Investigating the Correspondence Between Transcriptomic and Proteomic Expression Profiles Using Coupled Cluster Models.
Modern transcriptomics and proteomics enable us to survey the expression of RNAs and proteins at large scales. While these data are usually generated and analysed separately, there is an increasing interest in comparing and co-analysing transcriptome and proteome expression data. A major open question is whether transcriptome and proteome expression is linked and how it is coordinated. Results: Here we have developed a probabilistic clustering model that permits analysis of the links between transcriptomic and proteomic profiles in a sensible and flexible manner. Our coupled mixture model defines a prior probability distribution over the component to which a protein profile should be assigned conditioned on which component the associated mRNA profile belongs to. By providing probabilistic assignments this approach sits between the two extremes of concatenating the data on the assumption that mRNA and protein clusters would have a one-to-one relationship, and independent clustering where the mRNA profile provides no information on the protein profile and vice-versa. We apply this approach to a large dataset of quantitative transcriptomic and proteomic expression data obtained from a human breast epithelial cell line (HMEC) stimulated by epidermal growth factor (EGF) over a series of timepoints corresponding to one cell cycle. The results reveal a complex relationship between transcriptome and proteome with most mRNA clusters linked to at least two protein clusters, and vice versa. A more detailed analysis incorporating information on gene function from the gene ontology database shows that a high correlation of mRNA and protein expression is limited to the components of some molecular machines, such as the ribosome, cell adhesion complexes and the TCP-1 chaperonin involved in protein folding. Conclusions: The dynamic regulation of the transcriptome and proteome in mammalian cells in response to an acute mitogenic stimulus appears largely independent with very little correspondence between mRNA and protein expression. The exceptions involve a few selected multi-protein complexes that require the stoichiometric expression of components for correct function. This finding has wide ramifications regarding the understanding of gene and protein expression including its control and evolution. It also shows that transcriptomic and proteomic expression analysis are complementary and non-redundant.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Environmental Molecular Sciences Lab. (EMSL)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 946373
- Report Number(s):
- PNNL-SA-61286; 10198; TRN: US0900934
- Journal Information:
- Bioinformatics, 24(24):2894-2900, Vol. 24, Issue 24
- Country of Publication:
- United States
- Language:
- English
Similar Records
Integration of transcriptomic and proteomic data identifies biological functions in cell populations from human infant lung
Integrative analysis of transcriptomic and proteomic data of Shewanella oneidensis: missing value imputation using temporal datasets