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Chapter 12 : in the Proteomics Era

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Abstract:

More than 60 years ago, Swedish biochemist Pehr Edman introduced the first technique for single peptide sequencing ( ). The underlying principle involves a phenylisothiocyanate reaction with the free N-terminal amino group of a given peptide. The modified amino acid is then cleaved off and identified by chromatography or electrophoresis. Further cycles of the same process allow consecutive determination of up to 30 amino acids and thus the N-terminal amino acid sequence of a polypeptide. Major drawbacks of the so-called Edman degradation are that (i) N-terminal residues of a polypeptide must be freely accessible and unmodified and (ii) disulfide bonds cannot be directly identified. Nevertheless, Edman paved the way to modern protein identification. Proteomics has come a long way and is currently in transition from pure basic research to medical application. The reasons are obvious. The genome can be viewed as the blueprint of a cell; the transcriptome encompasses the first step, transcribing parts of the genome, which is active at a given time point. The proteome, however, describes the sum of the working parts of a cell. Thus, proteomics is the most direct platform for measuring cellular activity. Importantly, both during transcription from DNA to RNA and during translation from RNA to protein, changes occur, which can multiply the different variants of the encoding gene. These include transcription errors, epigenetic changes, and other events, as well as translation errors, posttranslational modifications such as phosphorylation, and differential modes of protein folding. These changes increase complexity markedly, thereby allowing the most direct and most precise insight into a cell.

Citation: Gengenbacher M, Mouritsen J, Schubert O, Aebersold R, Kaufmann S. 2014. in the Proteomics Era, p 241-260. In Hatfull G, Jacobs W (ed), Molecular Genetics of Mycobacteria, Second Edition. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.MGM2-0020-2013

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Figures

Image of Figure 1
Figure 1

Functional annotation and abundance distribution of the proteome. Distribution of functional classes of the proteome as annotated in TubercuList v2.6 release 27 (March 2013), updated with functional annotation for many of the “conserved hypotheticals” and “unknowns” ( ). Distribution of SRM-based absolute label-free abundance estimates for 2,195 proteins of H37Rv in a 1:1 mix of exponential and stationary cultures in rich medium ( ). The concentration is given in femtomoles per microgram of extracted protein. doi:10.1128/microbiolspec.MGM2-0020-2013.f1

Citation: Gengenbacher M, Mouritsen J, Schubert O, Aebersold R, Kaufmann S. 2014. in the Proteomics Era, p 241-260. In Hatfull G, Jacobs W (ed), Molecular Genetics of Mycobacteria, Second Edition. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.MGM2-0020-2013
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Figure 2

Technology-advanced proteomics. Early approaches identified only a small number of proteins. Combination of 2D-GE and untargeted shotgun mass spectrometry (MS), as well as extensive prefractionation of proteins or peptides, improved identification rates significantly. The latest targeted proteomics techniques, namely SRM, demonstrated identification of virtually all expressed proteins at given states in unfractionated cultures ( ). doi:10.1128/microbiolspec.MGM2-0020-2013.f2

Citation: Gengenbacher M, Mouritsen J, Schubert O, Aebersold R, Kaufmann S. 2014. in the Proteomics Era, p 241-260. In Hatfull G, Jacobs W (ed), Molecular Genetics of Mycobacteria, Second Edition. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.MGM2-0020-2013
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Figure 3

Uncovering the proteome. Most of the current knowledge on proteomics has been generated by comparison of bacterial cultures, i.e., rich medium versus hypoxia or nutrient deprivation, conditions that might experience . Subfractions such as culture supernatant, cell wall debris, or the bacterial cytosol were separated by 2D-GE and subsequently analyzed by MS techniques. Study of the proteome during infection remained difficult: due to the overwhelming protein abundance of the host as compared to the pathogen, enrichment for bacterial fractions was required prior to analysis by 2D-GE and shotgun MS. With the availability of the complete proteome libraries for ( ) and the human host (U. Kusebauch et al., in preparation), SRM will allow simultaneous proteome analysis of the pathogen and the host in complex mixtures. doi:10.1128/microbiolspec.MGM2-0020-2013.f3

Citation: Gengenbacher M, Mouritsen J, Schubert O, Aebersold R, Kaufmann S. 2014. in the Proteomics Era, p 241-260. In Hatfull G, Jacobs W (ed), Molecular Genetics of Mycobacteria, Second Edition. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.MGM2-0020-2013
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Figure 4

The Mtb Proteome Library. Proteome mapping: After harvesting bacterial cultures, proteins were extracted and digested with the proteolytic enzyme trypsin. The resulting peptides were fractionated using off-gel isoelectric focusing to reduce sample complexity, and each fraction was analyzed by shotgun MS. The peptide and protein identifications, as well as the corresponding spectra, can be browsed interactively in the PeptideAtlas database (http://www.PeptideAtlas.org). Proteome Library generation: From the collected data, the most MS-suited, unique peptides were selected for every annotated protein of . For proteins that had never been observed previously, representative peptides were predicted. The peptides were synthesized, pooled, and analyzed in SRM-triggered MS2 mode (SRM-MS2). From the resulting spectra the most intense fragment ions, as well as the chromatographic retention times, can be extracted. These MS coordinates, called SRM assays, constitute the synthetic Mtb Proteome Library and can be downloaded from the SRMAtlas database (http://www.SRMAtlas.org). Proteome Library validation: The SRM assays in the synthetic Mtb Proteome Library were validated for the detection of proteins in unfractionated mycobacterial lysates by SRM. The resulting quantitative SRM traces and statistical scores can be viewed in the PASSEL database (http://www.PeptideAtlas.org/passel). Reprinted from reference with permission from Elsevier. doi:10.1128/microbiolspec.MGM2-0020-2013.f4

Citation: Gengenbacher M, Mouritsen J, Schubert O, Aebersold R, Kaufmann S. 2014. in the Proteomics Era, p 241-260. In Hatfull G, Jacobs W (ed), Molecular Genetics of Mycobacteria, Second Edition. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.MGM2-0020-2013
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Figure 5

SRM assay specificity in mycobacteria or host background. The theoretical specificity of SRM assays determined by the SRMCollider algorithm is shown as a cumulative plot of the number of peptides that can be uniquely identified with a given number of peptide-fragment ion pairs selected with decreasing intensity. Only background peptides with a chromatographic retention time close to that of the target peptide are considered as interfering background. The solid line indicates mycobacterial background. The dashed line indicates human background. Reprinted from reference with permission from Elsevier. doi:10.1128/microbiolspec.MGM2-0020-2013.f5

Citation: Gengenbacher M, Mouritsen J, Schubert O, Aebersold R, Kaufmann S. 2014. in the Proteomics Era, p 241-260. In Hatfull G, Jacobs W (ed), Molecular Genetics of Mycobacteria, Second Edition. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.MGM2-0020-2013
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Tables

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Table 1

Potential of proteomics for design of TB intervention measures

Citation: Gengenbacher M, Mouritsen J, Schubert O, Aebersold R, Kaufmann S. 2014. in the Proteomics Era, p 241-260. In Hatfull G, Jacobs W (ed), Molecular Genetics of Mycobacteria, Second Edition. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.MGM2-0020-2013

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