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Proteostasis is defined as the processes required maintaining the equilibrium between protein synthesis and degradation. Tight regulations from gene transcription to post-translational proteins modifications of these processes are required in cell cycle and survival to balance cellular development and environmental stresses. It has been shown repeatedly that the abundance of proteins only moderately correlates with that of transcripts. While the determination of protein turnover using pulsed SILAC metabolic labelling is popular for large scale proteomics investigations. Although used with small mammals (e.g. mice), such approach remains difficult with whole organisms and almost impossible with autotroph species like plants. Alternatively, it has been proposed to use inorganic sources of 15N isotopes that could be metabolically incorporated to de novo synthetized amino acids but full 15N protein labelling is almost impossible due to the high amino acid recycling rate in plant. Instead of reaching complete 14N/15N exchange as previously done by Millar et al. 1,2 , we decide to perform a 15N metabolic pulse at very low incorporation yield (around 5 % 15N enrichment). This limited incorporation rate offers some advantages of which a limited increase of mass spectrometry signal complexity. Indeed, the 15N-labelled products moderately modify the peptide natural isotopic distribution and both labelled and unlabelled product appears as an overlapping isotopic distribution. Then, the recorded signal is extensively processed to filter out the outliers. Finally, the filtered signal is finally used to first perform label free protein quantitation which provide the "Protein Fold Change" (PFC) evolution over time. Second, the experimental peptide isotopic distribution is used to determine the apparent 15N labelling rate and the evolution of the 15N "Labelled Protein Fraction" (LPF) over the labelling time. Thanks to our local bioinformatics developments including i2MassChroQ, MassChroQ and the MCQR package, we propose an approach of MS signal processing that make it possible to determine the fold change in protein (FCP) and the labelled protein fraction (LPF). Both values (FCP & LPF) are key elements and are required to estimate the turnover rate individually for each protein 3 at the proteomic scale. Furthermore, this presentation highlights that accurate isotopic distribution measurement is possible and could be used from ordinary largescale proteomics LC-MS/MS instruments to provide an interesting alternative to determine protein turnover determination at the proteome scale

IntroductionX!TandemPipeline (Langella et al. 2017) is a proteomics free and open sourceJava software program designed to filter and group peptide/proteinidentifications from MS/MS mass spectra. After a complete rewrite in C++17,X!TandemPipeline++ now features both native support for the timsTOF raw dataformat and peptide/protein quantification. X!TandemPipeline++ performs peptideidentifications and area under the curve XIC-based quantifications using theBruker's native raw data format. Using a common HeLa data set published byMeier et al. (2018, PXDO1OO12), we demonstrate that X!TandemPipeline++identifies and quantifies significantly more proteins than competitors MaxQuantand MSFragger. It is also significantly faster.MethodsX!TandemPipeline++ is written in portable C++17 and makes use of the Qtlibraries for the graphical user interface. Binary packages are available forLinux and MS Windows. The timsTOF native raw data reader was developed in-house withthe technical specifications provided by Bruker. The sofware wastightly optimized to ensure very fast access to the binary data. The currentversion provides real time MS/MS peptide annotation and performs extremely fastion current extractions and XIC chromatogram visualizations (typically, in lessthan five seconds for a 2 hour PASEF run).Preliminary Data or Plenary Speakers AbstractThe Bruker timsTOF line of instruments improves the identification of peptidesand proteins in complex mixtures by implementing a peculiar ion mobilitytechnology. X!TandemPipeline++ has the distinct feature, with respect to theMaxQuant and MSFragger competitors, of natively parsing the timsTOF raw datawith original software code that puts us in total control of the dataprocessing, in particular where tight optimizations are needed for speed or incases where special data processing features need adding for increased accuracy.This is exemplified by X!TandemPipeline++ using the renowned X!Tandem engine(Craig et al. 2004) to perform database searches right on optimized databrokered to it by our reader of native timsTOF binary data. Our software identifies roughly the same amount of peptides as when using theX!Tandem engine on MGF data provided by the Bruker Data analysis tool butperforms much faster, reducing processing time from 55 to 12 minutes on atypical quality control HeLa sample. For peptide quantifications,X!TandemPipeline++ uses the ion mobility-enabled version of MassChroQ (Valot etal. 2011), both software pieces sharing our optimized native timsTOF datareader. Using a common HeLa data set (Meier et al. (2018), PXDO1OO12, 4 technicalreplicates), we found more quantified proteins with a minimum of 2 quantifiedions : 5783 vs 4977 (MSFragger), 3526 (MaxQuant). Correlation of quantifiedproteins between samples was 0.99. Median protein coefficient of variation (CV)was also very good : 0.059 vs 0.049 (MSFragger), 0.072 (Peaks) and 0.070(MaxQuant). Comparable results have been found using data set PXD014777(Prianichnikov et al., 2020, a mixture of proteins from three organisms, H.sapiens, S. cerevisiae, and E. coli). Overall, while producing similar orbetter results than competing software, X!TandemPipeline++ always performsfaster than all the other software offerings.Novel AspectFast and accurate timsTOF native raw data support for quantitative proteomics.

To decipher the biochemical bases of nitrogen (N) utilization and metabolism of silage maize inrelation to growth and productivity, an untargeted metabolomic (proton NMR- and LC-QTOF-MSbased) and proteomic approach was conducted on leaves of 29 hybrids cultivated in the field underoptimal and reduced N fertilization. The corresponding biochemical data were analyzed eitherindividually (PCA, ANOVA), or integrated with that of eco-physiological, developmental and yield-related traits (multi-block sparse PLS-DA). Such integrated analysis was conducted to interpret theunderlying physiology concerning the plant response to a mild N deficit often occurring underagronomic conditions. The genetic diversity of the core panel of 29 European dent hybrids crossed toa flint tester was exploited to highlight common N-responsive metabolites and proteins in order toidentify putative biological markers that could be used to pilot and rationalize N fertilization. Theresponses of metabolites, proteins, and yield-related traits to the reduced N treatment were alsoexploited to identify biochemical markers representative of a maize ideotype exhibiting betteragronomic performances when N fertilization is limited. These markers could be used to select high-yielding commercial maize hybrids used for silage production requiring less N fertilizer inputs.

Plants in their physical environment face multiple biotic and abiotic stresses duringtheir life cycle. In nature, environmental stresses often co-occur – such as water deficit andnutrient deficiencies – and can have synergistic, antagonistic or additive effects on the plantmolecular responses. In pea (Pisum sativum), combination of water stress (WS) and sulfur (S)deficiency showed a mitigation effect on the seed protein composition, as compared to Sdeficiency occurring alone (Henriet et al., 2019). To better understand how pea responds toWS and/or S deficiency, a multi-omics (transcriptomics, proteomics, metabolomics, ionomics)analysis has been performed from leaf samples collected during a time course, on differentlots of plants subjected to either WS, S deficiency, or to the combination of the two stressors.Our analyses revealed that 21% of the stress-responsive mRNAs, proteins, metabolites andions showed a stronger response when S deficiency occurs with WS, suggesting synergisticor additive effects. Using a weighted-gene co-expression network approach, we evidencedgenes strongly regulated by changes in the S concentration in leaves. The comparisonbetween our transcriptomics and proteomics data allows us to formulate hypotheses on thelevel of regulation (transcriptional or post-transcriptional) of these genes. We also proposecandidate genes that might help plants to better tolerate combinatorial stresses.

Dans leur environnement naturel, les plantes doivent faire face à plusieurs stress biotiqueset abiotiques au cours de leur cycle de développement. Certains de ces stress peuventsurvenir au même moment – comme le stress hydrique et les carences nutritionnelles – etpeuvent avoir des effets synergiques, antagonistes ou additifs sur les réponses moléculairesdes plantes. Chez le pois (Pisum sativum), il a été observé que l’effet de la carence en soufre(S) sur la composition protéique des graines peut être atténué lorsque cette carence estcombinée à un stress hydrique (Henriet et al., 2019). Afin de mieux caractériser les réponsesmoléculaires du pois au stress hydrique et/ou à la carence en S, une analyse multi-omiques(transcriptomique, protéomique, métabolomique, ionomique) a été réalisée à partir defeuilles récoltées à différents jours post-floraison, sur différents lots de plantes ayant subi ounon un stress hydrique, une carence en S, ou une combinaison des deux stress. Nos analysesont révélé que 21% des ARNm, protéines, métabolites et ions qui répondentsignificativement aux stress, ont une réponse accentuée lorsque les deux types de stresssont combinés, suggérant des effets synergiques ou additifs. En utilisant une approcheintégrative de réseau de co-expression, nous avons mis en évidence des gènes fortementrégulés par les changements de concentration en S dans les feuilles. La comparaison desdonnées de transcriptomique et protéomique nous permet de formuler des hypothèsesquant au niveau de régulation (transcriptionnel ou post-transcriptionnel) des gènes deréponse aux stress identifiés. Enfin, ce travail nous permet de proposer des listes de gènescandidats qui pourraient jouer un rôle dans la tolérance des plantes aux situations de multistress.

IntroductionX!TandemPipeline (Langella et al. 2017) is a proteomics free and open sourceJava software program designed to filter and group peptide/proteinidentifications from MS/MS mass spectra. After a complete rewrite in C++17,X!TandemPipeline++, now named i2MassChroQ, features both native support for the timsTOF raw dataformat and peptide/protein quantification. i2MassChroQ performs peptideidentifications and area under the curve XIC-based quantifications using theBruker's native raw data format. Using a common HeLa data set published byMeier et al. (2018, PXDO1OO12), we demonstrate that i2MassChroQidentifies and quantifies significantly more proteins than competitors MaxQuantand MSFragger. It is also significantly faster.Methodsi2MassChroQ is written in portable C++17 and makes use of the Qtlibraries for the graphical user interface. Binary packages are available forLinux and MS Windows. The timsTOF native raw data reader was developed in-house withthe technical specifications provided by Bruker. The sofware wastightly optimized to ensure very fast access to the binary data. The currentversion provides real time MS/MS peptide annotation and performs extremely fastion current extractions and XIC chromatogram visualizations (typically, in lessthan five seconds for a 2 hour PASEF run).ResultsThe Bruker timsTOF line of instruments improves the identification of peptidesand proteins in complex mixtures by implementing a peculiar ion mobilitytechnology. i2MassChroQ has the distinct feature, with respect to theMaxQuant and MSFragger competitors, of natively parsing the timsTOF raw datawith original software code that puts us in total control of the dataprocessing, in particular where tight optimizations are needed for speed or incases where special data processing features need adding for increased accuracy.This is exemplified by i2MassChroQ using the renowned X!Tandem engine(Craig et al. 2004) to perform database searches right on optimized databrokered to it by our reader of native timsTOF binary data.Our software identifies roughly the same amount of peptides as when using theX!Tandem engine on MGF data provided by the Bruker Data analysis tool butperforms much faster, reducing processing time from 55 to 12 minutes on atypical quality control HeLa sample. For peptide quantifications,i2MassChroQ uses the ion mobility-enabled version of MassChroQ (Valot etal. 2011), both software pieces sharing our optimized native timsTOF datareader.Using a common HeLa data set (Meier et al. (2018), PXDO1OO12, 4 technicalreplicates), we found more quantified proteins with a minimum of 2 quantifiedions : 5783 vs 4977 (MSFragger), 3526 (MaxQuant). Correlation of quantifiedproteins between samples was 0.99. Median protein coefficient of variation (CV)was also very good : 0.059 vs 0.049 (MSFragger), 0.072 (Peaks) and 0.070(MaxQuant). Comparable results have been found using data set PXD014777(Prianichnikov et al., 2020, a mixture of proteins from three organisms, H.sapiens, S. cerevisiae, and E. coli).ConclusionOverall, while producing similar orbetter results than competing software, i2MassChroQ always performsfaster than all the other software offerings.

OBJECTIVE This study aims to search for reliable serological biomarkers allowing the early prediction of cystic echinococcosis (CE) post-operative outcomes. METHODS We applied immunoprecipitation (IP) of Echinococcus granulosus protoscolex antigens with pediatric CE patients' plasma collected at 1-month and 1-year post-surgery, followed by Liquid Chromatography with tandem mass spectrometry (LC-MS/MS). We compared IP proteomic content from relapsed patients within the first-year post-surgery (RCE) to cases with no relapses until 3 post-operative years (NRCE). Selected proteins were recombinantly synthesized and assessed for their prognostic performance by Enzyme-linked immunosorbent assay (ELISA). RESULTS A total of 305 immunoreactive parasitic proteins were identified, 59 of which were significantly more abundant in RCE than NRCE for both time-points. Four proteins showed the most promising characteristics for predicting CE outcomes: cytoplasmic malate dehydrogenase (Eg-cMDH), citrate synthase (Eg-CS), annexin A6 and severin. ELISA-IgG against the four markers were significantly lower at 1-year post-surgery than 1-month in NRCE, in contrast to RCE that displayed either stable or higher levels. The Eg-cMDH and Eg-CS showed the best prognostic performance, with respective probabilities of being "relapse-free" of 83% and 81%, if a decrease of IgG levels occurred between 1-month and 1-year post-surgery. CONCLUSION The Eg-cMDH and Eg-CS are promising biomarkers to predict early CE post-surgical outcomes.

Le protéome des graines en développement de lignées recombinantes issues du croisement entre desgénotypes de féverole résistants et sensibles aux bruches a été étudié par l’approche shotgun. Cetravail a permis d’identifier 749 protéines au début du remplissage des graines, dont 80 sontdifférentiellement accumulées entre les lignées sensibles et résistantes aux bruches. En plus de fournirun premier aperçu des protéines présentes à ce stade clé du développement des graines chez laféverole, ces données ont fait émerger des protéines candidates pour améliorer la résistance desgraines aux bruches. Parmi les protéines préférentiellement accumulée dans les lignées résistantes estune glycoprotéine pariétale inhibitrice des polygalacturonases d’origine pathogène qui dégradent lesparois cellulaires végétales. Cette glycoprotéine est donc susceptible de jouer un rôle clé dans lesmécanismes de défense lors du développement des graines. Ces résultats et les perspectives visant àmieux comprendre les mécanismes de résistance des graines de légumineuses aux bruches serontprésentés.

International audience; The physiopathology of pulmonary arterial hypertension (PAH) is characterized by pulmonary artery smooth muscle cell (PASMC) and endothelial cell (PAEC) dysfunction, contributing to pulmonary arterial obstruction and PAH progression. KCNK3 loss of function mutations are responsible for the first channelopathy identified in PAH. Loss of KCNK3 function/expression is a hallmark of PAH. However, the molecular mechanisms involved in KCNK3 dysfunction are mostly unknown. To identify the pathological molecular mechanisms downstream of KCNK3 in human PASMCs (hPASMCs) and human PAECs (hPAECs), we used a Liquid Chromatography-Tandem Mass Spectrometry-based proteomic approach to identify the molecular pathways regulated by KCNK3. KCNK3 loss of expression was induced in control hPASMCs or hPAECs by specific siRNA targeting KCNK3. We found that the loss of KCNK3 expression in hPAECs and hPASMCs leads to 326 and 222 proteins differentially expressed, respectively. Among them, 53 proteins were common to hPAECs and hPASMCs. The specific proteome remodeling in hPAECs in absence of KCNK3 was mostly related to the activation of glycolysis, the superpathway of methionine degradation, and the mTOR signaling pathways, and to a reduction in EIF2 signaling pathways. In hPASMCs, we found an activation of the PI3K/AKT signaling pathways and a reduction in EIF2 signaling and the Purine Nucleotides De Novo Biosynthesis II and IL-8 signaling pathways. Common to hPAECs and hPASMCs, we found that the loss of KCNK3 expression leads to the activation of the NRF2-mediated oxidative stress response and a reduction in the interferon pathway. In the hPAECs and hPASMCs, we found an increased expression of HO-1 (heme oxygenase-1) and a decreased IFIT3 (interferon-induced proteins with tetratricopeptide repeats 3) (confirmed by Western blotting), allowing us to identify these axes to understand the consequences of KCNK3 dysfunction. Our experiments, based on the loss of KCNK3 expression by a specific siRNA strategy in control hPAECs and hPASMCs, allow us to identify differences in the activation of several signaling pathways, indicating the key role played by KCNK3 dysfunction in the development of PAH. Altogether, these results allow us to better understand the consequences of KCNK3 dysfunction and suggest that KCNK3 loss of expression acts in favor of the proliferation and migration of hPASMCs and promotes the metabolic shift and apoptosis resistance of hPAECs.

The fungal phytopathogen Colletotrichum lupini is responsible for lupin anthracnose, resulting in significant yield losses worldwide. The molecular mechanisms underlying this infectious process are yet to be elucidated. This study proposes to evaluate C. lupini gene expression and protein synthesis during lupin infection, using, respectively, an RNAseq-based transcriptomic approach and a mass spectrometry-based proteomic approach. Patterns of differentially-expressed genes in planta were evaluated from 24 to 84 hours post-inoculation, and compared to in vitro cultures. A total of 897 differentially-expressed genes were identified from C. lupini during interaction with white lupin, of which 520 genes were predicted to have a putative function, including carbohydrate active enzyme, effector, protease or transporter-encoding genes, commonly described as pathogenicity factors for other Colletotrichum species during plant infection, and 377 hypothetical proteins. Simultaneously, a total of 304 proteins produced during the interaction were identified and quantified by mass spectrometry. Taken together, the results highlight that the dynamics of symptoms, gene expression and protein synthesis shared similarities to those of hemibiotrophic pathogens. In addition, a few genes with unknown or poorly-described functions were found to be specifically associated with the early or late stages of infection, suggesting that they may be of importance for pathogenicity. This study, conducted for the first time on a species belonging to the Colletotrichum acutatum species complex, presents an opportunity to deepen functional analyses of the genes involved in the pathogenicity of Colletotrichum spp. during the onset of plant infection.

National audience; Climate change is a major concern because of its potential effects on biodiversity and the agricultural sector. The domesticated sunflower, Helianthus annuus L., is the fourth most important oilseed crop in the world [1], is cultivated as hybrids and is promising for agriculture adaptation because it can maintain stable yields across a wide variety of environmental conditions, especially during drought stress [2]. To highlight the molecular processes related to the drought adaptation strategies, we studied the responses of eight parental lines (four males and four females) and their 16 hybrids cultivated on the outdoor high-throughput automated phenotyping platform Heliaphen [3] where two water regimes were implemented and ecophysiological traits were measured. Leaf samples from 144 samples were collected for multi-omics analyses including label-free shotgun analysis and protein identification and quantification. Statistical learning methods without a priori allowed us to reduce this complex molecular system [4] and to model proteomics and phenomics relationships to identify molecular players of heterotic behaviors and their roles during drought stress. We showed dramatic behavioral differences between hybrids and parental lines with solid interaction with water deficit that are exemplified by some highly interesting protein candidates. Further analysis of these genes or proteins highlights the impact of genetic variation on regulatory networks that could be selected to improve heterotic groups and breed for drought stress tolerance. Current integration of eco-physiological, proteomics, transcriptomics and metabolomics levels in order to achieve a more holistic view will be presented and discussed.