Li-Yu Daisy Liu1, Ya-Chun Hsiao2, Hung-Chi Chen3, Yun-Wei Yang2, Men-Chi Chang2. 1. Department of Agronomy, National Taiwan University, Taipei, 106, Taiwan. lyliu@ntu.edu.tw. 2. Department of Agronomy, National Taiwan University, Taipei, 106, Taiwan. 3. Department of Horticulture and Landscape Architecture, National Taiwan University, Taipei, 106, Taiwan.
Abstract
BACKGROUND: In the past two decades, biologists have been able to identify the gene signatures associated with various phenotypes through the monitoring of gene expressions with high-throughput biotechnologies. These gene signatures have in turn been successfully applied to drug development, disease prevention, crop improvement, etc. However, ignoring the interactions among genes has weakened the predictive power of gene signatures in practical applications. Gene regulatory networks, in which genes are represented by nodes and the associations between genes are represented by edges, are typically constructed to analyze and visualize such gene interactions. More specifically, the present study sought to measure gene-gene associations by using the coefficient of intrinsic dependence (CID) to capture more nonlinear as well as cause-effect gene relationships. RESULTS: A stepwise procedure using the CID along with the partial coefficient of intrinsic dependence (pCID) was demonstrated for the rebuilding of simulated networks and the well-known CBF-COR pathway under cold stress using Arabidopsis microarray data. The procedure was also applied to the construction of bHLH gene regulatory pathways under abiotic stresses using rice microarray data, in which OsbHLH104, a putative phytochrome-interacting factor (OsPIF14), and OsbHLH060, a positive regulator of iron homeostasis (OsPRI1) were inferred as the most affiliated genes. The inferred regulatory pathways were verified through literature reviews. CONCLUSIONS: The proposed method can efficiently decipher gene regulatory pathways and may assist in achieving higher predictive power in practical applications. The lack of any mention in the literature of some of the regulatory event may have been due to the high complexity of the regulatory systems in the plant transcription, a possibility which could potentially be confirmed in the near future given ongoing rapid developments in bio-technology.
BACKGROUND: In the past two decades, biologists have been able to identify the gene signatures associated with various phenotypes through the monitoring of gene expressions with high-throughput biotechnologies. These gene signatures have in turn been successfully applied to drug development, disease prevention, crop improvement, etc. However, ignoring the interactions among genes has weakened the predictive power of gene signatures in practical applications. Gene regulatory networks, in which genes are represented by nodes and the associations between genes are represented by edges, are typically constructed to analyze and visualize such gene interactions. More specifically, the present study sought to measure gene-gene associations by using the coefficient of intrinsic dependence (CID) to capture more nonlinear as well as cause-effect gene relationships. RESULTS: A stepwise procedure using the CID along with the partial coefficient of intrinsic dependence (pCID) was demonstrated for the rebuilding of simulated networks and the well-known CBF-COR pathway under cold stress using Arabidopsis microarray data. The procedure was also applied to the construction of bHLH gene regulatory pathways under abiotic stresses using rice microarray data, in which OsbHLH104, a putative phytochrome-interacting factor (OsPIF14), and OsbHLH060, a positive regulator of iron homeostasis (OsPRI1) were inferred as the most affiliated genes. The inferred regulatory pathways were verified through literature reviews. CONCLUSIONS: The proposed method can efficiently decipher gene regulatory pathways and may assist in achieving higher predictive power in practical applications. The lack of any mention in the literature of some of the regulatory event may have been due to the high complexity of the regulatory systems in the plant transcription, a possibility which could potentially be confirmed in the near future given ongoing rapid developments in bio-technology.
Authors: Terri A Long; Hironaka Tsukagoshi; Wolfgang Busch; Brett Lahner; David E Salt; Philip N Benfey Journal: Plant Cell Date: 2010-07-30 Impact factor: 11.277
Authors: Ruijie Liu; Aliaksei Z Holik; Shian Su; Natasha Jansz; Kelan Chen; Huei San Leong; Marnie E Blewitt; Marie-Liesse Asselin-Labat; Gordon K Smyth; Matthew E Ritchie Journal: Nucleic Acids Res Date: 2015-04-29 Impact factor: 16.971
Authors: Juan I Fuxman Bass; Alos Diallo; Justin Nelson; Juan M Soto; Chad L Myers; Albertha J M Walhout Journal: Nat Methods Date: 2013-12 Impact factor: 28.547
Authors: A M Pérez-de-Castro; S Vilanova; J Cañizares; L Pascual; J M Blanca; M J Díez; J Prohens; B Picó Journal: Curr Genomics Date: 2012-05 Impact factor: 2.236