Specific Aims
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 nontrivial 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 falsepositive 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 set2) using two published 2D electrophoresis and two MudPit datasets. Succinctly (more
information is available on our website at [http://bioinfo.mbb.yale.edu/expression/mrnav-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 posttranscriptional 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
Fig. 1: The differences in correlation between
computationally measure these rates (Lian,
mRNA and protein expression values using
2002).
novel categories
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.
Literature Cited
Anderson L, Seilhamer J (1997) A comparison of selected mRNA and protein abundances in
human liver. Electrophoresis 18: 533-537.
Arava Y, Wang Y, Storey JD, Liu CL, Brown PO, Herschlag D (2003) Genome-wide analysis of
mRNA translation profiles in Saccharomyces cerevisiae. Proc Natl Acad Sci USA 100:
3889-3894.
Arthur, JM (2003) Proteomics : Curr Opin Nephrol Hypertens. 12(4): 423-30.
Bader, G.D. and Hogue, C.W. (2000). BINDA data specification for storing and describing
biomolecular interactions, molecular complexes and pathways. Bioinformatics 16: 465-477.
Chen G, Gharib TG, Huang CC, Taylor JM, Misek DE, Kardia SL, Giordano TJ, Iannettoni MD,
Orringer MB, Hanash SM, et al. (2002) Discordant protein and mRNA expression in lung
adenocarcinomas. Mol Cell Proteomics 1:304-313.
Chothia, C. and Gerstein, M. (1997) Protein evolution. How far can sequences diverge? Nature
385 : 579, 581.
Coghlan A, Wolfe KH (2000) Relationship of codon bias to mRNA concentration and protein
length in Saccharomyces cerevisiae.Yeast 16:1131-1145.
Day, D. A. and M. F. Tuite (1998). Post-transcriptional gene regulatory mechanisms in
eukaryotes:an overview. J Endocrinol 157(3): 361-71.
Drawid A, Gerstein M. (2000) A Bayesian system integrating expression data with sequence
patterns for localizing proteins: comprehensive application to the yeast genome. J Mol Biol.
301(4):1059-75.
Futcher B, Latter GI, Monardo P, McLaughlin CS, Garrels JI (1999) A sampling of the yeast
proteome. Mol Cell Biol 19:7357-7368.
Gerner C, Vejda S, Gelbmann D, Bayer E, Gotzmann J, Schulte-Hermann R, Mikulits W (2002)
Concomitant determination of absolute values of cellular protein amounts, synthesis rates,
and turnover rates by quantitative proteome profiling. Mol Cell Proteomics 1:528-537.
Gerstein, M. (1998) Measurement of the effectiveness of transitive sequence comparison, through
a third 'intermediate' sequence. Bioinformatics 14 : 707-14.
Gerstein, M. and Jansen, R. (2000). The current excitement in bioinformatics-analysis of wholegenome expression data: how does it relate to protein structure and function?
Curr Opin Struct Biol 10 : 574-84.
Glickman MH, Ciechanover A (2002) The ubiquitin-proteasome proteolytic pathway: destruction
for the sake of construction. Physiol Rev 82:373-428.
Greenbaum, D.,Colangelo, C., Williams, K., and Gerstein, M. (2003) Comparing protein
abundance and mRNA expression levels on a genomic scale. Genome Biology, 4, 117.1117.8.
Greenbaum D, Jansen R, Gerstein M (2002) Analysis of mRNA expression and protein
abundance data: an approach for the comparison of the enrichment of features in the cellular
population of proteins and transcripts. Bioinformatics 18:585-596.
Greenbaum D, Luscombe NM, Jansen R, Qian J, Gerstein M (2001) Interrelating different types
of genomic data, from proteome to secretome: 'oming in on function. Genome Res 11:14631468.
Gygi, SP, Corthals, GL, Zhang, Y, Rochon, Y & Aebersold, R (2000) Evaluation of twodimensional gel electrophoresis-based proteome analysis technology. Proc Natl Acad Sci
USA, 97, 9390-5.
Gygi, SP, Rochon, Y, Franza, BR & Aebersold, R (1999). Correlation between protein and
mRNA abundance in yeast. Mol Cell Biol 19,1720-30.
Hegyi H, Gerstein M. (2001) Annotation transfer for genomics: measuring functional divergence
in multi-domain proteins. Genome Res.11(10):1632-40.
Holstege, F. C., E. G. Jennings, et al. (1998). Dissecting the regulatory circuitry of a eukaryotic
genome. Cell 95(5): 717-728.
Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., and Sakaki, Y.(2001). A comprehensive
two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. 98:
4569-4574.
Jacobs Anderson JS, Parker R. Computational identification of cis-acting elements affecting post
transcriptional control of gene expression in Saccharomyces cerevisiae. Nucleic Acids Res.
2000 Apr 1;28(7):1604-17.
Jackson, R. J. and M. Wickens (1997). Translational controls impinging on the 5'-untranslated
region and initiation factor proteins. Curr Opin Genet Dev 7(2): 233-41.
Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ, Chung S, Emili A, Snyder M, Greenblatt
JF, Gerstein M. (2003) A bayesian networks approach for predicting protein-protein
interactions from genomic data. Science. 302(5644):449-53.
Jansen R, Bussemaker HJ, Gerstein M (2003) Revisiting the codon adaptation index from a
whole-genome perspective: analyzing the relationship between gene expression and codon
occurrence in yeast using a variety of models. Nucleic Acids Res 31:2242-2251.
Jansen R, Greenbaum D, Gerstein M (2002) Relating whole-genome expression data with
protein-protein interactions. Genome Res 12:37-46.
Jansen R, Lan N, Qian J, Gerstein M. (2002) Integration of genomic datasets to predict protein
complexes in yeast J Struct Funct Genomics. 2002b;2(2):71-81.
Jelinsky, S. A. and L. D. Samson (1999). Global response of Saccharomyces cerevisiae to an
alkylating agent. Proc Natl Acad Sci U S A 96(4): 1486-91.
Lian Z, Kluger Y, Greenbaum DS, Tuck D, Gerstein M, Berliner N, Weissman SM, Newburger
PE (2002) Genomic and proteomic analysis of the myeloid differentiation program: global
analysis of gene expression during induced differentiation in the MPRO cell line. Blood
100:3209-3220.
Lindahl, L. and A. Hinnebusch (1992). Diversity of mechanisms in the regulation of translation in
prokaryotes and lower eukaryotes. Curr Opin Genet Dev 2(5): 720-6.
McCarthy, J. E. (1998). Posttranscriptional control of gene expression in yeast. Microbiol Mol
Biol Rev 62(4): 1492-553.
Mewes HW, Frishman D, Guldener U, Mannhaupt G, Mayer K, Mokrejs M, Morgenstern B,
Munsterkotter M, Rudd S, Weil B. (2002) MIPS: a database for genomes and protein
sequences. Nucleic Acids Res. 30(1):31-4.
Morris, D. R. and A. P. Geballe (2000). Upstream open reading frames as regulators of mRNA
translation. Mol Cell Biol 20(23): 8635-42.
Naylor, G. J. and Gerstein, M. (2000) Measuring shifts in function and evolutionary opportunity
using variability profiles: A case study of the globins. J Mol Evol 51: 223-33.
Orntoft TF, Thykjaer T, Waldman FM, Wolf H, Celis JE (2002) Genome-wide study of gene
copy numbers, transcripts, and protein levels in pairs of non-invasive and invasive human
transitional cell carcinomas. Mol Cell Proteomics 1: 37-45.
Peng J, Elias JE, Thoreen CC, Licklider LJ, Gygi SP (2003) Evaluation of multidimensional
chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale
protein analysis: the yeast proteome. J Proteome Res 2: 43-50.
Pratt JM, Petty J, Riba-Garcia I, Robertson DH, Gaskell SJ, Oliver SG, Beynon RJ (2002)
Dynamics of protein turnover, a missing dimension in proteomics. Mol Cell Proteomics
1:579-591.
Qian J, Lin J, Luscombe NM, Yu H, Gerstein M. (2003) Prediction of regulatory networks:
genome-wide identification of transcription factor targets from gene expression data.
Bioinformatics.19(15):1917-26.
Roth, F. P., J. D. Hughes, et al. (1998). Finding DNA regulatory motifs within unaligned
noncoding sequences clustered by whole-genome mRNA quantitation. Nat Biotechnology
16(10): 939-45.
Serikawa KA, Xu XL, MacKay VL, Law GL, Zong Q, Zhao LP, Bumgarner R, Morris DR
(2003) The transcriptome and its translation during recovery from cell cycle arrest in
Saccharomyces cerevisiae. Mol Cell Proteomics 2: 191-204.
Sharp PM, Li WH (1987) The codon adaptation index --- a measure of directional
synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15:12811295.
Uetz, P., Giot, L., Cagney, G., Mansfield, T.A., Judson, R.S., Knight, J.R., Lockshon, D.,
Narayan, V., Srinivasan, M., Pochart, P. (2000). A comprehensive analysis of proteinprotein interactions in Saccharomyces cerevisiae. Nature 403: 623-627.
Velculescu VE, Zhang L, Zhou W, Vogelstein J, Basrai MA, Bassett DE Jr, Hieter P, Vogelstein
B, Kinzler KW (1997). Characterization of the yeast transcriptome. Cell 88(2):243-51.
Vilela, C., B. Linz, et al. (1998). The yeast transcription factor genes YAP1 and YAP2 are subject
to differential control at the levels of both translation and mRNA stability. Nucleic
AcidsRes 26(5): 1150-9.
Vilela, C., C. V. Ramirez, et al. (1999). Post-termination ribosome interactions with the 5'UTR
modulate yeast mRNA stability. Embo J 18(11): 3139-52.
Washburn MP, Wolters D, Yates 3rd JR (2001) Large-scale analysis of the yeast proteome by
multidimensional protein identification technology. Nat Biotechnol 19:242-247.
Washburn MP, Koller A, Oshiro G, Ulaszek RR, Plouffe D, Deciu C, Winzeler E, Yates 3rd JR
(2003) Protein pathway and complex clustering of correlated mRNA and protein expression
analyses in Saccharomyces cerevisiae. Proc Natl Acad Sci USA 100:3107-3112.
Wilson CA, Kreychman J, Gerstein M. (2000) Assessing annotation transfer for genomics:
quantifying the relations between protein sequence, structure and function through
traditional and probabilistic scores. J Mol Biol. 297(1):233-49.
Xenarios, I., Rice, D.W., Salwinski, L., Baron, M.K., Marcotte, E.M., and Eisenberg, D. (2000).
DIP: The database of interacting proteins. Nucleic Acids Res. 28: 289-291.