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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 protein levels and transcript levels.A) Histogram of correlation coefficients computed peptides and probesets representing the same gene. The median correlation coefficient is 0.27. B) Classification of correlations between probeset-peptides based on signal to noise ratio in the peptide data (larger signal to noise depicts less technical variation in the peptide measurement).
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pgen-1001393-g003: Relationships between protein levels and transcript levels.A) Histogram of correlation coefficients computed peptides and probesets representing the same gene. The median correlation coefficient is 0.27. B) Classification of correlations between probeset-peptides based on signal to noise ratio in the peptide data (larger signal to noise depicts less technical variation in the peptide measurement).

Mentions: We next examined the degree of concordance between the transcript and protein levels. For this, we compared the transcript and peptide measurements for every peptide-probeset pair that mapped to the same Ensembl gene. This “gene-level” analysis included 2010 peptide-probeset pairs (1342 peptides and 607 probesets) representing 396 Ensembl genes. Figure 3A shows the correlation coefficient distribution for these 2010 peptide-probeset pairs. Highly significant positive correlation (p-value<1e-06, r>0.46) between RNA and protein was found for 21% of the genes (85 out of 396) and ∼15% of the peptide-probeset pairs (291 out of 2010). The most significant correlation (r = 0.87) was found for the glyoxalase 1 gene (Glo1) where the peptide and transcript of this gene correlated (Figure S3). Overall, we found that the relationship between mRNA and protein levels was modest (mean r = 0.27) and for 39% of the pairs (761 of 2010) the mRNA and protein levels did not correlate significantly at the nominal 0.05 p-value threshold. Our estimate of average correlation between mRNA and protein was slightly higher than those reported in other organisms, perhaps due to recent improvements in the LC-MS technology and/or statistical power.


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 protein levels and transcript levels.A) Histogram of correlation coefficients computed peptides and probesets representing the same gene. The median correlation coefficient is 0.27. B) Classification of correlations between probeset-peptides based on signal to noise ratio in the peptide data (larger signal to noise depicts less technical variation in the peptide measurement).
© Copyright Policy
Related In: Results  -  Collection

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

pgen-1001393-g003: Relationships between protein levels and transcript levels.A) Histogram of correlation coefficients computed peptides and probesets representing the same gene. The median correlation coefficient is 0.27. B) Classification of correlations between probeset-peptides based on signal to noise ratio in the peptide data (larger signal to noise depicts less technical variation in the peptide measurement).
Mentions: We next examined the degree of concordance between the transcript and protein levels. For this, we compared the transcript and peptide measurements for every peptide-probeset pair that mapped to the same Ensembl gene. This “gene-level” analysis included 2010 peptide-probeset pairs (1342 peptides and 607 probesets) representing 396 Ensembl genes. Figure 3A shows the correlation coefficient distribution for these 2010 peptide-probeset pairs. Highly significant positive correlation (p-value<1e-06, r>0.46) between RNA and protein was found for 21% of the genes (85 out of 396) and ∼15% of the peptide-probeset pairs (291 out of 2010). The most significant correlation (r = 0.87) was found for the glyoxalase 1 gene (Glo1) where the peptide and transcript of this gene correlated (Figure S3). Overall, we found that the relationship between mRNA and protein levels was modest (mean r = 0.27) and for 39% of the pairs (761 of 2010) the mRNA and protein levels did not correlate significantly at the nominal 0.05 p-value threshold. Our estimate of average correlation between mRNA and protein was slightly higher than those reported in other organisms, perhaps due to recent improvements in the LC-MS technology and/or statistical power.

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