Global Mouse mRNA and Protein Levels Comparison Figure 2. Proteome and transcriptome data quality. A) Reliability of peptide measurement in LC-MS. The distribution of variance among the technical replicates in the LC-MS data (grey plot) and in the HMDP population (blue plot). B) The frequency of peptides with varying amount as defined by the ‘‘signal to noise’’ ratio. C) Distribution of heritability (fraction of total variance attributed to genetics) in the transcript dataset. The dashed line depicts the significant heritability estimates (p-value,0.05) D) Comparison of Affymetrix data with the Next Generation Sequencing data. E) Number of peptides per gene in the filtered peptide dataset. doi:10.1371/journal.pgen.1001393.g002 the ‘‘noisy’’ measurements, we were left with 1543 peptides (see Dataset S1 for the expression and Dataset S2 for the annotation of the peptides). These 1543 peptides represented 486 Ensembl Genes from which 39% were represented by only one peptide and the remaining 60% were represented by two or more peptides (Figure 2E). The most abundant number of peptides (.20 peptides per gene) was found for 2 genes, aldehyde dehydrogenase 1 family, member L1 (Aldh1l1), and carbamoyl-phosphate synthetase 1 (Cps1). For the transcript data, we focused only on those transcripts that had a) a significant genetic component underlying their variation (heritability p-value,0.05), and b) unambiguous annotation in the Ensembl database. To comprehensively compare the tran- scripts and peptides, we also included those probesets that were annotated as the same Ensembl gene/transcript as one of the peptides in the protein data. This resulted in the total of 9896 probesets (representing 7185 Ensembl genes) from the initial 22670 probesets. Proteome and transcriptome representation To investigate the range of gene products present in the filtered datasets, we generated a separate list of ‘‘GO Slim’’ terms for each of the three major GO categories (Cellular Compartment or ‘‘CC’’, Molecular Function or ‘‘MF’’, and Biological Process or ‘‘BP’’) and used the ‘‘GO Term Mapper’’ website (http://go. princeton.edu/cgi-bin/GOTermMapper) to classify and count the number of proteins and transcripts in each of the 3 major GO categories (Table S3). We also compared our results to the background set (all genes annotated by Mouse Genome Infor- matics) and using Fisher’s Exact test calculated the degree of enrichment or underrepresentation for each GO class. Figure S2 depicts the results of this analysis for the Cellular Compartment GO terms. While the proteome and transcriptome datasets represent a wide range of gene products present in various cellular compartments, the compartments are not equally represented. In the protein data, mitochondrial genes were overwhelmingly the largest set and in the transcript data the nuclear compartment was at the top of the list for enriched CC GO terms. The enrichment analysis showed that for both protein and transcript datasets the majority of the GO terms tested were significantly over and under- represented and these differences were more pronounced for the protein data (Table S3). A likely explanation for this observation is that the LC-MS analysis of the liver provides a biased sampling of the proteome data (due to the abundance and/or cellular location of the protein). The significant differences between the transcript data and the background set may be partially explained by the bias introduced in the design of the Affymetrix microarray. Alterna- tively, since the transcript data by the virtue of filtering represent the significantly heritable subset of all transcripts present on the Affymetrix chip, one could postulate that in some cellular compartments or cellular processes transcripts are more or less likely to exhibit common genetic variation. Modest concordance between transcript and protein levels 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. Next, we examined if the amount of technical noise and/or lack of genetic variation could explain the modest correlation between mRNA and peptide data. For this we classified each peptide based on the signal to noise ratio (defined earlier) and looked at the median correlation between mRNA and peptides within each group. As shown in Figure 3B, we found that as the ratio of signal to noise increases so does the correlation between the mRNA and peptide levels of the gene. In fact the median correlation for the least noisy group, comprising peptides with signal to noise ratio .90%, was twice as large as the noisiest group of peptides (peptides with signal to noise ratio ,60%). These results suggests that the modest correlation between peptides and mRNA observed in our study is partially due to either the presence of significant nongenetic variation or small genetic variation in some proteins. Alternative splicing is not a significant contributing factor to the overall modest correlation of transcript and protein levels 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 39 end. Such design will fail to accurately measure the levels of isoforms which are identical at the 39 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- PLoS Genetics | www.plosgenetics.org 5 June 2011 | Volume 7 | Issue 6 | e1001393