Figure 3. 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). doi:10.1371/journal.pgen.1001393.g003 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 (‘‘GHPEPLDLHLGMFLPTLLHQATEEQ- QER’’) 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 Global Mouse mRNA and Protein Levels Comparison 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. To investigate if our inability to measure isoform specific expression by microarrays could explain the lack of concordance between mRNA and peptides, we utilized the next generation sequencing data generated for the two inbred strains described earlier. This dataset provided us with an opportunity to examine the transcript level expression of the exons measured by NGS with the protein level expression of exons measured by the LC- MS. To investigate this, for each peptide we calculated the count of exons in RNA-Seq data for two strains. We then compared the DBA to B6 ratio of each exon in the peptide data to the DBA to B6 ratio of normalized sequence counts (reported in FPKM units) in the RNA-Seq data. The results of these comparisons are shown in Figure 4C. Similar to the gene-level analysis, the exon level analysis of all the filtered data also suggested a modest relationship between the exon counts in the mRNA data vs exon levels in the LC-MS data (r2 = 0.02). This global analysis provided no support for the presence of differential splicing/ isoform regulation as being a significant factor in the mRNA and protein overall relationships observed between LC-MS and microarray data. The relationship between RNA-Seq data and LC-MS peptide data is particularly strong (r = 0.42) for those peptides exhibiting a strong correlation (r.0.5) with microarray data (Figure 4C). In an alternate approach to study the effect of differential splicing on the correlation pattern, we examined our LC-MS data at the isoform level and compared the results to the gene level analysis. For this, we grouped various peptides of each protein into unique and mutually exclusive clusters of known isoforms as defined by the Ensembl database. In this classification we allowed peptides to only represent one Ensembl protein ID and excluded any peptide which matched with two or more Ensembl proteins. Focusing on clusters with at least two peptides assigned to a cluster, we calculated the within cluster correlation of of peptides and compared the average within-cluster correlation to the average correlation of peptides at the gene level analysis. The average correlation of peptides at the gene level analysis was estimated at 0.47. In comparison, the average within cluster correlation of peptides representing the same isoforms was estimated to be 0.52. Combined with the NGS analysis described earlier, the small and nonsignificant increase in the peptide concordance after taking into account the isoform membership provides little support for differential regulation of splicing/isoform expression as a signifi- cant factor underlying the observed modest correlation between transcripts and proteins. Differential relationships of proteins and transcripts to clinical traits 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 PLoS Genetics | www.plosgenetics.org 6 June 2011 | Volume 7 | Issue 6 | e1001393