Metabolic reprogramming promotes glioblastoma cell migration and invasion. Integrin αvβ3 is one of the major integrin family members in glioblastoma multiforme cell surface mediating interactions with extracellular matrix proteins that are important for glioblastoma progression. The role of αvβ3 integrin in regulating metabolic reprogramming and its mechanism of action have not been determined in glioblastoma cells. Integrin αvβ3 engagement with osteopontin promotes glucose uptake and aerobic glycolysis, while inhibiting mitochondrial oxidative phosphorylation. Blocking or downregulation of integrin αvβ3 inhibits glucose uptake and aerobic glycolysis and promotes mitochondrial oxidative phosphorylation, resulting in decreased migration and growth in glioblastoma cells. Pharmacological inhibition of focal adhesion kinase (FAK) or downregulation of protein arginine methyltransferase 5 (PRMT5) blocks metabolic shift toward glycolysis and inhibits glioblastoma cell migration and invasion. These results support that integrin αvβ3 and osteopontin engagement plays an important role in promoting the metabolic shift toward glycolysis and inhibiting mitochondria oxidative phosphorylation in glioblastoma cells. The metabolic shift in cell energy metabolism is coupled to changes in migration, invasion, and growth, which are mediated by downstream FAK and PRMT5 in glioblastoma cells.

Introduction: Matrix stiffening during idiopathic pulmonary fibrosis (IPF) creates a mechanically altered microenvironment that promotes fibroblast activation, yet the metabolic consequences of these mechanical cues remain incompletely defined. Pyruvate kinase M2 (PKM2) catalyzes the rate-limiting step of glycolysis, but whether it serves as a mechanosensitive link coupling matrix stiffness to fibroblast activation has not been clarified. This study examined whether substrate stiffness drives glycolytic reprogramming through PKM2 upregulation and whether this metabolic adaptation is required for mechanically driven fibroblast activation. Methods: PKM2 expression was analyzed in the NCBI GEO dataset GSE24206, human IPF lung tissues, and the bleomycin-induced mouse pulmonary fibrosis model. Primary mouse lung fibroblasts were cultured on collagen I–coated polyacrylamide hydrogels mimicking normal (2 kPa) or fibrotic (25 kPa) stiffness with or without TGF-β1 treatment. Glucose uptake, lactate secretion, pyruvate kinase activity, LDH activity, NAD ⁺ /NADH ratio, and oxygen consumption were measured; PKM2 oligomeric states were resolved by DSS cross-linking; and PKM2 was depleted by lentiviral shRNA. Results: PKM2 transcripts were progressively elevated in early- and advanced-stage IPF, and corresponding increases in protein and mRNA were confirmed in human IPF lungs and in bleomycin-treated mouse lungs. Stiff substrates upregulated PKM2 expression and reprogrammed fibroblast metabolism, with TGF-β1 eliciting increases in glucose uptake, lactate secretion, and oxygen consumption that were predominantly observed on stiff substrates. Cross-linking analysis showed that soft substrates favored catalytically active PKM2 tetramers, whereas stiff substrates constrained tetramer assembly and promoted lower-order species, providing a mechanistic basis for the dissociation between increased PKM2 expression and unchanged pyruvate kinase activity. PKM2 knockdown attenuated stiffness-induced increases in glucose uptake, lactate secretion, and LDH activity, reduced the cellular NAD ⁺ /NADH ratio, and decreased α-SMA–positive stress fiber formation under combined mechanical and TGF-β1 stimulation. Discussion: These findings indicate that PKM2 contributes to a mechanosensitive metabolic program coupling matrix stiffness to fibroblast activation, with substrate stiffness regulating both PKM2 expression and oligomeric state. Targeting PKM2-mediated metabolic adaptation may interrupt the self-reinforcing cycle of matrix stiffening and fibroblast activation in pulmonary fibrosis.

Type 2 diabetes mellitus (T2D) affects hundreds of millions worldwide, with recent estimates indicating approximately 589 million adults living with diabetes, most with type 2 disease. Beyond classical insulin signaling pathways, increasing evidence implicates altered protein glycosylation in metabolic dysfunction. The solute carrier 35 (SLC35) family of nucleotide sugar transporters mediates the import of activated sugars into the endoplasmic reticulum and Golgi lumen, thereby influencing global glycosylation patterns. Dysregulation of these transporters can perturb glucose homeostasis, insulin responsiveness, and nutrient-sensing pathways through changes in glycosylation flux. In this review, we dissect the molecular mechanisms by which these transporters modulate glucose homeostasis, insulin signaling pathways, protein O-GlcN acylation, and broader glycosylation processes. We integrate findings from human genetic studies, rodent models, and in vitro functional analyses to characterize how altered SLC35 activity is associated with T2D and metabolic syndrome. Four members demonstrate particularly compelling evidence: SLC35B4 modulates hepatic glucose metabolism, SLC35D3 mutations impair dopaminergic signaling and energy balance, and SLC35F3 variants interact with high-carbohydrate intake to increase metabolic-syndrome risk. SLC35A3, though less studied, may influence glycosylation-dependent insulin signaling through its role in N-glycan biosynthesis. Beyond these characterized transporters, this review identifies potential metabolic roles for understudied family members, suggesting broader implications across the entire SLC35 family. We also discuss how such alterations can lead to disrupted hexosamine flux, impaired glycoprotein processing, aberrant cellular signaling, and micronutrient imbalances. Finally, we evaluate the therapeutic potential of targeting SLC35 transporters, outlining both opportunities and challenges in translating these insights into novel T2D treatments.

Organelle morphology is increasingly recognized as a computationally accessible readout of molecular state, yet whether subcellular architecture can identify which signaling pathway is active, not merely whether a cell is perturbed, remains untested. Here we introduce SIGMA (SIgnaling via Golgi Morphology Analysis), a framework that reframes pathway identification as a morphological classification problem centered on the Golgi apparatus. SIGMA integrates imaging flow cytometry for population-scale Golgi profiling, teacher-student knowledge distillation to transfer fine morphological cues from limited high-resolution images to an efficient lower-resolution classifier, and mechanistic generalization testing with chemically distinct activators. Applied to MRC-5 fibroblasts across six conditions spanning four signaling cascades plus controls, SIGMA achieved 90.02% cell-level accuracy and correctly assigned five of seven structurally unrelated external compounds to their expected pathway class. These results establish Golgi morphology as a multiplexed, machine-readable fingerprint of signaling state.

Inflammatory activation remodels the Golgi apparatus to meet increased secretory demand, yet whether these structural changes carry computationally accessible information about immune cell state has not been explored. Here we show that machine learning applied to imaging flow cytometry images of the Golgi can distinguish lipopolysaccharide (LPS) treated from control cells. We trained models on Golgi images from human THP-1 monocytes before and 16 h after LPS stimulation in vitro, and from circulating myeloid cells of mice 24 h after intratracheal LPS or saline in vivo. In each dataset, the best-performing model was a deep learning model trained directly on images rather than a classifier using predefined morphometric features. Cross-domain performance varied with the model, transfer direction and use of labelled target data: several deep learning models trained on mouse images classified THP-1 cells above chance without changing model parameters. Grad-CAM highlighted regions within or adjacent to the GM130 fluorescence pattern. These findings indicate that GM130 images carry information associated with LPS treatment that supports classification within and across the two datasets. To our knowledge, this is the first application of supervised deep learning to imaging flow cytometry images of a subcellular organelle for inflammatory phenotyping, establishing organelle level image analysis as a potential complement to molecular assays for immune cell classification.