Quality criteria for finding genes with high mRNA-protein expression correlation and coexpression correlation

mRNA expression is widely used as a proxy for protein expression. However, their true relation is not known and two genes with the same mRNA levels might have different abundances of respective proteins. A related question is whether the coexpression of mRNA for gene pairs is reflected by the corresponding protein pairs. We examined the mRNA-protein correlation for both expression and coexpression. This analysis yielded insights into the relationship between mRNA and protein abundance, and allowed us to identify subsets of greater mRNA-protein coherence. The correlation between mRNA and protein was low for both expression and coexpression, 0.12 and 0.06 respectively. However, applying the best-performing quality measure, high-quality subsets reached a Spearman correlation of 0.31 for expression, 0.34 for coexpression and 0.49 for coexpression when restricted to functionally coupled genes. Our methodology can thus identify subsets for which the mRNA levels are expected to be the strongest correlated with protein levels.     

Evolution of proteins and gene expression levels are coupled in Drosophila and are independently associated with mRNA abundance, protein length, and number of protein-protein interactions

Organismic evolution requires that variation at distinct hierarchical levels and attributes be coherently integrated, often in the face of disparate environmental and genetic pressures. A central part of the evolutionary analysis of biological systems remains to decipher the causal connections between organism-wide (or genome-wide) attributes (e.g., mRNA abundance, protein length, codon bias, recombination rate, genomic position, mutation rate, etc) as well as their role-together with mutation, selection, and genetic drift-in shaping patterns of evolutionary variation in any of the attributes themselves. Here we combine genome-wide evolutionary analysis of protein and gene expression data to highlight fundamental relationships among genomic attributes and their associations with the evolution of both protein sequences and gene expression levels. Our results show that protein divergence is positively coupled with both gene expression polymorphism and divergence. We show moreover that although the number of protein-protein interactions in Drosophila is negatively associated with protein divergence as well as gene expression polymorphism and divergence, protein-protein interactions cannot account for the observed coupling between regulatory and structural evolution. Furthermore, we show that proteins with higher rates of amino acid substitutions tend to have larger sizes and tend to be expressed at lower mRNA abundances, whereas genes with higher levels of gene expression divergence and polymorphism tend to have shorter sizes and tend to be expressed at higher mRNA abundances. Finally, we show that protein length is negatively associated with both number of protein-protein interactions and mRNA abundance and that interacting proteins in Drosophila show similar amounts of divergence. We suggest that protein sequences and gene expression are subjected to similar evolutionary dynamics, possibly because of similarity in the fitness effect (i.e., strength of stabilizing selection) of disruptions in a gene's protein sequence or its mRNA expression. We conclude that, as more and better data accumulate, understanding the causal connections among biological traits and how they are integrated over time to constrain or promote structural and regulatory evolution may finally become possible.     

Quantification of mRNA and protein and integration with protein turnover in a bacterium

Biological function and cellular responses to environmental perturbations are regulated by a complex interplay of DNA, RNA, proteins and metabolites inside cells. To understand these central processes in living systems at the molecular level, we integrated experimentally determined abundance data for mRNA, proteins, as well as individual protein half‐lives from the genome‐reduced bacterium Mycoplasma pneumoniae. We provide a fine‐grained, quantitative analysis of basic intracellular processes under various external conditions. Proteome composition changes in response to cellular perturbations reveal specific stress response strategies. The regulation of gene expression is largely decoupled from protein dynamics and translation efficiency has a higher regulatory impact on protein abundance than protein turnover. Stochastic simulations using in vivo data show how low translation efficiency and long protein half‐lives effectively reduce biological noise in gene expression. Protein abundances are regulated in functional units, such as complexes or pathways, and reflect cellular lifestyles. Our study provides a detailed integrative analysis of average cellular protein abundances and the dynamic interplay of mRNA and proteins, the central biomolecules of a cell.     

Including shared peptides for estimating protein abundances: A significant improvement for quantitative proteomics

Inferring protein abundances from peptide intensities is the key step in quantitative proteomics. The inference is necessarily more accurate when many peptides are taken into account for a given protein. Yet, the information brought by the peptides shared by different proteins is commonly discarded. We propose a statistical framework based on a hierarchical modeling to include that information. Our methodology, based on a simultaneous analysis of all the quantified peptides, handles the biological and technical errors as well as the peptide effect. In addition, we propose a practical implementation suitable for analyzing large data sets. Compared to a method based on the analysis of one protein at a time (that does not include shared peptides), our methodology proved to be far more reliable for estimating protein abundances and testing abundance changes. The source codes are available at http://pappso.inra.fr/bioinfo/all_p/.     

A probabilistic treatment of the missing spot problem in 2D gel electrophoresis experiments

Two-dimensional SDS-PAGE gel electrophoresis using post-run staining is widely used to measure the abundances of thousands of protein spots simultaneously. Usually, the protein abundances of two or more biological groups are compared using biological and technical replicates. After gel separation and staining, the spots are detected, spot volumes are quantified, and spots are matched across gels. There are almost always many missing values in the resulting data set. The missing values arise either because the corresponding proteins have very low abundances (or are absent) or because of experimental errors such as incomplete/over focusing in the first dimension or varying run times in the second dimension as well as faulty spot detection and matching. In this study, we show that the probability for a spot to be missing can be modeled by a logistic regression function of the logarithm of the volume. Furthermore, we present an algorithm that takes a set of gels with technical and biological replicates as input and estimates the average protein abundances in the biological groups from the number of missing spots and measured volumes of the present spots using a maximum likelihood approach. Confidence intervals for abundances and p-values for differential expression between two groups are calculated using bootstrap sampling. The algorithm is compared to two standard approaches, one that discards missing values and one that sets all missing values to zero. We have evaluated this approach in two different gel data sets of different biological origin. An R-program, implementing the algorithm, is freely available at http://bioinfo.thep .lu.se/MissingValues2Dgels.html.     

Comparative Analysis of Proteome and Transcriptome in Human Hepatocellular Carcinoma using 2D-DIGE and SAGE

Proteome analysis of human hepatocellular carcinoma was conducted using two-dimensional difference gel electrophoresis, and the protein expression profiles were compared to the mRNA expression profiles made from serial analysis of gene expression (SAGE) in identical samples from a single patient. Image-to-image analysis of protein abundances together with protein identification by peptide mass fingerprinting yielded the protein expression profiles. A total of 188 proteins were identified, and the expression profiles of 164 proteins which had the corresponding SAGE data were compared to the mRNA expression profiles. Among them, 40 proteins showed significant differences in the mRNA expression levels between non HCC and HCC. We compared expression changes of proteins with those of mRNAs. We found that the expression tendency of 24 proteins were similar to that of mRNA, whereas 16 proteins showed different or opposite tendency to the mRNA expression.     

Influence of Protein Abundance on High-Throughput Protein-Protein Interaction Detection

Experimental protein-protein interaction (PPI) networks are increasingly being exploited in diverse ways for biological discovery. Accordingly, it is vital to discern their underlying natures by identifying and classifying the various types of deterministic (specific) and probabilistic (nonspecific) interactions detected. To this end, we have analyzed PPI networks determined using a range of high-throughput experimental techniques with the aim of systematically quantifying any biases that arise from the varying cellular abundances of the proteins. We confirm that PPI networks determined using affinity purification methods for yeast and Eschericia coli incorporate a correlation between protein degree, or number of interactions, and cellular abundance. The observed correlations are small but statistically significant and occur in both unprocessed (raw) and processed (high-confidence) data sets. In contrast, the yeast two-hybrid system yields networks that contain no such relationship. While previously commented based on mRNA abundance, our more extensive analysis based on protein abundance confirms a systematic difference between PPI networks determined from the two technologies. We additionally demonstrate that the centrality-lethality rule, which implies that higher-degree proteins are more likely to be essential, may be misleading, as protein abundance measurements identify essential proteins to be more prevalent than nonessential proteins. In fact, we generally find that when there is a degree/abundance correlation, the degree distributions of nonessential and essential proteins are also disparate. Conversely, when there is no degree/abundance correlation, the degree distributions of nonessential and essential proteins are not different. However, we show that essentiality manifests itself as a biological property in all of the yeast PPI networks investigated here via enrichments of interactions between essential proteins. These findings provide valuable insights into the underlying natures of the various high-throughput technologies utilized to detect PPIs and should lead to more effective strategies for the inference and analysis of high-quality PPI data sets.     

Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data

Mass spectrometry (MS)-based shotgun proteomics allows protein identifications even in complex biological samples. Protein abundances can then be estimated from the counts of tandem MS (MS/MS) spectra attributable to each protein, provided one accounts for differential MS detectability of contributing peptides. We developed a method, APEX, which calculates Absolute Protein EXpression levels based upon learned correction factors, MS/MS spectral counts and each protein's probability of correct identification. This protocol describes APEX-based calculations in three parts. (i) Using training data, peptide sequences and their sequence properties, a model is built to estimate MS detectability (O(i)) for any given protein. (ii) Absolute protein abundances are calculated from spectral counts, identification probabilities and the learned O(i)-values. (iii) Simple statistics allow calculation of differential expression in two distinct biological samples, i.e., measuring relative protein abundances. APEX-based protein abundances span 3-4 orders of magnitude and are applicable to mixtures of 100s to 1,000s of proteins.     

How is mRNA expression predictive for protein expression? A correlation study on human circulating monocytes

A key assumption in studying mRNA expression is that it is informative in the prediction of protein expression. However, only limited studies have explored the mRNA-protein expression correlation in yeast or human tissues and the results have been relatively inconsistent. We carried out correlation analyses on mRNA-protein expressions in freshly isolated human circulating monocytes from 30 unrelated women. The expressed proteins for 71 genes were quantified and identified by 2-D electrophoresis coupled with mass spectrometry. The corresponding mRNA expressions were quantified by Affymetrix gene chips. Significant correlation ( r =0.235, P <0.0001) was observed for the whole dataset including all studied genes and all samples. The correlations varied in different biological categories of gene ontology. For example, the highest correlation was achieved for genes of the extracellular region in terms of cellular component ( r =0.643, P <0.0001) and the lowest correlation was obtained for genes of regulation ( r =0.099, P=0.213) in terms of biological process. In the genome, half of the samples showed significant positive correlation for the 71 genes and significant correlation was found between the average mRNA and the average protein expression levels in all samples ( r =0.296, P <0.01). However, at the study group level, only five studied genes had significant positive correlation across all the samples. Our results showed an overall positive correlation between mRNA and protein expression levels. However, the moderate and varied correlations suggest that mRNA expression might be sometimes useful, but certainly far from perfect, in predicting protein expression levels.     

Correlation between mRNA and protein abundance in Desulfovibrio vulgaris: a multiple regression to identify sources of variations

Parallel profiling of mRNA and protein on a global scale and integrative analysis of these two data types could provide additional insights into the metabolic mechanisms underlying complex biological systems. However, because mRNA and protein abundance are affected by many cellular and physical processes, there have been conflicting results on their correlation. Using whole-genome microarray and LC-MS/MS proteomic data collected from Desulfovibrio vulgaris grown under three different conditions, we systematically investigate the relationship between mRNA and protein abundance by a multiple regression approach, in which some of the key covariates that may affect mRNA-protein relationship were included. The results showed that mRNA abundance alone can explain only 20-28% of the total variation of protein abundance, suggesting mRNA-protein correlation can not be determined by mRNA abundance alone. Among various covariates, analytic variation of protein abundance is the major source for the variation of mRNA-protein correlation, which contributes to 34-44% of the total variation of mRNA-protein correlation. The cellular functional category of genes/proteins contributes 10-15% of the total variation of mRNA-protein correlation, with a more pronounced correlation of the two properties was observed for "central intermediary metabolism" and "energy metabolism" categories. In addition, protein stability also contributes 5% of the total variation of mRNA-protein correlation. The study presents the first quantitative analysis of the contributions of various biochemical and physical sources to the correlation of mRNA and protein abundance in D. vulgaris.     

A Comparison of Protein and mRNA Expression during Development of the Soil Dwelling Prokaryote (S. coelicolor)

In this paper, correlation analysis of protein and mRNA levels in the soil dwelling bacteria Streptomyces coelicolor (S. coelicolor M145) is presented during development of the population as it grew in liquid medium using three biological and two technical replicates, measured during exponential growth, and its entry into the stationary phase. The proteome synthesis time series are compared with the gene expression time series measured previously under identical experimental conditions. Results reveal that about one third of protein/mRNA synthesis profiles are well correlated while another third are correlated negatively. Functional analysis of the highly correlated groups is presented. Based on numerical simulation, the negative correlation between protein and mRNA is shown to be caused by the difference between the rate of translation and protein degradation.     

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues

The increasingly large amount of proteomics data in the public domain enables, among other applications, the combined analyses of datasets to create comparative protein expression maps covering different organisms and different biological conditions. Here we have reanalysed public proteomics datasets from mouse and rat tissues (14 and 9 datasets, respectively), to assess baseline protein abundance. Overall, the aggregated dataset contained 23 individual datasets, including a total of 211 samples coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively.In all cases, we studied the distribution of canonical proteins between the different organs. The number of canonical proteins per dataset ranged from 273 (tendon) and 9,715 (liver) in mouse, and from 101 (tendon) and 6,130 (kidney) in rat. Then, we studied how protein abundances compared across different datasets and organs for both species. As a key point we carried out a comparative analysis of protein expression between mouse, rat and human tissues. We observed a high level of correlation of protein expression among orthologs between all three species in brain, kidney, heart and liver samples, whereas the correlation of protein expression was generally slightly lower between organs within the same species. Protein expression results have been integrated into the resource Expression Atlas for widespread dissemination.