Model-based comparative prediction of transcription-factor binding motifs in anabolic responses in bone.
10.1016/S1672-0229(08)60003-0
- Author:
Andy B CHEN
1
;
Kazunori HAMAMURA
;
Guohua WANG
;
Weirong XING
;
Subburaman MOHAN
;
Hiroki YOKOTA
;
Yunlong LIU
Author Information
1. Department of Biomedical Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Animals;
Base Sequence;
Binding Sites;
genetics;
Biomechanical Phenomena;
Bone Morphogenetic Proteins;
pharmacology;
Consensus Sequence;
DNA;
genetics;
metabolism;
Databases, Genetic;
Gene Expression Profiling;
statistics & numerical data;
Genomics;
statistics & numerical data;
Mice;
Oligonucleotide Array Sequence Analysis;
statistics & numerical data;
Osteoblasts;
drug effects;
metabolism;
Osteogenesis;
drug effects;
genetics;
physiology;
Transcription Factors;
metabolism
- From:
Genomics, Proteomics & Bioinformatics
2007;5(3-4):158-165
- CountryChina
- Language:English
-
Abstract:
Understanding the regulatory mechanism that controls the alteration of global gene expression patterns continues to be a challenging task in computational biology. We previously developed an ant algorithm, a biologically-inspired computational technique for microarray data, and predicted putative transcription-factor binding motifs (TFBMs) through mimicking interactive behaviors of natural ants. Here we extended the algorithm into a set of web-based software, Ant Modeler, and applied it to investigate the transcriptional mechanism underlying bone formation. Mechanical loading and administration of bone morphogenic proteins (BMPs) are two known treatments to strengthen bone. We addressed a question: Is there any TFBM that stimulates both "anabolic responses of mechanical loading" and "BMP-mediated osteogenic signaling"? Although there is no significant overlap among genes in the two responses, a comparative model-based analysis suggests that the two independent osteogenic processes employ common TFBMs, such as a stress responsive element and a motif for peroxisome proliferator-activated receptor (PPAR). The post-modeling in vitro analysis using mouse osteoblast cells supported involvements of the predicted TFBMs such as PPAR, Ikaros 3, and LMO2 in response to mechanical loading. Taken together, the results would be useful to derive a set of testable hypotheses and examine the role of specific regulators in complex transcriptional control of bone formation.
