Josselin Noirel

iTRAQ underestimation in simple and complex mixtures: "the good, the bad and the ugly".

The increasing popularity of iTRAQ for quantitative proteomics applications makes it necessary to evaluate its relevance, accuracy, and precision for biological interpretation. Here, we have assessed (a) the accuracy and precision of iTRAQ quantification in a controlled experimental setup, using low- and high-complexity protein mixtures; and (b) the potential pitfalls that hamper the applicability and attainable dynamic range of iTRAQ: isotopic contamination, background interference, and signal-to-noise ratio. Our data suggest greater dynamic crosstalk between interfering factors affecting underestimations, and that these interferences were largely scenario-specific, dependent on sample complexity. The good is the potential for iTRAQ to provide accurate quantification spanning 2 orders of magnitude. This potential is however limited by two factors. (1) The bad: the existence of isotopic impurities that can be corrected for; provided accurate isotopic factors are at ones disposal. (2) The ugly: we demonstrate here the interference of mixed MS/MS contribution occurring during precursor selection, an issue that is currently very difficult to minimize. In light of our results, we propose a list of advice for iTRAQ data analysis that could routinely ameliorate quantitative interpretation of proteomic data sets.

A quantitative proteomic analysis of biofilm adaptation by the periodontal pathogen Tannerella forsythia

Tannerella forsythia is a Gram‐negative anaerobe that is one of the most prominent inhabitants of the sub‐gingival plaque biofilm, which is crucial for causing periodontitis. We have used iTRAQ proteomics to identify and quantify alterations in global protein expression of T. forsythia during growth in a biofilm. This is the first proteomic study concentrating on biofilm growth in this key periodontal pathogen, and this study has identified several changes in protein expression. Moreover, we introduce a rigorous statistical method utilising peptide‐level intensities of iTRAQ reporters to determine which proteins are significantly regulated. In total, 348 proteins were identified and quantified with the expression of 44 proteins being significantly altered between biofilm and planktonic cells. We identified proteins from all cell compartments, and highlighted a marked upregulation in the relative abundances of predicted outer membrane proteins in biofilm cells. These included putative transport systems and the T. forsythia S‐layer proteins. These data and our finding that the butyrate production pathway is markedly downregulated in biofilms indicate possible alterations in host interaction capability. We also identified upregulation of putative oxidative stress response proteins, and showed that biofilm cells are 10 to 20 fold more resistant to oxidative stress. This may represent an important adaptation of this organism to prolonged persistence and immune evasion in the oral cavity.

Minimising iTRAQ ratio compression through understanding LC-MS elution dependence and high-resolution HILIC fractionation.

Application of iTRAQ‐based workflows for protein profiling has become widespread. Concomitantly, the idiosyncratic limitations of iTRAQ, such as its tendency to underestimate quantifications, have been studied and recognised. This report shows that the influence of ratio compression and limiting transmission in iTRAQ MS/MS in high‐complexity mixtures (iTRAQ‐labelled lysates) can be partly alleviated using high‐resolution sample fractionation. Here, we also investigate in greater detail the dependency of iTRAQ quantification on the dynamics of online chromatography in low‐complexity mixtures (iTRAQ‐labelled standards). These findings will allow more efficient strategies to be designed for iTRAQ proteomics, alleviating iTRAQ underestimation and thus facilitating the detection of subtle abundance changes.

Quantitative overview of N2 fixation in Nostoc punctiforme ATCC 29133 through cellular enrichments and iTRAQ shotgun proteomics.

Nostoc punctiforme ATCC 29133 is a photoautotrophic cyanobacterium with the capacity to fix atmospheric N 2. Its ability to mediate this process is similar to that described for Nostoc sp. PCC 7120, where vegetative cells differentiate into heterocysts. Quantitative proteomic investigations at both the filament level and the heterocyst level are presented using isobaric tagging technology (iTRAQ), with 721 proteins at the 95% confidence interval quantified across both studies. Observations from both experiments yielded findings confirmatory of both transcriptional studies, and published Nostoc sp. PCC 7120 iTRAQ data. N. punctiforme exhibits similar metabolic trends, though changes in a number of metabolic pathways are less pronounced than in Nostoc sp. PCC 7120. Results also suggest a number of proteins that may benefit from future investigations. These include ATP dependent Zn-proteases, N-reserve degraders and also redox balance proteins. Complementary proteomic data sets from both organisms present key precursor knowledge that is important for future cyanobacterial biohydrogen research.

HILIC- and SCX-based quantitative proteomics of Chlamydomonas reinhardtii during nitrogen starvation induced lipid and carbohydrate accumulation

Nitrogen starvation induced changes in carbohydrate and lipid content is described in several algal species. Although these phenotypic changes are desirable, such manipulations also significantly deteriorate culture health, ultimately halting growth. To optimize biofuel production from algae, it is desirable to induce lipid accumulation without compromising cell growth and survival. In this study, we utilized an 8-plex iTRAQ-based proteomic approach to assess the model alga Chlamydomonas reinhardtii CCAP 11/32CW15+ under nitrogen starvation. First-dimension fractionation was conducted using HILIC and SCX. A total of 587 proteins were identified (≥3 peptides) of which 71 and 311 were differentially expressed at significant levels (p<0.05), during nitrogen stress induced carbohydrate and lipid production, respectively. Forty-seven percent more changes with significance were observed with HILIC compared to SCX. Several trends were observed including increase in energy metabolism, decrease in translation machinery, increase in cell wall production and a change of balance between photosystems I and II. These findings point to a severely compromised system where lipid is accumulated at the expense of normal functioning of the organism, suggesting that a more informed and controlled method of lipid induction than gross nutrient manipulation would be needed for development of sustainable processes.

A review of current proteomics technologies with a survey on their widespread use in reproductive biology investigations

Proteomics is very much a technology-driven field. The ambition is to identify, quantify and to assess the state of posttranslational modification and interaction partners for every protein in the cell. The proteome is in a state of flux and is thus extremely complex. Analysis of the proteome is exacerbated by the huge dynamic concentration range of proteins in the cellular environment. The impact that mass spectrometry-based proteomics has had on the field of biology has heavily depended on dramatic improvements in mass spectrometry that have been made in recent years. We examined 1541 reports indexed in PubMed relating to proteomics and reproduction to identify trends in the field and to make some broad observations for future work. To set the scene, in the first part of the report, we give a comprehensive overview of proteomics and associated techniques and technologies (such as separations and mass spectrometry). The second part examines the field in light of these techniques and suggests some opportunities for application of these tools in the area of reproduction.

MMG: a probabilistic tool to identify submodules of metabolic pathways

MOTIVATION A fundamental task in systems biology is the identification of groups of genes that are involved in the cellular response to particular signals. At its simplest level, this often reduces to identifying biological quantities (mRNA abundance, enzyme concentrations, etc.) which are differentially expressed in two different conditions. Popular approaches involve using t-test statistics, based on modelling the data as arising from a mixture distribution. A common assumption of these approaches is that the data are independent and identically distributed; however, biological quantities are usually related through a complex (weighted) network of interactions, and often the more pertinent question is which subnetworks are differentially expressed, rather than which genes. Furthermore, in many interesting cases (such as high-throughput proteomics and metabolomics), only very partial observations are available, resulting in the need for efficient imputation techniques. RESULTS We introduce Mixture Model on Graphs (MMG), a novel probabilistic model to identify differentially expressed submodules of biological networks and pathways. The method can easily incorporate information about weights in the network, is robust against missing data and can be easily generalized to directed networks. We propose an efficient sampling strategy to infer posterior probabilities of differential expression, as well as posterior probabilities over the model parameters. We assess our method on artificial data demonstrating significant improvements over standard mixture model clustering. Analysis of our model results on quantitative high-throughput proteomic data leads to the identification of biologically significant subnetworks, as well as the prediction of the expression level of a number of enzymes, some of which are then verified experimentally. AVAILABILITY MATLAB code is available from http://www.dcs.shef.ac.uk/~guido/software.html

Improving N-glycosylation efficiency in Escherichia coli using shotgun proteomics, metabolic network analysis, and selective reaction monitoring

Recently, the prospect of using Escherichia coli as a host for human glycoprotein production has increased due to detailed characterization of the prokaryotic N‐glycosylation process and the ability to transfer the system into this bacterium. Although functionality of the native Campylobacter jejuni N‐glycosylation system in E. coli has been demonstrated, the efficiency of the process using the well‐characterized C. jejuni glycoprotein AcrA, was found to be low at 13.4 ± 0.9% of total extracted protein. A combined approach using isobaric labeling of peptides and probability‐based network analysis of metabolic changes was applied to forward engineer E. coli to improve glycosylation efficiency of AcrA. Enhancing flux through the glyoxylate cycle was identified as a potential metabolic manipulation to improve modification efficiency and was achieved by increasing the expression of isocitrate lyase. While the overall recombinant protein titre did not change significantly, the amount of glycosylated protein increased by approximately 300%. Biotechnol. Bioeng. 2011; 108:902–912.

Eight-plex iTRAQ analysis of variant metastatic human prostate cancer cells identifies candidate biomarkers of progression: An exploratory study

Due to the heterogeneity in the biological behavior of prostate cancer, biomarkers that can reliably distinguish indolent from aggressive disease are urgently needed to inform treatment choices.

Archaeal Signal Transduction: Impact of Protein Phosphatase Deletions on Cell Size, Motility, and Energy Metabolism in Sulfolobus acidocaldarius

In this study, the in vitro and in vivo functions of the only two identified protein phosphatases, Saci-PTP and Saci-PP2A, in the crenarchaeal model organism Sulfolobus acidocaldarius were investigated. Biochemical characterization revealed that Saci-PTP is a dual-specific phosphatase (against pSer/pThr and pTyr), whereas Saci-PP2A exhibited specific pSer/pThr activity and inhibition by okadaic acid. Deletion of saci_pp2a resulted in pronounced alterations in growth, cell shape and cell size, which could be partially complemented. Transcriptome analysis of the three strains (Δsaci_ptp, Δsaci_pp2a and the MW001 parental strain) revealed 155 genes that were differentially expressed in the deletion mutants, and showed significant changes in expression of genes encoding the archaella (archaeal motility structure), components of the respiratory chain and transcriptional regulators. Phosphoproteome studies revealed 801 unique phosphoproteins in total, with an increase in identified phosphopeptides in the deletion mutants. Proteins from most functional categories were affected by phosphorylation, including components of the motility system, the respiratory chain, and regulatory proteins. In the saci_pp2a deletion mutant the up-regulation at the transcript level, as well as the observed phosphorylation pattern, resembled starvation stress responses. Hypermotility was also observed in the saci_pp2a deletion mutant. The results highlight the importance of protein phosphorylation in regulating essential cellular processes in the crenarchaeon S. acidocaldarius.