David Bouyssié

Extensive Analysis of the Cytoplasmic Proteome of Human Erythrocytes Using the Peptide Ligand Library Technology and Advanced Mass Spectrometry

The erythrocyte cytoplasmic proteome is composed of 98% hemoglobin; the remaining 2% is largely unexplored. Here we used a combinatorial library of hexameric peptides as a capturing agent to lower the signal of hemoglobin and amplify the signal of low to very low abundance proteins in the cytoplasm of human red blood cells (RBCs). Two types of hexapeptide library beads have been adopted: amino-terminal hexapeptide beads and beads in which the peptides have been further derivatized by carboxylation. The amplification of the signal of low abundance and suppression of the signal of high abundance species were fully demonstrated by two-dimensional gel maps and nano-LC-MSMS analysis. The effect of this new methodology on quantitative information also was explored. Moreover using this approach on an LTQ-Orbitrap mass spectrometer, we could identify with high confidence as many as 1578 proteins in the cytoplasmic fraction of a highly purified preparation of RBCs, allowing a deep exploration of the classical RBC pathways as well as the identification of unexpected minor proteins. In addition, we were able to detect the presence of eight different hemoglobin chains including embryonic and newly discovered globin chains. Thus, this extensive study provides a huge data set of proteins that are present in the RBC cytoplasm that may help to better understand the biology of this simplified cell and may open the way to further studies on blood pathologies using targeted approaches.

Comprehensive human urine standards for comparability and standardization in clinical proteome analysis

Purpose: Urine proteomics is emerging as a powerful tool for biomarker discovery. The purpose of this study is the development of a well‐characterized “real life” sample that can be used as reference standard in urine clinical proteomics studies.

In-depth Exploration of Cerebrospinal Fluid by Combining Peptide Ligand Library Treatment and Label-free Protein Quantification

Cerebrospinal fluid (CSF) is the biological fluid in closest contact with the brain and thus contains proteins of neural cell origin. Hence, CSF is a biochemical window into the brain and is particularly attractive for the search for biomarkers of neurological diseases. However, as in the case of other biological fluids, one of the main analytical challenges in proteomic characterization of the CSF is the very wide concentration range of proteins, largely exceeding the dynamic range of current analytical approaches. Here, we used the combinatorial peptide ligand library technology (ProteoMiner) to reduce the dynamic range of protein concentration in CSF and unmask previously undetected proteins by nano-LC-MS/MS analysis on an LTQ-Orbitrap mass spectrometer. This method was first applied on a large pool of CSF from different sources with the aim to better characterize the protein content of this fluid, especially for the low abundance components. We were able to identify 1212 proteins in CSF, and among these, 745 were only detected after peptide library treatment. However, additional difficulties for clinical studies of CSF are the low protein concentration of this fluid and the low volumes typically obtained after lumbar puncture, precluding the conventional use of ProteoMiner with large volume columns for treatment of patient samples. The method has thus been optimized to be compatible with low volume samples. We could show that the treatment is still efficient with this miniaturized protocol and that the dynamic range of protein concentration is actually reduced even with small amounts of beads, leading to an increase of more than 100% of the number of identified proteins in one LC-MS/MS run. Moreover, using a dedicated bioinformatics analytical work flow, we found that the method is reproducible and applicable for label-free quantification of series of samples processed in parallel.

Mascot File Parsing and Quantification (MFPaQ), a New Software to Parse, Validate, and Quantify Proteomics Data Generated by ICAT and SILAC Mass Spectrometric Analyses Application To the Proteomics Study of Membrane Proteins from Primary Human Endothelial Cells

Proteomics strategies based on nanoflow (nano-) LC-MS/MS allow the identification of hundreds to thousands of proteins in complex mixtures. When combined with protein isotopic labeling, quantitative comparison of the proteome from different samples can be achieved using these approaches. However, bioinformatics analysis of the data remains a bottleneck in large scale quantitative proteomics studies. Here we present a new software named Mascot File Parsing and Quantification (MFPaQ) that easily processes the results of the Mascot search engine and performs protein quantification in the case of isotopic labeling experiments using either the ICAT or SILAC (stable isotope labeling with amino acids in cell culture) method. This new tool provides a convenient interface to retrieve Mascot protein lists; sort them according to Mascot scoring or to user-defined criteria based on the number, the score, and the rank of identified peptides; and to validate the results. Moreover the software extracts quantitative data from raw files obtained by nano-LC-MS/MS, calculates peptide ratios, and generates a non-redundant list of proteins identified in a multisearch experiment with their calculated averaged and normalized ratio. Here we apply this software to the proteomics analysis of membrane proteins from primary human endothelial cells (ECs), a cell type involved in many physiological and pathological processes including chronic inflammatory diseases such as rheumatoid arthritis. We analyzed the EC membrane proteome and set up methods for quantitative analysis of this proteome by ICAT labeling. EC microsomal proteins were fractionated and analyzed by nano-LC-MS/MS, and database searches were performed with Mascot. Data validation and clustering of proteins were performed with MFPaQ, which allowed identification of more than 600 unique proteins. The software was also successfully used in a quantitative differential proteomics analysis of the EC membrane proteome after stimulation with a combination of proinflammatory mediators (tumor necrosis factor-α, interferon-γ, and lymphotoxin α/β) that resulted in the identification of a full spectrum of EC membrane proteins regulated by inflammation.

Quantitative Proteomics Reveals a Dynamic Association of Proteins to Detergent-resistant Membranes upon Elicitor Signaling in Tobacco

A large body of evidence from the past decade supports the existence, in membrane from animal and yeast cells, of functional microdomains playing important roles in protein sorting, signal transduction, or infection by pathogens. In plants, as previously observed for animal microdomains, detergent-resistant fractions, enriched in sphingolipids and sterols, were isolated from plasma membrane. A characterization of their proteic content revealed their enrichment in proteins involved in signaling and response to biotic and abiotic stress and cell trafficking suggesting that these domains were likely to be involved in such physiological processes. In the present study, we used 14N/15N metabolic labeling to compare, using a global quantitative proteomics approach, the content of tobacco detergent-resistant membranes extracted from cells treated or not with cryptogein, an elicitor of defense reaction. To analyze the data, we developed a software allowing an automatic quantification of the proteins identified. The results obtained indicate that, although the association to detergent-resistant membranes of most proteins remained unchanged upon cryptogein treatment, five proteins had their relative abundance modified. Four proteins related to cell trafficking (four dynamins) were less abundant in the detergent-resistant membrane fraction after cryptogein treatment, whereas one signaling protein (a 14-3-3 protein) was enriched. This analysis indicates that plant microdomains could, like their animal counterpart, play a role in the early signaling process underlying the setup of defense reaction. Furthermore proteins identified as differentially associated to tobacco detergent-resistant membranes after cryptogein challenge are involved in signaling and vesicular trafficking as already observed in similar studies performed in animal cells upon biological stimuli. This suggests that the ways by which the dynamic association of proteins to microdomains could participate in the regulation of the signaling process may be conserved between plant and animals.

Label-free Quantification and Shotgun Analysis of Complex Proteomes by One-dimensional SDS-PAGE/NanoLC-MS EVALUATION FOR THE LARGE SCALE ANALYSIS OF INFLAMMATORY HUMAN ENDOTHELIAL CELLS

To perform differential studies of complex protein mixtures, strategies for reproducible and accurate quantification are needed. Here, we evaluated a quantitative proteomic workflow based on nanoLC-MS/MS analysis on an LTQ-Orbitrap-VELOS mass spectrometer and label-free quantification using the MFPaQ software. In such label-free quantitative studies, a compromise has to be found between two requirements: repeatability of sample processing and MS measurements, allowing an accurate quantification, and high proteomic coverage of the sample, allowing quantification of minor species. The latter is generally achieved through sample fractionation, which may induce experimental bias during the label-free comparison of samples processed, and analyzed independently. In this work, we wanted to evaluate the performances of MS intensity-based label-free quantification when a complex protein sample is fractionated by one-dimensional SDS-PAGE. We first tested the efficiency of the analysis without protein fractionation and could achieve quite good quantitative repeatability in single-run analysis (median coefficient of variation of 5%, 99% proteins with coefficient of variation <48%). We show that sample fractionation by one-dimensional SDS-PAGE is associated with a moderate decrease of quantitative measurement repeatability while largely improving the depth of proteomic coverage. We then applied the method for a large scale proteomic study of the human endothelial cell response to inflammatory cytokines, such as TNFα, interferon γ, and IL1β, which allowed us to finely decipher at the proteomic level the biological pathways involved in endothelial cell response to proinflammatory cytokines.

Lamellar Bodies of Human Epidermis Proteomics Characterization by High Throughput Mass Spectrometry and Possible Involvement of CLIP-170 in their Trafficking/Secretion

Lamellar bodies (LBs) are tubulovesicular secretory organelles of epithelial cells related to lysosomes. In the epidermis, they play a crucial role in permeability barrier homeostasis, secreting their contents, lipids, a variety of hydrolases, protease inhibitors, and antimicrobial peptides, in the upper keratinocyte layers. The identification of proteins transported in epidermal LBs is still far from complete, and the way their secretion is controlled unknown. In this study, we describe the first proteomics characterization by nano-LC-MS/MS of a fraction enriched in epidermal LBs. We identified 984 proteins, including proteins known or thought to be secreted by LBs. Moreover 31 proteins corresponded to lysosomal components further suggesting that LBs are a new class of secretory lysosomes. Many of the newly found proteins could play a role in the epidermal barrier and desquamation (one acid ceramidase-like protein, apolipoproteins, glycosidases, protease inhibitors, and peptidases) and in LB trafficking (e.g. Rab, Arf, and motor complex proteins). We focus here on CLIP-170/restin, a protein that mediates interactions between organelles and microtubules. Western blotting confirmed the presence of CLIP-170 and its known effectors IQGAP1 and Cdc42 in the LB-enriched fraction. We showed, by confocal microscopy analysis of skin cryosections, that CLIP-170 was expressed in differentiated keratinocytes, first at the periphery of the nucleus then with a granular cytoplasmic labeling evocative of LBs. It was preferentially co-localized with Cdc42 and with the known LB protein cathepsin D. CLIP-170 was also largely co-localized with Rab7. This study strongly suggests a new function for CLIP-170, its involvement together with Cdc42 and/or Rab7 in the intracellular trafficking of LBs, and provides evidence that nano-LC-MS/MS combined with monodimensional electrophoresis separation constitutes a powerful method for identifying proteins in a complex mixture such as subcellular structures.

Chronic ethanol feeding affects proteasome-interacting proteins.

Studies on alcoholic liver injury mechanisms show a significant inhibition of the proteasome activity. To investigate this phenomenon, we isolated proteasome complexes from the liver of rats fed ethanol chronically, and from the liver of their pair‐fed controls, using a non‐denaturing multiple centrifugations procedure to preserve proteasome‐interacting proteins (PIPs). ICAT and MS/MS spectral counting, further confirmed by Western blot, showed that the levels of several PIPs were significantly decreased in the isolated ethanol proteasome fractions. This was the case of PA28α/β proteasome activator subunits, and of three proteasome‐associated deubiquitinases, Rpn11, ubiquitin C‐terminal hydrolase 14, and ubiquitin carboxyl‐terminal hydrolase L5. Interestingly, Rpn13 C‐terminal end was missing in the ethanol proteasome fraction, which probably altered the linking of ubiquitin carboxyl‐terminal hydrolase L5 to the proteasome. 20S proteasome and most 19S subunits were however not changed but Ecm29, a protein known to stabilize the interactions between the 20S and its activators, was decreased in the isolated ethanol proteasome fractions. It is proposed that ethanol metabolism causes proteasome inhibition by several mechanisms, including by altering PIPs and proteasome regulatory complexes binding to the proteasome.

Computational and Mass-Spectrometry-Based Workflow for the Discovery and Validation of Missing Human Proteins: Application to Chromosomes 2 and 14.

In the framework of the C-HPP, our Franco-Swiss consortium has adopted chromosomes 2 and 14, coding for a total of 382 missing proteins (proteins for which evidence is lacking at protein level). Over the last 4 years, the French proteomics infrastructure has collected high-quality data sets from 40 human samples, including a series of rarely studied cell lines, tissue types, and sample preparations. Here we described a step-by-step strategy based on the use of bioinformatics screening and subsequent mass spectrometry (MS)-based validation to identify what were up to now missing proteins in these data sets. Screening database search results (85,326 dat files) identified 58 of the missing proteins (36 on chromosome 2 and 22 on chromosome 14) by 83 unique peptides following the latest release of neXtProt (2014-09-19). PSMs corresponding to these peptides were thoroughly examined by applying two different MS-based criteria: peptide-level false discovery rate calculation and expert PSM quality assessment. Synthetic peptides were then produced and used to generate reference MS/MS spectra. A spectral similarity score was then calculated for each pair of reference-endogenous spectra and used as a third criterion for missing protein validation. Finally, LC-SRM assays were developed to target proteotypic peptides from four of the missing proteins detected in tissue/cell samples, which were still available and for which sample preparation could be reproduced. These LC-SRM assays unambiguously detected the endogenous unique peptide for three of the proteins. For two of these, identification was confirmed by additional proteotypic peptides. We concluded that of the initial set of 58 proteins detected by the bioinformatics screen, the consecutive MS-based validation criteria led to propose the identification of 13 of these proteins (8 on chromosome 2 and 5 on chromosome 14) that passed at least two of the three MS-based criteria. Thus, a rigorous step-by-step approach combining bioinformatics screening and MS-based validation assays is particularly suitable to obtain protein-level evidence for proteins previously considered as missing. All MS/MS data have been deposited in ProteomeXchange under identifier PXD002131.

Label-free quantitative urinary proteomics identifies the arginase pathway as a new player in congenital obstructive nephropathy.

Obstructive nephropathy is a frequently encountered situation in newborns. In previous studies, the urinary peptidome has been analyzed for the identification of clinically useful biomarkers of obstructive nephropathy. However, the urinary proteome has not been explored yet and should allow additional insight into the pathophysiology of the disease. We have analyzed the urinary proteome of newborns (n = 5/group) with obstructive nephropathy using label free quantitative nanoLC-MS/MS allowing the identification and quantification of 970 urinary proteins. We next focused on proteins exclusively regulated in severe obstructive nephropathy and identified Arginase 1 as a potential candidate molecule involved in the development of obstructive nephropathy, located at the crossroad of pro- and antifibrotic pathways. The reduced urinary abundance of Arginase 1 in obstructive nephropathy was verified in independent clinical samples using both Western blot and MRM analysis. These data were confirmed in situ in kidneys obtained from a mouse obstructive nephropathy model. In addition, we also observed increased expression of Arginase 2 and increased total arginase activity in obstructed mouse kidneys. mRNA expression analysis of the related arginase pathways indicated that the pro-fibrotic arginase-related pathway is activated during obstructive nephropathy. Taken together we have identified a new actor in the development of obstructive nephropathy in newborns using quantitative urinary proteomics and shown its involvement in an in vivo model of disease. The present study demonstrates the relevance of such a quantitative urinary proteomics approach with clinical samples for a better understanding of the pathophysiology and for the discovery of potential therapeutic targets.