Dynamic covariation between gene expression and proteome characteristics.
BACKGROUND
Cells react to changing intra- and extracellular signals by dynamically modulating complex biochemical networks. Cellular responses to extracellular signals lead to changes in gene and protein expression. Since the majority of genes encode proteins, we investigated possible correlations between protein parameters and gene expression patterns to identify proteome-wide characteristics indicative of trends common to expressed proteins.


RESULTS
Numerous bioinformatics methods were used to filter and merge information regarding gene and protein annotations. A new statistical time point-oriented analysis was developed for the study of dynamic correlations in large time series data. The method was applied to investigate microarray datasets for different cell types, organisms and processes, including human B and T cell stimulation, Drosophila melanogaster life span, and Saccharomyces cerevisiae cell cycle.


CONCLUSIONS
We show that the properties of proteins synthesized correlate dynamically with the gene expression profile, indicating that not only is the actual identity and function of expressed proteins important for cellular responses but that several physicochemical and other protein properties correlate with gene expression as well. Gene expression correlates strongly with amino acid composition, composition- and sequence-derived variables, functional, structural, localization and gene ontology parameters. Thus, our results suggest that a dynamic relationship exists between proteome properties and gene expression in many biological systems, and therefore this relationship is fundamental to understanding cellular mechanisms in health and disease.
Sharabiani
Mansour Taghavi Azar
MT
Institute of Medical Technology, FI-33014 University of Tampere, Finland. memata@gmail.com
Siermala
Markku
M
Lehtinen
Tommi O
TO
Vihinen
Mauno
M
D000818:Animals; D001402:B-Lymphocytes / Q000502:physiology; D002453:Cell Cycle / Q000235:genetics; D019295:Computational Biology / Q000379:methods; D003626:Data Display; D004331:Drosophila melanogaster / Q000235:genetics; D001330:Electronic Data Processing; D020869:Gene Expression Profiling / Q000379:methods*; D005787:Gene Frequency; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008213:Lymphocyte Activation / Q000235:genetics; D008390:Markov Chains; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D020543:Proteome / Q000145:classification*; D012441:Saccharomyces cerevisiae / Q000235:genetics; D020539:Sequence Analysis, Protein / Q000379:methods; D015398:Signal Transduction / Q000235:genetics; D012984:Software; D013601:T-Lymphocytes / Q000502:physiology
D020543:Proteome