Limits...
Modularity of Escherichia coli sRNA regulation revealed by sRNA-target and protein network analysis.

Wu TH, Chang IY, Chu LC, Huang HC, Ng WV - BMC Bioinformatics (2010)

Bottom Line: As was found for the protein-protein interaction network, the targets that are regulated by the same sRNA also tend to be closely knit within the transcription-regulatory network (larger density, p-val = 0.036), and inward interactions between them are greater than the outward interactions (higher in-degree ratio, p-val = 0.023).Our results indicate that sRNA targeting tends to show a clustering pattern that is similar to the human microRNA regulation associated with protein-protein interaction network that was observed in a previous study.Our results indicate that sRNA targeting shows different properties when compared to the proteins that form cellular networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan.

ABSTRACT

Background: sRNAs, which belong to the non-coding RNA family and range from approximately 50 to 400 nucleotides, serve various important gene regulatory roles. Most are believed to be trans-regulating and function by being complementary to their target mRNAs in order to inhibiting translation by ribosome occlusion. Despite this understanding of their functionality, the global properties associated with regulation by sRNAs are not yet understood. Here we use topological analysis of sRNA targets in terms of protein-protein interaction and transcription-regulatory networks in Escherichia coli to shed light on the global correlation between sRNA regulation and cellular control networks.

Results: The analysis of sRNA targets in terms of their networks showed that some specific network properties could be identified. In protein-protein interaction network, sRNA targets tend to occupy more central positions (higher closeness centrality, p-val = 0.022) and more cliquish (larger clustering coefficient, p-val = 0.037). The targets of the same sRNA tend to form a network module (shorter characteristic path length, p-val = 0.015; larger density, p-val = 0.019; higher in-degree ratio, p-val = 0.009). Using the transcription-regulatory network, sRNA targets tend to be under multiple regulation (higher indegree, p-val = 0.013) and the targets usually are important to the transfer of regulatory signals (higher betweenness, p-val = 0.012). As was found for the protein-protein interaction network, the targets that are regulated by the same sRNA also tend to be closely knit within the transcription-regulatory network (larger density, p-val = 0.036), and inward interactions between them are greater than the outward interactions (higher in-degree ratio, p-val = 0.023). However, after incorporating information on predicted sRNAs and down-stream targets, the results are not as clear-cut, but the overall network modularity is still evident.

Conclusions: Our results indicate that sRNA targeting tends to show a clustering pattern that is similar to the human microRNA regulation associated with protein-protein interaction network that was observed in a previous study. Namely, the sRNA targets show close interaction and forms a closely knit network module for both the protein-protein interaction and the transcription-regulatory networks. Thus, targets of the same sRNA work in a concerted way toward a specific goal. In addition, in the transcription-regulatory network, sRNA targets act as "multiplexor", accepting regulatory control from multiple sources and acting accordingly. Our results indicate that sRNA targeting shows different properties when compared to the proteins that form cellular networks.

Show MeSH

Related in: MedlinePlus

sRNA oxyS and its targets in the gene-regulation and protein-protein interaction networks. The dark green lines represent experimentally verified regulation and the dark yellow lines represent predicted regulation. The teal lines indicate indirect regulation (e.g., dinF downstream of lexA in the same operon). Yellow lines are also indirect regulation, but indicate genes of a predicted target. Dashed lines indicate regulated genes extended from an operon structure. Transcription factors under regulation of sRNA have pink borders. Arrow, T, and diamond heads represent positive, negative, and dual regulators, respectively. Circular heads represent predicted, thus unknown, regulation. OxyS regulation deals with multiple stress responses, such as oxidative and osmotic stresses.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2957679&req=5

Figure 1: sRNA oxyS and its targets in the gene-regulation and protein-protein interaction networks. The dark green lines represent experimentally verified regulation and the dark yellow lines represent predicted regulation. The teal lines indicate indirect regulation (e.g., dinF downstream of lexA in the same operon). Yellow lines are also indirect regulation, but indicate genes of a predicted target. Dashed lines indicate regulated genes extended from an operon structure. Transcription factors under regulation of sRNA have pink borders. Arrow, T, and diamond heads represent positive, negative, and dual regulators, respectively. Circular heads represent predicted, thus unknown, regulation. OxyS regulation deals with multiple stress responses, such as oxidative and osmotic stresses.

Mentions: To demonstrate our findings, we will discuss the concentrated interactions of a sRNA exemplar, OxyS, in the protein-protein interaction network. The interactions between the sRNA OxyS and its experimental and predicted targets, and neighbors of these targets are depicted in Figure 1 (The graph was generated with Cytoscape [13]). This network shows that OxyS is responsible for regulating a number of genes participating in the stress response. As an antioxidant defense pleiotropic regulator, OxyS is positively regulated by OxyR, which is a transcriptional activator under oxidative stress [14]. In the OxyS network, targets regulated by OxyS roughly forms three clusters with other interacting molecules. These clusters are centered on rpoS, dps, and gadB. Among these, dps is a DNA binding protein involved in a number of stress responses including oxidative stress [15] and fatty acid starvation [16]. GadB is the subunit of glutamate decarboxylase B, part of the glutamate-dependent acid resistance system 2, which protects the cell during anaerobic phosphate starvation. RpoS (σs) encodes the RNA polymerase subunit sigma 38, which responses to osmotic and oxidative stresses. Since some of the genes participating in stress response, including katG, dps, gadB and gorA, are regulated by both σs and OxyR, it was suggested that repression of rpoS by OxyS may prevent redundant utilization of transcriptional regulators [14]. In addition, OxyR induces transcription of fur, whose product represses rpoS transcription [17,18]. Therefore, OxyR and OxyS together regulate rpoS on both the transcription level and the translation level. The gene gadC, which is downstream of gadB in the same operon, is required for decarboxylase-based acid resistance [19]. Other than the three major clusters in the interaction networks, several other targets not having protein interactions are also present. Two targets, fhlA and rpoS, encodes transcriptional regulators. FhlA is an activator required for the formate hydrogenlyase complex [20]. This metal-cofactor containing complex is primarily synthesized under anaerobic condition and may be detrimental to the cell during oxidative stress. Indirect repression by oxyS thus may reduce hydrogen-peroxide induced damage [21]. Three predicted targets, lexA, ogrK, and dinF, which are present in the network, are suggested to be regulated by oxyS. The genes lexA and orgK are predicted by TargetRNA and IntaRNA. LexA is part of the inducible DNA repair system. It is a global repressor of the SOS response regulon that allows bacteria to survive a sudden increase in DNA damage [22]. Upon DNA damage, such as that caused by UV light, the LexA repressor undergoes self-cleavage and the expression of SOS genes are thus activated [23]. DinF is downstream of lexA in term of genome position and is possibly a member of the family of MATE (multidrug and toxic compound extrusion) transporters induced by DNA damage [24,25]. It should also be noticed in the oxyS network that there are many other sRNAs that tend to work together as part of gene regulation. For instance, tp2 is predicted to regulate rplW, which encodes the 50 S ribosomal subunit protein L23. SsrS, RprA, and DsrA also regulate rpoS together with oxyS. In addition, SsrS also regulates rpoC, another subunit of RNA polymerase. Overall in the interaction network, we can see that oxyS, with other sRNAs, orchestrates a variety of genes participating in multiple stress responses, and these are mostly DNA damage associated. We can also see that targets represented in protein-protein interaction networks have many neighbors and their average clustering coefficient is approximately three times as high as average in the networks (3.5E-1 versus 1.1E-1). Other network properties were also found in the transcription regulatory network and an example is shown in Additional file 3.


Modularity of Escherichia coli sRNA regulation revealed by sRNA-target and protein network analysis.

Wu TH, Chang IY, Chu LC, Huang HC, Ng WV - BMC Bioinformatics (2010)

sRNA oxyS and its targets in the gene-regulation and protein-protein interaction networks. The dark green lines represent experimentally verified regulation and the dark yellow lines represent predicted regulation. The teal lines indicate indirect regulation (e.g., dinF downstream of lexA in the same operon). Yellow lines are also indirect regulation, but indicate genes of a predicted target. Dashed lines indicate regulated genes extended from an operon structure. Transcription factors under regulation of sRNA have pink borders. Arrow, T, and diamond heads represent positive, negative, and dual regulators, respectively. Circular heads represent predicted, thus unknown, regulation. OxyS regulation deals with multiple stress responses, such as oxidative and osmotic stresses.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2957679&req=5

Figure 1: sRNA oxyS and its targets in the gene-regulation and protein-protein interaction networks. The dark green lines represent experimentally verified regulation and the dark yellow lines represent predicted regulation. The teal lines indicate indirect regulation (e.g., dinF downstream of lexA in the same operon). Yellow lines are also indirect regulation, but indicate genes of a predicted target. Dashed lines indicate regulated genes extended from an operon structure. Transcription factors under regulation of sRNA have pink borders. Arrow, T, and diamond heads represent positive, negative, and dual regulators, respectively. Circular heads represent predicted, thus unknown, regulation. OxyS regulation deals with multiple stress responses, such as oxidative and osmotic stresses.
Mentions: To demonstrate our findings, we will discuss the concentrated interactions of a sRNA exemplar, OxyS, in the protein-protein interaction network. The interactions between the sRNA OxyS and its experimental and predicted targets, and neighbors of these targets are depicted in Figure 1 (The graph was generated with Cytoscape [13]). This network shows that OxyS is responsible for regulating a number of genes participating in the stress response. As an antioxidant defense pleiotropic regulator, OxyS is positively regulated by OxyR, which is a transcriptional activator under oxidative stress [14]. In the OxyS network, targets regulated by OxyS roughly forms three clusters with other interacting molecules. These clusters are centered on rpoS, dps, and gadB. Among these, dps is a DNA binding protein involved in a number of stress responses including oxidative stress [15] and fatty acid starvation [16]. GadB is the subunit of glutamate decarboxylase B, part of the glutamate-dependent acid resistance system 2, which protects the cell during anaerobic phosphate starvation. RpoS (σs) encodes the RNA polymerase subunit sigma 38, which responses to osmotic and oxidative stresses. Since some of the genes participating in stress response, including katG, dps, gadB and gorA, are regulated by both σs and OxyR, it was suggested that repression of rpoS by OxyS may prevent redundant utilization of transcriptional regulators [14]. In addition, OxyR induces transcription of fur, whose product represses rpoS transcription [17,18]. Therefore, OxyR and OxyS together regulate rpoS on both the transcription level and the translation level. The gene gadC, which is downstream of gadB in the same operon, is required for decarboxylase-based acid resistance [19]. Other than the three major clusters in the interaction networks, several other targets not having protein interactions are also present. Two targets, fhlA and rpoS, encodes transcriptional regulators. FhlA is an activator required for the formate hydrogenlyase complex [20]. This metal-cofactor containing complex is primarily synthesized under anaerobic condition and may be detrimental to the cell during oxidative stress. Indirect repression by oxyS thus may reduce hydrogen-peroxide induced damage [21]. Three predicted targets, lexA, ogrK, and dinF, which are present in the network, are suggested to be regulated by oxyS. The genes lexA and orgK are predicted by TargetRNA and IntaRNA. LexA is part of the inducible DNA repair system. It is a global repressor of the SOS response regulon that allows bacteria to survive a sudden increase in DNA damage [22]. Upon DNA damage, such as that caused by UV light, the LexA repressor undergoes self-cleavage and the expression of SOS genes are thus activated [23]. DinF is downstream of lexA in term of genome position and is possibly a member of the family of MATE (multidrug and toxic compound extrusion) transporters induced by DNA damage [24,25]. It should also be noticed in the oxyS network that there are many other sRNAs that tend to work together as part of gene regulation. For instance, tp2 is predicted to regulate rplW, which encodes the 50 S ribosomal subunit protein L23. SsrS, RprA, and DsrA also regulate rpoS together with oxyS. In addition, SsrS also regulates rpoC, another subunit of RNA polymerase. Overall in the interaction network, we can see that oxyS, with other sRNAs, orchestrates a variety of genes participating in multiple stress responses, and these are mostly DNA damage associated. We can also see that targets represented in protein-protein interaction networks have many neighbors and their average clustering coefficient is approximately three times as high as average in the networks (3.5E-1 versus 1.1E-1). Other network properties were also found in the transcription regulatory network and an example is shown in Additional file 3.

Bottom Line: As was found for the protein-protein interaction network, the targets that are regulated by the same sRNA also tend to be closely knit within the transcription-regulatory network (larger density, p-val = 0.036), and inward interactions between them are greater than the outward interactions (higher in-degree ratio, p-val = 0.023).Our results indicate that sRNA targeting tends to show a clustering pattern that is similar to the human microRNA regulation associated with protein-protein interaction network that was observed in a previous study.Our results indicate that sRNA targeting shows different properties when compared to the proteins that form cellular networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan.

ABSTRACT

Background: sRNAs, which belong to the non-coding RNA family and range from approximately 50 to 400 nucleotides, serve various important gene regulatory roles. Most are believed to be trans-regulating and function by being complementary to their target mRNAs in order to inhibiting translation by ribosome occlusion. Despite this understanding of their functionality, the global properties associated with regulation by sRNAs are not yet understood. Here we use topological analysis of sRNA targets in terms of protein-protein interaction and transcription-regulatory networks in Escherichia coli to shed light on the global correlation between sRNA regulation and cellular control networks.

Results: The analysis of sRNA targets in terms of their networks showed that some specific network properties could be identified. In protein-protein interaction network, sRNA targets tend to occupy more central positions (higher closeness centrality, p-val = 0.022) and more cliquish (larger clustering coefficient, p-val = 0.037). The targets of the same sRNA tend to form a network module (shorter characteristic path length, p-val = 0.015; larger density, p-val = 0.019; higher in-degree ratio, p-val = 0.009). Using the transcription-regulatory network, sRNA targets tend to be under multiple regulation (higher indegree, p-val = 0.013) and the targets usually are important to the transfer of regulatory signals (higher betweenness, p-val = 0.012). As was found for the protein-protein interaction network, the targets that are regulated by the same sRNA also tend to be closely knit within the transcription-regulatory network (larger density, p-val = 0.036), and inward interactions between them are greater than the outward interactions (higher in-degree ratio, p-val = 0.023). However, after incorporating information on predicted sRNAs and down-stream targets, the results are not as clear-cut, but the overall network modularity is still evident.

Conclusions: Our results indicate that sRNA targeting tends to show a clustering pattern that is similar to the human microRNA regulation associated with protein-protein interaction network that was observed in a previous study. Namely, the sRNA targets show close interaction and forms a closely knit network module for both the protein-protein interaction and the transcription-regulatory networks. Thus, targets of the same sRNA work in a concerted way toward a specific goal. In addition, in the transcription-regulatory network, sRNA targets act as "multiplexor", accepting regulatory control from multiple sources and acting accordingly. Our results indicate that sRNA targeting shows different properties when compared to the proteins that form cellular networks.

Show MeSH
Related in: MedlinePlus