Project description:We used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Two condition (flagellin and LPS) time course exposure of RAW 264.7 cell line at 1, 2, 4, and 24 hours. Two replicates for each condition and time point. All conditions compared to a pool of untreated cells at a 0 hour time point.
Project description:Our comprehensive model identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimization, providin a framework to understand metabolic adaptation in eukaryal cells.
Project description:We used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions.
Project description:Development of an updated genome-scale metabolic model of Clostridium thermocellum and its application for integration of multi-omics datasets
Project description:The ketogenic diet (KD) has demonstrated anti-proliferative effects across multiple tumor types, yet the underlying metabolic and transcriptomic mechanisms remain incompletely understood. This study employed integrated multi-omics analysis combining targeted metabolomics and RNA-sequencing to elucidate KD-induced metabolic reprogramming in BRAF/NRAS wild-type, BRAF mutant, and NRAS mutant melanoma xenografts, which showed delayed tumor growth when treated with KD. Despite pronounced metabolic and transcriptional heterogeneity across models with minimal overlap in individual KD-responsive genes, pathway-level analysis revealed convergent biological signatures. Using correlation-based integration and supervised latent variable modeling (mixOmics DIABLO), we identified consistent KD-associated alterations in cancer-related pathways including PI3K-Akt and MAPK signaling, as well as sphingolipid, HIF-1, and shigellosis pathways. Mechanistically, KD enhanced sphingomyelin and ceramide levels in addition to the expression of ceramide synthesis genes while reducing ceramide breakdown. Moreover, KD induced downregulation of critical tumor regulators including PI3K, AKT, HIF, MEK, and ERK. These findings demonstrate that despite metabolic and transcriptomic heterogeneity, KD drives coordinated metabolic reprogramming at the pathway level, shifting lipid metabolism toward pro-apoptotic ceramides and attenuating key oncogenic signaling cascades. Our results provide mechanistic insights into KD's anti-tumor efficacy and identify metabolic nodes amenable to therapeutic intervention in melanoma. This record contains the count matrix generated with the RNA-seq experiment.