Protein abundances are more conserved than mRNA abundances across diverse taxa

On the Dependency of Cellular Protein Levels on mRNA Abundance

The question of how genomic information is expressed to determine phenotypes is of central importance for basic and translational life science research and has been studied by transcriptomic and proteomic profiling. Here, we review the relationship between protein and mRNA levels under various scenarios, such as steady state, long-term state changes, and short-term adaptation, demonstrating the complexity of gene expression regulation, especially during dynamic transitions. The spatial and temporal variations of mRNAs, as well as the local availability of resources for protein biosynthesis, strongly influence the relationship between protein levels and their coding transcripts. We further discuss the buffering of mRNA fluctuations at the level of protein concentrations. We conclude that transcript levels by themselves are not sufficient to predict protein levels in many scenarios and to thus explain genotype-phenotype relationships and that high-quality data quantifying different levels of gene expression are indispensable for the complete understanding of biological processes.

Transient protein-protein interactions perturb E. coli metabolome and cause gene dosage toxicity

Gene dosage toxicity (GDT) is an important factor that determines optimal levels of protein abundances, yet its molecular underpinnings remain unknown. Here, we demonstrate that overexpression of DHFR in E. coli causes a toxic metabolic imbalance triggered by interactions with several functionally related enzymes. Though deleterious in the overexpression regime, surprisingly, these interactions are beneficial at physiological concentrations, implying their functional significance in vivo. Moreover, we found that overexpression of orthologous DHFR proteins had minimal effect on all levels of cellular organization - molecular, systems, and phenotypic, in sharp contrast to E. coli DHFR. Dramatic difference of GDT between 'E. coli's self' and 'foreign' proteins suggests the crucial role of evolutionary selection in shaping protein-protein interaction (PPI) networks at the whole proteome level. This study shows how protein overexpression perturbs a dynamic metabolon of weak yet potentially functional PPI, with consequences for the metabolic state of cells and their fitness.

Multi-omic data integration enables discovery of hidden biological regularities

Rapid growth in size and complexity of biological data sets has led to the ‘Big Data to Knowledge' challenge. We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule. Second, we show that genome-scale models, based on genomic and bibliomic data, enable quantitative synchronization of disparate data types. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing. These regularities can be formally represented in a computable format allowing for coherent interpretation and prediction of fitness and selection that underlies cellular physiology.

Translating omics data sets into biological insight is one of the great challenges of our time. Here, the authors make headway by synchronising pairs of omics data types via invariants across conditions and by integrating datasets into a genome-scale model of E. coli metabolism and gene expression.

High-Throughput Proteomics Reveals the Unicellular Roots of Animal Phosphosignaling and Cell Differentiation

Cell-specific regulation of protein levels and activity is essential for the distribution of functions among multiple cell types in animals. The finding that many genes involved in these regulatory processes have a premetazoan origin raises the intriguing possibility that the mechanisms required for spatially regulated cell differentiation evolved prior to the appearance of animals. Here, we use high-throughput proteomics in Capsaspora owczarzaki, a close unicellular relative of animals, to characterize the dynamic proteome and phosphoproteome profiles of three temporally distinct cell types in this premetazoan species. We show that life-cycle transitions are linked to extensive proteome and phosphoproteome remodeling and that they affect key genes involved in animal multicellularity, such as transcription factors and tyrosine kinases. The observation of shared features between Capsaspora and metazoans indicates that elaborate and conserved phosphosignaling and proteome regulation supported temporal cell-type differentiation in the unicellular ancestor of animals.

Avoidance of stochastic RNA interactions can be harnessed to control protein expression levels in bacteria and archaea

A critical assumption of gene expression analysis is that mRNA abundances broadly correlate with protein abundance, but these two are often imperfectly correlated. Some of the discrepancy can be accounted for by two important mRNA features: codon usage and mRNA secondary structure. We present a new global factor, called mRNA:ncRNA avoidance, and provide evidence that avoidance increases translational efficiency. We also demonstrate a strong selection for the avoidance of stochastic mRNA:ncRNA interactions across prokaryotes, and that these have a greater impact on protein abundance than mRNA structure or codon usage. By generating synonymously variant green fluorescent protein (GFP) mRNAs with different potential for mRNA:ncRNA interactions, we demonstrate that GFP levels correlate well with interaction avoidance. Therefore, taking stochastic mRNA:ncRNA interactions into account enables precise modulation of protein abundance.

DOI:http://dx.doi.org/10.7554/eLife.13479.001

Many genes carry information for making proteins. To make a protein, a working copy of the information stored in DNA is first copied into a molecule of messenger RNA. These RNA messages are then interpreted by the ribosome, the molecular machine that makes proteins. Many messages are produced from each gene, and each message can be read multiple times. Thus, it should follow that the number of messages produced dictates the number of proteins made. However, this is not the case and the number of proteins produced cannot be completely predicted from knowing the number of messenger RNAs.

Cells control how much of a given protein they produce through interactions between the messenger RNAs and other regulatory RNAs. The regulatory RNAs bind directly to a message and impede protein production. Because there are millions of RNAs in a cell, these interactions have evolved to be highly specific. Nevertheless, it seems inevitable that messenger RNAs would encounter other RNAs too, which could short-circuit gene regulation and lead to less protein being produced.

Umu et al. have now asked if such short-circuit events are selected against during evolution. Computational tools were used to predict the strength of binding between the RNAs found in the dominant forms of microbial life on Earth: the bacteria and the archaea. This approach revealed that the majority of messenger RNAs bind more weakly to the most common RNA molecules found in cells than would be expected by chance. Weakened binding should prevent the RNA molecules from becoming tangled with each other and ensure that protein levels are not perturbed by unintended interactions between highly expressed messages and other RNAs.

To test this hypothesis further, Umu et al. generated versions of the gene for a green fluorescent protein that differed only in how well their messenger RNAs could avoid interacting with the most abundant RNAs in E. coli cells. Those messengers that were designed to avoid interacting with other RNAs yielded far more protein than those that were not. The findings show that taking this kind of avoidance into account can improve predictions about how much protein will be produced and should therefore make it easier to control protein production in experimental systems.

Finally, the messenger RNAs of some bacteria do not show such clear avoidance. However, these bacteria have a more complex internal cell structure. This finding hints at an alternative means for avoiding short-circuiting events that could be used by more complicated cells, such of those of animals and plants, which also contain much larger numbers of RNAs.

DOI:http://dx.doi.org/10.7554/eLife.13479.002

Understanding and predicting protein misfolding and aggregation: Insights from proteomics

Protein misfolding and aggregation are being found to be associated with an increasing number of human diseases and premature aging, either because they promote a loss of protein function or, more frequently, because the aggregated species gain a toxic activity. Despite potentially harmful, aggregation seems to be a generic property of polypeptide chains and aggregation-prone protein sequences seem to be ubiquitous, which, counterintuitively, suggests that they serve evolutionary conserved functions. The in vitro study of individual aggregation reactions of a large number of proteins has provided important insights on the structural and sequential determinants of this process. However, it is clear that understanding the role played by protein aggregation and its regulation in health and disease at the cellular, developmental, and evolutionary levels require more global approaches. The use of model organisms and their proteomic analysis hold the power to provide answers to such issues. In the present review, we address how, initially, computational large-scale analysis and, more recently, experimental proteomics are helping us to rationalize how, why and when proteins aggregate, as well as to decipher the strategies organisms have developed to control proteins aggregation propensities.

Defining the consequences of genetic variation on a proteome-wide scale

Genetic variation modulates protein expression through both transcriptional and post-transcriptional mechanisms. To characterize the consequences of natural genetic diversity on the proteome, here we combine a multiplexed, mass spectrometry-based method for protein quantification with an emerging outbred mouse model containing extensive genetic variation from eight inbred founder strains. By measuring genome-wide transcript and protein expression in livers from 192 Diversity outbred mice, we identify 2,866 protein quantitative trait loci (pQTL) with twice as many local as distant genetic variants. These data support distinct transcriptional and post-transcriptional models underlying the observed pQTL effects. Using a sensitive approach to mediation analysis, we often identified a second protein or transcript as the causal mediator of distant pQTL. Our analysis reveals an extensive network of direct protein-protein interactions. Finally, we show that local genotype can provide accurate predictions of protein abundance in an independent cohort of collaborative cross mice.

Structural hot spots for the solubility of globular proteins

Natural selection shapes protein solubility to physiological requirements and recombinant applications that require higher protein concentrations are often problematic. This raises the question whether the solubility of natural protein sequences can be improved. We here show an anti-correlation between the number of aggregation prone regions (APRs) in a protein sequence and its solubility, suggesting that mutational suppression of APRs provides a simple strategy to increase protein solubility. We show that mutations at specific positions within a protein structure can act as APR suppressors without affecting protein stability. These hot spots for protein solubility are both structure and sequence dependent but can be computationally predicted. We demonstrate this by reducing the aggregation of human α-galactosidase and protective antigen of Bacillus anthracis through mutation. Our results indicate that many proteins possess hot spots allowing to adapt protein solubility independently of structure and function.

Mutations in aggregation prone regions of recombinant proteins often improve their solubility, although they might cause negative effects on their structure and function. Here, the authors identify proteins hot spots that can be exploited to optimize solubility without compromising stability.

Functionalities of expressed messenger RNAs revealed from mutant phenotypes

Total messenger RNAs mRNAs that are produced from a given gene under a certain set of conditions include both functional and nonfunctional transcripts. The high prevalence of nonfunctional mRNAs that have been detected in cells has raised questions regarding the functional implications of mRNA expression patterns and divergences. Phenotypes that result from the mutagenesis of protein-coding genes have provided the most straightforward descriptions of gene functions, and such data obtained from model organisms have facilitated investigations of the functionalities of expressed mRNAs. Mutant phenotype data from mouse tissues have revealed various attributes of functional mRNAs, including tissue-specificity, strength of expression, and evolutionary conservation. In addition, the role that mRNA expression evolution plays in driving morphological evolution has been revealed from studies designed to exploit morphological and physiological phenotypes of mouse mutants. Investigations into yeast essential genes (defined by an absence of colony growth after gene deletion) have further described gene regulatory strategies that reduce protein expression noise by mediating the rates of transcription and translation. In addition to the functional significance of expressed mRNAs as described in the abovementioned findings, the functionalities of other type of RNAs (i.e., noncoding RNAs) remain to be characterized with systematic mutations and phenotyping of the DNA regions that encode these RNA molecules. WIREs RNA 2016, 7:416-427. doi: 10.1002/wrna.1329 For further resources related to this article, please visit the WIREs website.

Direct and Absolute Quantification of over 1800 Yeast Proteins via Selected Reaction Monitoring

Defining intracellular protein concentration is critical in molecular systems biology. Although strategies for determining relative protein changes are available, defining robust absolute values in copies per cell has proven significantly more challenging. Here we present a reference data set quantifying over 1800 Saccharomyces cerevisiae proteins by direct means using protein-specific stable-isotope labeled internal standards and selected reaction monitoring (SRM) mass spectrometry, far exceeding any previous study. This was achieved by careful design of over 100 QconCAT recombinant proteins as standards, defining 1167 proteins in terms of copies per cell and upper limits on a further 668, with robust CVs routinely less than 20%. The selected reaction monitoring-derived proteome is compared with existing quantitative data sets, highlighting the disparities between methodologies. Coupled with a quantification of the transcriptome by RNA-seq taken from the same cells, these data support revised estimates of several fundamental molecular parameters: a total protein count of ∼100 million molecules-per-cell, a median of ∼1000 proteins-per-transcript, and a linear model of protein translation explaining 70% of the variance in translation rate. This work contributes a “gold-standard” reference yeast proteome (including 532 values based on high quality, dual peptide quantification) that can be widely used in systems models and for other comparative studies.

Yeast Interspecies Comparative Proteomics Reveals Divergence in Expression Profiles and Provides Insights into Proteome Resource Allocation and Evolutionary Roles of Gene Duplication

Omics analysis is a versatile approach for understanding the conservation and diversity of molecular systems across multiple taxa. In this study, we compared the proteome expression profiles of four yeast species (Saccharomyces cerevisiae, Saccharomyces mikatae, Kluyveromyces waltii, and Kluyveromyces lactis) grown on glucose- or glycerol-containing media. Conserved expression changes across all species were observed only for a small proportion of all proteins differentially expressed between the two growth conditions. Two Kluyveromyces species, both of which exhibited a high growth rate on glycerol, a nonfermentative carbon source, showed distinct species-specific expression profiles. In K. waltii grown on glycerol, proteins involved in the glyoxylate cycle and gluconeogenesis were expressed in high abundance. In K. lactis grown on glycerol, the expression of glycolytic and ethanol metabolic enzymes was unexpectedly low, whereas proteins involved in cytoplasmic translation, including ribosomal proteins and elongation factors, were highly expressed. These marked differences in the types of predominantly expressed proteins suggest that K. lactis optimizes the balance of proteome resource allocation between metabolism and protein synthesis giving priority to cellular growth. In S. cerevisiae, about 450 duplicate gene pairs were retained after whole-genome duplication. Intriguingly, we found that in the case of duplicates with conserved sequences, the total abundance of proteins encoded by a duplicate pair in S. cerevisiae was similar to that of protein encoded by nonduplicated ortholog in Kluyveromyces yeast. Given the frequency of haploinsufficiency, this observation suggests that conserved duplicate genes, even though minor cases of retained duplicates, do not exhibit a dosage effect in yeast, except for ribosomal proteins. Thus, comparative proteomic analyses across multiple species may reveal not only species-specific characteristics of metabolic processes under nonoptimal culture conditions but also provide valuable insights into intriguing biological principles, including the balance of proteome resource allocation and the role of gene duplication in evolutionary history.

Next-generation analysis of gene expression regulation--comparing the roles of synthesis and degradation

Technological advances now enable routine measurement of mRNA and protein abundances, and estimates of their rates of synthesis and degradation that inform on their values and the degree of change in response to stimuli. Importantly, more and more data on time-series experiments are emerging, e.g. of cells responding to stress, enabling first insights into a new dimension of gene expression regulation - its dynamics and how it allows for very different response signals across genes. This review discusses recently published methods and datasets, their impact on what we now know about the relationships between concentrations and synthesis rates of mRNAs and proteins in yeast and mammalian cells, their evolution, and new hypotheses on translation regulatory mechanisms generated by approaches that involve ribosome footprinting.

Quantitative Mass Spectrometry Reveals Partial Translational Regulation for Dosage Compensation in Chicken

There is increasing evidence that dosage compensation is not a ubiquitous feature following sex chromosome evolution, especially not in organisms where females are the heterogametic sex, like in birds. Even when it occurs, compensation can be incomplete and limited to dosage-sensitive genes. However, previous work has mainly studied transcriptional regulation of sex-linked genes, which may not reflect expression at the protein level. Here, we used liquid chromatography-tandem mass spectrometry to detect and quantify expressed levels of more than 2,400 proteins in ten different tissues of male and female chicken embryos. For comparison, transcriptome sequencing was performed in the same individuals, five of each sex. The proteomic analysis revealed that dosage compensation was incomplete, with a mean male-to-female (M:F) expression ratio of Z-linked genes of 1.32 across tissues, similar to that at the RNA level (1.29). The mean Z chromosome-to-autosome expression ratio was close to 1 in males and lower than 1 in females, consistent with partly reduced Z chromosome expression in females. Although our results exclude a general mechanism for chromosome-wide dosage compensation at translation, 30% of all proteins encoded from Z-linked genes showed a significant change in the M:F ratio compared with the corresponding ratio at the RNA level. This resulted in a pattern where some genes showed balanced expression between sexes and some close to 2-fold higher expression in males. This suggests that proteomic analyses will be necessary to reveal a more complete picture of gene regulation and sex chromosome evolution.

Co-option and evolution of non-olfactory proteinaceous pheromones in a terrestrial lungless salamander

Gene co-option is a major force in the evolution of novel biological functions. In plethodontid salamanders, males deliver proteinaceous courtship pheromones to the female olfactory system or transdermally to the bloodstream. Molecular studies identified three families of highly duplicated, rapidly evolving pheromones (PRF, PMF, and SPF). Analyses for Plethodon salamanders revealed pheromone mixtures of primarily PRF and PMF. The current study demonstrates that in Desmognathus ocoee--a plesiomorphic species with transdermal delivery--SPF is the major pheromone component representing >30% of total protein. Chromatographic profiles of D. ocoee pheromones were consistent from May through October. LC/MS-MS analysis suggested uniform SPF isoform expression between individual male D. ocoee. A gene ancestry for SPF with the Three-Finger Protein superfamily was supported by intron-exon boundaries, but not by the disulfide bonding pattern. Further analysis of the pheromone mixture revealed paralogs to peptide hormones that contained mutations in receptor binding regions, such that these novel molecules may alter female physiology by acting as hormone agonists/antagonists. Cumulatively, gene co-option, duplication, and neofunctionalization have permitted recruitment of additional gene families for pheromone activity. Such independent co-option events may be playing a key role in salamander speciation by altering male traits that influence reproductive success.

Post-transcriptional Mechanisms Contribute Little to Phenotypic Variation in Snake Venoms

Protein expression is a major link in the genotype–phenotype relationship, and processes affecting protein abundances, such as rates of transcription and translation, could contribute to phenotypic evolution if they generate heritable variation. Recent work has suggested that mRNA abundances do not accurately predict final protein abundances, which would imply that post-transcriptional regulatory processes contribute significantly to phenotypes. Post-transcriptional processes also appear to buffer changes in transcriptional patterns as species diverge, suggesting that the transcriptional changes have little or no effect on the phenotypes undergoing study. We tested for concordance between mRNA and protein expression levels in snake venoms by means of mRNA-seq and quantitative mass spectrometry for 11 snakes representing 10 species, six genera, and three families. In contrast to most previous work, we found high correlations between venom gland transcriptomes and venom proteomes for 10 of our 11 comparisons. We tested for protein-level buffering of transcriptional changes during species divergence by comparing the difference between transcript abundance and protein abundance for three pairs of species and one intraspecific pair. We found no evidence for buffering during divergence of our three species pairs but did find evidence for protein-level buffering for our single intraspecific comparison, suggesting that buffering, if present, was a transient phenomenon in venom divergence. Our results demonstrated that post-transcriptional mechanisms did not contribute significantly to phenotypic evolution in venoms and suggest a more prominent and direct role for cis-regulatory evolution in phenotypic variation, particularly for snake venoms.

Molecular portraits: the evolution of the concept of transcriptome-based cancer signatures

Cancer results from dysregulation of multiple steps of gene expression programs. We review how transcriptome profiling has been widely explored for cancer classification and biomarker discovery but resulted in limited clinical impact. Therefore, we discuss alternative and complementary omics approaches.

Sequential metabolic phases as a means to optimize cellular output in a constant environment

Temporal changes of gene expression are a well-known regulatory feature of all cells, which is commonly perceived as a strategy to adapt the proteome to varying external conditions. However, temporal (rhythmic and non-rhythmic) changes of gene expression are also observed under virtually constant external conditions. Here we hypothesize that such changes are a means to render the synthesis of the metabolic output more efficient than under conditions of constant gene activities. In order to substantiate this hypothesis, we used a flux-balance model of the cellular metabolism. The total time span spent on the production of a given set of target metabolites was split into a series of shorter time intervals (metabolic phases) during which only selected groups of metabolic genes are active. The related flux distributions were calculated under the constraint that genes can be either active or inactive whereby the amount of protein related to an active gene is only controlled by the number of active genes: the lower the number of active genes the more protein can be allocated to the enzymes carrying non-zero fluxes. This concept of a predominantly protein-limited efficiency of gene expression clearly differs from other concepts resting on the assumption of an optimal gene regulation capable of allocating to all enzymes and transporters just that fraction of protein necessary to prevent rate limitation. Applying this concept to a simplified metabolic network of the central carbon metabolism with glucose or lactate as alternative substrates, we demonstrate that switching between optimally chosen stationary flux modes comprising different sets of active genes allows producing a demanded amount of target metabolites in a significantly shorter time than by a single optimal flux mode at fixed gene activities. Our model-based findings suggest that temporal expression of metabolic genes can be advantageous even under conditions of constant external substrate supply.

Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell-lines

Protein quantification at proteome‐wide scale is an important aim, enabling insights into fundamental cellular biology and serving to constrain experiments and theoretical models. While proteome‐wide quantification is not yet fully routine, many datasets approaching proteome‐wide coverage are becoming available through biophysical and MS techniques. Data of this type can be accessed via a variety of sources, including publication supplements and online data repositories. However, access to the data is still fragmentary, and comparisons across experiments and organisms are not straightforward. Here, we describe recent updates to our database resource “PaxDb” (Protein Abundances Across Organisms). PaxDb focuses on protein abundance information at proteome‐wide scope, irrespective of the underlying measurement technique. Quantification data is reprocessed, unified, and quality‐scored, and then integrated to build a meta‐resource. PaxDb also allows evolutionary comparisons through precomputed gene orthology relations. Recently, we have expanded the scope of the database to include cell‐line samples, and more systematically scan the literature for suitable datasets. We report that a significant fraction of published experiments cannot readily be accessed and/or parsed for quantitative information, requiring additional steps and efforts. The current update brings PaxDb to 414 datasets in 53 organisms, with (semi‐) quantitative abundance information covering more than 300 000 proteins.

Statistical analysis of the individual variability of 1D protein profiles as a tool in ecology: an application to parasitoid venom

Understanding the forces that shape eco-evolutionary patterns often requires linking phenotypes to genotypes, allowing characterization of these patterns at the molecular level. DNA-based markers are less informative in this aim compared to markers associated with gene expression and, more specifically, with protein quantities. The characterization of eco-evolutionary patterns also usually requires the analysis of large sample sizes to accurately estimate interindividual variability. However, the methods used to characterize and compare protein samples are generally expensive and time-consuming, which constrains the size of the produced data sets to few individuals. We present here a method that estimates the interindividual variability of protein quantities based on a global, semi-automatic analysis of 1D electrophoretic profiles, opening the way to rapid analysis and comparison of hundreds of individuals. The main original features of the method are the in silico normalization of sample protein quantities using pictures of electrophoresis gels at different staining levels, as well as a new method of analysis of electrophoretic profiles based on a median profile. We demonstrate that this method can accurately discriminate between species and between geographically distant or close populations, based on interindividual variation in venom protein profiles from three endoparasitoid wasps of two different genera (Psyttalia concolor, Psyttalia lounsburyi and Leptopilina boulardi). Finally, we discuss the experimental designs that would benefit from the use of this method.

Negative feedback buffers effects of regulatory variants

Mechanisms conferring robustness against regulatory variants have been controversial. Previous studies suggested widespread buffering of RNA misexpression on protein levels during translation. We do not find evidence that translational buffering is common. Instead, we find extensive buffering at the level of RNA expression, exerted through negative feedback regulation acting in trans, which reduces the effect of regulatory variants on gene expression. Our approach is based on a novel experimental design in which allelic differential expression in a yeast hybrid strain is compared to allelic differential expression in a pool of its spores. Allelic differential expression in the hybrid is due to cis-regulatory differences only. Instead, in the pool of spores allelic differential expression is not only due to cis-regulatory differences but also due to local trans effects that include negative feedback. We found that buffering through such local trans regulation is widespread, typically compensating for about 15% of cis-regulatory effects on individual genes. Negative feedback is stronger not only for essential genes, indicating its functional relevance, but also for genes with low to middle levels of expression, for which tight regulation matters most. We suggest that negative feedback is one mechanism of Waddington's canalization, facilitating the accumulation of genetic variants that might give selective advantage in different environments.

Effects of different per translational kinetics on the dynamics of a core circadian clock model

Living beings display self-sustained daily rhythms in multiple biological processes, which persist in the absence of external cues since they are generated by endogenous circadian clocks. The period (per) gene is a central player within the core molecular mechanism for keeping circadian time in most animals. Recently, the modulation PER translation has been reported, both in mammals and flies, suggesting that translational regulation of clock components is important for the proper clock gene expression and molecular clock performance. Because translational regulation ultimately implies changes in the kinetics of translation and, therefore, in the circadian clock dynamics, we sought to study how and to what extent the molecular clock dynamics is affected by the kinetics of PER translation. With this objective, we used a minimal mathematical model of the molecular circadian clock to qualitatively characterize the dynamical changes derived from kinetically different PER translational mechanisms. We found that the emergence of self-sustained oscillations with characteristic period, amplitude, and phase lag (time delays) between per mRNA and protein expression depends on the kinetic parameters related to PER translation. Interestingly, under certain conditions, a PER translation mechanism with saturable kinetics introduces longer time delays than a mechanism ruled by a first-order kinetics. In addition, the kinetic laws of PER translation significantly changed the sensitivity of our model to parameters related to the synthesis and degradation of per mRNA and PER degradation. Lastly, we found a set of parameters, with realistic values, for which our model reproduces some experimental results reported recently for Drosophila melanogaster and we present some predictions derived from our analysis.

Global quantitative proteomics reveals novel factors in the ecdysone signaling pathway in Drosophila melanogaster

The ecdysone signaling pathway plays a major role in various developmental transitions in insects. Recent advances in the understanding of ecdysone action have relied to a large extent on the application of molecular genetic tools in Drosophila. Here, we used a comprehensive quantitative SILAC MS-based approach to study the global, dynamic proteome of a Drosophila cell line to investigate how hormonal signals are transduced into specific cellular responses. Global proteome data after ecdysone treatment after various time points were then integrated with transcriptome data. We observed a substantial overlap in terms of affected targets between the dynamic proteome and transcriptome, although there were some clear differences in timing effects. Also, downregulation of several specific mRNAs did not always correlate with downregulation of their corresponding protein counterparts, and in some cases there was no correlation between transcriptome and proteome dynamics whatsoever. In addition, we performed a comprehensive interactome analysis of EcR, the major target of ecdysone. Proteins copurified with EcR include factors involved in transcription, chromatin remodeling, ecdysone signaling, ecdysone biosynthesis, and other signaling pathways. Novel ecdysone-responsive proteins identified in this study might link previously unknown proteins to the ecdysone signaling pathway and might be novel targets for developmental studies. To our knowledge, this is the first time that ecdysone signaling is studied by global quantitative proteomics. All MS data have been deposited in the ProteomeXchange with identifier PXD001455 (http://proteomecentral.proteomexchange.org/dataset/PXD001455).

Genomic variation. Impact of regulatory variation from RNA to protein

The phenotypic consequences of expression quantitative trait loci (eQTLs) are presumably due to their effects on protein expression levels. Yet the impact of genetic variation, including eQTLs, on protein levels remains poorly understood. To address this, we mapped genetic variants that are associated with eQTLs, ribosome occupancy (rQTLs), or protein abundance (pQTLs). We found that most QTLs are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, eQTLs tend to have significantly reduced effect sizes on protein levels, which suggests that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we identified a class of cis QTLs that affect protein abundance with little or no effect on messenger RNA or ribosome levels, which suggests that they may arise from differences in posttranslational regulation.

Next-generation proteomics: toward customized biomarkers for environmental biomonitoring

Because of their ecological representativeness, invertebrates are commonly employed as test organisms in ecotoxicological assessment; however, to date, biomarkers employed for these species were the result of a direct transposition from vertebrates, despite deep evolutionary divergence. To gain efficiency in the diagnostics of ecosystem health, specific biomarkers must be developed. In this sense, next-generation proteomics enables the specific identification of proteins involved in key physiological functions or defense mechanisms, which are responsive to ecotoxicological challenges. However, the analytical investment required restricts use in biomarker discovery. Routine biomarker validation and assays rely on more conventional mass spectrometers. Here, we describe how proteomics remains a challenge for ecotoxicological test organisms because of the lack of appropriate protein sequences databases, thus restricting the analysis on conserved and ubiquitous proteins. These limits and some strategies used to overcome them are discussed. These new tools, such as proteogenomics and targeted proteomics, should result in new biomarkers specific to relevant environmental organisms and applicable to routine ecotoxicological assessment.

Protein expression analyses at the single cell level

The central dogma of molecular biology explains how genetic information is converted into its end product, proteins, which are responsible for the phenotypic state of the cell. Along with the protein type, the phenotypic state depends on the protein copy number. Therefore, quantification of the protein expression in a single cell is critical for quantitative characterization of the phenotypic states. Protein expression is typically a dynamic and stochastic phenomenon that cannot be well described by standard experimental methods. As an alternative, fluorescence imaging is being explored for the study of protein expression, because of its high sensitivity and high throughput. Here we review key recent progresses in fluorescence imaging-based methods and discuss their application to proteome analysis at the single cell level.

Proteomics for systems toxicology

Current toxicology studies frequently lack measurements at molecular resolution to enable a more mechanism-based and predictive toxicological assessment. Recently, a systems toxicology assessment framework has been proposed, which combines conventional toxicological assessment strategies with system-wide measurement methods and computational analysis approaches from the field of systems biology. Proteomic measurements are an integral component of this integrative strategy because protein alterations closely mirror biological effects, such as biological stress responses or global tissue alterations. Here, we provide an overview of the technical foundations and highlight select applications of proteomics for systems toxicology studies. With a focus on mass spectrometry-based proteomics, we summarize the experimental methods for quantitative proteomics and describe the computational approaches used to derive biological/mechanistic insights from these datasets. To illustrate how proteomics has been successfully employed to address mechanistic questions in toxicology, we summarized several case studies. Overall, we provide the technical and conceptual foundation for the integration of proteomic measurements in a more comprehensive systems toxicology assessment framework. We conclude that, owing to the critical importance of protein-level measurements and recent technological advances, proteomics will be an integral part of integrative systems toxicology approaches in the future.

The emergence of proteome-wide technologies: systematic analysis of proteins comes of age

During the lifetime of a cell proteins can change their localization, alter their abundance and undergo modifications, all of which cannot be assayed by tracking mRNAs alone. Methods to study proteomes directly are coming of age, thereby opening new perspectives on the role of post-translational regulation in stabilizing the cellular milieu. Proteomics has undergone a revolution, and novel technologies for the systematic analysis of proteins have emerged. These methods can expand our ability to acquire information from single proteins to proteomes, from static to dynamic measures and from the population level to the level of single cells. Such approaches promise that proteomes will soon be studied at a similar level of dynamic resolution as has been the norm for transcriptomes.

Functional effects of Toll-like receptor (TLR)3, 7, 9, RIG-I and MDA-5 stimulation in nasal epithelial cells

BACKGROUND

The human nasal epithelium is an important physical barrier, and a part of the innate immune defense that protect against pathogens. The epithelial cells recognize microbial components by pattern-recognition receptors (PRRs), and thereby trigger an immune response. Even though TLR3, TLR7, TLR9, RIG-I and MDA-5 are all known to respond to viral stimulation, their potential role in chronic airway inflammation triggered by local cytokine release remains to be established.

METHODS

mRNA and corresponding protein expression of TLR3, TLR7, TLR9, RIG-I and MDA-5 were analyzed in nasal biopsies and various upper airway epithelial cell lines using real-time reverse transcription PCR, immunohistochemistry and flow cytometry. Ligand induced, cytokine release, was evaluated with ELISA.

RESULTS

Nasal biopsies were found to express TLR3, TLR7, TLR9, RIG-I and MDA-5, with the most abundant expression in the surface epithelium. These receptors were verified in primary human nasal epithelial cell (HNEC) as well as in the airway epithelial cell lines Detroit-562 and FaDu. Poly(I:C) (TLR3) and R-837 (TLR7) stimulation increased secretion of IL-6 and GM-CSF from the nasal mucosa and the epithelial cell lines. CpG (TLR9) stimulation caused release of IL-8 in the nasal mucosa and in FaDu. Poly(I:C)/LyoVec (RIG-I/MDA-5) stimulation activated the secretion of IFN-β in the nasal mucosa. A corresponding release was also detected from HNEC and Detroit-562.

CONCLUSION

The nasal epithelium has the ability to recognize viral intrusion through TLR and RLR receptors, and the subsequent response might have a role in exacerbation of inflammatory diseases like allergic rhinitis and chronic rhinosinusitis.

Universal pacemaker of genome evolution in animals and fungi and variation of evolutionary rates in diverse organisms

Gene evolution is traditionally considered within the framework of the molecular clock (MC) model whereby each gene is characterized by an approximately constant rate of evolution. Recent comparative analysis of numerous phylogenies of prokaryotic genes has shown that a different model of evolution, denoted the Universal PaceMaker (UPM), which postulates conservation of relative, rather than absolute evolutionary rates, yields a better fit to the phylogenetic data. Here, we show that the UPM model is a better fit than the MC for genome wide sets of phylogenetic trees from six species of Drosophila and nine species of yeast, with extremely high statistical significance. Unlike the prokaryotic phylogenies that include distant organisms and multiple horizontal gene transfers, these are simple data sets that cover groups of closely related organisms and consist of gene trees with the same topology as the species tree. The results indicate that both lineage-specific and gene-specific rates are important in genome evolution but the lineage-specific contribution is greater. Similar to the MC, the gene evolution rates under the UPM are strongly overdispersed, approximately 2-fold compared with the expectation from sampling error alone. However, we show that neither Drosophila nor yeast genes form distinct clusters in the tree space. Thus, the gene-specific deviations from the UPM, although substantial, are uncorrelated and most likely depend on selective factors that are largely unique to individual genes. Thus, the UPM appears to be a key feature of genome evolution across the history of cellular life.

Mass spectrometric analysis of differentially expressed proteins in an endangered medicinal herb, Picrorhiza kurroa

Picrorhiza kurroa grown in the Northwestern Himalayan region is used in various herbal formulations but extensive harvesting of this plant has led it to near extinction. The active constituents responsible for the medicinal properties of P. kurroa have been identified as picroside-I and picroside-II which are present in a particular ratio (1:1.5) in herbal formulations like Picroliv. The biosynthetic pathway of picrosides has been partially deciphered till date and needs to be elucidated completely. Review of literature revealed that no information is available as of today on the proteome analysis of Picrorhiza kurroa w.r.t. picroside-II biosynthesis. Therefore, with the aim of identifying proteins associated with picroside biosynthesis in Picrorhiza kurroa, differential protein expression was studied under picroside accumulating versus nonaccumulating conditions using SDS-PAGE. A total of 19 differentially expressed proteins were identified using MALDI-TOF/TOF MS followed by MASCOT search. Proteins involved in diverse functions were identified amongst which the most important proteins were glyceraldehyde-3-phosphate dehydrogenase, 1-aminocyclopropane-1-carboxylate oxidase, photosystem I reaction centre subunit V, 2-oxoglutarate ferrous-dependent oxygenase and putative cytochrome P450 superfamily protein because of their role in picroside biosynthesis. These identified proteins provide an insight and a basic platform for thorough understanding of biosynthesis of secondary metabolites and various other physiological processes of P. kurroa.

Protein-to-mRNA ratios are conserved between Pseudomonas aeruginosa strains

Recent studies have shown that the concentrations of proteins expressed from orthologous genes are often conserved across organisms and to a greater extent than the abundances of the corresponding mRNAs. However, such studies have not distinguished between evolutionary (e.g., sequence divergence) and environmental (e.g., growth condition) effects on the regulation of steady-state protein and mRNA abundances. Here, we systematically investigated the transcriptome and proteome of two closely related Pseudomonas aeruginosa strains, PAO1 and PA14, under identical experimental conditions, thus controlling for environmental effects. For 703 genes observed by both shotgun proteomics and microarray experiments, we found that the protein-to-mRNA ratios are highly correlated between orthologous genes in the two strains to an extent comparable to protein and mRNA abundances. In spite of this high molecular similarity between PAO1 and PA14, we found that several metabolic, virulence, and antibiotic resistance genes are differentially expressed between the two strains, mostly at the protein but not at the mRNA level. Our data demonstrate that the magnitude and direction of the effect of protein abundance regulation occurring after the setting of mRNA levels is conserved between bacterial strains and is important for explaining the discordance between mRNA and protein abundances.

Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast

Understanding the patterns and causes of phenotypic divergence is a central goal in evolutionary biology. Much work has shown that mRNA abundance is highly variable between closely related species. However, the extent and mechanisms of post-transcriptional gene regulatory evolution are largely unknown. Here we used ribosome profiling to compare transcript abundance and translation efficiency in two closely related yeast species (S. cerevisiae and S. paradoxus). By comparing translation regulatory divergence to interspecies differences in mRNA sequence features, we show that differences in transcript leaders and codon bias substantially contribute to divergent translation. Globally, we find that translation regulatory divergence often buffers species differences in mRNA abundance, such that ribosome occupancy is more conserved than transcript abundance. We used allele-specific ribosome profiling in interspecies hybrids to compare the relative contributions of cis- and trans-regulatory divergence to species differences in mRNA abundance and translation efficiency. The mode of gene regulatory divergence differs for these processes, as trans-regulatory changes play a greater role in divergent mRNA abundance than in divergent translation efficiency. Strikingly, most genes with aberrant transcript abundance in F1 hybrids (either over- or underexpressed compared to both parent species) did not exhibit aberrant ribosome occupancy. Our results show that interspecies differences in translation contribute substantially to the evolution of gene expression. Compensatory differences in transcript abundance and translation efficiency may increase the robustness of gene regulation.

A systems-wide comparison of red rice (Oryza longistaminata) tissues identifies rhizome specific genes and proteins that are targets for cultivated rice improvement

BACKGROUND

The rhizome, the original stem of land plants, enables species to invade new territory and is a critical component of perenniality, especially in grasses. Red rice (Oryza longistaminata) is a perennial wild rice species with many valuable traits that could be used to improve cultivated rice cultivars, including rhizomatousness, disease resistance and drought tolerance. Despite these features, little is known about the molecular mechanisms that contribute to rhizome growth, development and function in this plant.

RESULTS

We used an integrated approach to compare the transcriptome, proteome and metabolome of the rhizome to other tissues of red rice. 116 Gb of transcriptome sequence was obtained from various tissues and used to identify rhizome-specific and preferentially expressed genes, including transcription factors and hormone metabolism and stress response-related genes. Proteomics and metabolomics approaches identified 41 proteins and more than 100 primary metabolites and plant hormones with rhizome preferential accumulation. Of particular interest was the identification of a large number of gene transcripts from Magnaportha oryzae, the fungus that causes rice blast disease in cultivated rice, even though the red rice plants showed no sign of disease.

CONCLUSIONS

A significant set of genes, proteins and metabolites appear to be specifically or preferentially expressed in the rhizome of O. longistaminata. The presence of M. oryzae gene transcripts at a high level in apparently healthy plants suggests that red rice is resistant to this pathogen, and may be able to provide genes to cultivated rice that will enable resistance to rice blast disease.

Deregulated expression of Nucleophosmin 1 in gastric cancer and its clinicopathological implications

Background

The process of gastric carcinogenesis still remains to be elucidated. The identification of genes related to this process may help to reduce mortality rates through early diagnosis and the development of new anticancer therapies. Nucleophosmin 1 (NPM1) acts in ribosome biogenesis, centrosome duplication, maintenance of genomic stability, and embryonic development. Recently, NPM1 has been implicated in the tumorigenesis processes. Here, we evaluated NPM1 gene and protein expression in gastric tumors and in corresponding non-neoplastic gastric samples.

Methods

NPM1 protein expression was determined by Western blot in 17 pairs of gastric tumors and corresponding non-neoplastic gastric tissue. The protein immunoreactivity was observed in 12 tumor samples. mRNA expression was evaluated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in 22 pairs of gastric tumors and in matched non-neoplastic gastric tissue.

Results

NPM1 protein expression was significantly reduced in gastric cancer samples compared to matched non-neoplastic gastric samples (P = 0.019). The protein level of NPM1 was reduced at least 1.5-fold in 35% of tumors compared to paired non-neoplastic gastric tissue. However, NPM1 immunoreactivity was detected in neoplastic and non-neoplastic cells, including in intestinal metaplastic, gastritis and inflammatory cells. NPM1 was mainly expressed in nucleus and nucleolus subcellular compartments. The staining intensity and the percentage of immunoreactive cells varied among the studied cases. The NPM1 mRNA level was reduced at least 1.5-fold in 45.5% of samples and increased in 27.3% of samples. An inverse correlation between protein and mRNA expression was detected (r = -0.509, P = 0.037). Intestinal-type gastric cancer presented higher mRNA levels than diffuse-type (P = 0.026). However, reduced NPM1 protein expression was associated with intestinal-type gastric cancer compared to matched non-neoplastic gastric samples (P = 0.018). In addition, tumors from patients with known distant metastasis presented reduced NPM1 protein levels compared to tumors from patients without distant metastasis (P < 0.001).

Conclusion

Although the expression of NPM1 is heterogeneous in gastric tumors, our results suggest that NPM1 down-regulation may have a role in gastric carcinogenesis and may help in the selection of anticancer treatment strategies.

Primate transcript and protein expression levels evolve under compensatory selection pressures

Changes in gene regulation have likely played an important role in the evolution of primates. Differences in messenger RNA (mRNA) expression levels across primates have often been documented; however, it is not yet known to what extent measurements of divergence in mRNA levels reflect divergence in protein expression levels, which are probably more important in determining phenotypic differences. We used high-resolution, quantitative mass spectrometry to collect protein expression measurements from human, chimpanzee, and rhesus macaque lymphoblastoid cell lines and compared them to transcript expression data from the same samples. We found dozens of genes with significant expression differences between species at the mRNA level yet little or no difference in protein expression. Overall, our data suggest that protein expression levels evolve under stronger evolutionary constraint than mRNA levels.

Conserved translatome remodeling in nematode species executing a shared developmental transition

Nematodes of the genus Caenorhabditis enter a developmental diapause state after hatching in the absence of food. To better understand the relative contributions of distinct regulatory modalities to gene expression changes associated with this developmental transition, we characterized genome-wide changes in mRNA abundance and translational efficiency associated with L1 diapause exit in four species using ribosome profiling and mRNA-seq. We found a strong tendency for translational regulation and mRNA abundance processes to act synergistically, together effecting a dramatic remodeling of the gene expression program. While gene-specific differences were observed between species, overall translational dynamics were broadly and functionally conserved. A striking, conserved feature of the response was strong translational suppression of ribosomal protein production during L1 diapause, followed by activation upon resumed development. On a global scale, ribosome footprint abundance changes showed greater similarity between species than changes in mRNA abundance, illustrating a substantial and genome-wide contribution of translational regulation to evolutionary maintenance of stable gene expression.

Network signatures of survival in glioblastoma multiforme

To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included “protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.

Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor in adults, and, while the median survival time for treated patients is approximately one year, subgroups of patients respond differently to the same treatments, with some patients showing little improvement and other patients living far longer than expected. These differences in treatment response indicate that the tumors may show molecular differences that we can harness to tailor cancer therapy. To this end, we sought to identify biomarkers of patient survival in GBM. To improve the applicability of our molecular markers to other patient groups, we constrained our markers using maps of protein-protein interactions, and we also employed a unique computational strategy that incorporates patient-to-patient molecular variability into the results. We identified a set of 50 genes comprising a subnetwork signature that successfully separated GBM patients by their survival times. Our approach to identifying this subnetwork signature also improved our ability to identify its protein products in an independent cohort of patients. In the ongoing search to improve cancer detection and treatment, our work represents a successful strategy for identifying reproducible biomarkers that can more efficiently lead to the discovery of druggable protein targets.

Toward a genome-wide landscape of translational control

Genome-wide analysis of translational control has taken strides in recent years owing to the advent of high-throughput technologies, including DNA microarrays and deep sequencing. Global studies have unraveled a principal role, among posttranscriptional mechanisms, for mRNA translation in determining protein levels in the cell. The impact of translational control in dynamic regulation of the proteome under different conditions is increasingly appreciated. Here we review genome-wide studies that use high-throughput techniques and bioinformatics to assess the role of mRNA translation in the regulation of protein levels; we also discuss how genome-wide data on mRNA translation can be obtained, analyzed, and used to identify mechanisms of translational control.

The initiative on Model Organism Proteomes (iMOP) Session September 6, 2011, Geneva, Switzerland

iMOP--the Initiative on Model Organism Proteomes--was accepted as a new HUPO initiative at the Ninth HUPO meeting in Sydney in 2010. A goal of iMOP is to integrate research groups working on a great diversity of species into a model organism community. At the Tenth HUPO meeting in Geneva this variety was reflected in the iMOP session on Tuesday September 6, 2011. The presentations covered the quantitative proteome database PaxDb, proteomics projects studying farm animals, Arabidopsis thaliana, as well as host-pathogen interactions.

Widespread uncoupling between transcriptome and translatome variations after a stimulus in mammalian cells

BACKGROUND

The classical view on eukaryotic gene expression proposes the scheme of a forward flow for which fluctuations in mRNA levels upon a stimulus contribute to determine variations in mRNA availability for translation. Here we address this issue by simultaneously profiling with microarrays the total mRNAs (the transcriptome) and the polysome-associated mRNAs (the translatome) after EGF treatment of human cells, and extending the analysis to other 19 different transcriptome/translatome comparisons in mammalian cells following different stimuli or undergoing cell programs.

RESULTS

Triggering of the EGF pathway results in an early induction of transcriptome and translatome changes, but 90% of the significant variation is limited to the translatome and the degree of concordant changes is less than 5%. The survey of other 19 different transcriptome/translatome comparisons shows that extensive uncoupling is a general rule, in terms of both RNA movements and inferred cell activities, with a strong tendency of translation-related genes to be controlled purely at the translational level. By different statistical approaches, we finally provide evidence of the lack of dependence between changes at the transcriptome and translatome levels.

CONCLUSIONS

We propose a model of diffused independency between variation in transcript abundances and variation in their engagement on polysomes, which implies the existence of specific mechanisms to couple these two ways of regulating gene expression.

Insights into the regulation of protein abundance from proteomic and transcriptomic analyses

Recent advances in next-generation DNA sequencing and proteomics provide an unprecedented ability to survey mRNA and protein abundances. Such proteome-wide surveys are illuminating the extent to which different aspects of gene expression help to regulate cellular protein abundances. Current data demonstrate a substantial role for regulatory processes occurring after mRNA is made - that is, post-transcriptional, translational and protein degradation regulation - in controlling steady-state protein abundances. Intriguing observations are also emerging in relation to cells following perturbation, single-cell studies and the apparent evolutionary conservation of protein and mRNA abundances. Here, we summarize current understanding of the major factors regulating protein expression.

Comparative analysis of different label-free mass spectrometry based protein abundance estimates and their correlation with RNA-Seq gene expression data

An increasing number of studies involve integrative analysis of gene and protein expression data taking advantage of new technologies such as next-generation transcriptome sequencing (RNA-Seq) and highly sensitive mass spectrometry (MS) instrumentation. Thus, it becomes interesting to revisit the correlative analysis of gene and protein expression data using more recently generated data sets. Furthermore, within the proteomics community there is a substantial interest in comparing the performance of different label-free quantitative proteomic strategies. Gene expression data can be used as an indirect benchmark for such protein-level comparisons. In this work we use publicly available mouse data to perform a joint analysis of genomic and proteomic data obtained on the same organism. First, we perform a comparative analysis of different label-free protein quantification methods (intensity based and spectral count based and using various associated data normalization steps) using several software tools on the proteomic side. Similarly, we perform correlative analysis of gene expression data derived using microarray and RNA-Seq methods on the genomic side. We also investigate the correlation between gene and protein expression data, and various factors affecting the accuracy of quantitation at both levels. It is observed that spectral count based protein abundance metrics, which are easy to extract from any published data, are comparable to intensity based measures with respect to correlation with gene expression data. The results of this work should be useful for designing robust computational pipelines for extraction and joint analysis of gene and protein expression data in the context of integrative studies.

Proteomics in evolutionary ecology: linking the genotype with the phenotype

The study of the proteome (proteomics), which includes the dynamics of protein expression, regulation, interactions and its function, has played a less prominent role in evolutionary and ecological investigations in comparison with the study of the genome and transcriptome. There are, however, a number of arguments suggesting that this situation should change. First, the proteome is closer to the phenotype than the genome or the transcriptome, and as such may be more directly responsive to natural selection, and thus closely linked to adaptation. Second, there is evidence of a low correlation between protein and transcript expression levels across genes in many different organisms. Finally, there have been some recent important technological improvements in proteomics methods that make them feasible, practical and useful to address a wide range of evolutionary questions even in nonmodel organisms. The different proteomic methods, their limitations and problems when interpreting empirical data are described and discussed. In addition, the proteomic literature pertaining to evolutionary ecology is reviewed with examples, and potential applications of proteomics in a variety of evolutionary contexts are outlined. New proteomic research trends such as the study of posttranslational modifications and protein-protein interactions, as well as the combined use of the different -omics approaches, are discussed in relation to the development of a more functional and integrated perspective, needed for achieving a more comprehensive knowledge of evolutionary change.

Label-free protein quantitation using weighted spectral counting

Mass spectrometry (MS)-based shotgun proteomics allows protein identifications even in complex biological samples. Protein abundances can then be estimated from the counts of MS/MS spectra attributable to each protein, provided that one corrects for differential MS-detectability of the contributing peptides. We describe the use of a method, APEX, which calculates Absolute Protein EXpression levels based on learned correction factors, MS/MS spectral counts, and each protein's probability of correct identification.The APEX-based calculations consist of three parts: (1) Using training data, peptide sequences and their sequence properties, a model is built that can be used to estimate MS-detectability (O (i)) for any given protein. (2) Absolute abundances of proteins measured in an MS/MS experiment are calculated with information from spectral counts, identification probabilities and the learned O (i)-values. (3) Simple statistics allow for significance analysis of differential expression in two distinct biological samples, i.e., measuring relative protein abundances. APEX-based protein abundances span more than four orders of magnitude and are applicable to mixtures of hundreds to thousands of proteins from any type of organism.

PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification

Many software tools have been developed for the automated identification of peptides from tandem mass spectra. The accuracy and sensitivity of the identification software via database search are critical for successful proteomics experiments. A new database search tool, PEAKS DB, has been developed by incorporating the de novo sequencing results into the database search. PEAKS DB achieves significantly improved accuracy and sensitivity over two other commonly used software packages. Additionally, a new result validation method, decoy fusion, has been introduced to solve the issue of overconfidence that exists in the conventional target decoy method for certain types of peptide identification software.

Extensive divergence of yeast stress responses through transitions between induced and constitutive activation

Closely related species show a high degree of differences in gene expression, but the functional significance of these differences remains unclear. Similarly, stress responses in yeast typically involve differential expression of numerous genes, and it is unclear how many of these are functionally significant. To address these issues, we compared the expression programs of four yeast species under different growth conditions, and found that the response of these species to stress has diverged extensively. On an individual gene basis, most transcriptional responses are not conserved in any pair of species, and there are very limited common responses among all four species. We present evidence that many evolutionary changes in stress responses are compensated either (i) by the response of related genes or (ii) by changes in the basal expression levels of the genes whose responses have diverged. Thus, stress-related genes are often induced upon stress in some species but maintain high levels even in the absence of stress at other species, indicating a transition between induced and constitutive activation. In addition, ~15% of the stress responses are specific to only one of the four species, with no evidence for compensating effects or stress-related annotations, and these may reflect fortuitous regulation that is unimportant for the stress response (i.e., biological noise). Frequent compensatory changes and biological noise may explain how diverged expression responses support similar physiological responses.