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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.

Understanding the mechanisms of adaptation to the environment in cultivated plants is a promising way to meet the challenge of maintaining food security in the context of global warming. In the case of maize, high-throughput sequencing has revealed that structural variations represent a large part of the genome and could have huge phenotypic effects. Among these, Presence Absence Variants (PAVs, which include insertion/deletion of large DNA sequences) may be involved in adaptation of maize to its environment, but their contribution to the genetic determinism of traits and genotype by environment interactions remains largely unknown. To address this issue, we performed a genome-wide association study between two types of polymorphisms, SNPs and InDels, and molecular traits obtained from proteomics and metabolomics analyses to detect quantitative trait loci (QTLs). The genetic panel used for this study was composed of 254 dent inbred lines genotyped with 978,134 SNPs and 72,041 InDels. The latter encompassed from 37 to 129,700 pb, including thousands of PAVs that are not present in the B73 reference genome. Proteins and metabolites were quantified by mass spectrometry in leaf samples from F1 hybrids obtained by crossing the inbred lines with one flint tester line. Hybrid plants were grown under two watering conditions (well-watered and water deficit) in greenhouse. In total, we detected 61,225 QTLs associated with proteome or metabolome variations. Among these, 4,766 QTLs were exclusively detected by InDels. To take into account the difference of marker density between InDels and SNPs, we used a re-sampling approach which showed that there was no difference between InDels and SNPs regarding the number and effect of the QTLs detected . Additionally, the QTLs detected by the two types of polymorphism were equally distributed in the two watering conditions. These preliminary results show that InDels are worse considering to detect new genetic regions of interest. They also suggest that InDels and SNPs equally contribute to molecular trait variation and response to drought stress.

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.