ABSTRACT
BACKGROUND: Obtaining physiological insights from microarray experiments requires computational techniques that relate gene expression data to functional information. Traditionally, this has been done in two consecutive steps. The first step identifies important genes through clustering or statistical techniques, while the second step assigns biological functions to the identified groups. Recently, techniques have been developed that identify such relationships in a single step. RESULTS: We have developed an algorithm that relates patterns of gene expression in a set of microarray experiments to functional groups in one step. Our only assumption is that patterns co-occur frequently. The effectiveness of the algorithm is demonstrated as part of a study of regulation by two-component systems in Escherichia coli. The significance of the relationships between expression data and functional annotations is evaluated based on density histograms that are constructed using product similarity among expression vectors. We present a biological analysis of three of the resulting functional groups of proteins, develop hypotheses for further biological studies, and test one of these hypotheses experimentally. A comparison with other algorithms and a different data set is presented. CONCLUSION: Our new algorithm is able to find interesting and biologically meaningful relationships, not found by other algorithms, in previously analyzed data sets. Scaling of the algorithm to large data sets can be achieved based on a theoretical model.
Subject(s)
Escherichia coli/genetics , Escherichia coli/metabolism , Gene Expression Profiling/methods , Gene Expression Regulation, Bacterial , Neural Networks, Computer , Adaptation, Biological/genetics , Algorithms , Artificial Intelligence , Chi-Square Distribution , Databases, Genetic , Gene Expression/physiology , Oligonucleotide Array Sequence Analysis/methods , Oligonucleotide Array Sequence Analysis/statistics & numerical data , Pattern Recognition, Automated , Protein Kinases/genetics , Protein Kinases/metabolism , Signal Transduction , Transcription Factors/genetics , Transcription Factors/metabolismABSTRACT
BACKGROUND: The Yersinia enterocolitica flagellar master regulator FlhD/FlhC affects the expression levels of non-flagellar genes, including 21 genes that are involved in central metabolism. The sigma factor of the flagellar system, FliA, has a negative effect on the expression levels of seven plasmid-encoded virulence genes in addition to its positive effect on the expression levels of eight of the flagellar operons. This study investigates the phenotypes of flhD and fliA mutants that result from the complex gene regulation. RESULTS: Phenotypes relating to central metabolism were investigated with Phenotype MicroArrays. Compared to the wild-type strain, isogenic flhD and fliA mutants exhibited increased growth on purines and reduced growth on N-acetyl-D-glucosamine and D-mannose, when used as a sole carbon source. Both mutants grew more poorly on pyrimidines and L-histidine as sole nitrogen source. Several intermediates of the tricarboxylic acid and the urea cycle, as well as several dipeptides, provided differential growth conditions for the two mutants. Gene expression was determined for selected genes and correlated with the observed phenotypes. Phenotypes relating to virulence were determined with the chicken embryo lethality assay. The assay that was previously established for Escherichia coli strains was modified for Y. enterocolitica. The flhD mutant caused reduced chicken embryo lethality when compared to wild-type bacteria. In contrast, the fliA mutant caused wild-type lethality. This indicates that the virulence phenotype of the flhD mutant might be due to genes that are regulated by FlhD/FlhC but not FliA, such as those that encode the flagellar type III secretion system. CONCLUSION: Phenotypes of flhD and fliA mutants are related to central metabolism and virulence and correlate with gene regulation.
