Characterization of global yeast quantitative proteome data generated from the wild-type and glucose repression saccharomyces cerevisiae strains: the comparison of two quantitative methods

The effect of obesity on uterine receptivity is mediated by endometrial extracellular vesicles that control human endometrial stromal cell decidualization and trophoblast invasion

The objectives of the present study were to determine whether obesity impacts human decidualization and the endometrial control of trophoblast invasion (both of which are required for embryo implantation) and evaluate the potential involvement of endometrial extracellular vesicles (EVs) in the regulation of these physiological processes. Using primary human cell cultures, we first demonstrated that obesity is associated with significantly lower in vitro decidualization of endometrial stromal cells (ESCs). We then showed that a trophoblastic cell line's invasive ability was greater in the presence of conditioned media from cultures of ESCs from obese women. The results of functional assays indicated that supplementation of the culture medium with EVs from nonobese women can rescue (at least in part) the defect in in vitro decidualization described in ESCs from obese women. Furthermore, exposure to endometrial EVs from obese women (vs. nonobese women) was associated with significantly greater invasive activity by HTR-8/SVneo cells. Using mass-spectrometry-based quantitative proteomics, we found that EVs isolated from uterine supernatants of biopsies from obese women (vs. nonobese women) presented a molecular signature focused on cell remodelling and angiogenesis. The proteomics analysis revealed two differentially expressed proteins (fibronectin and angiotensin-converting enzyme) that might be involved specifically in the rescue of the decidualization capacity in ESCs from obese women; both of these proteins are abundantly present in endometrial EVs from nonobese women, and both are involved in the decidualization process. In conclusion, our results provided new insights into the endometrial EVs' pivotal role in the poor uterine receptivity observed in obese women.

Insights into the Structure and Protein Composition of Moorella thermoacetica Spores Formed at Different Temperatures

The bacterium Moorella thermoacetica produces the most heat-resistant spores of any spoilage-causing microorganism known in the food industry. Previous work by our group revealed that the resistance of these spores to wet heat and biocides was lower when spores were produced at a lower temperature than the optimal temperature. Here, we used electron microcopy to characterize the ultrastructure of the coat of the spores formed at different sporulation temperatures; we found that spores produced at 55 °C mainly exhibited a lamellar inner coat tightly associated with a diffuse outer coat, while spores produced at 45 °C showed an inner and an outer coat separated by a less electron-dense zone. Moreover, misarranged coat structures were more frequently observed when spores were produced at the lower temperature. We then analyzed the proteome of the spores obtained at either 45 °C or 55 °C with respect to proteins putatively involved in the spore coat, exosporium, or in spore resistance. Some putative spore coat proteins, such as CotSA, were only identified in spores produced at 55 °C; other putative exosporium and coat proteins were significantly less abundant in spores produced at 45 °C. Altogether, our results suggest that sporulation temperature affects the structure and protein composition of M. thermoacetica spores.

Established and Upcoming Yeast Expression Systems

Yeast was the first microorganism used by mankind for biotransformation of feedstock that laid the foundations of industrial biotechnology. Long historical use, vast amount of data, and experience paved the way for Saccharomyces cerevisiae as a first yeast cell factory, and still it is an important expression platform as being the production host for several large volume products. Continuing special needs of each targeted product and different requirements of bioprocess operations have led to identification of different yeast expression systems. Modern bioprocess engineering and advances in omics technology, i.e., genomics, transcriptomics, proteomics, secretomics, and interactomics, allow the design of novel genetic tools with fine-tuned characteristics to be used for research and industrial applications. This chapter focuses on established and upcoming yeast expression platforms that have exceptional characteristics, such as the ability to utilize a broad range of carbon sources or remarkable resistance to various stress conditions. Besides the conventional yeast S. cerevisiae, established yeast expression systems including the methylotrophic yeasts Pichia pastoris and Hansenula polymorpha, the dimorphic yeasts Arxula adeninivorans and Yarrowia lipolytica, the lactose-utilizing yeast Kluyveromyces lactis, the fission yeast Schizosaccharomyces pombe, and upcoming yeast platforms, namely, Kluyveromyces marxianus, Candida utilis, and Zygosaccharomyces bailii, are compiled with special emphasis on their genetic toolbox for recombinant protein production.

Biomaterial Scaffolds as Pre-metastatic Niche Mimics Systemically Alter the Primary Tumor and Tumor Microenvironment

Primary tumor (PT) immune cells and pre-metastatic niche (PMN) sites are critical to metastasis. Recently, synthetic biomaterial scaffolds used as PMN mimics are shown to capture both immune and metastatic tumor cells. Herein, studies are performed to investigate whether the scaffold-mediated redirection of immune and tumor cells would alter the primary tumor microenvironment (TME). Transcriptomic analysis of PT cells from scaffold-implanted and mock-surgery mice identifies differentially regulated pathways relevant to invasion and metastasis progression. Transcriptomic differences are hypothesized to result from scaffold-mediated modulations of immune cell trafficking and phenotype in the TME. Culturing tumor cells with conditioned media generated from PT immune cells of scaffold-implanted mice decrease invasion in vitro more than two-fold relative to mock surgery controls and reduce activity of invasion-promoting transcription factors. Secretomic characterization of the conditioned media delineates interactions between immune cells in the TME and tumor cells, showing an increase in the pan-metastasis inhibitor decorin and a concomitant decrease in invasion-promoting chemokine (C-C motif) ligand 2 (CCL2) in scaffold-implanted mice. Flow cytometric and transcriptomic profiling of PT immune cells identify phenotypically distinct tumor-associated macrophages (TAMs) in scaffold-implanted mice, which may contribute to an invasion-suppressive TME. Taken together, this study demonstrates biomaterial scaffolds systemically influence metastatic progression through manipulation of the TME.

Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome

Protein activity is the ultimate arbiter of function in most cellular pathways, and protein concentration is fundamentally connected to protein action. While the proteome of yeast has been subjected to the most comprehensive analysis of any eukaryote, existing datasets are difficult to compare, and there is no consensus abundance value for each protein. We evaluated 21 quantitative analyses of the S. cerevisiae proteome, normalizing and converting all measurements of protein abundance into the intuitive measurement of absolute molecules per cell. We estimate the cellular abundance of 92% of the proteins in the yeast proteome and assess the variation in each abundance measurement. Using our protein abundance dataset, we find that a global response to diverse environmental stresses is not detected at the level of protein abundance, we find that protein tags have only a modest effect on protein abundance, and we identify proteins that are differentially regulated at the mRNA abundance, mRNA translation, and protein abundance levels.

Recent advances in capillary ultrahigh pressure liquid chromatography

In the twenty years since its initial demonstration, capillary ultrahigh pressure liquid chromatography (UHPLC) has proven to be one of most powerful separation techniques for the analysis of complex mixtures. This review focuses on the most recent advances made since 2010 towards increasing the performance of such separations. Improvements in capillary column preparation techniques that have led to columns with unprecedented performance are described. New stationary phases and phase supports that have been reported over the past decade are detailed, with a focus on their use in capillary formats. A discussion on the instrument developments that have been required to ensure that extra-column effects do not diminish the intrinsic efficiency of these columns during analysis is also included. Finally, the impact of these capillary UHPLC topics on the field of proteomics and ways in which capillary UHPLC may continue to be applied to the separation of complex samples are addressed.

A peptide-retrieval strategy enables significant improvement of quantitative performance without compromising confidence of identification

Reliable quantification of low-abundance proteins in complex proteomes is challenging largely owing to the limited number of spectra/peptides identified. In this study we developed a straightforward method to improve the quantitative accuracy and precision of proteins by strategically retrieving the less confident peptides that were previously filtered out using the standard target-decoy search strategy. The filtered-out MS/MS spectra matched to confidently-identified proteins were recovered, and the peptide-spectrum-match FDR were re-calculated and controlled at a confident level of FDR≤1%, while protein FDR maintained at ~1%. We evaluated the performance of this strategy in both spectral count- and ion current-based methods. >60% increase of total quantified spectra/peptides was respectively achieved for analyzing a spike-in sample set and a public dataset from CPTAC. Incorporating the peptide retrieval strategy significantly improved the quantitative accuracy and precision, especially for low-abundance proteins (e.g. one-hit proteins). Moreover, the capacity of confidently discovering significantly-altered proteins was also enhanced substantially, as demonstrated with two spike-in datasets. In summary, improved quantitative performance was achieved by this peptide recovery strategy without compromising confidence of protein identification, which can be readily implemented in a broad range of quantitative proteomics techniques including label-free or labeling approaches.

SIGNIFICANCE

We hypothesize that more quantifiable spectra and peptides in a protein, even including less confident peptides, could help reduce variations and improve protein quantification. Hence the peptide retrieval strategy was developed and evaluated in two spike-in sample sets with different LC-MS/MS variations using both MS1- and MS2-based quantitative approach. The list of confidently identified proteins using the standard target-decoy search strategy was fixed and more spectra/peptides with less confidence matched to confident proteins were retrieved. However, the total peptide-spectrum-match false discovery rate (PSM FDR) after retrieval analysis was still controlled at a confident level of FDR≤1%. As expected, the penalty for occasionally incorporating incorrect peptide identifications is negligible by comparison with the improvements in quantitative performance. More quantifiable peptides, lower missing value rate, better quantitative accuracy and precision were significantly achieved for the same protein identifications by this simple strategy. This strategy is theoretically applicable for any quantitative approaches in proteomics and thereby provides more quantitative information, especially on low-abundance proteins.

Protein abundance changes of Zygosaccharomyces rouxii in different sugar concentrations

Zygosaccharomyces rouxii is a yeast which can cause spoilage in the concentrated juice industries. It exhibits resistance to high sugar concentrations but genome- and proteome-wide studies on Z. rouxii in response to high sugar concentrations have been poorly investigated. Herein, by using a 2-D electrophoresis based workflow, the proteome of a wild strain of Z. rouxii under different sugar concentrations has been analyzed. Proteins were extracted, quantified, and subjected to 2-DE analysis in the pH range 4-7. Differences in growth (lag phase), protein content (13.97-19.23mg/g cell dry weight) and number of resolved spots (196-296) were found between sugar concentrations. ANOVA test showed that 168 spots were different, and 47 spots, corresponding to 40 unique gene products have been identified. These protein species are involved in carbohydrate and energy metabolism, amino acid metabolism, response to stimulus, protein transport and vesicle organization, cell morphogenesis regulation, transcription and translation, nucleotide metabolism, amino-sugar nucleotide-sugar pathways, oxidoreductases balancing, and ribosome biogenesis. The present study provides important information about how Z. rouxii acts to cope with high sugar concentration at molecular levels, which might enhance our global understanding of Z. rouxii's high sugar-tolerance trait.

In Vitro Metabolic Labeling of Intestinal Microbiota for Quantitative Metaproteomics

Intestinal microbiota is emerging as one of the key environmental factors influencing or causing the development of numerous human diseases. Metaproteomics can provide invaluable information on the functional activities of intestinal microbiota and on host-microbe interactions as well. However, the application of metaproteomics in human microbiota studies is still largely limited, in part due to the lack of accurate quantitative intestinal metaproteomic methods. Most current metaproteomic microbiota studies are based on label-free quantification, which may suffer from variability during the separate sample processing and mass spectrometry runs. In this study, we describe a quantitative metaproteomic strategy, using in vitro stable isotopically ((15)N) labeled microbiota as a spike-in reference, to study the intestinal metaproteomes. We showed that the human microbiota were efficiently labeled (>95% (15)N enrichment) within 3 days under in vitro conditions, and accurate light-to-heavy protein/peptide ratio measurements were obtained using a high-resolution mass spectrometer and the quantitative proteomic software tool Census. We subsequently employed our approach to study the in vitro modulating effects of fructo-oligosaccharide and five different monosaccharides on the microbiota. Our methodology improves the accuracy of quantitative intestinal metaproteomics, which would promote the application of proteomics for functional studies of intestinal microbiota.

A multi-model statistical approach for proteomic spectral count quantitation

The rapid development of mass spectrometry (MS) technologies has solidified shotgun proteomics as the most powerful analytical platform for large-scale proteome interrogation. The ability to map and determine differential expression profiles of the entire proteome is the ultimate goal of shotgun proteomics. Label-free quantitation has proven to be a valid approach for discovery shotgun proteomics, especially when sample is limited. Label-free spectral count quantitation is an approach analogous to RNA sequencing whereby count data is used to determine differential expression. Here we show that statistical approaches developed to evaluate differential expression in RNA sequencing experiments can be applied to detect differential protein expression in label-free discovery proteomics. This approach, termed MultiSpec, utilizes open-source statistical platforms; namely edgeR, DESeq and baySeq, to statistically select protein candidates for further investigation. Furthermore, to remove bias associated with a single statistical approach a single ranked list of differentially expressed proteins is assembled by comparing edgeR and DESeq q-values directly with the false discovery rate (FDR) calculated by baySeq. This statistical approach is then extended when applied to spectral count data derived from multiple proteomic pipelines. The individual statistical results from multiple proteomic pipelines are integrated and cross-validated by means of collapsing protein groups.

BIOLOGICAL SIGNIFICANCE

Spectral count data from shotgun proteomics experiments is semi-quantitative and semi-random, yet a robust way to estimate protein concentration. Tag-count approaches are routinely used to analyze RNA sequencing data sets. This approach, termed MultiSpec, utilizes multiple tag-count based statistical tests to determine differential protein expression from spectral counts. The statistical results from these tag-count approaches are combined in order to reach a final MultiSpec q-value to re-rank protein candidates. This re-ranking procedure is completed to remove bias associated with a single approach in order to better understand the true proteomic differences driving the biology in question. The MultiSpec approach can be extended to multiple proteomic pipelines. In such an instance, MultiSpec statistical results are integrated by collapsing protein groups across proteomic pipelines to provide a single ranked list of differentially expressed proteins. This integration mechanism is seamlessly integrated with the statistical analysis and provides the means to cross-validate protein inferences from multiple proteomic pipelines.

Methods and applications of absolute protein quantification in microbial systems

In the last years the scientific community faced an increased need to provide high-quality data on the concentration of single proteins within a cell. Especially against the background of the fast evolving field of systems biology this does not only apply for a few proteins but preferably for the whole proteome of the organism. Therefore there has been a rapid development from pure identification of proteins via characterization of changes between different conditions by relative protein quantification towards determination of absolute protein amounts for hundreds of protein species in a cell. This review aims for discussion of different small-scale and large-scale approaches for absolute protein quantification in bacterial cells to picture biological processes and explore life in deeper detail. The presented advantages and limitations of various methods may provide interested researchers help to appraise available methods, select the most appropriate technique and avoid common pitfalls during determination of protein concentration in a complex sample.

Secretome identification of immune cell factors mediating metastatic cell homing

Metastatic cell homing is a complex process mediated in part by diffusible factors secreted from immune cells found at a pre-metastatic niche. We report on connecting secretomics and TRanscriptional Activity CEll aRray (TRACER) data to identify functional paracrine interactions between immune cells and metastatic cells as novel mediators of homing. Metastatic breast cancer mouse models were used to generate a diseased splenocyte conditioned media (D-SCM) containing immune cell secreted factors. MDA-MB-231 metastatic cell activity including cell invasion, migration, transendothelial migration, and proliferation were increased in D-SCM relative to control media. Our D-SCM secretome analysis yielded 144 secreted factor candidates that contribute to increased metastatic cell activity. The functional mediators of homing were identified using MetaCore software to determine interactions between the immune cell secretome and the TRACER-identified active transcription factors within metastatic cells. Among the 5 candidate homing factors identified, haptoglobin was selected and validated in vitro and in vivo as a key mediator of homing. Our studies demonstrate a novel systems biology approach to identify functional signaling factors associated with a cellular phenotype, which provides an enabling tool that complements large-scale protein identification provided by proteomics.

The proteome and lipidome of Synechocystis sp. PCC 6803 cells grown under light-activated heterotrophic conditions

Cyanobacteria are photoautotrophic prokaryotes with a plant-like photosynthetic machinery. Because of their short generation times, the ease of their genetic manipulation, and the limited size of their genome and proteome, cyanobacteria are popular model organisms for photosynthetic research. Although the principal mechanisms of photosynthesis are well-known, much less is known about the biogenesis of the thylakoid membrane, hosting the components of the photosynthetic, and respiratory electron transport chain in cyanobacteria. Here we present a detailed proteome analysis of the important model and host organism Synechocystis sp. PCC 6803 under light-activated heterotrophic growth conditions. Because of the mechanistic importance and severe changes in thylakoid membrane morphology under light-activated heterotrophic growth conditions, a focus was put on the analysis of the membrane proteome, which was supported by a targeted lipidome analysis. In total, 1528 proteins (24.5% membrane integral) were identified in our analysis. For 641 of these proteins quantitative information was obtained by spectral counting. Prominent changes were observed for proteins associated with oxidative stress response and protein folding. Because of the heterotrophic growth conditions, also proteins involved in carbon metabolism and C/N-balance were severely affected. Although intracellular thylakoid membranes were significantly reduced, only minor changes were observed in their protein composition. The increased proportion of the membrane-stabilizing sulfoqinovosyl diacyl lipids found in the lipidome analysis, as well as the increased content of lipids with more saturated acyl chains, are clear indications for a coordinated synthesis of proteins and lipids, resulting in stabilization of intracellular thylakoid membranes under stress conditions.

Multiplexed, Quantitative Workflow for Sensitive Biomarker Discovery in Plasma Yields Novel Candidates for Early Myocardial Injury

We have developed a novel plasma protein analysis platform with optimized sample preparation, chromatography, and MS analysis protocols. The workflow, which utilizes chemical isobaric mass tag labeling for relative quantification of plasma proteins, achieves far greater depth of proteome detection and quantification while simultaneously having increased sample throughput than prior methods. We applied the new workflow to a time series of plasma samples from patients undergoing a therapeutic, "planned" myocardial infarction for hypertrophic cardiomyopathy, a unique human model in which each person serves as their own biologic control. Over 5300 proteins were confidently identified in our experiments with an average of 4600 proteins identified per sample (with two or more distinct peptides identified per protein) using iTRAQ four-plex labeling. Nearly 3400 proteins were quantified in common across all 16 patient samples. Compared with a previously published label-free approach, the new method quantified almost fivefold more proteins/sample and provided a six- to nine-fold increase in sample analysis throughput. Moreover, this study provides the largest high-confidence plasma proteome dataset available to date. The reliability of relative quantification was also greatly improved relative to the label-free approach, with measured iTRAQ ratios and temporal trends correlating well with results from a 23-plex immunoMRM (iMRM) assay containing a subset of the candidate proteins applied to the same patient samples. The functional importance of improved detection and quantification was reflected in a markedly expanded list of significantly regulated proteins that provided many new candidate biomarker proteins. Preliminary evaluation of plasma sample labeling with TMT six-plex and ten-plex reagents suggests that even further increases in multiplexing of plasma analysis are practically achievable without significant losses in depth of detection relative to iTRAQ four-plex. These results obtained with our novel platform provide clear demonstration of the value of using isobaric mass tag reagents in plasma-based biomarker discovery experiments.

Exocytosis and Endocytosis of Small Vesicles across the Plasma Membrane in Saccharomyces cerevisiae

When Saccharomyces cerevisiae is starved of glucose, the gluconeogenic enzymes fructose-1,6-bisphosphatase (FBPase), phosphoenolpyruvate carboxykinase, isocitrate lyase, and malate dehydrogenase, as well as the non-gluconeogenic enzymes glyceraldehyde-3-phosphate dehydrogenase and cyclophilin A, are secreted into the periplasm. In the extracellular fraction, these secreted proteins are associated with small vesicles that account for more than 90% of the total number of extracellular structures observed. When glucose is added to glucose-starved cells, FBPase is internalized and associated with clusters of small vesicles in the cytoplasm. Specifically, the internalization of FBPase results in the decline of FBPase and vesicles in the extracellular fraction and their appearance in the cytoplasm. The clearance of extracellular vesicles and vesicle-associated proteins from the extracellular fraction is dependent on the endocytosis gene END3. This internalization is regulated when cells are transferred from low to high glucose. It is rapidly occurring and is a high capacity process, as clusters of vesicles occupy 10%–20% of the total volume in the cytoplasm in glucose re-fed cells. FBPase internalization also requires the VPS34 gene encoding PI3K. Following internalization, FBPase is delivered to the vacuole for degradation, whereas proteins that are not degraded may be recycled.

Functional characterization of the small regulatory subunit PetP from the cytochrome b6f complex in Thermosynechococcus elongatus

The cyanobacterial cytochrome b(6)f complex is central for the coordination of photosynthetic and respiratory electron transport and also for the balance between linear and cyclic electron transport. The development of a purification strategy for a highly active dimeric b(6)f complex from the thermophilic cyanobacterium Thermosynechococcus elongatus BP-1 enabled characterization of the structural and functional role of the small subunit PetP in this complex. Moreover, the efficient transformability of this strain allowed the generation of a ΔpetP mutant. Analysis on the whole-cell level by growth curves, photosystem II light saturation curves, and P700(+) reduction kinetics indicate a strong decrease in the linear electron transport in the mutant strain versus the wild type, while the cyclic electron transport via photosystem I and cytochrome b(6)f is largely unaffected. This reduction in linear electron transport is accompanied by a strongly decreased stability and activity of the isolated ΔpetP complex in comparison with the dimeric wild-type complex, which binds two PetP subunits. The distinct behavior of linear and cyclic electron transport may suggest the presence of two distinguishable pools of cytochrome b(6)f complexes with different functions that might be correlated with supercomplex formation.

Improving production of malonyl coenzyme A-derived metabolites by abolishing Snf1-dependent regulation of Acc1

Acetyl coenzyme A (acetyl-CoA) carboxylase (ACCase) plays a central role in carbon metabolism and has been the site of action for the development of therapeutics or herbicides, as its product, malonyl-CoA, is a precursor for production of fatty acids and other compounds. Control of Acc1 activity in the yeast Saccharomyces cerevisiae occurs mainly at two levels, i.e., regulation of transcription and repression by Snf1 protein kinase at the protein level. Here, we demonstrate a strategy for improving the activity of ACCase in S. cerevisiae by abolishing posttranslational regulation of Acc1 via site-directed mutagenesis. It was found that introduction of two site mutations in Acc1, Ser659 and Ser1157, resulted in an enhanced activity of Acc1 and increased total fatty acid content. As Snf1 regulation of Acc1 is particularly active under glucose-limited conditions, we evaluated the effect of the two site mutations in chemostat cultures. Finally, we showed that our modifications of Acc1 could enhance the supply of malonyl-CoA and therefore successfully increase the production of two industrially important products derived from malonyl-CoA, fatty acid ethyl esters and 3-hydroxypropionic acid.

ACCase is responsible for carboxylation of acetyl-CoA to produce malonyl-CoA, which is a crucial step in the control of fatty acid metabolism. ACCase opened the door for pharmaceutical treatments of obesity and diabetes as well as the development of new herbicides. ACCase is also recognized as a promising target for developing cell factories, as its malonyl-CoA product serves as a universal precursor for a variety of high-value compounds in white biotechnology. Yeast ACCase is a good model in understanding the enzyme’s catalysis, regulation, and inhibition. The present study describes the importance of protein phosphorylation in regulation of yeast ACCase and identifies potential regulation sites. This study led to the generation of a more efficient ACCase, which was applied in the production of two high-value compounds derived from malonyl-CoA, i.e., fatty acid ethyl esters that can be used as biodiesel and 3-hydroxypropionic acid that is considered an important platform chemical.

Contribution of network connectivity in determining the relationship between gene expression and metabolite concentration changes

One of the primary mechanisms through which a cell exerts control over its metabolic state is by modulating expression levels of its enzyme-coding genes. However, the changes at the level of enzyme expression allow only indirect control over metabolite levels, for two main reasons. First, at the level of individual reactions, metabolite levels are non-linearly dependent on enzyme abundances as per the reaction kinetics mechanisms. Secondly, specific metabolite pools are tightly interlinked with the rest of the metabolic network through their production and consumption reactions. While the role of reaction kinetics in metabolite concentration control is well studied at the level of individual reactions, the contribution of network connectivity has remained relatively unclear. Here we report a modeling framework that integrates both reaction kinetics and network connectivity constraints for describing the interplay between metabolite concentrations and mRNA levels. We used this framework to investigate correlations between the gene expression and the metabolite concentration changes in Saccharomyces cerevisiae during its metabolic cycle, as well as in response to three fundamentally different biological perturbations, namely gene knockout, nutrient shock and nutrient change. While the kinetic constraints applied at the level of individual reactions were found to be poor descriptors of the mRNA-metabolite relationship, their use in the context of the network enabled us to correlate changes in the expression of enzyme-coding genes to the alterations in metabolite levels. Our results highlight the key contribution of metabolic network connectivity in mediating cellular control over metabolite levels, and have implications towards bridging the gap between genotype and metabolic phenotype.

Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms

The mitochondrial free radical theory of aging (MFRTA) states that reactive oxygen species (ROS) generated at the respiratory electron transport chain are active in causing age-related damage of biomolecules like lipids, nucleic acids and proteins. Accumulation of this kind of damage results in functional impairments, aging and death of biological systems. Here we report data of an analysis to monitor the age-related quantitative protein composition of the mitochondria of the fungal aging model Podospora anserina. The impact of senescence on mitochondrial protein composition was analyzed by LC-MS. In an untargeted proteomic approach, we identified 795 proteins in samples from juvenile and senescent wild-type cultures and obtained quantitative information for 226 of these proteins by spectral counting. Despite the broad coverage of the proteome, no substantial changes in known age-related pathways could be observed. For a more detailed analysis, a targeted proteome analysis was applied focusing on 15 proteins from respiratory, ROS-scavenging and quality control pathways. Analyzing six distinct age-stages from juvenile to senescent P. anserina cultures revealed low, but statistically significant changes for the mitochondrial respiratory complexes. A P. anserina PaSod3 over-expression mutant with a phenotype of mitochondrial ROS over-production was used for biological evaluation of changes observed during aging. LC-MS analysis of the mutant revealed severe changes to the mitochondrial proteome--substantially larger than observed during senescence. Interestingly the amount of ATP synthase subunit g, involved in cristae formation is significantly decreased in the mutant implicating ROS-induced impairments in ATP synthase dimer and cristae formation. The difference between protein-profiles of aging wild type and ROS stressed mutant suggests that oxidative stress within the mitochondria is not the dominating mechanism for the aging process in P. anserina. Collectively, while our data do not exclude an effect of ROS on specific proteins and in signaling and control of pathways which are governing aging of P. anserina, it contradicts increasing ROS as a cause of a gross general and non-selective accumulation of damaged proteins during senescence. Instead, ROS may be effective by controlling specific regulators of mitochondrial function.

Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

Analysis of protein interaction networks and protein complexes using affinity purification and mass spectrometry (AP/MS) is among most commonly used and successful applications of proteomics technologies. One of the foremost challenges of AP/MS data is a large number of false-positive protein interactions present in unfiltered data sets. Here we review computational and informatics strategies for detecting specific protein interaction partners in AP/MS experiments, with a focus on incomplete (as opposite to genome wide) interactome mapping studies. These strategies range from standard statistical approaches, to empirical scoring schemes optimized for a particular type of data, to advanced computational frameworks. The common denominator among these methods is the use of label-free quantitative information such as spectral counts or integrated peptide intensities that can be extracted from AP/MS data. We also discuss related issues such as combining multiple biological or technical replicates, and dealing with data generated using different tagging strategies. Computational approaches for benchmarking of scoring methods are discussed, and the need for generation of reference AP/MS data sets is highlighted. Finally, we discuss the possibility of more extended modeling of experimental AP/MS data, including integration with external information such as protein interaction predictions based on functional genomics data.

Adaptation to potassium starvation of wild-type and K(+)-transport mutant (trk1,2) of Saccharomyces cerevisiae: 2-dimensional gel electrophoresis-based proteomic approach

Saccharomyces cerevisiae wild-type (BY4741) and the corresponding mutant lacking the plasma membrane main potassium uptake systems (trk1,trk2) were used to analyze the consequences of K(+) starvation following a proteomic approach. In order to trigger high-affinity mode of potassium transport, cells were transferred to potassium-free medium. Protein profile was followed by two-dimensional (2-D) gels in samples taken at 0, 30, 60, 120, 180, and 300 min during starvation. We observed a general decrease of protein content during starvation that was especially drastic in the mutant strain as it was the case of an important number of proteins involved in glycolysis. On the contrary, we identified proteins related to stress response and alternative energetic metabolism that remained clearly present. Neural network-based analysis indicated that wild type was able to adapt much faster than the mutant to the stress process. We conclude that complete potassium starvation is a stressful process for yeast cells, especially for potassium transport mutants, and we propose that less stressing conditions should be used in order to study potassium homeostasis in yeast.

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.

Comparative proteomic analysis of transition of saccharomyces cerevisiae from glucose-deficient medium to glucose-rich medium

Background

When glucose is added to Saccharomyces cerevisiae grown in non-fermentable carbon sources, genes encoding ribosomal, cell-cycle, and glycolytic proteins are induced. By contrast, genes involved in mitochondrial functions, gluconeogenesis, and the utilization of other carbon sources are repressed. Glucose also causes the activation of the plasma membrane ATPase and the inactivation of gluconeogenic enzymes and mitochondrial enzymes. The goals of this study were to use the iTRAQ-labeling mass spectrometry technique to identify proteins whose relative levels change in response to glucose re-feeding and to correlate changes in protein abundance with changes in transcription and enzymatic activities. We used an experimental condition that causes the degradation of gluconeogenic enzymes when glucose starved cells are replenished with glucose. Identification of these enzymes as being down-regulated by glucose served as an internal control. Furthermore, we sought to identify new proteins that were either up-regulated or down-regulated by glucose.

Results

We have identified new and known proteins that change their relative levels in cells that were transferred from medium containing low glucose to medium containing high glucose. Up-regulated proteins included ribosomal subunits, proteins involved in protein translation, and the plasma membrane ATPase. Down-regulated proteins included small heat shock proteins, mitochondrial proteins, glycolytic enzymes, and gluconeogenic enzymes. Ach1p is involved in acetate metabolism and is also down-regulated by glucose.

Conclusions

We have identified known proteins that have previously been reported to be regulated by glucose as well as new glucose-regulated proteins. Up-regulation of ribosomal proteins and proteins involved in translation may lead to an increase in protein synthesis and in nutrient uptake. Down-regulation of glycolytic enzymes, gluconeogenic enzymes, and mitochondrial proteins may result in changes in glycolysis, gluconeogenesis, and mitochondrial functions. These changes may be beneficial for glucose-starved cells to adapt to the addition of glucose.

An insight into iTRAQ: where do we stand now?

The iTRAQ (isobaric tags for relative and absolute quantification) technique is widely employed in proteomic workflows requiring relative quantification. Here, we review the iTRAQ literature; in particular, we focus on iTRAQ usage in relation to other commonly used quantitative techniques e.g. stable isotope labelling in culture (SILAC), label-free methods and selected reaction monitoring (SRM). As a result, we identify several issues arising with respect to iTRAQ. Perhaps frustratingly, iTRAQ's attractiveness has been undermined by a number of technical and analytical limitations: it may not be truly quantitative, as the changes in abundance reported will generally be underestimated. We discuss weaknesses and strengths of iTRAQ as a methodology for relative quantification in the light of this and other technical issues. We focus on technical developments targeted at iTRAQ accuracy and precision, use of 4-plex over 8-plex reagents and application of iTRAQ to post-translational modification (PTM) workflows. We also discuss iTRAQ in relation to label-free approaches, to which iTRAQ is losing ground.

Proteomic profile of reversible protein oxidation using PROP, purification of reversibly oxidized proteins

Signal transduction pathways that are modulated by thiol oxidation events are beginning to be uncovered, but these discoveries are limited by the availability of relatively few analytical methods to examine protein oxidation compared to other signaling events such as protein phosphorylation. We report here the coupling of PROP, a method to purify reversibly oxidized proteins, with the proteomic identification of the purified mixture using mass spectrometry. A gene ontology (GO), KEGG enrichment and Wikipathways analysis of the identified proteins indicated a significant enrichment in proteins associated with both translation and mRNA splicing. This methodology also enabled the identification of some of the specific cysteine residue targets within identified proteins that are reversibly oxidized by hydrogen peroxide treatment of intact cells. From these identifications, we determined a potential consensus sequence motif associated with oxidized cysteine residues. Furthermore, because we identified proteins and specific sites of oxidation from both abundant proteins and from far less abundant signaling proteins (e.g. hepatoma derived growth factor, prostaglandin E synthase 3), the results suggest that the PROP procedure was efficient. Thus, this PROP-proteomics methodology offers a sensitive means to identify biologically relevant redox signaling events that occur within intact cells.

Accelerated identification of proteins by mass spectrometry by employing covalent pre-gel staining with Uniblue A

Background

The identification of proteins by mass spectrometry is a standard method in biopharmaceutical quality control and biochemical research. Prior to identification by mass spectrometry, proteins are usually pre-separated by electrophoresis. However, current protein staining and de-staining protocols are tedious and time consuming, and therefore prolong the sample preparation time for mass spectrometry.

Methodology and Principal Findings

We developed a 1-minute covalent pre-gel staining protocol for proteins, which does not require de-staining before the mass spectrometry analysis. We investigated the electrophoretic properties of derivatized proteins and peptides and studied their behavior in mass spectrometry. Further, we elucidated the preferred reaction of proteins with Uniblue A and demonstrate the integration of the peptide derivatization into typical informatics tools.

Conclusions and Significance

The Uniblue A staining method drastically speeds up the sample preparation for the mass spectrometry based identification of proteins. The application of this chemo-proteomic strategy will be advantageous for routine quality control of proteins and for time-critical tasks in protein analysis.

Enabling technologies for yeast proteome analysis

Whilst the study of yeast genomes and transcriptomes is in an advanced state, there is still much to learn about the resulting proteins in terms of cataloging, characterization of post-translational modifications, turnover, and the dynamics of sub-cellular localization and interactions. Analysis of the transcripts gives little insight into function or diversity as changes in RNA levels do not always correlate with the resulting protein abundance. A number of global and targeted attempts have been made to catalog and characterize the yeast proteome and we describe here the methods used to gain a greater understanding of the yeast proteome. This comprehensive review also describes future approaches that will aid completion in identifying and characterizing the remaining 20% of the undetermined yeast proteome as well as giving new insight into protein dynamics.

Comprehensive comparison of iTRAQ and label-free LC-based quantitative proteomics approaches using two Chlamydomonas reinhardtii strains of interest for biofuels engineering

Comprehensive comparisons of quantitative proteomics techniques are rare in the literature, yet they are crucially important for optimal selection of approaches and methodologies that are ideal for a given proteomics initiative. In this study, two LC-based quantitative proteomics approaches--iTRAQ and label-free--were implemented using the LTQ-Orbitrap Velos platform. For this comparison, the model used was the total protein content from two Chlamydomonas reinhardtii strains in the context of alternative biofuels production. The strain comparison includes sta6 (a starch-less mutant of cw15) that produces twice as many lipid bodies (LB) containing triacylglycerols (TAGs) as its parental strain cw15 (a cell wall-deficient C. reinhardtii strain) under nitrogen starvation. Internal standard addition was used to rigorously assess the quantitation accuracy and precision of each method. Results from iTRAQ-4plex labeling using HCD (higher energy collision-induced dissociation) fragmentation were compared to those obtained using a label-free approach based on the peak area of intact peptides and collision-induced dissociation. The accuracy and precision, number of identified/quantified proteins and statistically significant protein differences detected, as well as efficiency of these two quantitative proteomics methods were evaluated and compared. Four technical and three biological replicates of each strain were performed to assess both the technical and biological variation of both approaches. A total of 896 and 639 proteins were identified with high confidence, and 329 and 124 proteins were quantified significantly with label-free and iTRAQ, respectively, using biological replicates. The results showed that both iTRAQ labeling and label-free methods provide high quality quantitative and qualitative data using nano-LC coupled with the LTQ-Orbitrap Velos mass spectrometer, but the selection of the optimal approach is dependent on experimental design and the biological question to be addressed. The functional categorization of the differential proteins between cw15 and sta6 reveals already known but also new mechanisms likely responsible for the production of lipids in sta6 and sets the baseline for future studies aimed at engineering these strains for high oil production.

Phosphatidylinositol 3,4,5-trisphosphate activity probes for the labeling and proteomic characterization of protein binding partners

Phosphatidylinositol polyphosphate lipids, such as phosphatidylinositol 3,4,5-trisphosphate [PI(3,4,5)P₃], regulate critical biological processes, many of which are aberrant in disease. These lipids often act as site-specific ligands in interactions that enforce membrane association of protein binding partners. Herein, we describe the development of bifunctional activity probes corresponding to the headgroup of PI(3,4,5)P₃ that are effective for identifying and characterizing protein binding partners from complex samples, namely cancer cell extracts. These probes contain both a photoaffinity tag for covalent labeling of target proteins and a secondary handle for subsequent detection or manipulation of labeled proteins. Probes bearing different secondary tags were exploited, either by direct attachment of a fluorescent dye for optical detection or by using an alkyne that can be derivatized after protein labeling via click chemistry. First, we describe the design and modular synthetic strategy used to generate multiple probes with different reporter tags of use for characterizing probe-labeled proteins. Next, we report initial labeling studies using purified protein, the PH domain of Akt, in which probes were found to label this target, as judged by in-gel detection. Furthermore, protein labeling was abrogated by controls including competition with an unlabeled PI(3,4,5)P₃ headgroup analogue as well as through protein denaturation, indicating specific labeling. In addition, probes featuring linkers of different lengths between the PI(3,4,5)P₃ headgroup and photoaffinity tag led to variations in protein labeling, indicating that a shorter linker was more effective in this case. Finally, proteomic labeling studies were performed using cell extracts; labeled proteins were observed by in-gel detection and characterized using postlabeling with biotin, affinity chromatography, and identification via tandem mass spectrometry. These studies yielded a total of 265 proteins, including both known and novel candidate PI(3,4,5)P₃-binding proteins.

A label-free quantitative shotgun proteomics analysis of rice grain development

Background

Although a great deal of rice proteomic research has been conducted, there are relatively few studies specifically addressing the rice grain proteome. The existing rice grain proteomic researches have focused on the identification of differentially expressed proteins or monitoring protein expression patterns during grain filling stages.

Results

Proteins were extracted from rice grains 10, 20, and 30 days after flowering, as well as from fully mature grains. By merging all of the identified proteins in this study, we identified 4,172 non-redundant proteins with a wide range of molecular weights (from 5.2 kDa to 611 kDa) and pI values (from pH 2.9 to pH 12.6). A Genome Ontology category enrichment analysis for the 4,172 proteins revealed that 52 categories were enriched, including the carbohydrate metabolic process, transport, localization, lipid metabolic process, and secondary metabolic process. The relative abundances of the 1,784 reproducibly identified proteins were compared to detect 484 differentially expressed proteins during rice grain development. Clustering analysis and Genome Ontology category enrichment analysis revealed that proteins involved in the metabolic process were enriched through all stages of development, suggesting that proteome changes occurred even in the desiccation phase. Interestingly, enrichments of proteins involved in protein folding were detected in the desiccation phase and in fully mature grain.

Conclusion

This is the first report conducting comprehensive identification of rice grain proteins. With a label free shotgun proteomic approach, we identified large number of rice grain proteins and compared the expression patterns of reproducibly identified proteins during rice grain development. Clustering analysis, Genome Ontology category enrichment analysis, and the analysis of composite expression profiles revealed dynamic changes of metabolisms during rice grain development. Interestingly, we detected that proteins involved in glycolysis, TCA-cycle, lipid metabolism, and proteolysis accumulated at higher levels in fully mature grain compared to grain developing stages, suggesting that the accumulation of these proteins during the desiccation stage may be associated with the preparation of proteins required in germination.

Comprehensive tissue processing strategy for quantitative proteomics of formalin-fixed multiple sclerosis lesions

Formalin-fixed (FF) autopsy tissue comprises the bulk of existing Multiple Sclerosis (MSc) pathology archives, providing a rich pool of material for biomarker discovery and disease characterization. Here, we present the development of a heat-induced extraction protocol for the proteomic analysis of FF brain tissue, its application to the study of lesion remyelination and its failure in MSc. A 4-round extraction strategy was optimized using FF tissue leading to a 35% increase in the number of proteins identified compared to a single extraction; and a 65% increase in proteins identified with ≥4 peptides. Histological staining of sections with oil red O and luxol fast blue-periodic acid Schiff, required to characterize MSc lesions was found to have minimal effect on LC-MS/MS. The application of the optimized protocol to chronic demyelinated and remyelinated FF MSc lesions and the adjacent periplaque white matter, isolated through laser guided manual dissection from 3 patients, identified 428 unique proteins (0.2% FDR) using LC-MS/MS. Comparison of the lesion types using iTRAQ and 2-D LC-MS/MS revealed 82 differentially expressed proteins. Protein quantitation by iTRAQ and spectral counting was well-correlated (r(s)= 0.7653; p < 10(-30)). The data generated from this work illustrates the scope of the methodology and provides insights into the pathogenesis of MSc and remyelination.

The plasma membrane of the cyanobacterium Gloeobacter violaceus contains segregated bioenergetic domains

The light reactions of oxygenic photosynthesis almost invariably take place in the thylakoid membranes, a highly specialized internal membrane system located in the stroma of chloroplasts and the cytoplasm of cyanobacteria. The only known exception is the primordial cyanobacterium Gloeobacter violaceus, which evolved before the appearance of thylakoids and harbors the photosynthetic complexes in the plasma membrane. Thus, studies on G. violaceus not only shed light on the evolutionary origin and the functional advantages of thylakoid membranes but also might include insights regarding thylakoid formation during chloroplast differentiation. Based on biochemical isolation and direct in vivo characterization, we report here structural and functional domains in the cytoplasmic membrane of a cyanobacterium. Although G. violaceus has no internal membranes, it does have localized domains with apparently specialized functions in its plasma membrane, in which both the photosynthetic and the respiratory complexes are concentrated. These bioenergetic domains can be visualized by confocal microscopy, and they can be isolated by a simple procedure. Proteomic analysis of these domains indicates their physiological function and suggests a protein sorting mechanism via interaction with membrane-intrinsic terpenoids. Based on these results, we propose specialized domains in the plasma membrane as evolutionary precursors of thylakoids.

GProX, a user-friendly platform for bioinformatics analysis and visualization of quantitative proteomics data

Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)(1). The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. The program is released as open-source and can be freely downloaded from the project webpage at http://gprox.sourceforge.net.

General statistical modeling of data from protein relative expression isobaric tags

Quantitative comparison of the protein content of biological samples is a fundamental tool of research. The TMT and iTRAQ isobaric labeling technologies allow the comparison of 2, 4, 6, or 8 samples in one mass spectrometric analysis. Sound statistical models that scale with the most advanced mass spectrometry (MS) instruments are essential for their efficient use. Through the application of robust statistical methods, we developed models that capture variability from individual spectra to biological samples. Classical experimental designs with a distinct sample in each channel as well as the use of replicates in multiple channels are integrated into a single statistical framework. We have prepared complex test samples including controlled ratios ranging from 100:1 to 1:100 to characterize the performance of our method. We demonstrate its application to actual biological data sets originating from three different laboratories and MS platforms. Finally, test data and an R package, named isobar, which can read Mascot, Phenyx, and mzIdentML files, are made available. The isobar package can also be used as an independent software that requires very little or no R programming skills.

Proteomic analysis of integrin adhesion complexes

Integrin receptors regulate cell fate by coupling the binding of extracellular adhesion proteins to the assembly of intracellular cytoskeletal and signaling complexes. A detailed, integrative view of adhesion complexes will provide insight into the molecular mechanisms that control cell morphology, survival, movement, and differentiation. To date, membrane receptor-associated signaling complexes have been refractory to proteomic analysis because of their inherent lability and inaccessibility. We developed a methodology to isolate ligand-induced integrin adhesion complexes, and we used this technique to analyze the composition of complexes associated with multiple receptor-ligand pairs and define core and receptor-specific subnetworks. In particular, we identified regulator of chromosome condensation-2 (RCC2) as a component of fibronectin-activated signaling pathways that regulate directional cell movement. The development of this proteomics pipeline provides the means to investigate the molecular composition and function of various adhesion complexes.

Improving proteome coverage on a LTQ-Orbitrap using design of experiments

Design of experiments (DOE) was used to determine improved settings for a LTQ-Orbitrap XL to maximize proteome coverage of Saccharomyces cerevisiae. A total of nine instrument parameters were evaluated with the best values affording an increase of approximately 60% in proteome coverage. Utilizing JMP software, 2 DOE screening design tables were generated and used to specify parameter values for instrument methods. DOE 1, a fractional factorial design, required 32 methods fully resolving the investigation of six instrument parameters involving only half the time necessary for a full factorial design of the same resolution. It was advantageous to complete a full factorial design for the analysis of three additional instrument parameters. Measured with a maximum of 1% false discovery rate, protein groups, unique peptides, and spectral counts gauged instrument performance. Randomized triplicate nanoLC-LTQ-Orbitrap XL MS/MS analysis of the S. cerevisiae digest demonstrated that the following five parameters significantly influenced proteome coverage of the sample: (1) maximum ion trap ionization time; (2) monoisotopic precursor selection; (3) number of MS/MS events; (4) capillary temperature; and (5) tube lens voltage. Minimal influence on the proteome coverage was observed for the remaining four parameters (dynamic exclusion duration, resolving power, minimum count threshold to trigger a MS/MS event, and normalized collision energy). The DOE approach represents a time- and cost-effective method for empirically optimizing MS-based proteomics workflows including sample preparation, LC conditions, and multiple instrument platforms.

Quantitative proteomic analysis of cold-responsive proteins in rice

Rice is susceptible to cold stress and with a future of climatic instability we will be unable to produce enough rice to satisfy increasing demand. A thorough understanding of the molecular responses to thermal stress is imperative for engineering cultivars, which have greater resistance to low temperature stress. In this study we investigated the proteomic response of rice seedlings to 48, 72 and 96 h of cold stress at 12-14°C. The use of both label-free and iTRAQ approaches in the analysis of global protein expression enabled us to assess the complementarity of the two techniques for use in plant proteomics. The approaches yielded a similar biological response to cold stress despite a disparity in proteins identified. The label-free approach identified 236 cold-responsive proteins compared to 85 in iTRAQ results, with only 24 proteins in common. Functional analysis revealed differential expression of proteins involved in transport, photosynthesis, generation of precursor metabolites and energy; and, more specifically, histones and vitamin B biosynthetic proteins were observed to be affected by cold stress.

Codon usage variability determines the correlation between proteome and transcriptome fold changes

Background

The availability of high throughput experimental methods has made possible to observe the relationships between proteome and transcirptome. The protein abundances show a positive but weak correlation with the concentrations of their cognate mRNAs. This weak correlation implies that there are other crucial effects involved in the regulation of protein translation, different from the sole availability of mRNA. It is well known that ribosome and tRNA concentrations are sources of variation in protein levels. Thus, by using integrated analysis of omics data, genomic information, transcriptome and proteome, we aim to unravel important variables affecting translation.

Results

We identified how much of the variability in the correlation between protein and mRNA concentrations can be attributed to the gene codon frequencies. We propose the hypothesis that the influence of codon frequency is due to the competition of cognate and near-cognate tRNA binding; which in turn is a function of the tRNA concentrations. Transcriptome and proteome data were combined in two analytical steps; first, we used Self-Organizing Maps (SOM) to identify similarities among genes, based on their codon frequencies, grouping them into different clusters; and second, we calculated the variance in the protein mRNA correlation in the sampled genes from each cluster. This procedure is justified within a mathematical framework.

Conclusions

With the proposed method we observed that in all the six studied cases most of the variability in the relation protein-transcript could be explained by the variation in codon composition.