外源基因诱导表达引发细胞反应的大肠杆菌定量蛋白质组学研究
|
摘要
定量蛋白质组学研究是蛋白质组学研究的新方向,也是揭示生命活动变化规律的有效工具。细胞培养条件下稳定同位素标记定量蛋白质组学技术(stable isotope labeling by amino acids in cell culture, SILAC)是当前定量蛋白质组学研究的金标准,定量准度好、精度高,常用于组学水平上对两种不同处理或状态的比较分析,进而研究特定因素对细胞或动物蛋白质组的影响,可作为信号通路研究方法。
大肠杆菌是一种与人类生活和生产有着密切关系的革兰氏阴性棒状杆菌,是生命科学研究中常用的模式生物,且因培养简单、方便、价廉、快速、研究透彻、没有毒性、遗传操作方法成熟等诸多优点,已经成为基因工程研究和工业发酵生产的常用工具。SILAC方法经过十年的发展,已经广泛应用于酵母、培养的哺乳动物细胞,甚至果蝇和整体小鼠,但作为原核模式生物的大肠杆菌却没有有效的经过严格评价的标记蛋白质组的SILAC培养基。
M9是大肠杆菌常用的无机盐培养基；SC是一种包含无机盐及各种氨基酸的成分可知的酵母完全培养基,通过调节氨基酸组成可用于筛选基因缺陷型菌株。本课题在两种培养基的基础上,通过调节氨基酸的组成,发展了一种可用于SILAC标记的具有自主知识产权的合成培养基,不仅可以满足大肠杆菌的快速、旺盛生长,适合大肠杆菌的代谢研究,而且可实现野生型大肠杆菌培养过程中重稳定同位素标记氨基酸的蛋白质组完全标记。
我们将大肠杆菌常用菌株BL21(DE3)分别在轻标和重标的培养基中进行培养,等量菌体混合后进行SILAC实验分析,即零差异试验(purenull实验)分析,由此得到定量数据的信噪比(S/N)卡值以及平台的定量精准度。进而我们又在该培养基中通过IPTG进行了外源蛋白的诱导表达,得到诱导前及诱导后1小时的轻重标菌体。菌体等OD混合后,通过正反标SILAC定量实验分析了IPTG处理前后蛋白质组的变化。结果显示,外源蛋白主要以可溶性形式存在；诱导1小时后菌体绝大部分内源蛋白表达下调,包括核糖体蛋白,鞭毛蛋白,tRNA合成,TCA循环蛋白等；在表达水平升高显著的11种蛋白中,半乳糖苷酶LacZ、T7RNA聚合酶和外缘基因本身等三种蛋白的变化幅度最大,证明菌体集中大量能量用于目的蛋白的合成；FTSI、PBPA和LPXC等三种蛋白与细胞壁的合成有关,暗示在诱导后菌体表面积略有升高,细胞形态发生变化,并通过电子显微镜证实了这种变化。该SILAC培养基的开发和高精度定量蛋白质组学的发展可为大肠杆菌定量发酵调控研究和合成生物学探索提供了有效的手段。
The rapid development of mass spectrometry-based proteomic technologies, and the increased accessibility of powerful data analysis tools, have provided proteomic researchers the ability to look at changes beyond only qualitative measurements and into the quantitative intricacies of biological systems. SILAC, or Stable Isotope labeling by Amino acids in Cell culture, is the one of such technologies that has been applied effectively and provided highly reproducible and accurate quantitative measurements. SILAC's initial applications were limited to yeast and cultured cells but more recently has been applied to fruit flies and mice. The technology has not been readily applied to such organism as Escherichia coli because of the lackness of the availability of SILAC medium. Here we present a mass spectrometry-based quantitative proteomics method to first introduce SILAC technology into E. coli via development of a novel growth medium and secondly to examine global proteomic profile changes during IPTG inducing.
E. coli is a rod-shaped gram-negative bacteria that has become one of the most useful and characterized models because of its ease in culture and manipulation. Its genome has been completely sequenced and its proteome has also been well annotated. While E. coli has been widely used both in industry and laboratory research, it has rarely used in SILAC based methods because of a lack of proper labeling medium. Complete labeling is essential to the success of SILAC and the proper medium is an important prerequisite. We have developed a medium with a base composition of M9minimal growth medium and yeast synthetic complete (SC) medium. By adjusting the composition of various amino acids and concentrations of carbon sources, we were able to create a medium that allowed fast growth and complete labeling within just10hours.
To test the accuracy of our quantitative platform we performed a pure null experiment involving both light and heavy labeling of BL21(DE3) competent cells in our SILAC medium. The two cellular populations were mixed at a ratio of1:1according to OD600and quantitative proteomics was performed. The quantitative results showed us the accuracy of our platform is about7%which was the highest so far based on our published literature. In addition, the pure null experiment also allowed us to figure out the necessary signal cutoff and filter thresholds for our following experiments.
Induction by IPTG is commonly used to differentially express proteins in E. coli. During induction, the cells are driven to mass transcribe objective gene of interest. We induced an exogenous protein by IPTG for1hour in BL21(DE3) recombinant cells, and collected the cells before and after induction which were differentially cultured in light and heavy medium respectively. After LC-MS/MS analysis we filtered the data using the cutoffs and thresholds from our pure null experiment and summarized the results according to differential expression. SILAC results showed that most proteins were down-regulated. These proteins were but not limited to tRNA synthesis, tricarboxylic acid cycle (TCA), ribosomal function, and flagella function. There were11proteins that showed over-expression profiles,3of which are expected as they were T7RNA polymerase, LacZ, and the target protein. The remaining over-expressed proteins included3related to cell wall synthesis suggesting that cellular morphology is changed. This was validated by the observation with electro-microscope. This experiment shows that under such conditions as induction the cellular state is changed and the media we developed can be as a good tool for quantitative proteomics research.
大肠杆菌是一种与人类生活和生产有着密切关系的革兰氏阴性棒状杆菌,是生命科学研究中常用的模式生物,且因培养简单、方便、价廉、快速、研究透彻、没有毒性、遗传操作方法成熟等诸多优点,已经成为基因工程研究和工业发酵生产的常用工具。SILAC方法经过十年的发展,已经广泛应用于酵母、培养的哺乳动物细胞,甚至果蝇和整体小鼠,但作为原核模式生物的大肠杆菌却没有有效的经过严格评价的标记蛋白质组的SILAC培养基。
M9是大肠杆菌常用的无机盐培养基；SC是一种包含无机盐及各种氨基酸的成分可知的酵母完全培养基,通过调节氨基酸组成可用于筛选基因缺陷型菌株。本课题在两种培养基的基础上,通过调节氨基酸的组成,发展了一种可用于SILAC标记的具有自主知识产权的合成培养基,不仅可以满足大肠杆菌的快速、旺盛生长,适合大肠杆菌的代谢研究,而且可实现野生型大肠杆菌培养过程中重稳定同位素标记氨基酸的蛋白质组完全标记。
我们将大肠杆菌常用菌株BL21(DE3)分别在轻标和重标的培养基中进行培养,等量菌体混合后进行SILAC实验分析,即零差异试验(purenull实验)分析,由此得到定量数据的信噪比(S/N)卡值以及平台的定量精准度。进而我们又在该培养基中通过IPTG进行了外源蛋白的诱导表达,得到诱导前及诱导后1小时的轻重标菌体。菌体等OD混合后,通过正反标SILAC定量实验分析了IPTG处理前后蛋白质组的变化。结果显示,外源蛋白主要以可溶性形式存在；诱导1小时后菌体绝大部分内源蛋白表达下调,包括核糖体蛋白,鞭毛蛋白,tRNA合成,TCA循环蛋白等；在表达水平升高显著的11种蛋白中,半乳糖苷酶LacZ、T7RNA聚合酶和外缘基因本身等三种蛋白的变化幅度最大,证明菌体集中大量能量用于目的蛋白的合成；FTSI、PBPA和LPXC等三种蛋白与细胞壁的合成有关,暗示在诱导后菌体表面积略有升高,细胞形态发生变化,并通过电子显微镜证实了这种变化。该SILAC培养基的开发和高精度定量蛋白质组学的发展可为大肠杆菌定量发酵调控研究和合成生物学探索提供了有效的手段。
The rapid development of mass spectrometry-based proteomic technologies, and the increased accessibility of powerful data analysis tools, have provided proteomic researchers the ability to look at changes beyond only qualitative measurements and into the quantitative intricacies of biological systems. SILAC, or Stable Isotope labeling by Amino acids in Cell culture, is the one of such technologies that has been applied effectively and provided highly reproducible and accurate quantitative measurements. SILAC's initial applications were limited to yeast and cultured cells but more recently has been applied to fruit flies and mice. The technology has not been readily applied to such organism as Escherichia coli because of the lackness of the availability of SILAC medium. Here we present a mass spectrometry-based quantitative proteomics method to first introduce SILAC technology into E. coli via development of a novel growth medium and secondly to examine global proteomic profile changes during IPTG inducing.
E. coli is a rod-shaped gram-negative bacteria that has become one of the most useful and characterized models because of its ease in culture and manipulation. Its genome has been completely sequenced and its proteome has also been well annotated. While E. coli has been widely used both in industry and laboratory research, it has rarely used in SILAC based methods because of a lack of proper labeling medium. Complete labeling is essential to the success of SILAC and the proper medium is an important prerequisite. We have developed a medium with a base composition of M9minimal growth medium and yeast synthetic complete (SC) medium. By adjusting the composition of various amino acids and concentrations of carbon sources, we were able to create a medium that allowed fast growth and complete labeling within just10hours.
To test the accuracy of our quantitative platform we performed a pure null experiment involving both light and heavy labeling of BL21(DE3) competent cells in our SILAC medium. The two cellular populations were mixed at a ratio of1:1according to OD600and quantitative proteomics was performed. The quantitative results showed us the accuracy of our platform is about7%which was the highest so far based on our published literature. In addition, the pure null experiment also allowed us to figure out the necessary signal cutoff and filter thresholds for our following experiments.
Induction by IPTG is commonly used to differentially express proteins in E. coli. During induction, the cells are driven to mass transcribe objective gene of interest. We induced an exogenous protein by IPTG for1hour in BL21(DE3) recombinant cells, and collected the cells before and after induction which were differentially cultured in light and heavy medium respectively. After LC-MS/MS analysis we filtered the data using the cutoffs and thresholds from our pure null experiment and summarized the results according to differential expression. SILAC results showed that most proteins were down-regulated. These proteins were but not limited to tRNA synthesis, tricarboxylic acid cycle (TCA), ribosomal function, and flagella function. There were11proteins that showed over-expression profiles,3of which are expected as they were T7RNA polymerase, LacZ, and the target protein. The remaining over-expressed proteins included3related to cell wall synthesis suggesting that cellular morphology is changed. This was validated by the observation with electro-microscope. This experiment shows that under such conditions as induction the cellular state is changed and the media we developed can be as a good tool for quantitative proteomics research.
引文
1. Hunter, T.C., et al., Peptide mass mapping constrained with stable isotope-tagged peptides for identification of protein mixtures. Anal Chem,2001.73(20):p.4891-902.
2. Ong, S.E., et al., Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics,2002.1(5):p.376-86.
3. Yu, K.H., et al., Stable isotope dilution multidimensional liquid chromatography-tandem mass spectrometry for pancreatic cancer serum biomarker discovery. J Proteome Res,2009.8(3):p. 1565-76.
4. Iwadate, Y., Clinical proteomics in cancer research-promises and limitations of current two-dimensional gel electrophoresis. Curr Med Chem,2008.15(23):p.2393-400.
5. Mann, M., Functional and quantitative proteomics using SILAC. Nat Rev Mol Cell Biol,2006. 7(12):p.952-8.
6. Sury, M.D., J.X. Chen, and M. Selbach, The SILAC fly allows for accurate protein quantification in vivo. Mol Cell Proteomics,2010.9(10):p.2173-83.
7. de Godoy, L.M., et al., Status of complete proteome analysis by mass spectrometry:SILAC labeled yeast as a model system. Genome Biol,2006.7(6):p. R50.
8. de Godoy, L.M., et al., Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature,2008.455(7217):p.1251-4.
9. Larance, M., et al., Stable-isotope labeling with amino acids in nematodes. Nat Methods,2011. 8(10):p.849-51.
10. Xu, P., et al., Stable isotope labeling with amino acids in Drosophila for quantifying proteins and modifications. J Proteome Res,2012.11(9):p.4403-12.
11. Kruger, M., et al., SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell,2008.134(2):p.353-64.
12. Blattner, F.R., et al., The complete genome sequence of Escherichia coli K-12. Science,1997. 277(5331):p.1453-62.
13. Wang, Y.K., et al., Inverse 15N-metabolic labeling/mass spectrometry for comparative proteomics and rapid identification of protein markers/targets. Rapid Commun Mass Spectrom, 2002.16(14):p.1389-97.
14. Krijgsveld, J., et al., Metabolic labeling of C. elegans and D.melanogaster for quantitative proteomics. Nat Biotechnol,2003.21(8):p.927-31.
15. Martinovic, S., et al., Selective incorporation of isotopically labeled amino acids for identification of intact proteins on a proteome-wide level. J Mass Spectrom,2002.37(1):p.99-107.
16. Chen, X., L.M. Smith, and E.M. Bradbury, Site-specific mass tagging with stable isotopes in proteins for accurate and efficient protein identification. Anal Chem,2000.72(6):p.1134-43.
17. Bolton, E.T., P.H. Abelson, and E. Aldous, Utilization of carbon dioxide in the synthesis of nucleic acid by escherichia coli. J Biol Chem,1952.198(1):p.179-85.
18. Abelson, P.H., E.T. Bolton, and E. Aldous, Utilization of carbon dioxide in the synthesis of proteins by Escherichia coli. Ⅱ. J Biol Chem,1952.198(1):p.173-8.
19. Choudhary, K., et al., Method for estimating the isotopic distributions of metabolically labeled proteins by MALDI-TOFMS:application to NMR samples. Anal Chem,2006.78(15):p.5419-23.
20. Ding, C., et al., Quantitative analysis of cohesin complex stoichiometry and SMC3 modification-dependent protein interactions. J Proteome Res,2011.10(8):p.3652-9.
21. Carroll, K.M., et al.. Absolute quantification of the glycolytic pathway in yeast:deployment of a complete QconCATapproach. Mol Cell Proteomics,2011.10(12):p. M111 007633.
22. Covert, M.W. and B.O. Palsson, Transcriptional regulation in constraints-based metabolic models of Escherichia coll J Biol Chem,2002.277(31):p.28058-64.
23. Kolisnychenko, V., et al., Engineering a reduced Escherichia coli genome. Genome Res,2002. 12(4):p.640-7.
24. Cohen, S.N., et al., Construction of biologically functional bacterial plasmids in vitro. Proc Natl Acad Sci U S A,1973.70(11):p.3240-4.
25. Edwards, M.C., et al., Addition of genes for cellobiase and pectinolytic activity in Escherichia coli for fuel ethanol production from pectin-rich lignocellulosic biomass. Appl Environ Microbiol, 2011.77(15):p.5184-91.
26. Schwartz, Y., et al., Effects of cholesterol and nuclear hormone receptor agonists on the production of transforming growth factor-beta in macrophages. Bull Exp Biol Med,2009.148(3): p.406-9.
27. Kuznetsova, T.V., et al., Producing human mechano growth factor (MGF) in Escherichia coli. Protein Expr Purif,2008.58(1):p.70-7.
28. Li, X.Y., H.L. Li, and G.Y. Zheng, [Construction and expression of recombinant cecropin B-binding site of luteinizing hormone releasing hormone gene and its anticancer function]. Zhonghua Fu Chan Ke Za Zhi,2007.42(7):p.477-81.
29. Redwan el, R.M., et al., Synthesis of the human insulin gene:protein expression, scaling up and bioactivity. Prep Biochem Biotechnol,2008.38(1):p.24-39.
30. Josefsberg, J.O. and B. Buckland, Vaccine process technology. Biotechnol Bioeng,2012.109(6): p.1443-60.
31. Kamionka, M., Engineering of therapeutic proteins production in Escherichia coli. Curr Pharm Biotechnol,2011.12(2):p.268-74.
32. Venkataraman, S., et al., Synthesis and biological activity of some novel pyrazolines. Acta Pol Pharm,2010.67(4):p.361-6.
33. Le, Y., et al., An approach to the production of soluble protein from a fungal gene encoding an aggregation-prone xylanase in Escherichia coli. PLoS One,2011.6(4):p. e18489.
34. Weyant, R.G., "The ferment of knowledge:studies in the historiography of eighteenth-century science". Essay review. J Hist Behav Sci,1983.19(3):p.226-33.
35. lacono, V.J., Periodontics 1988. A discipline in ferment. N Y State Dent J,1987.53(10):p.24-9.
36. Studier, F.W., et al., Use of 17 RNA polymerase to direct expression of cloned genes. Methods Enzymol,1990.185:p.60-89.
37. Han, M.J. and S.Y. Lee, The Escherichia coli proteome:past, present, and future prospects. Microbiol Mol Biol Rev,2006.70(2):p.362-439.
38. Eron, L. and R. Block, Mechanism of initiation and repression of in vitro transcription of the lac operon of Escherichia coli. Proc Natl Acad Sci U S A,1971.68(8):p.1828-32.
39. Fried, M.G. and D.M. Crothers, Equilibrium studies of the cyclic AMP receptor protein-DNA interaction. J Mol Biol,1984.172(3):p.241-62.
40. Dubendorff, J.W. and F.W. Studier, Creation of a T7 autogene. Cloning and expression of the gene for bacteriophage T7 RNA polymerase under control of its cognate promoter. J Mol Biol, 1991.219(1):p.61-8.
41. Grodberg, J., et al., Complete nucleotide sequence and deduced amino acid sequence of the ompT gene of Escherichia coli K-12. Nucleic Acids Res,1988.16(3):p.1209.
42. Oh, M.K. and J.C. Liao, DNA microarray detection of metabolic responses to protein overproduction in Escherichia coli. Metab Eng,2000.2(3):p.201-9.
43. Tomoyasu, T., et al., Levels of DnaK and DnaJ provide tight control of heat shock gene expression and protein repair in Escherichia coli. Mol Microbiol,1998.30(3):p.567-81.
44. Hoffmann, F. and U. Rinas, Stress induced by recombinant protein production in Escherichia coli. Adv Biochem Eng Biotechnol,2004.89:p.73-92.
45. Caspeta, L., et al., The effect of heating rate on Escherichia coli metabolism, physiological stress, transcriptional response, and production of temperature-induced recombinant protein:a scale-down study. Biotechnol Bioeng,2009.102(2):p.468-82.
46. Heyland, J., L.M. Blank, and A. Schmid, Quantification of metabolic limitations during recombinant protein production in Escherichia coli. J Biotechnol,2011.155(2):p.178-84.
47. Cheng, C.H. and W.C. Lee, Protein solubility and differential proteomic profiling of recombinant Escherichia coli overexpressing double-tagged fusion proteins. Microb Cell Fact,2010.9:p.63.
48. Wang, W., et al., Proteome analysis of a recombinant Bacillus megaterium strain during heterologous production of a glucosyltransferase. Proteome Sci,2005.3:p.4.
49. Buttgereit, F., G.R. Burmester, and M.D. Brand, Therapeutically targeting lymphocyte energy metabolism by high-dose glucocorticoids. Biochem Pharmacol,2000.59(6):p.597-603.
50. Tavernarakis, N., Protein synthesis and aging:elF4E and the soma vs. germline distinction. Cell Cycle,2007.6(10):p.1168-71.
51. Wilkins MR, S.J., Gooley AA, Appel RD, Humphery-Smith I, Hochstrasser DF, Williams KL, Progress with proteome projects:why all proteins expressed by a genome should be identified and how to do it Biotechnol Genet Eng Rev,1996.13:p.19-50.
52. Yates, J.R.,3rd, Mass spectral analysis in proteomics. Annu Rev Biophys Biomol Struct,2004.33: p.297-316.
53. McDonald, W.H. and J.R. Yates,3rd, Shotgun proteomics and biomarker discovery. Dis Markers, 2002.18(2):p.99-105.
54. Beynon, R.J. and J.M. Pratt, Metabolic labeling of proteins for proteomics. Mol Cell Proteomics, 2005.4(7):p.857-72.
55. Pratt, J.M., et al., Stable isotope labelling in vivo as an aid to protein identification in peptide mass fingerprinting. Proteomics,2002.2(2):p.157-63.
56. Pratt, J.M., et al., Dynamics of protein turnover, a missing dimension in proteomics. Mol Cell Proteomics,2002.1(8):p.579-91.
57. Zhang, R. and F.E. Regnier, Minimizing resolution of isotopically coded peptides in comparative proteomics. J Proteome Res,2002.1(2):p.139-47.
58. Zhang, R., et al., Controlling deuterium isotope effects in comparative proteomics. Anal Chem, 2002.74(15):p.3662-9.
59. Zhang, R., et al., Fractionation of isotopically labeled peptides in quantitative proteomics. Anal Chem,2001.73(21):p.5142-9.
60. Botstein, D., S.A. Chervitz, and J.M. Cherry, Yeast as a model organism. Science,1997. 277(5330):p.1259-60.
61. Williams, N., Yeast genome sequence ferments new research. Science,1996.272(5261):p.481.
62. Jiang, H. and A.M. English, Quantitative analysis of the yeast proteome by incorporation of isotopically labeled leucine. J Proteome Res,2002.1(4):p.345-50.
63. Gruhler, A., et al., Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway. Mol Cell Proteomics,2005.4(3):p.310-27.
64. Brenner, S., The genetics of Caenorhabditis elegans. Genetics,1974.77(1):p.71-94.
65. Waterston, R.H., H.F. Epstein, and S. Brenner, Paramyosin of Caenorhabditis elegans. J Mol Biol, 1974.90(2):p.285-90.
66. Herman, R.K., D.G. Albertson, and S. Brenner, Chromosome rearrangements in Caenorhabditis elegans. Genetics,1976.83(1):p.91-105.
67. Hodgkin, J.A. and S. Brenner, Mutations causing transformation of sexual phenotype in the nematode Caenorhabditis elegans. Genetics,1977.86(2 Pt.1):p.275-87.
68. Nusslein-Volhard, C. and E. Wieschaus, Mutations affecting segment number and polarity in Drosophila. Nature,1980.287(5785):p.795-801.
69. Chen, Y.J., et al., Tramtrack69 is required for the early repression of tailless expression. Mech Dev,2002.116(1-2):p.75-83.
70. Lanzuolo, C. and V. Orlando, Memories from the Polycomb Group Proteins. Annu Rev Genet, 2012.
71. Ye, Y. and M.E. Fortini, Apoptotic activities of wild-type and Alzheimer's disease-related mutant presenilins in Drosophila melanogaster. J Cell Biol,1999.146(6):p.1351-64.
72. Feany, M.B., Studying human neurodegenerative diseases in flies and worms. J Neuropathol Exp Neurol,2000.59(10):p.847-56.
73. Zhang, Y.Q., et al., Drosophila fragile X-related gene regulates the MAP1B homolog Futsch to control synoptic structure and function. Cell,2001.107(5):p.591-603.
74. Steffan, J.S., et al., SUMO modification of Huntingtin and Huntington's disease pathology. Science,2004.304(5667):p.100-4.
75. Liu, J., et al., Analysis of Drosophila segmentation network identifies a JNK pathway factor overexpressed in kidney cancer. Science,2009.323(5918):p.1218-22.
76. Crackower, M.A., et al., Angiotensin-converting enzyme 2 is an essential regulator of heart function. Nature,2002.417(6891):p.822-8.
77. Abeliovich, A., Parkinson's disease:Mitochondrial damage control. Nature,2010.463(7282):p. 744-5.
78. Clark, I.E., et al., Drosophila pinkl is required for mitochondrial function and interacts genetically with parkin. Nature,2006.441(7097):p.1162-6.
79. Deppmann, C.D., et al., A model for neuronal competition during development. Science,2008. 320(5874):p.369-73.
80. Liu, H., et al., Augmented Wnt signaling in a mammalian model of accelerated aging. Science, 2007.317(5839):p.803-6.
81. Graumann,J., et al., Stable isotope labeling by amino acids in cell culture (SILAC) and proteome quantitation of mouse embryonic stem cells to a depth of 5,111 proteins. Mol Cell Proteomics, 2008.7(4):p.672-83.
82. Pan, C., et al., Quantitative phosphoproteome analysis of a mouse liver cell line reveals specificity of phosphatase inhibitors. Proteomics,2008.8(21):p.4534-46.
83. Walther, D.M. and M. Mann, Accurate quantification of more than 4000 mouse tissue proteins reveals minimal proteome changes during aging. Mol Cell Proteomics,2011.10(2):p. M11O 004523.
84. Liao, L., et al., Quantitative proteomic analysis of primary neurons reveals diverse changes in synoptic protein content in fmrl knockout mice. Proc Natl Acad Sci U S A,2008.105(40):p. 15281-6.
85. Huang, T.C., et al., Regulation of lipid metabolism by Dicer revealed through SILAC mice. J Proteome Res,2012.11(4):p.2193-205.
86. Verleyen, P., et al., Identification of new immune induced molecules in the haemolymph of Drosophila melanogaster by 2D-nanoLC MS/MS. J Insect Physiol,2006.52(4):p.379-88.
87. Unlu, M., M.E. Morgan, and J.S. Minden, Difference gel electrophoresis:a single gel method for detecting changes in protein extracts. Electrophoresis,1997.18(11):p.2071-7.
88. Yang, C. and W. Yu, A regularized method for peptide quantification. J Proteome Res,2010.9(5): p.2705-12.
89. Kohlbacher,O., et al., TOPP--the OpenMS proteomics pipeline. Bioinformatics,2007.23(2):p. el91-7.
90. Bridges, S.M., et al., ProtQuant:a tool for the label-free quantification of MudPIT proteomics data. BMC Bioinformatics,2007.8 Suppl 7:p. S24.
91. Garber, E.A., S. Wehrli, and T.C. Hollocher,15N-tracer and NMR studies on the pathway of denitrification. Evidence against trioxodinitrate but for nitroxyl as an intermediate. J Biol Chem, 1983.258(6):p.3587-91.
92. Heine, W., et al., [15N tracer kinetics of the utilization of yeast nitrogen]. Nahrung,1989.33(4): p.349-54.
93. Smith, R.D., et al., An accurate mass tag strategy for quantitative and high-throughput proteome measurements. Proteomics,2002.2(5):p.513-23.
94. Huege, J., et al., Towards high throughput metabolic flux analysis in plants. Mol Biosyst,2012. 8(10):p.2466-9.
95. Voges, R. and S. Noack, Quantification of proteome dynamics in Corynebacterium glutamicum by (15)N-labeling and selected reaction monitoring. J Proteomics,2012.75(9):p.2660-9.
96. Khan, Z., et al., Accurate proteome-wide protein quantification from high-resolution 15N mass spectra. Genome Biol,2011.12(12):p. R122.
97. Zhao, Y., W.N. Lee, and G.G. Xiao, Quantitative proteomics and biomarker discovery in human cancer. Expert Rev Proteomics,2009.6(2):p.115-8.
98. Miyagi, M. and K.C. Rao, Proteolytic 180-labeling strategies for quantitative proteomics. Mass Spectrom Rev,2007.26(1):p.121-36.
99. Shadforth, I.P., et al., i-Tracker:for quantitative proteomics using iTRAQ. BMC Genomics,2005. 6:p.145.
100.Pichler, P., et al., Peptide labeling with isobaric tags yields higher identification rates using iTRAQ 4-plex compared to TMT 6-plex and iTRAQ 8-plex on LTQ Orbitrap. Anal Chem,2010. 82(15):p.6549-58.
101.Jadaliha, M., et al., Quantitative proteomic analysis of human embryonic stem cell differentiation by 8-plex iTRAQ labelling. PLoS One,2012.7(6):p. e38532.
102.Yang, L.S., et al., iTRAQ-based quantitative proteomic analysis for identification of oligodendroglioma biomarkers related with loss of heterozygosity on chromosomal arm lp. J Proteomics,2012.
2. Ong, S.E., et al., Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics,2002.1(5):p.376-86.
3. Yu, K.H., et al., Stable isotope dilution multidimensional liquid chromatography-tandem mass spectrometry for pancreatic cancer serum biomarker discovery. J Proteome Res,2009.8(3):p. 1565-76.
4. Iwadate, Y., Clinical proteomics in cancer research-promises and limitations of current two-dimensional gel electrophoresis. Curr Med Chem,2008.15(23):p.2393-400.
5. Mann, M., Functional and quantitative proteomics using SILAC. Nat Rev Mol Cell Biol,2006. 7(12):p.952-8.
6. Sury, M.D., J.X. Chen, and M. Selbach, The SILAC fly allows for accurate protein quantification in vivo. Mol Cell Proteomics,2010.9(10):p.2173-83.
7. de Godoy, L.M., et al., Status of complete proteome analysis by mass spectrometry:SILAC labeled yeast as a model system. Genome Biol,2006.7(6):p. R50.
8. de Godoy, L.M., et al., Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature,2008.455(7217):p.1251-4.
9. Larance, M., et al., Stable-isotope labeling with amino acids in nematodes. Nat Methods,2011. 8(10):p.849-51.
10. Xu, P., et al., Stable isotope labeling with amino acids in Drosophila for quantifying proteins and modifications. J Proteome Res,2012.11(9):p.4403-12.
11. Kruger, M., et al., SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell,2008.134(2):p.353-64.
12. Blattner, F.R., et al., The complete genome sequence of Escherichia coli K-12. Science,1997. 277(5331):p.1453-62.
13. Wang, Y.K., et al., Inverse 15N-metabolic labeling/mass spectrometry for comparative proteomics and rapid identification of protein markers/targets. Rapid Commun Mass Spectrom, 2002.16(14):p.1389-97.
14. Krijgsveld, J., et al., Metabolic labeling of C. elegans and D.melanogaster for quantitative proteomics. Nat Biotechnol,2003.21(8):p.927-31.
15. Martinovic, S., et al., Selective incorporation of isotopically labeled amino acids for identification of intact proteins on a proteome-wide level. J Mass Spectrom,2002.37(1):p.99-107.
16. Chen, X., L.M. Smith, and E.M. Bradbury, Site-specific mass tagging with stable isotopes in proteins for accurate and efficient protein identification. Anal Chem,2000.72(6):p.1134-43.
17. Bolton, E.T., P.H. Abelson, and E. Aldous, Utilization of carbon dioxide in the synthesis of nucleic acid by escherichia coli. J Biol Chem,1952.198(1):p.179-85.
18. Abelson, P.H., E.T. Bolton, and E. Aldous, Utilization of carbon dioxide in the synthesis of proteins by Escherichia coli. Ⅱ. J Biol Chem,1952.198(1):p.173-8.
19. Choudhary, K., et al., Method for estimating the isotopic distributions of metabolically labeled proteins by MALDI-TOFMS:application to NMR samples. Anal Chem,2006.78(15):p.5419-23.
20. Ding, C., et al., Quantitative analysis of cohesin complex stoichiometry and SMC3 modification-dependent protein interactions. J Proteome Res,2011.10(8):p.3652-9.
21. Carroll, K.M., et al.. Absolute quantification of the glycolytic pathway in yeast:deployment of a complete QconCATapproach. Mol Cell Proteomics,2011.10(12):p. M111 007633.
22. Covert, M.W. and B.O. Palsson, Transcriptional regulation in constraints-based metabolic models of Escherichia coll J Biol Chem,2002.277(31):p.28058-64.
23. Kolisnychenko, V., et al., Engineering a reduced Escherichia coli genome. Genome Res,2002. 12(4):p.640-7.
24. Cohen, S.N., et al., Construction of biologically functional bacterial plasmids in vitro. Proc Natl Acad Sci U S A,1973.70(11):p.3240-4.
25. Edwards, M.C., et al., Addition of genes for cellobiase and pectinolytic activity in Escherichia coli for fuel ethanol production from pectin-rich lignocellulosic biomass. Appl Environ Microbiol, 2011.77(15):p.5184-91.
26. Schwartz, Y., et al., Effects of cholesterol and nuclear hormone receptor agonists on the production of transforming growth factor-beta in macrophages. Bull Exp Biol Med,2009.148(3): p.406-9.
27. Kuznetsova, T.V., et al., Producing human mechano growth factor (MGF) in Escherichia coli. Protein Expr Purif,2008.58(1):p.70-7.
28. Li, X.Y., H.L. Li, and G.Y. Zheng, [Construction and expression of recombinant cecropin B-binding site of luteinizing hormone releasing hormone gene and its anticancer function]. Zhonghua Fu Chan Ke Za Zhi,2007.42(7):p.477-81.
29. Redwan el, R.M., et al., Synthesis of the human insulin gene:protein expression, scaling up and bioactivity. Prep Biochem Biotechnol,2008.38(1):p.24-39.
30. Josefsberg, J.O. and B. Buckland, Vaccine process technology. Biotechnol Bioeng,2012.109(6): p.1443-60.
31. Kamionka, M., Engineering of therapeutic proteins production in Escherichia coli. Curr Pharm Biotechnol,2011.12(2):p.268-74.
32. Venkataraman, S., et al., Synthesis and biological activity of some novel pyrazolines. Acta Pol Pharm,2010.67(4):p.361-6.
33. Le, Y., et al., An approach to the production of soluble protein from a fungal gene encoding an aggregation-prone xylanase in Escherichia coli. PLoS One,2011.6(4):p. e18489.
34. Weyant, R.G., "The ferment of knowledge:studies in the historiography of eighteenth-century science". Essay review. J Hist Behav Sci,1983.19(3):p.226-33.
35. lacono, V.J., Periodontics 1988. A discipline in ferment. N Y State Dent J,1987.53(10):p.24-9.
36. Studier, F.W., et al., Use of 17 RNA polymerase to direct expression of cloned genes. Methods Enzymol,1990.185:p.60-89.
37. Han, M.J. and S.Y. Lee, The Escherichia coli proteome:past, present, and future prospects. Microbiol Mol Biol Rev,2006.70(2):p.362-439.
38. Eron, L. and R. Block, Mechanism of initiation and repression of in vitro transcription of the lac operon of Escherichia coli. Proc Natl Acad Sci U S A,1971.68(8):p.1828-32.
39. Fried, M.G. and D.M. Crothers, Equilibrium studies of the cyclic AMP receptor protein-DNA interaction. J Mol Biol,1984.172(3):p.241-62.
40. Dubendorff, J.W. and F.W. Studier, Creation of a T7 autogene. Cloning and expression of the gene for bacteriophage T7 RNA polymerase under control of its cognate promoter. J Mol Biol, 1991.219(1):p.61-8.
41. Grodberg, J., et al., Complete nucleotide sequence and deduced amino acid sequence of the ompT gene of Escherichia coli K-12. Nucleic Acids Res,1988.16(3):p.1209.
42. Oh, M.K. and J.C. Liao, DNA microarray detection of metabolic responses to protein overproduction in Escherichia coli. Metab Eng,2000.2(3):p.201-9.
43. Tomoyasu, T., et al., Levels of DnaK and DnaJ provide tight control of heat shock gene expression and protein repair in Escherichia coli. Mol Microbiol,1998.30(3):p.567-81.
44. Hoffmann, F. and U. Rinas, Stress induced by recombinant protein production in Escherichia coli. Adv Biochem Eng Biotechnol,2004.89:p.73-92.
45. Caspeta, L., et al., The effect of heating rate on Escherichia coli metabolism, physiological stress, transcriptional response, and production of temperature-induced recombinant protein:a scale-down study. Biotechnol Bioeng,2009.102(2):p.468-82.
46. Heyland, J., L.M. Blank, and A. Schmid, Quantification of metabolic limitations during recombinant protein production in Escherichia coli. J Biotechnol,2011.155(2):p.178-84.
47. Cheng, C.H. and W.C. Lee, Protein solubility and differential proteomic profiling of recombinant Escherichia coli overexpressing double-tagged fusion proteins. Microb Cell Fact,2010.9:p.63.
48. Wang, W., et al., Proteome analysis of a recombinant Bacillus megaterium strain during heterologous production of a glucosyltransferase. Proteome Sci,2005.3:p.4.
49. Buttgereit, F., G.R. Burmester, and M.D. Brand, Therapeutically targeting lymphocyte energy metabolism by high-dose glucocorticoids. Biochem Pharmacol,2000.59(6):p.597-603.
50. Tavernarakis, N., Protein synthesis and aging:elF4E and the soma vs. germline distinction. Cell Cycle,2007.6(10):p.1168-71.
51. Wilkins MR, S.J., Gooley AA, Appel RD, Humphery-Smith I, Hochstrasser DF, Williams KL, Progress with proteome projects:why all proteins expressed by a genome should be identified and how to do it Biotechnol Genet Eng Rev,1996.13:p.19-50.
52. Yates, J.R.,3rd, Mass spectral analysis in proteomics. Annu Rev Biophys Biomol Struct,2004.33: p.297-316.
53. McDonald, W.H. and J.R. Yates,3rd, Shotgun proteomics and biomarker discovery. Dis Markers, 2002.18(2):p.99-105.
54. Beynon, R.J. and J.M. Pratt, Metabolic labeling of proteins for proteomics. Mol Cell Proteomics, 2005.4(7):p.857-72.
55. Pratt, J.M., et al., Stable isotope labelling in vivo as an aid to protein identification in peptide mass fingerprinting. Proteomics,2002.2(2):p.157-63.
56. Pratt, J.M., et al., Dynamics of protein turnover, a missing dimension in proteomics. Mol Cell Proteomics,2002.1(8):p.579-91.
57. Zhang, R. and F.E. Regnier, Minimizing resolution of isotopically coded peptides in comparative proteomics. J Proteome Res,2002.1(2):p.139-47.
58. Zhang, R., et al., Controlling deuterium isotope effects in comparative proteomics. Anal Chem, 2002.74(15):p.3662-9.
59. Zhang, R., et al., Fractionation of isotopically labeled peptides in quantitative proteomics. Anal Chem,2001.73(21):p.5142-9.
60. Botstein, D., S.A. Chervitz, and J.M. Cherry, Yeast as a model organism. Science,1997. 277(5330):p.1259-60.
61. Williams, N., Yeast genome sequence ferments new research. Science,1996.272(5261):p.481.
62. Jiang, H. and A.M. English, Quantitative analysis of the yeast proteome by incorporation of isotopically labeled leucine. J Proteome Res,2002.1(4):p.345-50.
63. Gruhler, A., et al., Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway. Mol Cell Proteomics,2005.4(3):p.310-27.
64. Brenner, S., The genetics of Caenorhabditis elegans. Genetics,1974.77(1):p.71-94.
65. Waterston, R.H., H.F. Epstein, and S. Brenner, Paramyosin of Caenorhabditis elegans. J Mol Biol, 1974.90(2):p.285-90.
66. Herman, R.K., D.G. Albertson, and S. Brenner, Chromosome rearrangements in Caenorhabditis elegans. Genetics,1976.83(1):p.91-105.
67. Hodgkin, J.A. and S. Brenner, Mutations causing transformation of sexual phenotype in the nematode Caenorhabditis elegans. Genetics,1977.86(2 Pt.1):p.275-87.
68. Nusslein-Volhard, C. and E. Wieschaus, Mutations affecting segment number and polarity in Drosophila. Nature,1980.287(5785):p.795-801.
69. Chen, Y.J., et al., Tramtrack69 is required for the early repression of tailless expression. Mech Dev,2002.116(1-2):p.75-83.
70. Lanzuolo, C. and V. Orlando, Memories from the Polycomb Group Proteins. Annu Rev Genet, 2012.
71. Ye, Y. and M.E. Fortini, Apoptotic activities of wild-type and Alzheimer's disease-related mutant presenilins in Drosophila melanogaster. J Cell Biol,1999.146(6):p.1351-64.
72. Feany, M.B., Studying human neurodegenerative diseases in flies and worms. J Neuropathol Exp Neurol,2000.59(10):p.847-56.
73. Zhang, Y.Q., et al., Drosophila fragile X-related gene regulates the MAP1B homolog Futsch to control synoptic structure and function. Cell,2001.107(5):p.591-603.
74. Steffan, J.S., et al., SUMO modification of Huntingtin and Huntington's disease pathology. Science,2004.304(5667):p.100-4.
75. Liu, J., et al., Analysis of Drosophila segmentation network identifies a JNK pathway factor overexpressed in kidney cancer. Science,2009.323(5918):p.1218-22.
76. Crackower, M.A., et al., Angiotensin-converting enzyme 2 is an essential regulator of heart function. Nature,2002.417(6891):p.822-8.
77. Abeliovich, A., Parkinson's disease:Mitochondrial damage control. Nature,2010.463(7282):p. 744-5.
78. Clark, I.E., et al., Drosophila pinkl is required for mitochondrial function and interacts genetically with parkin. Nature,2006.441(7097):p.1162-6.
79. Deppmann, C.D., et al., A model for neuronal competition during development. Science,2008. 320(5874):p.369-73.
80. Liu, H., et al., Augmented Wnt signaling in a mammalian model of accelerated aging. Science, 2007.317(5839):p.803-6.
81. Graumann,J., et al., Stable isotope labeling by amino acids in cell culture (SILAC) and proteome quantitation of mouse embryonic stem cells to a depth of 5,111 proteins. Mol Cell Proteomics, 2008.7(4):p.672-83.
82. Pan, C., et al., Quantitative phosphoproteome analysis of a mouse liver cell line reveals specificity of phosphatase inhibitors. Proteomics,2008.8(21):p.4534-46.
83. Walther, D.M. and M. Mann, Accurate quantification of more than 4000 mouse tissue proteins reveals minimal proteome changes during aging. Mol Cell Proteomics,2011.10(2):p. M11O 004523.
84. Liao, L., et al., Quantitative proteomic analysis of primary neurons reveals diverse changes in synoptic protein content in fmrl knockout mice. Proc Natl Acad Sci U S A,2008.105(40):p. 15281-6.
85. Huang, T.C., et al., Regulation of lipid metabolism by Dicer revealed through SILAC mice. J Proteome Res,2012.11(4):p.2193-205.
86. Verleyen, P., et al., Identification of new immune induced molecules in the haemolymph of Drosophila melanogaster by 2D-nanoLC MS/MS. J Insect Physiol,2006.52(4):p.379-88.
87. Unlu, M., M.E. Morgan, and J.S. Minden, Difference gel electrophoresis:a single gel method for detecting changes in protein extracts. Electrophoresis,1997.18(11):p.2071-7.
88. Yang, C. and W. Yu, A regularized method for peptide quantification. J Proteome Res,2010.9(5): p.2705-12.
89. Kohlbacher,O., et al., TOPP--the OpenMS proteomics pipeline. Bioinformatics,2007.23(2):p. el91-7.
90. Bridges, S.M., et al., ProtQuant:a tool for the label-free quantification of MudPIT proteomics data. BMC Bioinformatics,2007.8 Suppl 7:p. S24.
91. Garber, E.A., S. Wehrli, and T.C. Hollocher,15N-tracer and NMR studies on the pathway of denitrification. Evidence against trioxodinitrate but for nitroxyl as an intermediate. J Biol Chem, 1983.258(6):p.3587-91.
92. Heine, W., et al., [15N tracer kinetics of the utilization of yeast nitrogen]. Nahrung,1989.33(4): p.349-54.
93. Smith, R.D., et al., An accurate mass tag strategy for quantitative and high-throughput proteome measurements. Proteomics,2002.2(5):p.513-23.
94. Huege, J., et al., Towards high throughput metabolic flux analysis in plants. Mol Biosyst,2012. 8(10):p.2466-9.
95. Voges, R. and S. Noack, Quantification of proteome dynamics in Corynebacterium glutamicum by (15)N-labeling and selected reaction monitoring. J Proteomics,2012.75(9):p.2660-9.
96. Khan, Z., et al., Accurate proteome-wide protein quantification from high-resolution 15N mass spectra. Genome Biol,2011.12(12):p. R122.
97. Zhao, Y., W.N. Lee, and G.G. Xiao, Quantitative proteomics and biomarker discovery in human cancer. Expert Rev Proteomics,2009.6(2):p.115-8.
98. Miyagi, M. and K.C. Rao, Proteolytic 180-labeling strategies for quantitative proteomics. Mass Spectrom Rev,2007.26(1):p.121-36.
99. Shadforth, I.P., et al., i-Tracker:for quantitative proteomics using iTRAQ. BMC Genomics,2005. 6:p.145.
100.Pichler, P., et al., Peptide labeling with isobaric tags yields higher identification rates using iTRAQ 4-plex compared to TMT 6-plex and iTRAQ 8-plex on LTQ Orbitrap. Anal Chem,2010. 82(15):p.6549-58.
101.Jadaliha, M., et al., Quantitative proteomic analysis of human embryonic stem cell differentiation by 8-plex iTRAQ labelling. PLoS One,2012.7(6):p. e38532.
102.Yang, L.S., et al., iTRAQ-based quantitative proteomic analysis for identification of oligodendroglioma biomarkers related with loss of heterozygosity on chromosomal arm lp. J Proteomics,2012.