Project description:Biofuel production from lignocellulosic waste and residues is a promising option to mitigate the environmental costs associated to energy production. However, the difficulty to cost-effectively overcome lignocellulose recalcitrance hampers a widespread application of such bioprocesses. Through an integrated approach, we focused on the factors affecting cellulose reactivity and their impact on downstream fermentation. Three cellulosic manufactured materials were characterized in details: facial tissue, Whatman paper, cotton pads. The model mesophilic cellulolytic bacterium Clostridium cellulolyticum was used to study colonization and metabolic patterns during fermentation of these materials. Facial tissue was extensively colonized and exhibited the fastest degradation and the highest ethanol-to-acetate ratio. Comparing facial tissue fermentation to Whatman paper fermentation by label-free quantitative shotgun proteomics and statistical analyses, 187 proteins showed a different behavior; higher concentration levels were detected for many enzymes from the carbohydrate central metabolic pathway; distinct patterns of expression levels were observed for carbohydratases degrading cellulose and hemicellulose. Overall, lower degrees of polymerization, lower crystallinity index, and the presence of hemicelluloses could explain the higher biological reactivity and bioethanol production yields.
Project description:Oil rapeseed (Brassica napus L.) is a typical winter biennial plant, with high cold tolerance during vegetative stage. In recent years, more and more early-maturing rapeseed varieties were planted across China. Unfortunately, the early-maturing rapeseed varieties with low cold tolerance have higher risk of freeze injury in cold winter and spring. Little is known about the molecular mechanisms for coping with different low-temperature stress conditions in rapeseed. In this study, we investigated 47,328 differentially expressed genes (DEGs) of two early-maturing rapeseed varieties with different cold tolerance treated with cold shock at chilling (4°C) and freezing (−4°C) temperatures, as well as chilling and freezing stress following cold acclimation or control conditions. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that two conserved (the primary metabolism and plant hormone signal transduction) and two novel (plant-pathogen interaction pathway and circadian rhythms pathway) signaling pathways were significantly enriched with differentially-expressed transcripts. Our results provided a foundation for understanding the low-temperature stress response mechanisms of rapeseed. We also propose new ideas and candidate genes for genetic improvement of rapeseed tolerance to cold stresses.
Project description:Being inspired by metabolomic data processing, we have developed a bioinformatic pipeline that optimizes the processing of mass spectral data obtained from isobaric Tandem Mass Tag (TMT) experiments. Our method focuses on the tandem mass spectral level by first quantifying and then identifying (QtI), while preserving unidentified spectra for further investigations. The raw datasets were previously generated [1, 2]. Two-proteome model experiments were considered where identical pools of human CSF or plasma samples were mixed with E. coli samples at different concentrations. E. coli protein extract was spiked in 400 µL of CSF at amounts of 0, 2, 3, 5, 6.25, and 7.5 µg. Such sets of 6 spiked CSF samples were prepared in triplicate for comparison using sixplex isobaric tagging and analyzed in triplicates on two independent but identical LC MS/MS, for a total of 18 raw files [1]; this experiment is called “CSF-E.coli”. E. coli protein extract was spiked at 0, 2.5, 5, 6.25, 12.5, and 25 µg in 30 µL human plasma. Such sets of 6 spiked plasma samples were prepared in quadruplicate for comparison using sixplex isobaric tagging and analyzed in triplicates on one LC MS/MS, for a total of 12 raw files [2]; this experiment is referred as “Plasma-E.coli”.The so-called “96samples-CSF” experiment consists of 16 replicate TMT sixplex experiments measuring identical CSF samples from the pool described above [2], analyzed in triplicates on one LCMS/MS for a total of 48 raw files. The so-called “96samples-plasma” experiment consists of 16 replicate TMT sixplex experiments measuring identical plasma samples from the pool described before, analyzed in triplicates on one LC MS/MS for a total of 48 raw files [1]. References: [1] Dayon, L., Núñez Galindo, A., Corthésy, J., Cominetti, O. & Kussmann, M. Comprehensive and scalable highly automated MS-based proteomic workflow for clinical biomarker discovery in human plasma. J. Proteome Res. 13, 3837-3845 (2014). [2] Núñez Galindo, A., Kussmann, M. & Dayon, L. Proteomics of Cerebrospinal Fluid: Throughput and Robustness Using a Scalable Automated Analysis Pipeline for Biomarker Discovery. Anal. Chem. 87, 10755-10761 (2015).
Project description:C3-C4 intermediate Moricandia suffruticosa showed tolerance to drought and heat stresses, and high photosynthetic capacity under these abiotic stresses as comparing with C3 relative crop rapeseed (Brassica napus). In our study, systematic analysis was conducted to reveal photosynthetic difference between C3-C4 Moricandia suffruticosa and its relative C3 rapeseed from the same Brassiceae tribe. It was found that Moricandia leaf photosynthesis and anatomy were significantly changed compared to rapeseed under drought and heat stress conditions. De novo transcriptome of Moricandia was assembled by next generation sequencing, and unigenes were mapped to respective rapeseed gene locus. Then comparative transcriptome analysis was conducted in leaf tissues of Moricandia and rapeseed under both drought and heat stresses. Main pathways and candidate genes were revealed from this analysis, which may be associated with the stress induced change in Moricandia.
Project description:Precise and large-scale characterization of glycoproteome is critical for understanding the biological functions of glycoproteins. Due to the complexity of glycosylation, the overall throughput, data quality and accessibility of site-specific glycosylation analysis are overwhelmingly lower than those of routine proteomic studies. Here, we introduce a workflow that robustly identifies intact glycopeptides at a proteome scale using stepped-energy mass-spectrometry (MS) and pGlyco 2.0, a dedicated search engine for large-scale glycopeptide analysis with comprehensive quality control (false discovery rate evaluation on the glycan, peptide and glycopeptide matches).
Project description:Precise and large-scale characterization of glycoproteome is critical for understanding the biological functions of glycoproteins. Due to the complexity of glycosylation, the overall throughput, data quality and accessibility of site-specific glycosylation analysis are overwhelmingly lower than those of routine proteomic studies. Here, we introduce a workflow that robustly identifies intact glycopeptides at a proteome scale using stepped-energy mass-spectrometry (MS) and pGlyco 2.0, a dedicated search engine for large-scale glycopeptide analysis with comprehensive quality control (false discovery rate evaluation on the glycan, peptide and glycopeptide matches).
Project description:Precise and large-scale characterization of glycoproteome is critical for understanding the biological functions of glycoproteins. Due to the complexity of glycosylation, the overall throughput, data quality and accessibility of site-specific glycosylation analysis are overwhelmingly lower than those of routine proteomic studies. Here, we introduce a workflow that robustly identifies intact glycopeptides at a proteome scale using stepped-energy mass-spectrometry (MS) and pGlyco 2.0, a dedicated search engine for large-scale glycopeptide analysis with comprehensive quality control (false discovery rate evaluation on the glycan, peptide and glycopeptide matches).
Project description:Precise and large-scale characterization of glycoproteome is critical for understanding the biological functions of glycoproteins. Due to the complexity of glycosylation, the overall throughput, data quality and accessibility of site-specific glycosylation analysis are overwhelmingly lower than those of routine proteomic studies. Here, we introduce a workflow that robustly identifies intact glycopeptides at a proteome scale using stepped-energy mass-spectrometry (MS) and pGlyco 2.0, a dedicated search engine for large-scale glycopeptide analysis with comprehensive quality control (false discovery rate evaluation on the glycan, peptide and glycopeptide matches).
Project description:Precise and large-scale characterization of glycoproteome is critical for understanding the biological functions of glycoproteins. Due to the complexity of glycosylation, the overall throughput, data quality and accessibility of site-specific glycosylation analysis are overwhelmingly lower than those of routine proteomic studies. Here, we introduce a workflow that robustly identifies intact glycopeptides at a proteome scale using stepped-energy mass-spectrometry (MS) and pGlyco 2.0, a dedicated search engine for large-scale glycopeptide analysis with comprehensive quality control (false discovery rate evaluation on the glycan, peptide and glycopeptide matches).
Project description:Precise and large-scale characterization of glycoproteome is critical for understanding the biological functions of glycoproteins. Due to the complexity of glycosylation, the overall throughput, data quality and accessibility of site-specific glycosylation analysis are overwhelmingly lower than those of routine proteomic studies. Here, we introduce a workflow that robustly identifies intact glycopeptides at a proteome scale using stepped-energy mass-spectrometry (MS) and pGlyco 2.0, a dedicated search engine for large-scale glycopeptide analysis with comprehensive quality control (false discovery rate evaluation on the glycan, peptide and glycopeptide matches).