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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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TR Network of M.tb.Blue nodes represent regulatory genes that are not regulated by other nodes, while green ones are nodes that regulate the activity of other targets and are regulated by other transcription factors. Self-regulations are represented by black arcs, while feedbacks of mutual regulations are represented in green, thick lines. The picture has been done using the software Gephi.
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pone-0022178-g001: TR Network of M.tb.Blue nodes represent regulatory genes that are not regulated by other nodes, while green ones are nodes that regulate the activity of other targets and are regulated by other transcription factors. Self-regulations are represented by black arcs, while feedbacks of mutual regulations are represented in green, thick lines. The picture has been done using the software Gephi.

Mentions: Our starting point is the TR network proposed by Balázsi and colleagues a few years ago [17], which is the largest M.tb transcriptional network to date (see Table 1). Based on this TR network, we have performed a considerable expansion by using publicly available sources, most of which appeared after Balazsi's compilation see Materials and Methods. For such an expansion we have used resources that are based on two different experimental groups of methodologies. Within the first family of experimental procedures, we have considered techniques that are based on detecting significant changes of target-gene expression levels caused by disrupting, over-expressing, or inducing a certain regulator, compared with wild type reference expression levels. These techniques include microarrays analysis (genome-wide, poorly specific), or quantitative real time qRT-PCR analysis (that provides higher accuracy and reliability), as well as fusion in target promoters of sequences coding reporters like gfp or lacZ. On the other hand, the second family of methodologies covers procedures that are based on the identification of the DNA-transcription factor binding sites, and, eventually, the characterization of the physical protein-DNA interaction. Electrophoretic mobility shift assays, one hybrid reporter systems and ChiP-on-chip assays are examples of these methodologies. Moreover, once the new information coming from experimental sources and computational inference is compiled, we have further enlarged the network by operon-based expansion as done in [17], using the operon map predicted in [18]. See Materials and Methods for more details. Figure 1 shows the resulting TR network of M.tb. We next analyze its main topological properties at different resolution levels.


The transcriptional regulatory network of Mycobacterium tuberculosis.

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

TR Network of M.tb.Blue nodes represent regulatory genes that are not regulated by other nodes, while green ones are nodes that regulate the activity of other targets and are regulated by other transcription factors. Self-regulations are represented by black arcs, while feedbacks of mutual regulations are represented in green, thick lines. The picture has been done using the software Gephi.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0022178-g001: TR Network of M.tb.Blue nodes represent regulatory genes that are not regulated by other nodes, while green ones are nodes that regulate the activity of other targets and are regulated by other transcription factors. Self-regulations are represented by black arcs, while feedbacks of mutual regulations are represented in green, thick lines. The picture has been done using the software Gephi.
Mentions: Our starting point is the TR network proposed by Balázsi and colleagues a few years ago [17], which is the largest M.tb transcriptional network to date (see Table 1). Based on this TR network, we have performed a considerable expansion by using publicly available sources, most of which appeared after Balazsi's compilation see Materials and Methods. For such an expansion we have used resources that are based on two different experimental groups of methodologies. Within the first family of experimental procedures, we have considered techniques that are based on detecting significant changes of target-gene expression levels caused by disrupting, over-expressing, or inducing a certain regulator, compared with wild type reference expression levels. These techniques include microarrays analysis (genome-wide, poorly specific), or quantitative real time qRT-PCR analysis (that provides higher accuracy and reliability), as well as fusion in target promoters of sequences coding reporters like gfp or lacZ. On the other hand, the second family of methodologies covers procedures that are based on the identification of the DNA-transcription factor binding sites, and, eventually, the characterization of the physical protein-DNA interaction. Electrophoretic mobility shift assays, one hybrid reporter systems and ChiP-on-chip assays are examples of these methodologies. Moreover, once the new information coming from experimental sources and computational inference is compiled, we have further enlarged the network by operon-based expansion as done in [17], using the operon map predicted in [18]. See Materials and Methods for more details. Figure 1 shows the resulting TR network of M.tb. We next analyze its main topological properties at different resolution levels.

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