GO Enrichment Analysis for Differential Proteomics Using ProteoRE.

With the increased simplicity of producing proteomics data, the bottleneck has now shifted to the functional analysis of large lists of proteins to translate this primary level of information into meaningful biological knowledge. Tools implementing such approach are a powerful way to gain biological insights related to their samples, provided that biologists/clinicians have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed ProteoRE (Proteomics Research Environment), a unified online research service that provides end-users with a set of tools to interpret their proteomics data in a collaborative and reproducible manner. ProteoRE is built upon the Galaxy framework, a workflow system allowing for data and analysis persistence, and providing user interfaces to facilitate the interaction with tools dedicated to the functional and the visual analysis of proteomics datasets. A set of tools relying on computational methods selected for their complementarity in terms of functional analysis was developed and made accessible via the ProteoRE web portal. In this chapter, a step-by-step protocol linking these tools is designed to perform a functional annotation and GO-based enrichment analyses applied to a set of differentially expressed proteins as a use case. Analytical practices, guidelines as well as tips related to this strategy are also provided. Tools, datasets, and results are freely available at http://www.proteore.org , allowing researchers to reuse them.

Comprehensive and comparative exploration of the Atp7b-/- mouse plasma proteome.

Wilson's disease (WD), a rare genetic disease caused by mutations in the ATP7B gene, is associated with altered expression and/or function of the copper-transporting ATP7B protein, leading to massive toxic accumulation of copper in the liver and brain. The Atp7b-/- mouse, a genetic and phenotypic model of WD, was developed to provide new insights into the pathogenic mechanisms of WD. Many plasma proteins are secreted by the liver, and impairment of liver function can trigger changes to the plasma proteome. High standard proteomics workflows can identify such changes. Here, we explored the plasma proteome of the Atp7b-/- mouse using a mass spectrometry (MS)-based proteomics workflow combining unbiased discovery analysis followed by targeted quantification. Among the 367 unique plasma proteins identified, 7 proteins were confirmed as differentially abundant between Atp7b-/- mice and wild-type littermates, and were directly linked to WD pathophysiology (regeneration of liver parenchyma, plasma iron depletion, etc.). We then adapted our targeted proteomics assay to quantify human orthologues of these proteins in plasma from copper-chelator-treated WD patients. The plasma proteome changes observed in the Atp7b-/- mouse were not confirmed in these samples, except for alpha-1 antichymotrypsin, levels of which were decreased in WD patients compared to healthy individuals. Plasma ceruloplasmin was investigated in both the Atp7b-/- mouse model and human patients; it was significantly decreased in the human form of WD only. In conclusion, MS-based proteomics is a method of choice to identify proteome changes in murine models of disrupted metal homeostasis, and allows their validation in human cohorts.

Bioinformatics Tools and Workflow to Select Blood Biomarkers for Early Cancer Diagnosis: An Application to Pancreatic Cancer.

Secretome proteomics for the discovery of cancer biomarkers holds great potential to improve early cancer diagnosis. A knowledge-based approach relying on mechanistic criteria related to the type of cancer should help to identify candidates from available "omics" information. With the aim of accelerating the discovery process for novel biomarkers, a set of tools is developed and made available via a Galaxy-based instance to assist end-users biologists. These implemented tools proceed by a step-by-step strategy to mine transcriptomics and proteomics databases for information relating to tissue specificity, allow the selection of proteins that are part of the secretome, and combine this information with proteomics datasets to rank the most promising candidate biomarkers for early cancer diagnosis. Using pancreatic cancer as a case study, this strategy produces a list of 24 candidate biomarkers suitable for experimental assessment by MS-based proteomics. Among these proteins, three (SYCN, REG1B, and PRSS2) were previously reported as circulating candidate biomarkers of pancreatic cancer. Here, further refinement of this list allows to prioritize 14 candidate biomarkers along with their associated proteotypic peptides for further investigation, using targeted MS-based proteomics. The bioinformatics tools and the workflow implementing this strategy for the selection of candidate biomarkers are freely accessible at http://www.proteore.org.

Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR.

ProStaR is a software tool dedicated to differential analysis in label-free quantitative proteomics. Practically, once biological samples have been analyzed by bottom-up mass spectrometry-based proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, by means of precursor ion chromatogram integration. Then, it is classical to use these peptide-level pieces of information to derive the identity and quantity of the sample proteins before proceeding with refined statistical processing at protein-level, so as to bring out proteins which abundance is significantly different between different groups of samples. To achieve this statistical step, it is possible to rely on ProStaR, which allows the user to (1) load correctly formatted data, (2) clean them by means of various filters, (3) normalize the sample batches, (4) impute the missing values, (5) perform null hypothesis significance testing, (6) check the well-calibration of the resulting p-values, (7) select a subset of differentially abundant proteins according to some false discovery rate, and (8) contextualize these selected proteins into the Gene Ontology. This chapter provides a detailed protocol on how to perform these eight processing steps with ProStaR.

Designing an In Silico Strategy to Select Tissue-Leakage Biomarkers Using the Galaxy Framework.

Knowledge-based approaches using large-scale biological ("omics") data are a powerful way to identify mechanistic biomarkers, provided that scientists have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed a set of tools under the Galaxy framework to allow biologists to define their own strategy for reproducible biomarker selection. These tools rely on retrieving experimental data from public databases, and applying successive filters derived from information relating to disease pathophysiology. A step-by-step protocol linking these tools was implemented to select tissue-leakage biomarker candidates of myocardial infarction. A list of 24 candidates suitable for experimental assessment by MS-based proteomics is proposed. These tools have been made publicly available at http://www.proteore.org , allowing researchers to reuse them in their quest for biomarker discovery.

PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptides.

We propose a new hypothesis test for the differential abundance of proteins in mass-spectrometry based relative quantification. An important feature of this type of high-throughput analyses is that it involves an enzymatic digestion of the sample proteins into peptides prior to identification and quantification. Due to numerous homology sequences, different proteins can lead to peptides with identical amino acid chains, so that their parent protein is ambiguous. These so-called shared peptides make the protein-level statistical analysis a challenge and are often not accounted for. In this article, we use a linear model describing peptide-protein relationships to build a likelihood ratio test of differential abundance for proteins. We show that the likelihood ratio statistic can be computed in linear time with the number of peptides. We also provide the asymptotic null distribution of a regularized version of our statistic. Experiments on both real and simulated datasets show that our procedures outperforms state-of-the-art methods. The procedures are available via the pepa.test function of the DAPAR Bioconductor R package.

Proteomic characterization of human exhaled breath condensate.

To improve biomedical knowledge and to support biomarker discovery studies, it is essential to establish comprehensive proteome maps for human tissues and biofluids, and to make them publicly accessible. In this study, we performed an in-depth proteomics characterization of exhaled breath condensate (EBC), a sample obtained non-invasively by condensation of exhaled air that contains submicron droplets of airway lining fluid. Two pooled samples of EBC, each obtained from 10 healthy donors, were processed using a straightforward protocol based on sample lyophilization, in-gel digestion and liquid chromatography tandem-mass spectrometry analysis. Two 'technical' control samples were processed in parallel to the pooled samples to correct for exogenous protein contamination. A total of 229 unique proteins were identified in EBC among which 153 proteins were detected in both EBC pooled samples. A detailed bioinformatics analysis of these 153 proteins showed that most of the proteins identified corresponded to proteins secreted in the respiratory tract (lung, bronchi). Eight proteins were salivary proteins. Our dataset is described and has been made accessible through the ProteomeXchange database (dataset identifier: PXD007591) and is expected to be useful for future MS-based biomarker studies using EBC as the diagnostic specimen.

DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics.

DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments. DAPAR contains procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate. ProStaR is a graphical user interface that allows friendly access to the DAPAR functionalities through a web browser. AVAILABILITY AND IMPLEMENTATION: DAPAR and ProStaR are implemented in the R language and are available on the website of the Bioconductor project (http://www.bioconductor.org/). A complete tutorial and a toy dataset are accompanying the packages. CONTACT: samuel.wieczorek@cea.fr, florence.combes@cea.fr, thomas.burger@cea.fr.

Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments.

In MS-based quantitative proteomics, the FDR control (i.e. the limitation of the number of proteins that are wrongly claimed as differentially abundant between several conditions) is a major postanalysis step. It is classically achieved thanks to a specific statistical procedure that computes the adjusted p-values of the putative differentially abundant proteins. Unfortunately, such adjustment is conservative only if the p-values are well-calibrated; the false discovery control being spuriously underestimated otherwise. However, well-calibration is a property that can be violated in some practical cases. To overcome this limitation, we propose a graphical method to straightforwardly and visually assess the p-value well-calibration, as well as the R codes to embed it in any pipeline. All MS data have been deposited in the ProteomeXchange with identifier PXD002370 (http://proteomecentral.proteomexchange.org/dataset/PXD002370).

Uranium perturbs signaling and iron uptake response in Arabidopsis thaliana roots.

Uranium is a natural element which is mainly redistributed in the environment due to human activity, including accidents and spillages. Plants may be useful in cleaning up after incidents, although little is yet known about the relationship between metal speciation and plant response. Here, J-Chess modeling was used to predict U speciation and exposure conditions affecting U bioavailability for plants. The model was confirmed by exposing Arabidopsis thaliana plants to U under hydroponic conditions. The early root response was characterized using complete Arabidopsis transcriptome microarrays (CATMA). Expression of 111 genes was modified at the three timepoints studied. The associated biological processes were further examined by real-time quantitative RT-PCR. Annotation revealed that oxidative stress, cell wall and hormone biosynthesis, and signaling pathways (including phosphate signaling) were affected by U exposure. The main actors in iron uptake and signaling (IRT1, FRO2, AHA2, AHA7 and FIT1) were strongly down-regulated upon exposure to uranyl. A network calculated using IRT1, FRO2 and FIT1 as bait revealed a set of genes whose expression levels change under U stress. Hypotheses are presented to explain how U perturbs the iron uptake and signaling response. These results give preliminary insights into the pathways affected by uranyl uptake, which will be of interest for engineering plants to help clean areas contaminated with U.

Identification of a novel BET bromodomain inhibitor-sensitive, gene regulatory circuit that controls Rituximab response and tumour growth in aggressive lymphoid cancers.

Immuno-chemotherapy elicit high response rates in B-cell non-Hodgkin lymphoma but heterogeneity in response duration is observed, with some patients achieving cure and others showing refractory disease or relapse. Using a transcriptome-powered targeted proteomics screen, we discovered a gene regulatory circuit involving the nuclear factor CYCLON which characterizes aggressive disease and resistance to the anti-CD20 monoclonal antibody, Rituximab, in high-risk B-cell lymphoma. CYCLON knockdown was found to inhibit the aggressivity of MYC-overexpressing tumours in mice and to modulate gene expression programs of biological relevance to lymphoma. Furthermore, CYCLON knockdown increased the sensitivity of human lymphoma B cells to Rituximab in vitro and in vivo. Strikingly, this effect could be mimicked by in vitro treatment of lymphoma B cells with a small molecule inhibitor for BET bromodomain proteins (JQ1). In summary, this work has identified CYCLON as a new MYC cooperating factor that autonomously drives aggressive tumour growth and Rituximab resistance in lymphoma. This resistance mechanism is amenable to next-generation epigenetic therapy by BET bromodomain inhibition, thereby providing a new combination therapy rationale for high-risk lymphoma.

An evaluation of custom microarray applications: the oligonucleotide design challenge.

The increase in feature resolution and the availability of multipack formats from microarray providers has opened the way to various custom genomic applications. However, oligonucleotide design and selection remains a bottleneck of the microarray workflow. Several tools are available to perform this work, and choosing the best one is not an easy task, nor are the choices obvious. Here we review the oligonucleotide design field to help users make their choice. We have first performed a comparative evaluation of the available solutions based on a set of criteria including: ease of installation, user-friendly access, the number of parameters and settings available. In a second step, we chose to submit two real cases to a selection of programs. Finally, we used a set of tests for the in silico benchmark of the oligo sets obtained from each type of software. We show that the design software must be selected according to the goal of the scientist, depending on factors such as the organism used, the number of probes required and their localization on the target sequence. The present work provides keys to the choice of the most relevant software, according to the various parameters we tested.

Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders.

BACKGROUND: The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. METHODOLOGY/PRINCIPAL FINDINGS: The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss) could always be differentiated from non-responders (<4 kgs weight loss). We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%+/-2.2%. CONCLUSION: Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.

Goulphar: rapid access and expertise for standard two-color microarray normalization methods.

BACKGROUND: Raw data normalization is a critical step in microarray data analysis because it directly affects data interpretation. Most of the normalization methods currently used are included in the R/BioConductor packages but it is often difficult to identify the most appropriate method. Furthermore, the use of R commands for functions and graphics can introduce mistakes that are difficult to trace. We present here a script written in R that provides a flexible means of access to and monitoring of data normalization for two-color microarrays. This script combines the power of BioConductor and R analysis functions and reduces the amount of R programming required. RESULTS: Goulphar was developed in and runs using the R language and environment. It combines and extends functions found in BioConductor packages (limma and marray) to correct for dye biases and spatial artifacts. Goulphar provides a wide range of optional and customizable filters for excluding incorrect signals during the pre-processing step. It displays informative output plots, enabling the user to monitor the normalization process, and helps adapt the normalization method appropriately to the data. All these analyses and graphical outputs are presented in a single PDF report. CONCLUSION: Goulphar provides simple, rapid access to the power of the R/BioConductor statistical analysis packages, with precise control and visualization of the results obtained. Complete documentation, examples and online forms for setting script parameters are available from http://transcriptome.ens.fr/goulphar/.