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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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Related in: MedlinePlus

Isoform-specific analysis of peptide data. A) An example of differential regulation of isoforms detected in the LC-MS data. Top panel, comparison of similarity in expression variation of 20 peptides measured for Acox1. Grey plots illustrate the expression variation among inbred mice for 19 peptides which represent all four Acox1 isoforms. Red plot illustrated the expression profile of the peptide representing the isoforms skipping exon 4. Bottom panel, Ensembl genome browser's schematic representation of four Acox1 isoforms. Arrow points to Acox1-002 isoform which skips exon 4. B) Concordance between Acox1 peptides. The left boxplot depicts correlations among peptides that include Acox1-002 isoform. The right boxplot depicts correlations between the peptide mapping to exon 4 and all other peptides. The scatter points overlaid on each boxplot represent the pair-wise correlation values. C) Exon level analysis of peptide measurements by LC-MS and transcript measurements as measured by NGS in the livers of the B6 and DBA inbred strains. The black dots depict the relationships examined by comparing peptide data to microarray data and the red dots represent the highly significant relations found by peptide comparison with the microarray data. The lines depict the best fit as predicted by linear regression (black line = regression of all peptides, red line = regression of highly significant peptides).
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pgen-1001393-g004: Isoform-specific analysis of peptide data. A) An example of differential regulation of isoforms detected in the LC-MS data. Top panel, comparison of similarity in expression variation of 20 peptides measured for Acox1. Grey plots illustrate the expression variation among inbred mice for 19 peptides which represent all four Acox1 isoforms. Red plot illustrated the expression profile of the peptide representing the isoforms skipping exon 4. Bottom panel, Ensembl genome browser's schematic representation of four Acox1 isoforms. Arrow points to Acox1-002 isoform which skips exon 4. B) Concordance between Acox1 peptides. The left boxplot depicts correlations among peptides that include Acox1-002 isoform. The right boxplot depicts correlations between the peptide mapping to exon 4 and all other peptides. The scatter points overlaid on each boxplot represent the pair-wise correlation values. C) Exon level analysis of peptide measurements by LC-MS and transcript measurements as measured by NGS in the livers of the B6 and DBA inbred strains. The black dots depict the relationships examined by comparing peptide data to microarray data and the red dots represent the highly significant relations found by peptide comparison with the microarray data. The lines depict the best fit as predicted by linear regression (black line = regression of all peptides, red line = regression of highly significant peptides).

Mentions: Aside from lack of genetic variation in peptides, another plausible explanation for the lack of high correlation between peptides and probesets could be the analytic approach chosen to calculate correlations. In our study, we estimated the relationship between mRNA and proteins by examining the correlations between pairs of peptides and probesets that were annotated to the same gene without considering the isoform information for that gene. The choice of analytic approach presented here was mainly due to the limitation of the technology we used to measure the transcript levels. The probesets on the Affymetrix microarrays are designed to hybridize mainly to the transcripts 3′ end. Such design will fail to accurately measure the levels of isoforms which are identical at the 3′ end but are differentially regulated at the transcript level. The inability to measure isoform specific expression can clearly impact the mRNA-protein correlation results for certain peptides which represent specific isoforms as LC-MS data may include peptides unique to a gene's isoform. Figure 4A and 4B illustrate an example of differential isoform regulation identified in the LC-MS data. Acox1 (acyl-Coenzyme A oxidase 1, palmitoyl) is a peroxisomal gene involved in fatty acid beta-oxidation pathway and metabolism of very long chain fatty acids, and its deficiency causes pseudoneonatal adrenoleukodystrophy [25] in humans. This gene produces four protein-coding products (Acox1-001, Acox1-002, Acox1-003, and Acox1-201 as denoted in Ensembl genome browser) shown in Figure 4A (bottom panel). All isoforms except for “Acox1-002” include exon 4 of this gene. In LC-MS data, 20 peptides were measured for this protein. One of these 20 peptides (“GHPEPLDLHLGMFLPTLLHQATEEQQER”) maps to the exon 4 sequence of this gene, thus, does not represent the “Acox1-002” isoform which skips this exon. Examining the expression profile and correlation of these 20 peptides revealed that all peptides representing “Acox1-002” isoform are highly intercorrelated (mean r = 0.86, Figure 4B) and exhibit a similar expression profile (Figure 4A, top panel), but none have either similarity in expression profile or significant correlation with the peptide mapping to the exon 4 which is skipped in Acox1-002 isoform (mean r = 0.23, Figure 4A top panel, and 4B). This suggests that Acox1-002 isoform (with the skipped exon 4) is the main isoform underlying the significant correlation among 19 of the 20 peptides identified by LC-MS in our genetic population. This example illustrates that the LC-MS data contain information on differential regulation of isoforms, in contrast to the microarray data.


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)

Isoform-specific analysis of peptide data. A) An example of differential regulation of isoforms detected in the LC-MS data. Top panel, comparison of similarity in expression variation of 20 peptides measured for Acox1. Grey plots illustrate the expression variation among inbred mice for 19 peptides which represent all four Acox1 isoforms. Red plot illustrated the expression profile of the peptide representing the isoforms skipping exon 4. Bottom panel, Ensembl genome browser's schematic representation of four Acox1 isoforms. Arrow points to Acox1-002 isoform which skips exon 4. B) Concordance between Acox1 peptides. The left boxplot depicts correlations among peptides that include Acox1-002 isoform. The right boxplot depicts correlations between the peptide mapping to exon 4 and all other peptides. The scatter points overlaid on each boxplot represent the pair-wise correlation values. C) Exon level analysis of peptide measurements by LC-MS and transcript measurements as measured by NGS in the livers of the B6 and DBA inbred strains. The black dots depict the relationships examined by comparing peptide data to microarray data and the red dots represent the highly significant relations found by peptide comparison with the microarray data. The lines depict the best fit as predicted by linear regression (black line = regression of all peptides, red line = regression of highly significant peptides).
© Copyright Policy
Related In: Results  -  Collection

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

pgen-1001393-g004: Isoform-specific analysis of peptide data. A) An example of differential regulation of isoforms detected in the LC-MS data. Top panel, comparison of similarity in expression variation of 20 peptides measured for Acox1. Grey plots illustrate the expression variation among inbred mice for 19 peptides which represent all four Acox1 isoforms. Red plot illustrated the expression profile of the peptide representing the isoforms skipping exon 4. Bottom panel, Ensembl genome browser's schematic representation of four Acox1 isoforms. Arrow points to Acox1-002 isoform which skips exon 4. B) Concordance between Acox1 peptides. The left boxplot depicts correlations among peptides that include Acox1-002 isoform. The right boxplot depicts correlations between the peptide mapping to exon 4 and all other peptides. The scatter points overlaid on each boxplot represent the pair-wise correlation values. C) Exon level analysis of peptide measurements by LC-MS and transcript measurements as measured by NGS in the livers of the B6 and DBA inbred strains. The black dots depict the relationships examined by comparing peptide data to microarray data and the red dots represent the highly significant relations found by peptide comparison with the microarray data. The lines depict the best fit as predicted by linear regression (black line = regression of all peptides, red line = regression of highly significant peptides).
Mentions: Aside from lack of genetic variation in peptides, another plausible explanation for the lack of high correlation between peptides and probesets could be the analytic approach chosen to calculate correlations. In our study, we estimated the relationship between mRNA and proteins by examining the correlations between pairs of peptides and probesets that were annotated to the same gene without considering the isoform information for that gene. The choice of analytic approach presented here was mainly due to the limitation of the technology we used to measure the transcript levels. The probesets on the Affymetrix microarrays are designed to hybridize mainly to the transcripts 3′ end. Such design will fail to accurately measure the levels of isoforms which are identical at the 3′ end but are differentially regulated at the transcript level. The inability to measure isoform specific expression can clearly impact the mRNA-protein correlation results for certain peptides which represent specific isoforms as LC-MS data may include peptides unique to a gene's isoform. Figure 4A and 4B illustrate an example of differential isoform regulation identified in the LC-MS data. Acox1 (acyl-Coenzyme A oxidase 1, palmitoyl) is a peroxisomal gene involved in fatty acid beta-oxidation pathway and metabolism of very long chain fatty acids, and its deficiency causes pseudoneonatal adrenoleukodystrophy [25] in humans. This gene produces four protein-coding products (Acox1-001, Acox1-002, Acox1-003, and Acox1-201 as denoted in Ensembl genome browser) shown in Figure 4A (bottom panel). All isoforms except for “Acox1-002” include exon 4 of this gene. In LC-MS data, 20 peptides were measured for this protein. One of these 20 peptides (“GHPEPLDLHLGMFLPTLLHQATEEQQER”) maps to the exon 4 sequence of this gene, thus, does not represent the “Acox1-002” isoform which skips this exon. Examining the expression profile and correlation of these 20 peptides revealed that all peptides representing “Acox1-002” isoform are highly intercorrelated (mean r = 0.86, Figure 4B) and exhibit a similar expression profile (Figure 4A, top panel), but none have either similarity in expression profile or significant correlation with the peptide mapping to the exon 4 which is skipped in Acox1-002 isoform (mean r = 0.23, Figure 4A top panel, and 4B). This suggests that Acox1-002 isoform (with the skipped exon 4) is the main isoform underlying the significant correlation among 19 of the 20 peptides identified by LC-MS in our genetic population. This example illustrates that the LC-MS data contain information on differential regulation of isoforms, in contrast to the microarray data.

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