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Comparative analysis of proteome and transcriptome variation in mouse.

Ghazalpour A, Bennett B, Petyuk VA, Orozco L, Hagopian R, Mungrue IN, Farber CR, Sinsheimer J, Kang HM, Furlotte N, Park CC, Wen PZ, Brewer H, Weitz K, Camp DG, Pan C, Yordanova R, Neuhaus I, Tilford C, Siemers N, Gargalovic P, Eskin E, Kirchgessner T, Smith DJ, Smith RD, Lusis AJ - PLoS Genet. (2011)

Bottom Line: For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation.Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels.In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America. aghazalp@ucla.edu

ABSTRACT
The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

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Relationships between the peptide data and transcript data with clinical traits and biological pathways.A) Correlations of transcriptome and proteome with clinical traits. A scatter plot of correlation coefficients between 607 probesets and 1343 peptides with 42 clinical traits (peptide-trait correlations are plotted on the x-axis and probeset-trait correlations are plotted on the y-axis). Red points are those correlations which were significant for transcripts only, green points are those correlations which were significant for protein data only and black points are those which were not significant in either of the two datasets. B) Concordance of transcripts and proteins in 115 KEGG biological pathways.
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pgen-1001393-g005: Relationships between the peptide data and transcript data with clinical traits and biological pathways.A) Correlations of transcriptome and proteome with clinical traits. A scatter plot of correlation coefficients between 607 probesets and 1343 peptides with 42 clinical traits (peptide-trait correlations are plotted on the x-axis and probeset-trait correlations are plotted on the y-axis). Red points are those correlations which were significant for transcripts only, green points are those correlations which were significant for protein data only and black points are those which were not significant in either of the two datasets. B) Concordance of transcripts and proteins in 115 KEGG biological pathways.

Mentions: In light of the modest correlation observed between the transcript and protein pairs, we examined the relationship of each of these two datasets with clinical traits. In our HMDP panel, we have previously measured a set of 42, some interrelated, metabolic traits (see Materials and Methods). In this analysis, in order to make a direct comparison across the two datasets, we once again focused on the 396 genes for which we had at least one peptide and one transcript measurement. At the 5% false discovery rate, we observed that three quarters of probesets (457 from the total 607) significantly correlated with at least one of the clinical traits. In contrast, at the same false discovery rate, only 28% of the total peptides (380 out of 1342) showed significant correlation with at least one of the 42 phenotypes. Despite the fact that the starting number of peptides was twice the number of probesets (1342 and 607), the total number of significant correlations for the peptides was only about half the number found for the probesets (2206 vs 1107). The same biased pattern was also observed at other statistical thresholds as shown in Table 1. In addition to probeset-pair analysis, we also carried a similar analysis at the gene level to estimate what fraction of starting genes (396 total genes) a) exhibit consistent relationship with clinical traits both at the transcript level and the protein level b) exhibit trait relationships unique to either of the two molecular phenotypes. From the 396 genes, 325 genes had at least one significant correlation at the 5%FDR with clinical phenotypes and 162 had at least one significant correlation with phenotypes at the protein level (Table 1). At the transcript level, the total number of significant correlations amounted to 1781 vs 556 found at the protein level. From these, 234 relations were found to be common for transcript and protein of the same genes and 1547 were unique to transcripts only (Figure 5A). Despite this overwhelming bias toward better correlation of transcripts, we also found 322 unique relations at the protein level (Table 1 and Figure 5A). Altogether, about half the significant protein-trait correlations also exhibited transcript-trait correlations, but only 15% of the significant transcript-trait correlations exhibited corresponding protein-trait correlations.


Comparative analysis of proteome and transcriptome variation in mouse.

Ghazalpour A, Bennett B, Petyuk VA, Orozco L, Hagopian R, Mungrue IN, Farber CR, Sinsheimer J, Kang HM, Furlotte N, Park CC, Wen PZ, Brewer H, Weitz K, Camp DG, Pan C, Yordanova R, Neuhaus I, Tilford C, Siemers N, Gargalovic P, Eskin E, Kirchgessner T, Smith DJ, Smith RD, Lusis AJ - PLoS Genet. (2011)

Relationships between the peptide data and transcript data with clinical traits and biological pathways.A) Correlations of transcriptome and proteome with clinical traits. A scatter plot of correlation coefficients between 607 probesets and 1343 peptides with 42 clinical traits (peptide-trait correlations are plotted on the x-axis and probeset-trait correlations are plotted on the y-axis). Red points are those correlations which were significant for transcripts only, green points are those correlations which were significant for protein data only and black points are those which were not significant in either of the two datasets. B) Concordance of transcripts and proteins in 115 KEGG biological pathways.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3111477&req=5

pgen-1001393-g005: Relationships between the peptide data and transcript data with clinical traits and biological pathways.A) Correlations of transcriptome and proteome with clinical traits. A scatter plot of correlation coefficients between 607 probesets and 1343 peptides with 42 clinical traits (peptide-trait correlations are plotted on the x-axis and probeset-trait correlations are plotted on the y-axis). Red points are those correlations which were significant for transcripts only, green points are those correlations which were significant for protein data only and black points are those which were not significant in either of the two datasets. B) Concordance of transcripts and proteins in 115 KEGG biological pathways.
Mentions: In light of the modest correlation observed between the transcript and protein pairs, we examined the relationship of each of these two datasets with clinical traits. In our HMDP panel, we have previously measured a set of 42, some interrelated, metabolic traits (see Materials and Methods). In this analysis, in order to make a direct comparison across the two datasets, we once again focused on the 396 genes for which we had at least one peptide and one transcript measurement. At the 5% false discovery rate, we observed that three quarters of probesets (457 from the total 607) significantly correlated with at least one of the clinical traits. In contrast, at the same false discovery rate, only 28% of the total peptides (380 out of 1342) showed significant correlation with at least one of the 42 phenotypes. Despite the fact that the starting number of peptides was twice the number of probesets (1342 and 607), the total number of significant correlations for the peptides was only about half the number found for the probesets (2206 vs 1107). The same biased pattern was also observed at other statistical thresholds as shown in Table 1. In addition to probeset-pair analysis, we also carried a similar analysis at the gene level to estimate what fraction of starting genes (396 total genes) a) exhibit consistent relationship with clinical traits both at the transcript level and the protein level b) exhibit trait relationships unique to either of the two molecular phenotypes. From the 396 genes, 325 genes had at least one significant correlation at the 5%FDR with clinical phenotypes and 162 had at least one significant correlation with phenotypes at the protein level (Table 1). At the transcript level, the total number of significant correlations amounted to 1781 vs 556 found at the protein level. From these, 234 relations were found to be common for transcript and protein of the same genes and 1547 were unique to transcripts only (Figure 5A). Despite this overwhelming bias toward better correlation of transcripts, we also found 322 unique relations at the protein level (Table 1 and Figure 5A). Altogether, about half the significant protein-trait correlations also exhibited transcript-trait correlations, but only 15% of the significant transcript-trait correlations exhibited corresponding protein-trait correlations.

Bottom Line: For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation.Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels.In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America. aghazalp@ucla.edu

ABSTRACT
The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

Show MeSH
Related in: MedlinePlus