Dataset Information

Correlation of mRNA expression and protein abundance affected by multiple sequence features related to translational efficiency in Desulfovibrio vulgaris: a quantitative analysis.

ABSTRACT: The modest correlation between mRNA expression and protein abundance in large-scale data sets is explained in part by experimental challenges, such as technological limitations, and in part by fundamental biological factors in the transcription and translation processes. Among various factors affecting the mRNA-protein correlation, the roles of biological factors related to translation are poorly understood. In this study, using experimental mRNA expression and protein abundance data collected from Desulfovibrio vulgaris by DNA microarray and liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) proteomic analysis, we quantitatively examined the effects of several translational-efficiency-related sequence features on mRNA-protein correlation. Three classes of sequence features were investigated according to different translational stages: (i) initiation, Shine-Dalgarno sequences, start codon identity, and start codon context; (ii) elongation, codon usage and amino acid usage; and (iii) termination, stop codon identity and stop codon context. Surprisingly, although it is widely accepted that translation initiation is the rate-limiting step for translation, our results showed that the mRNA-protein correlation was affected the most by the features at elongation stages, i.e., codon usage and amino acid composition (5.3-15.7% and 5.8-11.9% of the total variation of mRNA-protein correlation, respectively), followed by stop codon context and the Shine-Dalgarno sequence (3.7-5.1% and 1.9-3.8%, respectively). Taken together, all sequence features contributed to 15.2-26.2% of the total variation of mRNA-protein correlation. This study provides the first comprehensive quantitative analysis of the mRNA-protein correlation in bacterial D. vulgaris and adds new insights into the relative importance of various sequence features in prokaryotic protein translation.

SUBMITTER: Nie L

PROVIDER: S-EPMC1698625 | biostudies-literature | 2006 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Correlation of mRNA expression and protein abundance affected by multiple sequence features related to translational efficiency in Desulfovibrio vulgaris: a quantitative analysis.

Nie Lei L Wu Gang G Zhang Weiwen W

Genetics 20061008 4

The modest correlation between mRNA expression and protein abundance in large-scale data sets is explained in part by experimental challenges, such as technological limitations, and in part by fundamental biological factors in the transcription and translation processes. Among various factors affecting the mRNA-protein correlation, the roles of biological factors related to translation are poorly understood. In this study, using experimental mRNA expression and protein abundance data collected f ...[more]

PMID: 17028312

Similar Datasets

Project description:Gene expression profiling technologies can generally produce mRNA abundance data for all genes in a genome. A dearth of proteomic data persists because identification range and sensitivity of proteomic measurements lag behind those of transcriptomic measurements. Using partial proteomic data, it is likely that integrative transcriptomic and proteomic analysis may introduce significant bias. Developing methodologies to accurately estimate missing proteomic data will allow better integration of transcriptomic and proteomic datasets and provide deeper insight into metabolic mechanisms underlying complex biological systems.In this study, we present a non-linear data-driven model to predict abundance for undetected proteins using two independent datasets of cognate transcriptomic and proteomic data collected from Desulfovibrio vulgaris. We use stochastic gradient boosted trees (GBT) to uncover possible non-linear relationships between transcriptomic and proteomic data, and to predict protein abundance for the proteins not experimentally detected based on relevant predictors such as mRNA abundance, cellular role, molecular weight, sequence length, protein length, guanine-cytosine (GC) content and triple codon counts. Initially, we constructed a GBT model using all possible variables to assess their relative importance and characterize the behavior of the predictive model. A strong plateau effect in the regions of high mRNA values and sparse data occurred in this model. Hence, we removed genes in those areas based on thresholds estimated from the partial dependency plots where this behavior was captured. At this stage, only the strongest predictors of protein abundance were retained to reduce the complexity of the GBT model. After removing genes in the plateau region, mRNA abundance, main cellular functional categories and few triple codon counts emerged as the top-ranked predictors of protein abundance. We then created a new tuned GBT model using the five most significant predictors. The construction of our non-linear model consists of a set of serial regression trees models with implicit strength in variable selection. The model provides variable relative importance measures using as a criterion mean square error. The results showed that coefficients of determination for our nonlinear models ranged from 0.393 to 0.582 in both datasets, providing better results than linear regression used in the past. We evaluated the validity of this non-linear model using biological information of operons, regulons and pathways, and the results demonstrated that the coefficients of variation of estimated protein abundance values within operons, regulons or pathways are indeed smaller than those for random groups of proteins.Supplementary data are available at Bioinformatics online.

Project description:Desulfovibrio vulgaris has been studied extensively for its potential in the bioremediation of heavy metals and radionuclides. Hydrocarbons and solvents, as frequent environmental co-contaminants, have been reported to inhibit microbial activities and thereby pose a limitation on bioremediation efficiency. As a part of the Genomes: GTL project to deduce the stress response pathways in metal/radionuclide reducing bacteria, we studied the responses of D. vulgaris to ethanol, which is a solvent and co-contaminant frequently encountered at contaminated DOE sites. Growth experiments in closed vessels at 37 °C indicated that D. vulgaris could maintain normal growth with 1%(v/v) ethanol. The growth rates were reduced with increasing ethanol concentrations at 2% and 5%. Growth ceased when ethanol concentration was raised to 5%(v/v). Cell lysis was apparent with decreasing optical density following 10% ethanol addition. To assess the mechanism of ethanol inhibition, genome-wide transcriptional profiles were analyzed from D. vulgaris cultures following ethanol (5% v/v) treatment using whole-genome microarrays. The ethanol treatment altered the expression of a large number of genes in the D. vulgaris genome, of which 354 were up-regulated greater than 2 fold and 217 were down-regulated by over 2 fold. As expected, changes in the transcriptional profile were similar to those of the stress response to acetone, which is also a solvent. Transcripts highly up-regulated included genes encoding the flagella structural subunits, suggesting motility as a mechanism of solvent stress response. Another group of genes highly induced were chaperones, such as dnaJ, groES, and hsp20, indicating the importance of maintaining proper protein folding under ethanol stress. Down-regulated genes included two groups of genes, ribosomal proteins and amino acid transporters, consistent with the growth inhibition by ethanol observed in growth studies. These results were interpreted that D. vulgaris responds to elevated solvent levels by increased motility and maintenance of proper protein functions. Current work is focused on the analysis of regulatory pathways based on temporal transcriptional dynamics. Keywords: Stress response

Dataset Information

Correlation of mRNA expression and protein abundance affected by multiple sequence features related to translational efficiency in Desulfovibrio vulgaris: a quantitative analysis.

Publications

Correlation of mRNA expression and protein abundance affected by multiple sequence features related to translational efficiency in Desulfovibrio vulgaris: a quantitative analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets