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.

Three-dimensional eukaryotic genomic organization is strongly correlated with codon usage expression and function

“…This was expected, as GC3 content and codon usage are known to be correlated. It should be noted, that both synonymous and non-synonymous features of the ORF are known to be related to protein function and expression [31][32][33][37][38][39][40][41] ; thus, our choice of CUFS attempts to capture and integrate as many as possible of the underlying signals in the coding sequence, for a better representation of the functional interactions between genes.…”

Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies

“…As summarized in one review (Nie et al, 2007), chances of capturing correlation patterns between transcriptomic and proteomic data could be improved if some of the statistical challenges can be properly addressed, such as proper data transformation and normalization, better statistical tools to deal with experimental measurement errors and missing proteomic data, and tools for handling the nonlinearity property of correlation between transcriptomic and proteomic data. Some progress has been made in recent years; for example, Nie et al (2006a) proposed a zero-inflated Poisson regression model to address issues with missing proteomics data. The key improvement of this model is that the undetected proteins are also taken into consideration, thus allowing an estimation of protein expression even when the proteins are experimentally undetectable due to technical limitations.…”

“…For example, the discrepancy between transcriptomics and proteomics data has been used to suggest possible posttranscription regulation involved in chill-adaptation in Bacillus subtilis (Budde et al, 2006) and physiological adjustment of Halobacterium salinarum in response to changes in oxygen availability (Schmid et al, 2007). In one recent study using proteomic and transcriptomic data, Nie et al (2006a) used multiple regression analysis of wholegenome mRNA expression and LC-MS/MS proteome abundance data collected from D. vulgaris grown in three conditions, to gain insights into how the mRNA-protein correlation may be affected by various sequence features related to translation efficiency. The analysis suggested that the mRNA-protein correlation is affected primarily by the factors important during the elongation stage, i.e.…”

Toward a Genome-Wide Landscape of Translational Control

“…Subsequent studies based on measurements of more genes estimated r 2 p from 0.14 to 0.73 (Lu et al 2007;Schmidt et al 2007;Ingolia et al 2009) or r s of 0.57 and 0.58 (Ghaemmaghami et al 2003;Beyer et al 2004). In bacteria, r 2 p between 0.20 and 0.47 has been reported (Nie et al 2006;Lu et al 2007;Jayapal et al 2008). Moreover, in a recent single-cell study in Escherichia coli, mean mRNA copies and protein copies showed r 2 p of 0.29 and 0.59 using deep sequencing of RNA (RNA-seq) and fluorescence in situ hybridization (FISH), respectively (Taniguchi et al 2010).…”