12.07.2015 Views

A computational study of bacterial gene regulation and adaptation ...

A computational study of bacterial gene regulation and adaptation ...

A computational study of bacterial gene regulation and adaptation ...

SHOW MORE
SHOW LESS

You also want an ePaper? Increase the reach of your titles

YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.

I.4. Transcriptional regulatory networks: structure <strong>and</strong> evolutionTranscription factors regulate the expression <strong>of</strong> only a specific set <strong>of</strong> <strong>gene</strong>s because <strong>of</strong>selectivity in the nature <strong>of</strong> DNA sequences they can recognise. Therefore, the associationbetween TFs <strong>and</strong> their target <strong>gene</strong>s can be represented as networks where each edge isdirected from a TF to a target <strong>gene</strong>. Such data are available for nearly all TFs in the yeastSaccharomyces cerevisiae. These data, a majority <strong>of</strong> which comes from a single <strong>study</strong>, wereobtained using genome-wide ChIP-chip studies (Harbison et al. 2004). For E. coli, theRegulonDB database makes available all known transcriptional regulatory interactionsdescribed in the literature (Gama-Castro et al. 2008). The most recent release <strong>of</strong> this database(January 2009, version 6.3) contains 3,289 regulatory interactions between 166 TFs (notincluding !-factors) <strong>and</strong> 1,477 target <strong>gene</strong>s. Recently, ChIP-chip studies have been performedon selected TFs in E. coli substantially adding to our current knowledge <strong>of</strong> regulatory targetsfor these TFs (Cho et al. 2008a; Cho et al. 2008b; Grainger et al. 2006; Grainger et al. 2005).But we still lack such information for a large proportion <strong>of</strong> TFs. The regulatory network inRegulonDB has been analysed as a whole <strong>and</strong> a number <strong>of</strong> biological insights have beenderived as a result. In this section, we review some <strong>of</strong> these, with emphasis on insights into(1) types <strong>of</strong> TFs, (2) modularity <strong>of</strong> network structure, (3) variation in network structure acrossdifferent functional target <strong>gene</strong> classes, (4) constraint imposed on genome organisation bytranscriptional <strong>regulation</strong>, (5) integration <strong>of</strong> signal sensing into the regulatory network <strong>and</strong> (6)evolution <strong>of</strong> regulatory interactions.I.4.1. Types <strong>of</strong> transcription factors:global <strong>and</strong> localIn the current release <strong>of</strong> RegulonDB, 10 TFs regulate two-thirds <strong>of</strong> all target <strong>gene</strong>s in thedatabase. Previously, Martinez-Antonio <strong>and</strong> Collado-Vides performed a detailed <strong>study</strong> that setthe rules for defining global TFs (Martinez-Antonio <strong>and</strong> Collado-Vides 2003). These rules gobeyond the number <strong>of</strong> <strong>gene</strong>s or operons regulated by a TF <strong>and</strong> include the following: (1)number <strong>of</strong>- <strong>and</strong> nature <strong>of</strong> co-regulating TFs, (2) ability to regulate <strong>gene</strong>s which belong totarget-groups <strong>of</strong> different !-factors, (3) potential to respond to a wide range <strong>of</strong> environmentalconditions, <strong>and</strong> (4) capacity to target <strong>gene</strong>s from a number <strong>of</strong> functional categories. In total,only seven TFs pass these criteria. These are the catabolite-responsive CRP, anaerobiosisregulators FNR <strong>and</strong> ArcA, the feast or famine LRP, <strong>and</strong> the histone-like FIS, IHF <strong>and</strong> H-NS.Of these, all except H-NS show an enrichment towards targeting <strong>gene</strong>s from a single broad11

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!