Literature DB >> 16077011

Mass spectrometry of the M. smegmatis proteome: protein expression levels correlate with function, operons, and codon bias.

Rong Wang1, John T Prince, Edward M Marcotte.   

Abstract

The fast-growing bacterium Mycobacterium smegmatis is a model mycobacterial system, a nonpathogenic soil bacterium that nonetheless shares many features with the pathogenic Mycobacterium tuberculosis, the causative agent of tuberculosis. The study of M. smegmatis is expected to shed light on mechanisms of mycobacterial growth and complex lipid metabolism, and provides a tractable system for antimycobacterial drug development. Although the M. smegmatis genome sequence is not yet completed, we used multidimensional chromatography and tandem mass spectrometry, in combination with the partially completed genome sequence, to detect and identify a total of 901 distinct proteins from M. smegmatis over the course of 25 growth conditions, providing experimental annotation for many predicted genes with an approximately 5% false-positive identification rate. We observed numerous proteins involved in energy production (9.8% of expressed proteins), protein translation (8.7%), and lipid biosynthesis (5.4%); 33% of the 901 proteins are of unknown function. Protein expression levels were estimated from the number of observations of each protein, allowing measurement of differential expression of complete operons, and the comparison of the stationary and exponential phase proteomes. Expression levels are correlated with proteins' codon biases and mRNA expression levels, as measured by comparison with codon adaptation indices, principle component analysis of codon frequencies, and DNA microarray data. This observation is consistent with notions that either (1) prokaryotic protein expression levels are largely preset by codon choice, or (2) codon choice is optimized for consistency with average expression levels regardless of the mechanism of regulating expression.

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Year:  2005        PMID: 16077011      PMCID: PMC1182224          DOI: 10.1101/gr.3994105

Source DB:  PubMed          Journal:  Genome Res        ISSN: 1088-9051            Impact factor:   9.043


  42 in total

1.  The use of gene clusters to infer functional coupling.

Authors:  R Overbeek; M Fonstein; M D'Souza; G D Pusch; N Maltsev
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Review 2.  The evolution of mycobacterial pathogenicity: clues from comparative genomics.

Authors:  R Brosch; A S Pym; S V Gordon; S T Cole
Journal:  Trends Microbiol       Date:  2001-09       Impact factor: 17.079

3.  Proteogenomic mapping as a complementary method to perform genome annotation.

Authors:  Jacob D Jaffe; Howard C Berg; George M Church
Journal:  Proteomics       Date:  2004-01       Impact factor: 3.984

4.  Integrating high-throughput and computational data elucidates bacterial networks.

Authors:  Markus W Covert; Eric M Knight; Jennifer L Reed; Markus J Herrgard; Bernhard O Palsson
Journal:  Nature       Date:  2004-05-06       Impact factor: 49.962

5.  Mycobacterium smegmatis D-Alanine Racemase Mutants Are Not Dependent on D-Alanine for Growth.

Authors:  Ofelia Chacon; Zhengyu Feng; N Beth Harris; Nancy E Cáceres; L Garry Adams; Raúl G Barletta
Journal:  Antimicrob Agents Chemother       Date:  2002-01       Impact factor: 5.191

6.  The codon Adaptation Index--a measure of directional synonymous codon usage bias, and its potential applications.

Authors:  P M Sharp; W H Li
Journal:  Nucleic Acids Res       Date:  1987-02-11       Impact factor: 16.971

7.  Direct analysis of protein complexes using mass spectrometry.

Authors:  A J Link; J Eng; D M Schieltz; E Carmack; G J Mize; D R Morris; B M Garvik; J R Yates
Journal:  Nat Biotechnol       Date:  1999-07       Impact factor: 54.908

8.  Complementary analysis of the Mycobacterium tuberculosis proteome by two-dimensional electrophoresis and isotope-coded affinity tag technology.

Authors:  Frank Schmidt; Samuel Donahoe; Kristine Hagens; Jens Mattow; Ulrich E Schaible; Stefan H E Kaufmann; Ruedi Aebersold; Peter R Jungblut
Journal:  Mol Cell Proteomics       Date:  2003-10-13       Impact factor: 5.911

9.  Cluster analysis and display of genome-wide expression patterns.

Authors:  M B Eisen; P T Spellman; P O Brown; D Botstein
Journal:  Proc Natl Acad Sci U S A       Date:  1998-12-08       Impact factor: 11.205

10.  Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence.

Authors:  S T Cole; R Brosch; J Parkhill; T Garnier; C Churcher; D Harris; S V Gordon; K Eiglmeier; S Gas; C E Barry; F Tekaia; K Badcock; D Basham; D Brown; T Chillingworth; R Connor; R Davies; K Devlin; T Feltwell; S Gentles; N Hamlin; S Holroyd; T Hornsby; K Jagels; A Krogh; J McLean; S Moule; L Murphy; K Oliver; J Osborne; M A Quail; M A Rajandream; J Rogers; S Rutter; K Seeger; J Skelton; R Squares; S Squares; J E Sulston; K Taylor; S Whitehead; B G Barrell
Journal:  Nature       Date:  1998-06-11       Impact factor: 49.962

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  35 in total

1.  Metabolomics Studies To Decipher Stress Responses in Mycobacterium smegmatis Point to a Putative Pathway of Methylated Amine Biosynthesis.

Authors:  Arshad Rizvi; Saleem Yousf; Kannan Balakrishnan; Harish Kumar Dubey; Shekhar C Mande; Jeetender Chugh; Sharmistha Banerjee
Journal:  J Bacteriol       Date:  2019-07-10       Impact factor: 3.490

2.  Protein fragment domains identified using 2D gel electrophoresis/MALDI-TOF.

Authors:  Maria D Person; Jianjun Shen; Angelina Traner; Sean C Hensley; Herng-Hsiang Lo; James L Abbruzzese; Donghui Li
Journal:  J Biomol Tech       Date:  2006-04

Review 3.  Expression profiling in granulomatous lung disease.

Authors:  Edward S Chen; David R Moller
Journal:  Proc Am Thorac Soc       Date:  2007-01

4.  Improving protein identification sensitivity by combining MS and MS/MS information for shotgun proteomics using LTQ-Orbitrap high mass accuracy data.

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5.  Whole proteome analysis of post-translational modifications: applications of mass-spectrometry for proteogenomic annotation.

Authors:  Nitin Gupta; Stephen Tanner; Navdeep Jaitly; Joshua N Adkins; Mary Lipton; Robert Edwards; Margaret Romine; Andrei Osterman; Vineet Bafna; Richard D Smith; Pavel A Pevzner
Journal:  Genome Res       Date:  2007-08-09       Impact factor: 9.043

6.  Spectral dictionaries: Integrating de novo peptide sequencing with database search of tandem mass spectra.

Authors:  Sangtae Kim; Nitin Gupta; Nuno Bandeira; Pavel A Pevzner
Journal:  Mol Cell Proteomics       Date:  2008-08-14       Impact factor: 5.911

7.  Acid stress response of a mycobacterial proteome: insight from a gene ontology analysis.

Authors:  Bryan Ap Roxas; Qingbo Li
Journal:  Int J Clin Exp Med       Date:  2009-11-10

8.  Comparative proteogenomics: combining mass spectrometry and comparative genomics to analyze multiple genomes.

Authors:  Nitin Gupta; Jamal Benhamida; Vipul Bhargava; Daniel Goodman; Elisabeth Kain; Ian Kerman; Ngan Nguyen; Noah Ollikainen; Jesse Rodriguez; Jian Wang; Mary S Lipton; Margaret Romine; Vineet Bafna; Richard D Smith; Pavel A Pevzner
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9.  Comparative proteogenomic analysis of the Leptospira interrogans virulence-attenuated strain IPAV against the pathogenic strain 56601.

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Journal:  Cell Res       Date:  2011-03-22       Impact factor: 25.617

10.  Ortho-proteogenomics: multiple proteomes investigation through orthology and a new MS-based protocol.

Authors:  Sébastien Gallien; Emmanuel Perrodou; Christine Carapito; Caroline Deshayes; Jean-Marc Reyrat; Alain Van Dorsselaer; Olivier Poch; Christine Schaeffer; Odile Lecompte
Journal:  Genome Res       Date:  2008-10-27       Impact factor: 9.043

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