova R, Toloue M, Tian B (2016) RNA-Seq methods for transcriptome analysis. Wiley Interdiscip Rev RNA. doi:10.1002/wrna.1364PubMedPubMedCentralGoogle Scholar
  15. 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:e1004957CrossRefPubMedPubMedCentralGoogle Scholar
  16. Ito T, Sakaki Y (1997) Fluorescent differential display. Methods Mol Biol 85:37–44PubMedGoogle Scholar
  17. Kodzius R, Gojobori T (2016) Single-cell technologies in environmental omics. Gene 576:701–707CrossRefPubMedGoogle Scholar
  18. Liu XS (2007) Getting started in tiling microarray analysis. PLoS Comput Biol 3:1842–1844PubMedGoogle Scholar
  19. 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 2:00096–02015Google Scholar
  20. Maaß S, Becher D (2016) Methods and applications of absolute protein quantification in microbial systems. J Proteome 136:222–233CrossRefGoogle Scholar
  21. Mazurkiewicz P, Tang CM, Boone C, Holden DW (2006) Signature-tagged mutagenesis: barcoding mutants for genome-wide screens. Nat Rev Genet 7:929–939CrossRefPubMedGoogle Scholar
  22. Myers KS, Park DM, Beauchene NA, Kiley PJ (2015) Defining bacterial regulons using ChIP-seq. Methods 86:80–88CrossRefPubMedPubMedCentralGoogle Scholar
  23. Ness SA (2007) Microarray analysis: basic strategies for successful experiments. Mol Biotechnol 36:205–219CrossRefPubMedGoogle Scholar
  24. Neufeld JD, Wagner M, Murrell JC (2007) Who eats what, where and when? Isotope-labelling experiments are coming of age. ISME J 1:103–110CrossRefPubMedGoogle Scholar
  25. Otto A, van Dijl JM, Hecker M, Becher D (2014) The Staphylococcus aureus proteome. Int J Med Microbiol 304:110–120CrossRefPubMedGoogle Scholar
  26. 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:bav058PubMedPubMedCentralGoogle Scholar
  27. Putri SP, Nakayama Y, Matsuda F, Uchikata T, Kobayashi S, Matsubara A, Fukusaki E (2013) Current metabolomics: practical applications. J Biosci Bioeng 115:579–589CrossRefPubMedGoogle Scholar
  28. 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 69:217–261CrossRefPubMedPubMedCentralGoogle Scholar
  29. Reva ON, Hallin PF, Willenbrock H, Sicheritz-Ponten T, Tümmler B, Ussery DW (2008) Global features of the Alcanivorax borkumensis SK2 genome. Environ Microbiol 10:614–625CrossRefPubMedGoogle Scholar
  30. 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 188:3763–3773CrossRefPubMedPubMedCentralGoogle Scholar
  31. Segata N, Boernigen D, Tickle TL, Morgan XC, Garrett WS, Huttenhower C (2013) Computational meta’omics for microbial community studies. Mol Syst Biol 9:666CrossRefPubMedPubMedCentralGoogle Scholar
  32. Soufi B, Macek B (2015) Global analysis of bacterial membrane proteins and their modifications. Int J Med Microbiol 305:203–208CrossRefPubMedGoogle Scholar
  33. 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 147:1621–1630CrossRefPubMedGoogle Scholar
  34. Van Oudenhove L, Devreese B (2013) A review on recent developments in mass spectrometry instrumentation and quantitative tools advancing bacterial proteomics. Appl Microbiol Biotechnol 97:4749–4762CrossRefPubMedGoogle Scholar
  35. 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 33:97–104CrossRefPubMedGoogle Scholar
  36. Yakimov MM, Timmis KN, Golyshin PN (2007) Obligate oil-degrading marine bacteria. Curr Opin Biotechnol 18:257–266CrossRefPubMedGoogle Scholar
  37. 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 12:635–645CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Centre for Bioinformatics and Computational Biology, Department of BiochemistryUniversity of Pretoria, HillcrestPretoriaSouth Africa
  2. 2.Klinische ForschergruppeMedizinische Hochschule HannoverHannoverGermany

Personalised recommendations