Limits...
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

A diagrammatic representation of our methodology.(A) Cis-regulatory element information is collected from PLACE database. (B) Promoter sequence data is collected from Rice Genome Annotation Project database. (C) RepeatMasker masks all the repeated regions in the promoter sequences. (D) Signal Scan tool scans the promoter sequences to estimate the occurrences and positions of the CREs. (E) A cartoon diagram shows the location of individual CREs in the promoter regions. (F, G) Selection of input gene-sets (differentially up-regulated genes) and background (rest of the genome). (H)COR values are estimated for all possible CRE pairs. (I) First filtering step: COR > 1.5 values are chosen. (J) Second filtering step: only the CRE pairs present in ≥ 3 promoters are selected for further analyses. (K) Third filtering step: significance of a COR value in the input gene-set is compared against the background by a Z-statistics (p < 0.05 is chosen). (L) Matrix representation of CRE pairs with statistically significant co-occurrence. (M) This matrix is transformed into an edge-weighted network (nodes represent individual CREs, edge weights represent the COR values). (N) Network analysis reveals the unique cliques of CREs.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4556519&req=5

pone.0137295.g001: A diagrammatic representation of our methodology.(A) Cis-regulatory element information is collected from PLACE database. (B) Promoter sequence data is collected from Rice Genome Annotation Project database. (C) RepeatMasker masks all the repeated regions in the promoter sequences. (D) Signal Scan tool scans the promoter sequences to estimate the occurrences and positions of the CREs. (E) A cartoon diagram shows the location of individual CREs in the promoter regions. (F, G) Selection of input gene-sets (differentially up-regulated genes) and background (rest of the genome). (H)COR values are estimated for all possible CRE pairs. (I) First filtering step: COR > 1.5 values are chosen. (J) Second filtering step: only the CRE pairs present in ≥ 3 promoters are selected for further analyses. (K) Third filtering step: significance of a COR value in the input gene-set is compared against the background by a Z-statistics (p < 0.05 is chosen). (L) Matrix representation of CRE pairs with statistically significant co-occurrence. (M) This matrix is transformed into an edge-weighted network (nodes represent individual CREs, edge weights represent the COR values). (N) Network analysis reveals the unique cliques of CREs.

Mentions: In the present work, we introduced a new methodology to identify significantly co-occurring CREs at the promoters of a gene-set. These co-occurrence relationships were further transformed into an edge-weighted network from which higher order combinatorics were estimated. A basic outline of our methodology is presented in Fig 1 and detailed descriptions are given below.


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

Deb A, Kundu S - PLoS ONE (2015)

A diagrammatic representation of our methodology.(A) Cis-regulatory element information is collected from PLACE database. (B) Promoter sequence data is collected from Rice Genome Annotation Project database. (C) RepeatMasker masks all the repeated regions in the promoter sequences. (D) Signal Scan tool scans the promoter sequences to estimate the occurrences and positions of the CREs. (E) A cartoon diagram shows the location of individual CREs in the promoter regions. (F, G) Selection of input gene-sets (differentially up-regulated genes) and background (rest of the genome). (H)COR values are estimated for all possible CRE pairs. (I) First filtering step: COR > 1.5 values are chosen. (J) Second filtering step: only the CRE pairs present in ≥ 3 promoters are selected for further analyses. (K) Third filtering step: significance of a COR value in the input gene-set is compared against the background by a Z-statistics (p < 0.05 is chosen). (L) Matrix representation of CRE pairs with statistically significant co-occurrence. (M) This matrix is transformed into an edge-weighted network (nodes represent individual CREs, edge weights represent the COR values). (N) Network analysis reveals the unique cliques of CREs.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0137295.g001: A diagrammatic representation of our methodology.(A) Cis-regulatory element information is collected from PLACE database. (B) Promoter sequence data is collected from Rice Genome Annotation Project database. (C) RepeatMasker masks all the repeated regions in the promoter sequences. (D) Signal Scan tool scans the promoter sequences to estimate the occurrences and positions of the CREs. (E) A cartoon diagram shows the location of individual CREs in the promoter regions. (F, G) Selection of input gene-sets (differentially up-regulated genes) and background (rest of the genome). (H)COR values are estimated for all possible CRE pairs. (I) First filtering step: COR > 1.5 values are chosen. (J) Second filtering step: only the CRE pairs present in ≥ 3 promoters are selected for further analyses. (K) Third filtering step: significance of a COR value in the input gene-set is compared against the background by a Z-statistics (p < 0.05 is chosen). (L) Matrix representation of CRE pairs with statistically significant co-occurrence. (M) This matrix is transformed into an edge-weighted network (nodes represent individual CREs, edge weights represent the COR values). (N) Network analysis reveals the unique cliques of CREs.
Mentions: In the present work, we introduced a new methodology to identify significantly co-occurring CREs at the promoters of a gene-set. These co-occurrence relationships were further transformed into an edge-weighted network from which higher order combinatorics were estimated. A basic outline of our methodology is presented in Fig 1 and detailed descriptions are given below.

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