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Deciphering Cis-Regulatory Element Mediated Combinatorial Regulation in Rice under Blast Infected Condition.

Deb A, Kundu S - PLoS ONE (2015)

Bottom Line: Our analysis includes a wide spectrum of biologically important results.We couple the network approach with experimental results of plant gene regulation and defense mechanisms and find evidences of auto and cross regulation among TF families, cross-talk among multiple hormone signaling pathways, similarities and dissimilarities in regulatory combinatorics between different tissues, etc.It can be further applied to unravel the tissue and/or condition specific combinatorial gene regulation in other eukaryotic systems with the availability of annotated genomic sequences and suitable experimental data.

View Article: PubMed Central - PubMed

Affiliation: Department of Biophysics Molecular Biology and Bioinformatics, University of Calcutta, Kolkata, West Bengal, India.

ABSTRACT
Combinations of cis-regulatory elements (CREs) present at the promoters facilitate the binding of several transcription factors (TFs), thereby altering the consequent gene expressions. Due to the eminent complexity of the regulatory mechanism, the combinatorics of CRE-mediated transcriptional regulation has been elusive. In this work, we have developed a new methodology that quantifies the co-occurrence tendencies of CREs present in a set of promoter sequences; these co-occurrence scores are filtered in three consecutive steps to test their statistical significance; and the significantly co-occurring CRE pairs are presented as networks. These networks of co-occurring CREs are further transformed to derive higher order of regulatory combinatorics. We have further applied this methodology on the differentially up-regulated gene-sets of rice tissues under fungal (Magnaporthe) infected conditions to demonstrate how it helps to understand the CRE-mediated combinatorial gene regulation. Our analysis includes a wide spectrum of biologically important results. The CRE pairs having a strong tendency to co-occur often exhibit very similar joint distribution patterns at the promoters of rice. We couple the network approach with experimental results of plant gene regulation and defense mechanisms and find evidences of auto and cross regulation among TF families, cross-talk among multiple hormone signaling pathways, similarities and dissimilarities in regulatory combinatorics between different tissues, etc. Our analyses have pointed a highly distributed nature of the combinatorial gene regulation facilitating an efficient alteration in response to fungal attack. All together, our proposed methodology could be an important approach in understanding the combinatorial gene regulation. It can be further applied to unravel the tissue and/or condition specific combinatorial gene regulation in other eukaryotic systems with the availability of annotated genomic sequences and suitable experimental data.

No MeSH data available.


Related in: MedlinePlus

The network transformation.(A) A CRE co-occurrences network. In this network, a node represents a CRE and an edge represents co-occurrence relation (COR) in between a pair of nodes (Wider line = higher COR value). Node-colour and size represent the degree of a node (darker and bigger = higher degree (B) Clique identification in the CRE co-occurrences network. (C) Search for common/sharing CRE(s) in between two cliques. (D) Represent each clique as single node and connect two cliques(nodes) if common CREs are found in between them. (E) A clique-clique network. Here a node represents a clique of CREs, while an edge indicates the relation in between two cliques in the terms of sharing/overlapping of CREs. Wider line of an edge indicates higher number of overlapping CREs in between two cliques. The node-size indicates the number of promoters in which the clique has occurred (bigger = more number of promoters) and the node-colour indicates the degree of a node (darker = higher degree).
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pone.0137295.g004: The network transformation.(A) A CRE co-occurrences network. In this network, a node represents a CRE and an edge represents co-occurrence relation (COR) in between a pair of nodes (Wider line = higher COR value). Node-colour and size represent the degree of a node (darker and bigger = higher degree (B) Clique identification in the CRE co-occurrences network. (C) Search for common/sharing CRE(s) in between two cliques. (D) Represent each clique as single node and connect two cliques(nodes) if common CREs are found in between them. (E) A clique-clique network. Here a node represents a clique of CREs, while an edge indicates the relation in between two cliques in the terms of sharing/overlapping of CREs. Wider line of an edge indicates higher number of overlapping CREs in between two cliques. The node-size indicates the number of promoters in which the clique has occurred (bigger = more number of promoters) and the node-colour indicates the degree of a node (darker = higher degree).

Mentions: For a set of promoters (gene-set of interest), significantly co-occurring CRE pairs were represented as undirected edge-weighted networks, termed as ‘CRE co-occurrence networks’. In these networks, each CRE represented a node and COR values between the CRE pairs represented the edge weights. The CFinder [36] was used to identify the cliques from each network. Clique(k) was defined as a complete subgraph with k number of nodes. Thus, a clique represents here the combinations of co-occurring CREs in a set of differentially up-regulated genes where their co-occurrences (each CRE co-occurred with every other CRE present into the clique) are statistically enriched. Further, we transformed each CRE co-occurrence network into a clique-clique network where each node represented a clique and an edge weight represented the number of overlapping/sharing CREs between two cliques (Fig 4). Different views of networks, represented here, were constructed using Cytoscape 3.1.1 [37].


Deciphering Cis-Regulatory Element Mediated Combinatorial Regulation in Rice under Blast Infected Condition.

Deb A, Kundu S - PLoS ONE (2015)

The network transformation.(A) A CRE co-occurrences network. In this network, a node represents a CRE and an edge represents co-occurrence relation (COR) in between a pair of nodes (Wider line = higher COR value). Node-colour and size represent the degree of a node (darker and bigger = higher degree (B) Clique identification in the CRE co-occurrences network. (C) Search for common/sharing CRE(s) in between two cliques. (D) Represent each clique as single node and connect two cliques(nodes) if common CREs are found in between them. (E) A clique-clique network. Here a node represents a clique of CREs, while an edge indicates the relation in between two cliques in the terms of sharing/overlapping of CREs. Wider line of an edge indicates higher number of overlapping CREs in between two cliques. The node-size indicates the number of promoters in which the clique has occurred (bigger = more number of promoters) and the node-colour indicates the degree of a node (darker = higher degree).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4556519&req=5

pone.0137295.g004: The network transformation.(A) A CRE co-occurrences network. In this network, a node represents a CRE and an edge represents co-occurrence relation (COR) in between a pair of nodes (Wider line = higher COR value). Node-colour and size represent the degree of a node (darker and bigger = higher degree (B) Clique identification in the CRE co-occurrences network. (C) Search for common/sharing CRE(s) in between two cliques. (D) Represent each clique as single node and connect two cliques(nodes) if common CREs are found in between them. (E) A clique-clique network. Here a node represents a clique of CREs, while an edge indicates the relation in between two cliques in the terms of sharing/overlapping of CREs. Wider line of an edge indicates higher number of overlapping CREs in between two cliques. The node-size indicates the number of promoters in which the clique has occurred (bigger = more number of promoters) and the node-colour indicates the degree of a node (darker = higher degree).
Mentions: For a set of promoters (gene-set of interest), significantly co-occurring CRE pairs were represented as undirected edge-weighted networks, termed as ‘CRE co-occurrence networks’. In these networks, each CRE represented a node and COR values between the CRE pairs represented the edge weights. The CFinder [36] was used to identify the cliques from each network. Clique(k) was defined as a complete subgraph with k number of nodes. Thus, a clique represents here the combinations of co-occurring CREs in a set of differentially up-regulated genes where their co-occurrences (each CRE co-occurred with every other CRE present into the clique) are statistically enriched. Further, we transformed each CRE co-occurrence network into a clique-clique network where each node represented a clique and an edge weight represented the number of overlapping/sharing CREs between two cliques (Fig 4). Different views of networks, represented here, were constructed using Cytoscape 3.1.1 [37].

Bottom Line: Our analysis includes a wide spectrum of biologically important results.We couple the network approach with experimental results of plant gene regulation and defense mechanisms and find evidences of auto and cross regulation among TF families, cross-talk among multiple hormone signaling pathways, similarities and dissimilarities in regulatory combinatorics between different tissues, etc.It can be further applied to unravel the tissue and/or condition specific combinatorial gene regulation in other eukaryotic systems with the availability of annotated genomic sequences and suitable experimental data.

View Article: PubMed Central - PubMed

Affiliation: Department of Biophysics Molecular Biology and Bioinformatics, University of Calcutta, Kolkata, West Bengal, India.

ABSTRACT
Combinations of cis-regulatory elements (CREs) present at the promoters facilitate the binding of several transcription factors (TFs), thereby altering the consequent gene expressions. Due to the eminent complexity of the regulatory mechanism, the combinatorics of CRE-mediated transcriptional regulation has been elusive. In this work, we have developed a new methodology that quantifies the co-occurrence tendencies of CREs present in a set of promoter sequences; these co-occurrence scores are filtered in three consecutive steps to test their statistical significance; and the significantly co-occurring CRE pairs are presented as networks. These networks of co-occurring CREs are further transformed to derive higher order of regulatory combinatorics. We have further applied this methodology on the differentially up-regulated gene-sets of rice tissues under fungal (Magnaporthe) infected conditions to demonstrate how it helps to understand the CRE-mediated combinatorial gene regulation. Our analysis includes a wide spectrum of biologically important results. The CRE pairs having a strong tendency to co-occur often exhibit very similar joint distribution patterns at the promoters of rice. We couple the network approach with experimental results of plant gene regulation and defense mechanisms and find evidences of auto and cross regulation among TF families, cross-talk among multiple hormone signaling pathways, similarities and dissimilarities in regulatory combinatorics between different tissues, etc. Our analyses have pointed a highly distributed nature of the combinatorial gene regulation facilitating an efficient alteration in response to fungal attack. All together, our proposed methodology could be an important approach in understanding the combinatorial gene regulation. It can be further applied to unravel the tissue and/or condition specific combinatorial gene regulation in other eukaryotic systems with the availability of annotated genomic sequences and suitable experimental data.

No MeSH data available.


Related in: MedlinePlus