Project description:The data set contains MS/MS data on teeth extracts for Ancient DNA teeth samples ran in both positive and Negative ionization modes
Project description:Seeds of Arabidopsis thaliana wild type and knockout-mutants for flavonoid genes (tt7 [3 lines], fls1, tt3, tt4, tt7tt4 [2 lines] ) were subjected to untargeted metabolomic analyses (LC-MS/MS) in positive and negative ionisation mode.
Project description:Punarnava [Boerhaavia diffusa L.] is a medicinal plant and constituent of several Indian traditional medicines. According to Ayurveda, this plant is a rich source of nutrients. Traditionally, it is used to provide relief against various gastrointestinal disorders, treat wounds, reduce joint pains, and as an anti-stress agent. Despite its pharmacological importance, detailed characterization of the metabolite composition of this plant has not been reported to date. Therefore, we have taken up metabolomic profiling of Punarnava choorna, as part of a larger project of metabolomic profiling of ayurvedic drugs. We carried out a global metabolomic analysis using a high-resolution mass spectrometry to investigate metabolite composition of Punarnava choorna. In total 1747 and 4031 features were identified at MS1 using XCMS in the positive and negative modes, respectively. Using MS2Compound, we identified 1229 and 709 features in the positive and negative modes, respectively. We also identified 362 and 191 metabolites at MS2 level in the positive and negative modes, respectively using the MS2Compound tool. The data were searched against the PlantCyc, KEGG, PhenolExplorer and HMDB databases. A large number of nutritionally important metabolites including amino acids, sugars and vitamins were identified in Purnarnava choorna. Further, the identified metabolites were mapped to their potential protein interactors using BindingDB tool. Highest number of interactions was observed for plant serotonins. The data provides molecular evidence to accelerate the discovery of mode of action of Purnarnava choorna.
Project description:Background: Renal dysfunction is a common and serious complication in patients with end-stage liver diseases. While some patients recover renal function after liver transplantation (LT), others do not. Additionally, patients with normal kidney function (Normal-KF) before LT may develop post-transplant renal dysfunction. Early identification of patients at risk for impaired kidney function (Impaired-KF) post-LT is critical to improving outcomes. This study integrated metabolomic and proteomic analyses to investigate molecular profiles distinguishing Normal-KF from Impaired-KF post-LT. Methods: Nine LT recipients were classified into Normal-KF (n=5) and Impaired-KF (n=4) groups. One additional recipient with pre-transplant renal function impairment who recovered renal function after LT, was analyzed separately. Plasma samples were collected at 2- and 5-weeks post-LT. The metabolomic and proteomic profiles were assessed by untargeted liquid chromatography-tandem mass spectrometry. Results: Metabolomic analysis identified 29 significantly altered metabolites between Normal-KF and Impaired-KF (fold change>2, p<0.05). Proteomic analysis revealed 45 differentially expressed proteins (fold change>1.25, p<0.05). For the recovered patient, the metabolomic profile closely resembled Normal-KF, whereas the proteomic profile remained aligned with Impaired-KF at both 14- and 35-days post-LT. From week 2 to week 5, both the metabolomic and proteomic profiles of the recovered patient showed trends toward the Normal-KF. Conclusion: This study revealed distinct metabolomic and proteomic signatures associated with renal dysfunction post-LT. Proteomic profiles indicated a delayed recovery compared to metabolomic profiles, suggesting a dynamic and muti-layered renal recovery process. Further research is warranted to elucidate the functional implications of the differential proteins and metabolites for improved monitoring and therapeutic strategies.
Project description:In the present study we carried out global and targeted metabolomics of differentiated IMR32 cells to study the neuroprotective mechanism of Yashtimadhu (Glycyrrhiza glabra L.) in rotenone induced cellular model of parkinsons disease. Our mass spectrometry data highlighted 2403 and 2934 aligned metabolites from the positive and negative modes respectively. Among the aligned metabolites, 756 metabolites from positive and 731 metabolites from negative polarity, were mapped to a known metabolite, and the others remained unassigned. A total of 1,102 non-redundant metabolites from the positive and negative modes were assigned.
Project description:Untargeted lipidomics of liver samples from female and male DBA/2J or C57BL/6J mice fed a control diet, Western diet, or high- or low-isoleucine Western diet. Both positive and negative mode are included.
Project description:Human iPSCs and NSCs were engineered by AAVS1 and/or C13 safe-harbor TALENs which mediated targeted integration of various reporter genes at single or dual safe-harbor loci. Multiple clones of targeted human iPSCs were used to compare with parental untargeted NCRM5 iPSCs. Polyclonal targeted human NSCs were used to compare with their parental untargeted NCRM1NSCs or H9NSCs. Total RNA obtained from targeted human iPSCs or NSCs compared to untargeted control iPSCs or NSCs.