Literature DB >> 17028312

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

Lei Nie1, Gang Wu, Weiwen Zhang.   

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.

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Year:  2006        PMID: 17028312      PMCID: PMC1698625          DOI: 10.1534/genetics.106.065862

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  70 in total

1.  Correlations between Shine-Dalgarno sequences and gene features such as predicted expression levels and operon structures.

Authors:  Jiong Ma; Allan Campbell; Samuel Karlin
Journal:  J Bacteriol       Date:  2002-10       Impact factor: 3.490

2.  Sequence analysis suggests that tetra-nucleotides signal the termination of protein synthesis in eukaryotes.

Authors:  C M Brown; P A Stockwell; C N Trotman; W P Tate
Journal:  Nucleic Acids Res       Date:  1990-11-11       Impact factor: 16.971

3.  Relationships among stop codon usage bias, its context, isochores, and gene expression level in various eukaryotes.

Authors:  Jingchun Sun; Ming Chen; Jinlin Xu; Jianhua Luo
Journal:  J Mol Evol       Date:  2005-09-13       Impact factor: 2.395

4.  Relation between mRNA expression and sequence information in Desulfovibrio vulgaris: combinatorial contributions of upstream regulatory motifs and coding sequence features to variations in mRNA abundance.

Authors:  Gang Wu; Lei Nie; Weiwen Zhang
Journal:  Biochem Biophys Res Commun       Date:  2006-04-17       Impact factor: 3.575

Review 5.  Translational termination efficiency in both bacteria and mammals is regulated by the base following the stop codon.

Authors:  W P Tate; E S Poole; J A Horsfield; S A Mannering; C M Brown; J G Moffat; M E Dalphin; K K McCaughan; L L Major; D N Wilson
Journal:  Biochem Cell Biol       Date:  1995 Nov-Dec       Impact factor: 3.626

6.  Correlation between the abundance of yeast transfer RNAs and the occurrence of the respective codons in protein genes. Differences in synonymous codon choice patterns of yeast and Escherichia coli with reference to the abundance of isoaccepting transfer RNAs.

Authors:  T Ikemura
Journal:  J Mol Biol       Date:  1982-07-15       Impact factor: 5.469

7.  Codon bias at the 3'-side of the initiation codon is correlated with translation initiation efficiency in Escherichia coli.

Authors:  C M Stenström; H Jin; L L Major; W P Tate; L A Isaksson
Journal:  Gene       Date:  2001-01-24       Impact factor: 3.688

8.  Non-canonical mechanism for translational control in bacteria: synthesis of ribosomal protein S1.

Authors:  I V Boni; V S Artamonova; N V Tzareva; M Dreyfus
Journal:  EMBO J       Date:  2001-08-01       Impact factor: 11.598

9.  Correlation between mRNA and protein abundance in Desulfovibrio vulgaris: a multiple regression to identify sources of variations.

Authors:  Lei Nie; Gang Wu; Weiwen Zhang
Journal:  Biochem Biophys Res Commun       Date:  2005-11-17       Impact factor: 3.575

10.  Post-transcriptional expression regulation in the yeast Saccharomyces cerevisiae on a genomic scale.

Authors:  Andreas Beyer; Jens Hollunder; Heinz-Peter Nasheuer; Thomas Wilhelm
Journal:  Mol Cell Proteomics       Date:  2004-08-23       Impact factor: 5.911

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  70 in total

1.  Comparison of mRNA and protein measures of cytokines following vaccination with human papillomavirus-16 L1 virus-like particles.

Authors:  Fatma M Shebl; Ligia A Pinto; Alfonso García-Piñeres; Richard Lempicki; Marcus Williams; Clayton Harro; Allan Hildesheim
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-03-23       Impact factor: 4.254

Review 2.  Protein abundance ratios for global studies of prokaryotes.

Authors:  Qiangwei Xia; Erik L Hendrickson; Tiansong Wang; Richard J Lamont; John A Leigh; Murray Hackett
Journal:  Proteomics       Date:  2007-08       Impact factor: 3.984

3.  Coupled global and targeted proteomics of human embryonic stem cells during induced differentiation.

Authors:  Anastasia K Yocum; Theresa E Gratsch; Nancy Leff; John R Strahler; Christie L Hunter; Angela K Walker; George Michailidis; Gilbert S Omenn; K Sue O'Shea; Philip C Andrews
Journal:  Mol Cell Proteomics       Date:  2008-02-26       Impact factor: 5.911

4.  Proteomic and transcriptomic analyses of "Candidatus Pelagibacter ubique" describe the first PII-independent response to nitrogen limitation in a free-living Alphaproteobacterium.

Authors:  Daniel P Smith; J Cameron Thrash; Carrie D Nicora; Mary S Lipton; Kristin E Burnum-Johnson; Paul Carini; Richard D Smith; Stephen J Giovannoni
Journal:  MBio       Date:  2013-11-26       Impact factor: 7.867

Review 5.  Global signatures of protein and mRNA expression levels.

Authors:  Raquel de Sousa Abreu; Luiz O Penalva; Edward M Marcotte; Christine Vogel
Journal:  Mol Biosyst       Date:  2009-10-01

6.  Gene expression signatures in tree shrew sclera in response to three myopiagenic conditions.

Authors:  Lin Guo; Michael R Frost; Li He; John T Siegwart; Thomas T Norton
Journal:  Invest Ophthalmol Vis Sci       Date:  2013-10-21       Impact factor: 4.799

7.  Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins.

Authors:  Wandaliz Torres-García; Weiwen Zhang; George C Runger; Roger H Johnson; Deirdre R Meldrum
Journal:  Bioinformatics       Date:  2009-05-15       Impact factor: 6.937

8.  Quantitative proteomic profiling of host-pathogen interactions: the macrophage response to Mycobacterium tuberculosis lipids.

Authors:  Wenqing Shui; Sarah A Gilmore; Leslie Sheu; Jun Liu; Jay D Keasling; Carolyn R Bertozzi
Journal:  J Proteome Res       Date:  2009-01       Impact factor: 4.466

9.  Comparative transcriptional and translational analysis of leptospiral outer membrane protein expression in response to temperature.

Authors:  Miranda Lo; Stuart J Cordwell; Dieter M Bulach; Ben Adler
Journal:  PLoS Negl Trop Dis       Date:  2009-12-08

10.  Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.

Authors:  Caroline Colijn; Aaron Brandes; Jeremy Zucker; Desmond S Lun; Brian Weiner; Maha R Farhat; Tan-Yun Cheng; D Branch Moody; Megan Murray; James E Galagan
Journal:  PLoS Comput Biol       Date:  2009-08-28       Impact factor: 4.475

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