Plasma ALS and Gal-3BP differentiate early from advanced liver fibrosis in MASLD patients.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is estimated to affect 30% of the world's population, and its prevalence is increasing in line with obesity. Liver fibrosis is closely related to mortality, making it the most important clinical parameter for MASLD. It is currently assessed by liver biopsy - an invasive procedure that has some limitations. There is thus an urgent need for a reliable non-invasive means to diagnose earlier MASLD stages. METHODS: A discovery study was performed on 158 plasma samples from histologically-characterised MASLD patients using mass spectrometry (MS)-based quantitative proteomics. Differentially abundant proteins were selected for verification by ELISA in the same cohort. They were subsequently validated in an independent MASLD cohort (n = 200). RESULTS: From the 72 proteins differentially abundant between patients with early (F0-2) and advanced fibrosis (F3-4), we selected Insulin-like growth factor-binding protein complex acid labile subunit (ALS) and Galectin-3-binding protein (Gal-3BP) for further study. In our validation cohort, AUROCs with 95% CIs of 0.744 [0.673 - 0.816] and 0.735 [0.661 - 0.81] were obtained for ALS and Gal-3BP, respectively. Combining ALS and Gal-3BP improved the assessment of advanced liver fibrosis, giving an AUROC of 0.796 [0.731. 0.862]. The {ALS; Gal-3BP} model surpassed classic fibrosis panels in predicting advanced liver fibrosis. CONCLUSIONS: Further investigations with complementary cohorts will be needed to confirm the usefulness of ALS and Gal-3BP individually and in combination with other biomarkers for diagnosis of liver fibrosis. With the availability of ELISA assays, these findings could be rapidly clinically translated, providing direct benefits for patients.

Beyond Target-Decoy Competition: Stable Validation of Peptide and Protein Identifications in Mass Spectrometry-Based Discovery Proteomics.

In bottom-up discovery proteomics, target-decoy competition (TDC) is the most popular method for false discovery rate (FDR) control. Despite unquestionable statistical foundations, this method has drawbacks, including its hitherto unknown intrinsic lack of stability vis-à-vis practical conditions of application. Although some consequences of this instability have already been empirically described, they may have been misinterpreted. This article provides evidence that TDC has become less reliable as the accuracy of modern mass spectrometers improved. We therefore propose to replace TDC by a totally different method to control the FDR at the spectrum, peptide, and protein levels, while benefiting from the theoretical guarantees of the Benjamini-Hochberg framework. As this method is simpler to use, faster to compute, and more stable than TDC, we argue that it is better adapted to the standardization and throughput constraints of current proteomic platforms.

Protein Biomarker Discovery in Non-depleted Serum by Spectral Library-Based Data-Independent Acquisition Mass Spectrometry.

In discovery proteomics experiments, tandem mass spectrometry and data-dependent acquisition (DDA) are classically used to identify and quantify peptides and proteins through database searching. This strategy suffers from known limitations such as under-sampling and lack of reproducibility of precursor ion selection in complex proteomics samples, leading to somewhat inconsistent analytical results across large datasets. Data-independent acquisition (DIA) based on fragmentation of all the precursors detected in predetermined isolation windows can potentially overcome this limitation. DIA promises reproducible peptide and protein quantification with deeper proteome coverage and fewer missing values than DDA strategies. This approach is particularly attractive in the field of clinical biomarker discovery, where large numbers of samples must be analyzed. Here, we describe a DIA workflow for non-depleted serum analysis including a straightforward approach through which to construct a dedicated spectral library, and indications on how to optimize chromatographic and mass spectrometry analytical methods to produce high-quality DIA data and results.

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.

Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies.

Missing values are a genuine issue in label-free quantitative proteomics. Recent works have surveyed the different statistical methods to conduct imputation and have compared them on real or simulated data sets and recommended a list of missing value imputation methods for proteomics application. Although insightful, these comparisons do not account for two important facts: (i) depending on the proteomics data set, the missingness mechanism may be of different natures and (ii) each imputation method is devoted to a specific type of missingness mechanism. As a result, we believe that the question at stake is not to find the most accurate imputation method in general but instead the most appropriate one. We describe a series of comparisons that support our views: For instance, we show that a supposedly "under-performing" method (i.e., giving baseline average results), if applied at the "appropriate" time in the data-processing pipeline (before or after peptide aggregation) on a data set with the "appropriate" nature of missing values, can outperform a blindly applied, supposedly "better-performing" method (i.e., the reference method from the state-of-the-art). This leads us to formulate few practical guidelines regarding the choice and the application of an imputation method in a proteomics context.

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.

Pandoraviruses: amoeba viruses with genomes up to 2.5 Mb reaching that of parasitic eukaryotes.

Ten years ago, the discovery of Mimivirus, a virus infecting Acanthamoeba, initiated a reappraisal of the upper limits of the viral world, both in terms of particle size (>0.7 micrometers) and genome complexity (>1000 genes), dimensions typical of parasitic bacteria. The diversity of these giant viruses (the Megaviridae) was assessed by sampling a variety of aquatic environments and their associated sediments worldwide. We report the isolation of two giant viruses, one off the coast of central Chile, the other from a freshwater pond near Melbourne (Australia), without morphological or genomic resemblance to any previously defined virus families. Their micrometer-sized ovoid particles contain DNA genomes of at least 2.5 and 1.9 megabases, respectively. These viruses are the first members of the proposed "Pandoravirus" genus, a term reflecting their lack of similarity with previously described microorganisms and the surprises expected from their future study.

Mass spectrometry-based absolute protein quantification: PSAQ™ strategy makes use of "noncanonical" proteotypic peptides.

Absolute quantification of proteins using isotope dilution mass spectrometry requires the selection of proteotypic peptides. When choosing these peptides, a certain number of rules must be respected. Several of these were established to safeguard against quantification errors resulting from the isotopically labeled standard peptides not behaving in the same way as the peptides to be quantified. Of all absolute quantification methods using isotope dilution, Protein Standard for Absolute Quantification (PSAQ(TM) ) offers the maximal protein sequence coverage. In the present study, we show that the PSAQ method presents a previously unreported advantage for protein quantification as it makes use of Met/Cys-containing peptides and peptides-containing miscleavages in addition to proteotypic peptides. By increasing the total number of peptides that can be considered, robustness of quantification is improved, paving the way for a facilitated quantification of low abundant and/or low-molecular-weight proteins.

Proteomic profiling of oil bodies isolated from the unicellular green microalga Chlamydomonas reinhardtii: with focus on proteins involved in lipid metabolism.

Oil bodies are sites of energy and carbon storage in many organisms including microalgae. As a step toward deciphering oil accumulation mechanisms in algae, we used proteomics to analyze purified oil bodies from the model microalga Chlamydomonas reinhardtii grown under nitrogen deprivation. Among the 248 proteins (≥ 2 peptides) identified by LC-MS/MS, 33 were putatively involved in the metabolism of lipids (mostly acyl-lipids and sterols). Compared with a recently reported Chlamydomonas oil body proteome, 19 new proteins of lipid metabolism were identified, spanning the key steps of the triacylglycerol synthesis pathway and including a glycerol-3-phosphate acyltransferase (GPAT), a lysophosphatidic acid acyltransferase (LPAT) and a putative phospholipid:diacylglycerol acyltransferase (PDAT). In addition, proteins putatively involved in deacylation/reacylation, sterol synthesis, lipid signaling and lipid trafficking were found to be associated with the oil body fraction. This data set thus provides evidence that Chlamydomonas oil bodies are not only storage compartments but also are dynamic structures likely to be involved in processes such as oil synthesis, degradation and lipid homeostasis. The proteins identified here should provide useful targets for genetic studies aiming at increasing our understanding of triacyglycerol synthesis and the role of oil bodies in microalgal cell functions.

AT_CHLORO, a comprehensive chloroplast proteome database with subplastidial localization and curated information on envelope proteins.

Recent advances in the proteomics field have allowed a series of high throughput experiments to be conducted on chloroplast samples, and the data are available in several public databases. However, the accurate localization of many chloroplast proteins often remains hypothetical. This is especially true for envelope proteins. We went a step further into the knowledge of the chloroplast proteome by focusing, in the same set of experiments, on the localization of proteins in the stroma, the thylakoids, and envelope membranes. LC-MS/MS-based analyses first allowed building the AT_CHLORO database (http://www.grenoble.prabi.fr/protehome/grenoble-plant-proteomics/), a comprehensive repertoire of the 1323 proteins, identified by 10,654 unique peptide sequences, present in highly purified chloroplasts and their subfractions prepared from Arabidopsis thaliana leaves. This database also provides extensive proteomics information (peptide sequences and molecular weight, chromatographic retention times, MS/MS spectra, and spectral count) for a unique chloroplast protein accurate mass and time tag database gathering identified peptides with their respective and precise analytical coordinates, molecular weight, and retention time. We assessed the partitioning of each protein in the three chloroplast compartments by using a semiquantitative proteomics approach (spectral count). These data together with an in-depth investigation of the literature were compiled to provide accurate subplastidial localization of previously known and newly identified proteins. A unique knowledge base containing extensive information on the proteins identified in envelope fractions was thus obtained, allowing new insights into this membrane system to be revealed. Altogether, the data we obtained provide unexpected information about plastidial or subplastidial localization of some proteins that were not suspected to be associated to this membrane system. The spectral counting-based strategy was further validated as the compartmentation of well known pathways (for instance, photosynthesis and amino acid, fatty acid, or glycerolipid biosynthesis) within chloroplasts could be dissected. It also allowed revisiting the compartmentation of the chloroplast metabolism and functions.

A toolbox for validation of mass spectrometry peptides identification and generation of database: IRMa.

SUMMARY: The IRMa toolbox provides an interactive application to assist in the validation of Mascot search results. It allows automatic filtering of Mascot identification results as well as manual confirmation or rejection of individual PSM (a match between a fragmentation mass spectrum and a peptide). Dynamic grouping and coherence of information are maintained by the software in real time. Validated results can be exported under various forms, including an identification database (MSIdb). This allows biologists to compile search results from a whole study in a unique repository in order to provide a summarized view of their project. IRMa also features a fully automated version that can be used in a high-throughput pipeline. Given filter parameters, it can delete hits with no significant PSM, regroup hits identified by the same peptide(s) and export the result to the specified format without user intervention. AVAILABILITY: http://biodev.extra.cea.fr/docs/irma (java 1.5 or higher needed).

Peptide storage: are you getting the best return on your investment? Defining optimal storage conditions for proteomics samples.

To comply with current proteomics guidelines, it is often necessary to analyze the same peptide samples several times. Between analyses, the sample must be stored in such a way as to conserve its intrinsic properties, without losing either peptides or signal intensity. This article describes two studies designed to define the optimal storage conditions for peptide samples between analyses. With the use of a label-free strategy, peptide conservation was compared over a 28-day period in three different recipients: standard plastic tubes, glass tubes, and low-adsorption plastic tubes. The results of this study showed that standard plastic tubes are unsuitable for peptide storage over the period studied. Glass tubes were found to perform better than standard plastic, but optimal peptide recovery was achieved using low-adsorption plastic tubes. The peptides showing poor recovery following storage were mainly hydrophobic in nature. The differences in peptide recovery between glass and low-adsorption plastic tubes were further studied using isotopically labeled proteins. This study allowed accurate comparison of peptide recovery between the two tube types within the same LC-MS run. The results of the label-free study were confirmed. Further, it was possible to demonstrate that peptide recovery in low-adsorption plastic tubes was optimal whatever the peptide concentration stored.

PepLine: a software pipeline for high-throughput direct mapping of tandem mass spectrometry data on genomic sequences.

PepLine is a fully automated software which maps MS/MS fragmentation spectra of trypsic peptides to genomic DNA sequences. The approach is based on Peptide Sequence Tags (PSTs) obtained from partial interpretation of QTOF MS/MS spectra (first module). PSTs are then mapped on the six-frame translations of genomic sequences (second module) giving hits. Hits are then clustered to detect potential coding regions (third module). Our work aimed at optimizing the algorithms of each component to allow the whole pipeline to proceed in a fully automated manner using raw nucleic acid sequences (i.e., genomes that have not been "reduced" to a database of ORFs or putative exons sequences). The whole pipeline was tested on controlled MS/MS spectra sets from standard proteins and from Arabidopsis thaliana envelope chloroplast samples. Our results demonstrate that PepLine competed with protein database searching softwares and was fast enough to potentially tackle large data sets and/or high size genomes. We also illustrate the potential of this approach for the detection of the intron/exon structure of genes.

A proteomics dissection of Arabidopsis thaliana vacuoles isolated from cell culture.

To better understand the mechanisms governing cellular traffic, storage of various metabolites, and their ultimate degradation, Arabidopsis thaliana vacuole proteomes were established. To this aim, a procedure was developed to prepare highly purified vacuoles from protoplasts isolated from Arabidopsis cell cultures using Ficoll density gradients. Based on the specific activity of the vacuolar marker alpha-mannosidase, the enrichment factor of the vacuoles was estimated at approximately 42-fold with an average yield of 2.1%. Absence of significant contamination by other cellular compartments was validated by Western blot using antibodies raised against specific markers of chloroplasts, mitochondria, plasma membrane, and endoplasmic reticulum. Based on these results, vacuole preparations showed the necessary degree of purity for proteomics study. Therefore, a proteomics approach was developed to identify the protein components present in both the membrane and soluble fractions of the Arabidopsis cell vacuoles. This approach includes the following: (i) a mild oxidation step leading to the transformation of cysteine residues into cysteic acid and methionine to methionine sulfoxide, (ii) an in-solution proteolytic digestion of very hydrophobic proteins, and (iii) a prefractionation of proteins by short migration by SDS-PAGE followed by analysis by liquid chromatography coupled to tandem mass spectrometry. This procedure allowed the identification of more than 650 proteins, two-thirds of which copurify with the membrane hydrophobic fraction and one-third of which copurifies with the soluble fraction. Among the 416 proteins identified from the membrane fraction, 195 were considered integral membrane proteins based on the presence of one or more predicted transmembrane domains, and 110 transporters and related proteins were identified (91 putative transporters and 19 proteins related to the V-ATPase pump). With regard to function, about 20% of the proteins identified were known previously to be associated with vacuolar activities. The proteins identified are involved in ion and metabolite transport (26%), stress response (9%), signal transduction (7%), and metabolism (6%) or have been described to be involved in typical vacuolar activities, such as protein and sugar hydrolysis. The subcellular localization of several putative vacuolar proteins was confirmed by transient expression of green fluorescent protein fusion constructs.