Quantitative modeling of human liver reveals dysregulation of glycosphingolipid pathways in nonalcoholic fatty liver disease

Hypothalamic JNK1-hepatic fatty acid synthase axis mediates a metabolic rewiring that prevents hepatic steatosis in male mice treated with olanzapine via intraperitoneal: Additional effects of PTP1B inhibition

“…In fact, the transcriptomic analysis revealed attenuated changes in gene families associated to hepatic steatosis/NAFLD progression in livers from PTP1B–KO mice fed an OLA-supplemented diet but, on the contrary, these gene families were markedly altered in WT mice ( i.e. circadian rhythm [ 104 , 105 ], PPAR signaling pathway [ 106 , 107 ], biosynthesis of unsaturated FAs [ 108 , 109 ], glycosaminoglycan biosynthesis [ 110 , 111 ], glycosphingolipid biosynthesis [ 112 , 113 ], terpenoid backbone biosynthesis and steroid biosynthesis [ 114 , 115 ]). These data highlight the protection of PTP1B deficiency against a broad spectrum of obesogenic inducers.…”

A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease

“…Sen et al . [29] employed transcriptomics of whole liver tissue and serum metabolomics from a cohort based on genome-scale metabolic models to identify dysregulated glycosphingolipid pathways across the disease spectrum. In the study by Luo et al .…”

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

“…This approach allows for a more precise investigation of metabolic pathways and therefore provides a foundation for precision medicine and the selection of optimal treatment strategies [ 34 , 35 ]. Furthermore, overlaying omics data with the metabolic networks has enabled researchers to infer metabolic regulations and identify patterns associated with metabolic states [ 7 , 8 , 28 , 36 ].…”

“…Genome-scale metabolic models (GEMs) provide a robust framework that enables the integration of multiple omics datasets [ 4 , 5 ]. By harnessing the power of GEMs, researchers can delve into the complexities of biological pathways, enabling a comprehensive understanding of cellular metabolism and its underlying mechanisms [ 2 , 6 , 7 , 8 , 9 ]. Furthermore, incorporating biochemical and genetic information into GEMs has enabled researchers to understand the interplay between genes, proteins, and metabolites involved in cellular metabolism.…”