Global Mouse mRNA and Protein Levels Comparison 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). doi:10.1371/journal.pgen.1001393.g004 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 correla- tions, but only 15% of the significant transcript-trait correlations exhibited corresponding protein-trait correlations. Relationships as a function of Gene Ontology categories and KEGG pathways We sought to examine whether the concordance between protein and transcript data was dependent on the biological function and/or cellular location of the gene product. For this we restricted the list of genes within each of the 3 major GO_slim terms described earlier to the 396 genes for which we had at least one probeset and one peptide measured. We then defined the average relationship between protein and transcript products of the genes within each GO category by computing the correlation between the gene products and taking the average of these correlations. The three panels in Figure S4 show the average correlations of the transcript and protein product of the genes grouped by their assigned GO categories. Striking differences in the concordance between proteins and transcripts across some of Table 1. Relationship of PT-pairs with clinical traits. the GO categories were observed. For example, for Cellular Compartment GO terms (CC), we found that peroxisomal and ER genes have on average a better correlation between protein and transcript products than other cellular compartments. We also found that for some of the GO categories the similarity between protein and transcript levels was almost non-existent (for example in BP the ‘‘cell growth’’ class, Figure S4C). To assess the significance of these observations, for each GO class we created 100,000 bootstrap datasets (each the size of the number of genes assigned to the respective GO category) containing correlation coefficient p-values randomly selected from the pool of peptide- probeset correlation p-values. We then assessed the significance of observed averaged correlation p-value for each class by comparing it to the distribution of the averaged p-values in the bootstrapped dataset (Table S4). In some GO groups, we found a class of genes for which the relationship between the transcript level and protein level is significantly better than for other GO groups. We also found GO classes in which the transcript levels and protein levels of the genes were significantly discordant (i.e. the relationship between protein and transcript was significantly less than what would be expected by chance). For example, in MF we found that genes classified as having a role in ‘‘electron carrier activity’’ had a strong relationship among the protein and transcript levels (p- value = 8.9e-03) and this relationship is significantly compromised for genes with ‘‘transporter activity’’ (p-value = 4e-05). Another example of discordant group was genes involved in the translation process (p-value,1e-05). Interestingly, the ‘‘translation’’ category has been proposed recently to be involved in phenotypic buffering in a yeast genetic interaction network. Overall, these results indicate that cellular compartments and biological processes vary in the degree to which the linear relationships between transcript levels and their protein products are conserved. We also examined the level of concordance among transcripts and proteins of genes that are members of the same biological pathway. For this, we focused on 212 biological pathways on the KEGG website (http://www.genome.jp/kegg/). We annotated the peptides and probesets according to their pathway member- ship as determined by their Ensembl gene IDs. Ninety nine out of 212 pathways contained genes for which we had both more than one transcript and more than one protein measured. Focusing on these 99 pathways, we then performed the following three correlation analyses: 1) correlation between peptides belonging 0.1% FDR 1% FDR 5% FDR 35 93 322 doi:10.1371/journal.pgen.1001393.t001 272 704 1547 17 71 234 8 # Trait Correlations Unique to Proteins # Trait Correlations Unique to Transcripts # Trait Correlations Shared Between Transcripts and Proteins # Proteins with Significant Correlation (%) # Transcripts with Significant Correlation (%) 24 (6) 64 (16) 162 (41) 122 (31) 217 (55) 325 (82) PLoS Genetics | www.plosgenetics.org June 2011 | Volume 7 | Issue 6 | e1001393