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The transcriptional regulatory network of Mycobacterium tuberculosis.

Sanz J, Navarro J, Arbués A, Martín C, Marijuán PC, Moreno Y - PLoS ONE (2011)

Bottom Line: Under the perspectives of network science and systems biology, the characterization of transcriptional regulatory (TR) networks beyond the context of model organisms offers a versatile tool whose potential remains yet mainly unexplored.We also present an exhaustive topological analysis of the new assembled network, focusing on the statistical characterization of motifs significances and the comparison with other model organisms.The expanded M.tb transcriptional regulatory network, considering its volume and completeness, constitutes an important resource for diverse tasks such as dynamic modeling of gene expression and signaling processes, computational reliability determination or protein function prediction, being the latter of particular relevance, given that the function of only a small percent of the proteins of M.tb is known.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Biocomputación y Física de Sistemas Complejos, Universidad de Zaragoza, Zaragoza, Spain.

ABSTRACT
Under the perspectives of network science and systems biology, the characterization of transcriptional regulatory (TR) networks beyond the context of model organisms offers a versatile tool whose potential remains yet mainly unexplored. In this work, we present an updated version of the TR network of Mycobacterium tuberculosis (M.tb), which incorporates newly characterized transcriptional regulations coming from 31 recent, different experimental works available in the literature. As a result of the incorporation of these data, the new network doubles the size of previous data collections, incorporating more than a third of the entire genome of the bacterium. We also present an exhaustive topological analysis of the new assembled network, focusing on the statistical characterization of motifs significances and the comparison with other model organisms. The expanded M.tb transcriptional regulatory network, considering its volume and completeness, constitutes an important resource for diverse tasks such as dynamic modeling of gene expression and signaling processes, computational reliability determination or protein function prediction, being the latter of particular relevance, given that the function of only a small percent of the proteins of M.tb is known.

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Most connected regulatory hubs in the M.tb transcriptional regulatory network.The figure reflects the high heterogeneity of the degree distribution. Namely, there are a few nodes with hundreds of interactions (regulating other genes), while most of the nodes in the network have a few transcriptional relations. For a list of all transcription factors identified see Materials and Methods.
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pone-0022178-g003: Most connected regulatory hubs in the M.tb transcriptional regulatory network.The figure reflects the high heterogeneity of the degree distribution. Namely, there are a few nodes with hundreds of interactions (regulating other genes), while most of the nodes in the network have a few transcriptional relations. For a list of all transcription factors identified see Materials and Methods.

Mentions: Concerning the kind of regulation, a similar plot but only taking into account incoming or outgoing links shows that both in-degree and out-degree distributions are also highly heterogeneous. However, the larger contribution to the many interactions of a few nodes mainly comes from transcription factors (see Figure 3). This can be appreciated already in Table 2, where we have summarized several topological properties. As a matter of fact, the average out-degree of transcription factors is much larger than the average in-degree of genes that have at least one regulator, indicating that most of the hubs are the formers (note that these quantities are not calculated in the usual way, otherwise ). Concerning other topological features, we see that they are within the typical range of values for other biological (and, in general, real complex) networks [14].


The transcriptional regulatory network of Mycobacterium tuberculosis.

Sanz J, Navarro J, Arbués A, Martín C, Marijuán PC, Moreno Y - PLoS ONE (2011)

Most connected regulatory hubs in the M.tb transcriptional regulatory network.The figure reflects the high heterogeneity of the degree distribution. Namely, there are a few nodes with hundreds of interactions (regulating other genes), while most of the nodes in the network have a few transcriptional relations. For a list of all transcription factors identified see Materials and Methods.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3139605&req=5

pone-0022178-g003: Most connected regulatory hubs in the M.tb transcriptional regulatory network.The figure reflects the high heterogeneity of the degree distribution. Namely, there are a few nodes with hundreds of interactions (regulating other genes), while most of the nodes in the network have a few transcriptional relations. For a list of all transcription factors identified see Materials and Methods.
Mentions: Concerning the kind of regulation, a similar plot but only taking into account incoming or outgoing links shows that both in-degree and out-degree distributions are also highly heterogeneous. However, the larger contribution to the many interactions of a few nodes mainly comes from transcription factors (see Figure 3). This can be appreciated already in Table 2, where we have summarized several topological properties. As a matter of fact, the average out-degree of transcription factors is much larger than the average in-degree of genes that have at least one regulator, indicating that most of the hubs are the formers (note that these quantities are not calculated in the usual way, otherwise ). Concerning other topological features, we see that they are within the typical range of values for other biological (and, in general, real complex) networks [14].

Bottom Line: Under the perspectives of network science and systems biology, the characterization of transcriptional regulatory (TR) networks beyond the context of model organisms offers a versatile tool whose potential remains yet mainly unexplored.We also present an exhaustive topological analysis of the new assembled network, focusing on the statistical characterization of motifs significances and the comparison with other model organisms.The expanded M.tb transcriptional regulatory network, considering its volume and completeness, constitutes an important resource for diverse tasks such as dynamic modeling of gene expression and signaling processes, computational reliability determination or protein function prediction, being the latter of particular relevance, given that the function of only a small percent of the proteins of M.tb is known.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Biocomputación y Física de Sistemas Complejos, Universidad de Zaragoza, Zaragoza, Spain.

ABSTRACT
Under the perspectives of network science and systems biology, the characterization of transcriptional regulatory (TR) networks beyond the context of model organisms offers a versatile tool whose potential remains yet mainly unexplored. In this work, we present an updated version of the TR network of Mycobacterium tuberculosis (M.tb), which incorporates newly characterized transcriptional regulations coming from 31 recent, different experimental works available in the literature. As a result of the incorporation of these data, the new network doubles the size of previous data collections, incorporating more than a third of the entire genome of the bacterium. We also present an exhaustive topological analysis of the new assembled network, focusing on the statistical characterization of motifs significances and the comparison with other model organisms. The expanded M.tb transcriptional regulatory network, considering its volume and completeness, constitutes an important resource for diverse tasks such as dynamic modeling of gene expression and signaling processes, computational reliability determination or protein function prediction, being the latter of particular relevance, given that the function of only a small percent of the proteins of M.tb is known.

Show MeSH
Related in: MedlinePlus