The Bioinformatics and Biostatistics Core (BBC) at the Yale/NIDA Neuroproteomics Research Center aims at meeting the computational, statistical, and database challenge (including data quality, management, integration, analysis, and archiving) posed by the Center’s ongoing neuroproteomics research. As a key bioinformatics participant, the Gerstein lab will interact synergistically with the other Core members to achieve the following specific aims:

1. Expanding the research directed at more accurately predicting protein levels from mRNA expression data to the analysis of relative changes in mRNA and protein expression;
2. Further develop our approaches to identify classes of proteins whose mRNA level and protein expression show high degrees of correlation (Greenbaum et al, 2001, 2003), and apply these approaches to existing mRNA expression data sets from other Neuroproteomics Center members (e.g., Dr. Ron Duman) to identify the proteins that are most likely to be significantly differentially regulated and target those among these proteins suitable for directed MS/MS and/or protein microarray analysis;
3. Routinely consult with other members of the Neuroproteomics Center to assist in interpreting the biological significance of the results of protein expression studies and their correlation with mRNA expression data.

Background and Significance

Even with the significant developments in the technologies used to quantify protein abundance over the past few years, protein identification and quantification still lags very far behind the high-throughput experimental techniques used to interrogate the mRNA expression levels of 25,000 or more genes and ESTs. In hope of determining protein abundance levels from the more copious and technically easier mRNA experiments, researchers have tried to find correlations between mRNA expression data and the limited protein abundance data. To date, there have been only a handful of efforts dedicated to this study, most notably in human cancers and yeast cells; for the most part, they have reported only minimal and/or limited correlations. One of the earliest analyses of correlation looked at only 19 proteins in the human liver. Anderson and Seilhamer (1997) found a somewhat positive correlation of 0.48. Another limited analysis, of only three genes MMP-2, MMP-9 and TIMP-1 in human prostate cancers, showed no significant relationship (Lichtinghagen, 2002). An additional cancer study (Chen, 2002) showed a significant correlation in only a small subset of the proteins studied. Conversely, Orntoft et al (2002) found highly significant correlations in human carcinomas when looking at changes in mRNA and protein expression levels.
Many of the present efforts at correlating mRNA and protein expression have been conducted in yeast as well. Using two-dimensional electrophoresis techniques, Gygi et al. (1999) found that even similar mRNA expression levels could be accompanied by a wide range (up to 20-fold difference) of protein abundance levels, and vice versa. These results contrast with those of Futcher et al (1999), who found relatively high correlations (r = 0.76) after transforming the data to normal distributions. In a previous analysis (Greenbaum, 2002), we merged the data from both of these datasets (referred to as 2DE-1 (Gygi, 1999) and 2DE-2 (Futcher, 1999)), comparing the resulting new larger protein abundance set ('merged data-set 1') with a comprehensive mRNA expression dataset. The mRNA expression reference set was constructed through iteratively combining, in a non-trivial fashion, three sets that used Affymetrix chips and a SAGE dataset (Greenbaum, 2002). Using these reference datasets, we were able to do an all-against-all comparison of mRNA and protein expression levels, in addition to a number of analyses comparing protein and mRNA expression using smaller, but broad categories (Greenbaum, 2001; Greenbaum, 2002).
Given the difficult, laborious, and limiting nature of two-dimensional electrophoresis analysis (Arthur, 2003), many of the newer protein abundance determinations have been done using MudPit and derivative technologies. Washburn et al. (2001) used MudPit to analyze and detect 1,484 arbitrary proteins: they were able to detect a somewhat random sampling of proteins independent of abundance, localization, size or hydrophobicity (we refer to this dataset as MudPit-1). In a further experiment the authors, comparing expression ratios for both proteins and mRNA levels, found that although they could not find correlations for individual loci, they could find overall correlations when looking at pathways and complexes of proteins that functioned together (Washburn, 2003). Peng et al. (2003) analyzed 1,504 yeast proteins with a false-positive rate - misidentification of a protein - of less than 1% (we refer to this dataset as MudPit-2). In their analysis, they contrasted their methodology with that of Washburn et al (2001) with which there was significant overlap of proteins.
Although the literature is ambiguous in terms of whether or not mRNA expression and protein abundance can be correlated, we believe that our newer methodologies described in Greenbaum et al (2003) and in Preliminary Results provide a context for finding correlations. One of the main limitations in finding correlations between mRNA and protein data has been the significant degree of error inherent to the experimental process of determining both mRNA and protein concentrations on a global scale. However, we have found that by examining smaller homogenous subpopulations of genes, as defined by information such as function (Mewes, 2002), subcellular localization (Drawid, 2000) or secondary structure, we minimize the noise resulting from experimental error. Using these smaller groups of genes, we have been able to find significantly higher correlations than are found in a global “all against all” comparison. Further examination of which functional classifications allow for very high correlations, along with incorporation of data associated with mRNA and protein turnover rates, will allow us to create a more rigorous methodology that may allow more accurate extrapolation of protein from mRNA abundance levels. Because of the paucity of published human protein and mRNA expression data on neurological (as well as on other tissues), our initial work has been carried out on yeast. Hence, one of the goals of the Bioinformatics Core will be to extend a similar type of analysis to the protein expression data that will be obtained by the proposed Protein and Lipid Separation and Profiling Core of the Neuroproteomics Research Center. In this regard we are fortunate that significant mRNA expression data has already been obtained by some members of the Center (e.g., Ron Duman). If we could confidently predict (at least some classes of) protein from mRNA expression, the resulting (relatively fewer) proteins of interest could then be targeted for directed MS/MS and/or protein microarray analysis.

Preliminary Results

We have made considerable progress on trying to predict gene and protein expression levels, based on various proteomic features. In particular, we have published three papers relating mRNA abundance and gene expression levels (Greenbaum et al, 2001, 2003 Lian et al 2002), and integrated this with a variety of protein features, looking at the degree to which mRNA abundance and gene expression levels were different with respect to different protein features. More specifically, we have proposed a methodology that creates reference data sets (Greenbaum et al., 2002) removing the biases of individual data sets. Additionally, instead of comparing individual genes, we compared broad categories of genomic information, finding significant trends in the underlying data. These included an overall weak correlation between mRNA and protein levels, although there were many outlying genes, which are also of interest given the degree to which their mRNA and protein values differ. We also found consistent enrichments of amino acids and depletions of random secondary structures in both forms of data relative to the coded DNA. We then went on from there to develop a model that tried to predict levels of gene expression and protein abundance from various features of the protein sequence (Jansen et al., 2003), in particular, from calculating the CAI (Codon Adaptation Index) from various compositional biases. We employed a statistical approach where we fit a number of simple models to the observed gene expression and protein abundance data, first trying to re-parameterize the classic CAI method that was developed ~15 years ago and then trying to do a simple linear regression, just a straight fit on the observed codon frequencies. We found, actually, that we could not improve that much on the classic CAI, though we could improve slightly by using the modern genomic data. We have made available over the web and in our paper (Jansen et al., 2003) our new models and parameters that investigators can use to better predict protein expression and CAI.
Expanding upon our previous merged protein and mRNA expression dataset, in our Greenbaum et al (2003) study we constructed a new merged dataset (merged data set-2) using two published 2D electrophoresis and two MudPit datasets. Succinctly (more information is available on our website at [http://bioinfo.mbb.yale.edu/expression/mrna-v-protein/]), we transformed each of the protein-abundance datasets into more quantitative data by fitting each protein dataset individually onto the reference mRNA expression dataset. Each of the new, fitted datasets was then inversely transformed back into protein space. These derived protein datasets were then combined into a larger reference dataset; when we had more than one abundance value for an open reading frame (ORF), we chose the value from the dataset according to a prescribed quality ranking. The resulting set contained protein abundance information for approximately 2,000 ORFs. Using the resulting data we could compare mRNA expression and protein abundance globally as well as looking at smaller, broad categories, such as function or localization (see Figure 1b, 1c in Greenbaum et al (2003). In particular, we show that some localization categories - for example, the nucleolus - have significantly higher correlations than the global correlation. Other localizations seem to present less of a correlation between mRNA and protein data, for example, the mitochondria - possibly reflecting the heterogeneous nature and function of the latter organelle. In terms of MIPS functional categories, we show that although some categories, such as cell rescue, show a lower correlation than the whole merged set, other functional categories, such as cell cycle, show a significant increase in correlation. Logically, this increased correlation reflects the co-regulated nature of the proteins in this functional category.
There are presumably at least three reasons for the poor correlations generally reported in the literature between the level of mRNA and the level of protein, and these may not be mutually exclusive. First, there are many complicated and varied post-transcriptional mechanisms involved in turning mRNA into protein that are not yet sufficiently well defined to be able to compute protein concentrations from mRNA; second, proteins may differ substantially in their in vivo half lives; and/or third, there is a significant amount of error and noise in both protein and mRNA experiments that limit our ability to get a clear picture.
Examining the first option - that there are a number of complex steps between transcription and translation - we looked at correlations between mRNA and protein abundance for those ORFs that had varied or steady levels of mRNA expression over the course of the cell cycle. To normalize for the varied degrees of expression for different ORFs, we took the standard deviation divided by the average expression level as representative of the variation of each ORF over the course of the yeast cell cycle (Fig. 1, below). Broadly speaking, the cell can control the levels of protein at the transcriptional level and/or at the translational level. Logically, we would assume that those ORFs that show a large degree of variation in their expression are controlled at the transcriptional level - the variability of the mRNA expression is indicative of the cell controlling mRNA expression at different points of the cell cycle to achieve the resulting and desired protein levels. Thus we would expect, and we found, a high degree of correlation (r = 0.89) between the reference mRNA and protein levels for these particular ORFs; the cell has already put significant energy into dictating the final level of protein through tightly controlling the mRNA expression, and we assume that there would then be minimal control at the protein level. In contrast, those genes that show minimal variation in their mRNA expression throughout the cell cycle are more likely to have little or no correlation with the final protein level; the cell would be controlling these ORFs at the translational and/or post-translational level, with the mRNA levels being somewhat independent of the final protein concentration. And indeed, we found only minimal correlation between protein and mRNA expression for these ORFs (r = 0.2). Furthermore, we found that those ORFs that have higher than average levels of ribosomal occupancy - that is that a large percentage of their cellular mRNA concentration is associated with ribosomes (being translated) - have well correlated mRNA and protein expression levels (Fig. 1). These cases probably represent a situation wherein the cell, having significantly controlled the mRNA expression to produce a specific level of protein, will probably not also employ mechanisms to control the translation. Alternatively, those proteins that have very low occupancy rates have uncorrelated mRNA and protein expression; thus, given that the cell has not tightly controlled the mRNA expression for this ORF, it will dictate the resulting protein levels through rigorous controls of its translation (that is, through tight limits on occupancy) (Arava, 2003).

A second option for a general lack of correlation between mRNA and protein abundance may be that proteins have very different half-lives as the result of varied protein synthesis and degradation. Protein turnover can vary significantly depending on a number of different conditions (Glickman, 2002); the cell can control the rates of degradation or synthesis for a given protein, and there is significant heterogeneity even within proteins that have similar functions (Pratt, 2002). Recent efforts have been made to computationally measure these rates (Lian, 2002).
Simplistically, it can be presumed that the change in a protein's concentration over time will be equal to the rate of translation minus the rate of degradation. By analogy to concepts in chemical kinetics, we can approximate this equation: dP(i,t)/dt = SE(i,t) - DP(i,t), where P is protein abundance i at time t, E is the mRNA expression level of protein P, S is a general rate of protein synthesis per mRNA, and D is a general rate of protein degradation per protein (Gerner, 2002). Additionally there are some experimental methods that can also be used to measure turnover and the translational control of protein levels (e.g., Serikawa, 2003). Given the degenerate nature of the genetic code, there are many synonymous codons (codons that translate into the same amino acid). As the cell is biased in its usage of synonymous codons - that is, the usage of a subset of codons results in a higher level of mRNA expression, possibly as a result of differing cellular tRNA levels - the CAI can be used to predict the expression of a gene (Sharp, 1987). We recently calculated new parameters for this model, with some improvement in predictive strength (Jansen, 2003). It is thought that the CAI will correlate differently with mRNA levels than with protein abundance levels due, in part, to protein turnover rates (Coghlan, 2000). Ranking the ORFs in terms of their CAI value, we found that although those ORFs that ranked the highest in terms of CAI did not show a very strong correlation between mRNA and protein levels, they nevertheless showed a significantly higher correlation than ORFs that were ranked as having the lower CAI values (r = 0.48 versus 0.02). The low correlations reflect the fact that the CAI will correlate differently for protein and mRNA values because of the additional cellular controls on protein translation, namely the effect of protein turnover rates. Nevertheless, the sizable difference in correlations between the two groups of ORFs with high- and low-ranking CAI values (Figure 1) shows there is some relationship between mRNA and protein values, possibly indicating that highly expressed genes tend to result in a more correlated level of protein abundance than lower expressed ones.
Although proteomics is still in its infancy, given the pace of technological advancement in protein quantification, mRNA expression analysis and noise reduction, more comprehensive correlation studies will soon be feasible. This will allow for more robust analyses of the relationship between mRNA expression and protein abundance values. Indeed, we are in the process of continuing this line of research by careful examination and correlation of the mRNA expression data already obtained by R. Duman and other Yale neurobiologists in the proposed Center of Excellence in Neuroproteomics. One obvious goal of these studies will be to be able to reach the point where we can more accurately extrapolate mRNA to protein expression data and thereby, for instance, guide the selection of antibodies to be spotted onto microarrays and those proteins that will be targeted by directed MS/MS-based technologies to permit the independent measurement of selected proteins of high potential interest.

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