Project description:Sweet cherry (Prunus avium L.) is a stone fruit widely consumed and appreciated for its organoleptic properties, as well as its nutraceutical potential. We here investigated the characteristics of six non-commercial Tuscan varieties of sweet cherry maintained at the Regional Germplasm Bank of the CNR-IBE in Follonica (Italy) and sampled at maturity over three consecutive years (2016-2017-2018). We adopted an approach merging genotyping and targeted gene expression profiling with metabolomics. To complement the data, a study of the soluble proteomes was also performed on two varieties showing the highest content of phenolics. The results obtained revealed that the highest differences were observed in the expression of genes involved in the phenylpropanoid pathway during the three years and among the varieties, while metabolomics identified the presence of flavanols and proanthocyanidins in highest abundance in the varieties Morellona and Crognola. Finally, proteomics on these two varieties showed differences in proteins involved in stress response, primary metabolism, and cell wall expansion. To the best of our knowledge, this is the first multi-pronged study focused on Tuscan sweet cherry varieties providing insights into the differential abundance of genes, proteins and metabolites.
Project description:Rice embryos are rich in high-quality protein, fat, vitamins and minerals, representing the most important nutritional part of brown rice. However, the molecular mechanism of rice embryo development is poorly understood. In this study, map-based cloning, metabolomics, and transcriptomics were applied to explore the giant embryo rice cultivar Dapeimi grown under natural conditions. The map-based cloning results demonstrated that Dapeimi is a novel allelic mutant of GE, and the functional mutation site is a single cytosine deletion in the exon1, which differed from the findings of previous reports. The identification and analysis of differential metabolites indicated that plants lacking the GE mainly promoted amino acid metabolism, energy metabolism, and lipid metabolism pathways in the rice embryo. Changes of gene expression in related metabolic regulatory networks were analyzed, and substantial changes in the transcriptome supported the metabolomics results. By analyzing rice embryo development through a combination of strategies, this research contributes to a greater understanding of the molecular mechanism of rice embryo development, and provides a theoretical foundation for improving rice nutrition and breeding high-nutrition varieties.
Project description:Molecular networking connects mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. As a result, the fragmentation spectra of these ion species often remain unconnected in tandem mass spectrometry-based molecular networks, leading to redundant and disconnected sub-networks of the same compound classes. To overcome this bottleneck, we develop Ion Identity Molecular Networking (IIMN) that integrates chromatographic peak shape correlation analysis into molecular networks to connect and collapse different ion species of the same molecule. The new feature relationships improve network connectivity for structurally related molecules, can be used to reveal unknown ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral reference libraries. IIMN is integrated into various open source feature finding tools and the GNPS environment. Moreover, IIMN-based spectral libraries with a broad coverage of ion species are publicly available.
Project description:The interplay between pathogens and hosts has been studied for decades using targeted approaches such as the analysis of mutants and host immunological responses. Although much has been learned from such studies, they focus on individual pathways and fail to reveal the global effects of infection on the host. To alleviate this issue, high-throughput methods such as transcriptomics and proteomics have been used to study host-pathogen interactions. Recently, metabolomics was established as a new method to study changes in the biochemical composition of host tissues. We report a metabolomics study of Salmonella enterica serovar Typhimurium infection. We used Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Direct Infusion to reveal that dozens of host metabolic pathways are affected by Salmonella in a murine infection model. In particular, multiple host hormone pathways are disrupted. Our results identify unappreciated effects of infection on host metabolism and shed light on mechanisms used by Salmonella to cause disease, and by the host to counter infection. Female C57BL/6 mice were infected with Salmonella enterica serovar Typhimurium SL1344 cells by oral gavage. Feces and livers were collected and metabolites extracted using acetonitrile. For experiments with feces, samples were collected from 4 mice before and after infection. For liver experiments, 11 uninfected and 11 infected mice were used. Samples were combined into 3 groups of 3-4 mice each, resulting in the analysis of 3 group samples of uninfected and 3 of infected mice. Extracts were infused into a 12-T Apex-Qe hybrid quadrupole-FT-ICR mass spectrometer equipped with an Apollo II electrospray ionization source, a quadrupole mass filter and a hexapole collision cell. Raw mass spectrometry data were processed as described elsewhere (Han et al. 2008. Metabolomics. 4:128-140 [PMID 19081807]). To identify differences in metabolite composition between uninfected and infected samples, we filtered the list of masses for metabolites which were present on one set of samples but not the other. Additionally, we calculated the ratios between averaged intensities of metabolites from uninfected and infected mice. To assign possible metabolite identities, monoisotopic neutral masses of interest were queried against MassTrix (http://masstrix.org). Masses were searched against the Mus musculus database within a mass error of 3 ppm. Data were analyzed by unpaired t tests with 95% confidence intervals.
Project description:The interplay between pathogens and hosts has been studied for decades using targeted approaches such as the analysis of mutants and host immunological responses. Although much has been learned from such studies, they focus on individual pathways and fail to reveal the global effects of infection on the host. To alleviate this issue, high-throughput methods such as transcriptomics and proteomics have been used to study host-pathogen interactions. Recently, metabolomics was established as a new method to study changes in the biochemical composition of host tissues. We report a metabolomics study of Salmonella enterica serovar Typhimurium infection. We used Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Direct Infusion to reveal that dozens of host metabolic pathways are affected by Salmonella in a murine infection model. In particular, multiple host hormone pathways are disrupted. Our results identify unappreciated effects of infection on host metabolism and shed light on mechanisms used by Salmonella to cause disease, and by the host to counter infection.