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

作者: Rong Wang , John T Prince , Edward M Marcotte

DOI: 10.1101/GR.3994105

关键词:

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

参考文章(45)
William R. Jacobs, Ganjam V. Kalpana, Jeffrey D. Cirillo, Lisa Pascopella, Scott B. Snapper, Rupa A. Udani, Wilbur Jones, Raúl G. Barletta, Barry R. Bloom, Genetic systems for mycobacteria. Methods in Enzymology. ,vol. 204, pp. 537- 555 ,(1991) , 10.1016/0076-6879(91)04027-L
Andrew J. Link, Jimmy Eng, David M. Schieltz, Edwin Carmack, Gregory J. Mize, David R. Morris, Barbara M. Garvik, John R. Yates, Direct analysis of protein complexes using mass spectrometry Nature Biotechnology. ,vol. 17, pp. 676- 682 ,(1999) , 10.1038/10890
Michael P. Washburn, Dirk Wolters, John R. Yates, Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nature Biotechnology. ,vol. 19, pp. 242- 247 ,(2001) , 10.1038/85686
J L Bennetzen, B D Hall, Codon selection in yeast. Journal of Biological Chemistry. ,vol. 257, pp. 3026- 3031 ,(1982) , 10.1016/S0021-9258(19)81068-2
Shiping Wu, Susan T. Howard, David L. Lakey, Andre Kipnis, Buka Samten, Hassan Safi, Veronica Gruppo, Benjamin Wizel, Homayoun Shams, Randall J. Basaraba, Ian M. Orme, Peter F. Barnes, The principal sigma factor sigA mediates enhanced growth of Mycobacterium tuberculosis in vivo. Molecular Microbiology. ,vol. 51, pp. 1551- 1562 ,(2004) , 10.1111/J.1365-2958.2003.03922.X
Colin Ratledge, Jeremy Dale, Mycobacteria : molecular biology and virulence Blackwell Science. ,(1999)
S. P. Gygi, G. L. Corthals, Y. Zhang, Y. Rochon, R. Aebersold, Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology Proceedings of the National Academy of Sciences of the United States of America. ,vol. 97, pp. 9390- 9395 ,(2000) , 10.1073/PNAS.160270797
John T Prince, Mark W Carlson, Rong Wang, Peng Lu, Edward M Marcotte, The need for a public proteomics repository. Nature Biotechnology. ,vol. 22, pp. 471- 472 ,(2004) , 10.1038/NBT0404-471
David N. Perkins, Darryl J. C. Pappin, David M. Creasy, John S. Cottrell, Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis. ,vol. 20, pp. 3551- 3567 ,(1999) , 10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2
Markus W. Covert, Eric M. Knight, Jennifer L. Reed, Markus J. Herrgard, Bernhard O. Palsson, Integrating high-throughput and computational data elucidates bacterial networks Nature. ,vol. 429, pp. 92- 96 ,(2004) , 10.1038/NATURE02456