Meta-analysis of published transcriptional and translational fold changes reveals a preference for low-fold inductions.
The goals of this study were to gain a better quantitative understanding of the dynamic range of transcriptional and translational response observed in biological systems and to examine the reporting of regulatory events for trends and biases. A straightforward pattern-matching routine extracted 3,408 independent observations regarding transcriptional fold-changes and 1,125 regarding translational fold-changes from over 15 million MEDLINE abstracts. Approximately 95% of reported changes were > or =2-fold. Further, the historical trend of reporting individual fold-changes is declining in favor of high-throughput methods for transcription but not translation. Where it was possible to compare the average fold-changes in transcription and translation for the same gene/product (203 examples), approximately 53% were a < or =2-fold difference, suggesting a loose tendency for the two to be coupled in magnitude. We found also that approximately three-fourths of reported regulatory events have been at the transcriptional level. The frequency distribution appears to be normally distributed and peaks near 2-fold, suggesting that nature selects for a low-energy solution to regulatory responses. Because high-throughput technologies ordinarily sacrifice measurement quality for quantity, this also suggests that many regulatory events may not be reliably detectable by such technologies. Text mining of regulatory events and responses provides additional information incorporable into microarray analysis, such as prior fold-change observations and flagging genes that are regulated post-transcription. All extracted regulation and response patterns can be downloaded at the following website: www.ou.edu/microarray/ oumcf/Meta_analysis.xls.
Wren
Jonathan D
JD
Advanced Center for Genome Technology, Department of Botany and Microbiology, The University of Oklahoma, Norman, 73019, USA. Jonathan.Wren@OU.edu
Conway
Tyrrell
T
D005786:Gene Expression Regulation; D016239:MEDLINE; D046228:Microarray Analysis; D014176:Protein Biosynthesis; D013223:Statistics as Topic; D014158:Transcription, Genetic