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Extensive mass spectrometry-based analysis of the fission yeast proteome: the Schizosaccharomyces pombe PeptideAtlas.

Gunaratne J, Schmidt A, Quandt A, Neo SP, Saraç OS, Gracia T, Loguercio S, Ahrné E, Xia RL, Tan KH, Lössner C, Bähler J, Beyer A, Blackstock W, Aebersold R - Mol. Cell Proteomics (2013)

Bottom Line: The identified proteins showed no biases in functional properties and allowed global estimation of protein abundances.Interestingly, the correlation was not equally tight for all functional categories ranging from r(s) >0.80 for proteins involved in translation to r(s) <0.45 for signal transduction proteins.In summary, the extensive and publicly available S. pombe PeptideAtlas together with the generated proteotypic peptide spectral library will be a useful resource for future targeted, in-depth, and quantitative proteomic studies on this microorganism.

View Article: PubMed Central - PubMed

Affiliation: Quantitative Proteomics Group, Institute of Molecular and Cell Biology, Agency for Science Technology and Research, 61 Biopolis Drive, Singapore 138673.

ABSTRACT
We report a high quality and system-wide proteome catalogue covering 71% (3,542 proteins) of the predicted genes of fission yeast, Schizosaccharomyces pombe, presenting the largest protein dataset to date for this important model organism. We obtained this high proteome and peptide (11.4 peptides/protein) coverage by a combination of extensive sample fractionation, high resolution Orbitrap mass spectrometry, and combined database searching using the iProphet software as part of the Trans-Proteomics Pipeline. All raw and processed data are made accessible in the S. pombe PeptideAtlas. The identified proteins showed no biases in functional properties and allowed global estimation of protein abundances. The high coverage of the PeptideAtlas allowed correlation with transcriptomic data in a system-wide manner indicating that post-transcriptional processes control the levels of at least half of all identified proteins. Interestingly, the correlation was not equally tight for all functional categories ranging from r(s) >0.80 for proteins involved in translation to r(s) <0.45 for signal transduction proteins. Moreover, many proteins involved in DNA damage repair could not be detected in the PeptideAtlas despite their high mRNA levels, strengthening the translation-on-demand hypothesis for members of this protein class. In summary, the extensive and publicly available S. pombe PeptideAtlas together with the generated proteotypic peptide spectral library will be a useful resource for future targeted, in-depth, and quantitative proteomic studies on this microorganism.

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Analysis of S. pombe protein abundance clusters and their associated physicochemical and functional properties. Protein abundances for proliferating cells (Experiment 2) were assessed using emPAI value calculation. A, all identified proteins were clustered into five major abundance categories based on protein density distribution against log emPAI value. B, number of proteins of the different abundance clusters. Bias analysis of protein length (C), pI (D), and GO-slim (E) for the high and low abundant protein clusters. *, frequency = (number of detected proteins associated with corresponding GO term within the cluster/number of total proteins in the cluster)/(number of total detected proteins associated with corresponding GO term/number of total detected proteins).
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Figure 2: Analysis of S. pombe protein abundance clusters and their associated physicochemical and functional properties. Protein abundances for proliferating cells (Experiment 2) were assessed using emPAI value calculation. A, all identified proteins were clustered into five major abundance categories based on protein density distribution against log emPAI value. B, number of proteins of the different abundance clusters. Bias analysis of protein length (C), pI (D), and GO-slim (E) for the high and low abundant protein clusters. *, frequency = (number of detected proteins associated with corresponding GO term within the cluster/number of total proteins in the cluster)/(number of total detected proteins associated with corresponding GO term/number of total detected proteins).

Mentions: Because the PeptideAtlas was generated from a set of different S. pombe samples that would complicate the interpretation of the calculated protein abundances, we estimated relative abundance levels only for proteins identified in experiment 2 from proliferating cells (Fig. 1A). Notably, this data subset accounted for the large majority of the MS data used to build the PeptideAtlas, and therefore most identified proteins could be quantified from this LC-MS dataset (supplemental Table S6). We used the emPAI calculation approach (36) and grouped these proteins according to their cellular concentrations. A good correlation (R2 = 0.78, see supplemental Table S7) with recently published absolute protein concentrations determined by fluorescence microscopy of 28 fission yeast proteins fused to yellow fluorescent protein in exponentially growing cells was observed (52). This demonstrates the following: (i) that we covered a protein concentration range of at least 3.5 orders of magnitude from 1.43 × 106 copies per cell (act1) to 600 copies per cell (cdc12) (52), and (ii) that the calculated emPAI scores can be employed as a good estimate of protein abundances in S. pombe. Additionally, we also compared the spectral counting-based emPAI values of this study with those obtained recently using more accurate MS intensity-based protein abundance estimation (22). The high number of overlapping proteins (supplemental Table S6) and the good correlation of protein abundances between the two datasets (supplemental Fig. S6, r = 0.7) further strengthen the validity of our calculated protein levels. This allowed us to cluster the quantified proteins according to their cellular concentrations into five categories as very high (proteins with abundance values greater than 90th percentile of the log (emPAI)), high (80–90% quantile of log emPAI), medium (20–80% quantile of log emPAI), low (10–20% quantile of log emPAI), and very low (abundance values less than 10th percentile of the log (emPAI)). The relative protein abundance distribution and cluster information of all quantified proteins are shown in Fig. 2, A and B.


Extensive mass spectrometry-based analysis of the fission yeast proteome: the Schizosaccharomyces pombe PeptideAtlas.

Gunaratne J, Schmidt A, Quandt A, Neo SP, Saraç OS, Gracia T, Loguercio S, Ahrné E, Xia RL, Tan KH, Lössner C, Bähler J, Beyer A, Blackstock W, Aebersold R - Mol. Cell Proteomics (2013)

Analysis of S. pombe protein abundance clusters and their associated physicochemical and functional properties. Protein abundances for proliferating cells (Experiment 2) were assessed using emPAI value calculation. A, all identified proteins were clustered into five major abundance categories based on protein density distribution against log emPAI value. B, number of proteins of the different abundance clusters. Bias analysis of protein length (C), pI (D), and GO-slim (E) for the high and low abundant protein clusters. *, frequency = (number of detected proteins associated with corresponding GO term within the cluster/number of total proteins in the cluster)/(number of total detected proteins associated with corresponding GO term/number of total detected proteins).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3675828&req=5

Figure 2: Analysis of S. pombe protein abundance clusters and their associated physicochemical and functional properties. Protein abundances for proliferating cells (Experiment 2) were assessed using emPAI value calculation. A, all identified proteins were clustered into five major abundance categories based on protein density distribution against log emPAI value. B, number of proteins of the different abundance clusters. Bias analysis of protein length (C), pI (D), and GO-slim (E) for the high and low abundant protein clusters. *, frequency = (number of detected proteins associated with corresponding GO term within the cluster/number of total proteins in the cluster)/(number of total detected proteins associated with corresponding GO term/number of total detected proteins).
Mentions: Because the PeptideAtlas was generated from a set of different S. pombe samples that would complicate the interpretation of the calculated protein abundances, we estimated relative abundance levels only for proteins identified in experiment 2 from proliferating cells (Fig. 1A). Notably, this data subset accounted for the large majority of the MS data used to build the PeptideAtlas, and therefore most identified proteins could be quantified from this LC-MS dataset (supplemental Table S6). We used the emPAI calculation approach (36) and grouped these proteins according to their cellular concentrations. A good correlation (R2 = 0.78, see supplemental Table S7) with recently published absolute protein concentrations determined by fluorescence microscopy of 28 fission yeast proteins fused to yellow fluorescent protein in exponentially growing cells was observed (52). This demonstrates the following: (i) that we covered a protein concentration range of at least 3.5 orders of magnitude from 1.43 × 106 copies per cell (act1) to 600 copies per cell (cdc12) (52), and (ii) that the calculated emPAI scores can be employed as a good estimate of protein abundances in S. pombe. Additionally, we also compared the spectral counting-based emPAI values of this study with those obtained recently using more accurate MS intensity-based protein abundance estimation (22). The high number of overlapping proteins (supplemental Table S6) and the good correlation of protein abundances between the two datasets (supplemental Fig. S6, r = 0.7) further strengthen the validity of our calculated protein levels. This allowed us to cluster the quantified proteins according to their cellular concentrations into five categories as very high (proteins with abundance values greater than 90th percentile of the log (emPAI)), high (80–90% quantile of log emPAI), medium (20–80% quantile of log emPAI), low (10–20% quantile of log emPAI), and very low (abundance values less than 10th percentile of the log (emPAI)). The relative protein abundance distribution and cluster information of all quantified proteins are shown in Fig. 2, A and B.

Bottom Line: The identified proteins showed no biases in functional properties and allowed global estimation of protein abundances.Interestingly, the correlation was not equally tight for all functional categories ranging from r(s) >0.80 for proteins involved in translation to r(s) <0.45 for signal transduction proteins.In summary, the extensive and publicly available S. pombe PeptideAtlas together with the generated proteotypic peptide spectral library will be a useful resource for future targeted, in-depth, and quantitative proteomic studies on this microorganism.

View Article: PubMed Central - PubMed

Affiliation: Quantitative Proteomics Group, Institute of Molecular and Cell Biology, Agency for Science Technology and Research, 61 Biopolis Drive, Singapore 138673.

ABSTRACT
We report a high quality and system-wide proteome catalogue covering 71% (3,542 proteins) of the predicted genes of fission yeast, Schizosaccharomyces pombe, presenting the largest protein dataset to date for this important model organism. We obtained this high proteome and peptide (11.4 peptides/protein) coverage by a combination of extensive sample fractionation, high resolution Orbitrap mass spectrometry, and combined database searching using the iProphet software as part of the Trans-Proteomics Pipeline. All raw and processed data are made accessible in the S. pombe PeptideAtlas. The identified proteins showed no biases in functional properties and allowed global estimation of protein abundances. The high coverage of the PeptideAtlas allowed correlation with transcriptomic data in a system-wide manner indicating that post-transcriptional processes control the levels of at least half of all identified proteins. Interestingly, the correlation was not equally tight for all functional categories ranging from r(s) >0.80 for proteins involved in translation to r(s) <0.45 for signal transduction proteins. Moreover, many proteins involved in DNA damage repair could not be detected in the PeptideAtlas despite their high mRNA levels, strengthening the translation-on-demand hypothesis for members of this protein class. In summary, the extensive and publicly available S. pombe PeptideAtlas together with the generated proteotypic peptide spectral library will be a useful resource for future targeted, in-depth, and quantitative proteomic studies on this microorganism.

Show MeSH
Related in: MedlinePlus