Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/413948

Autophagy regulates hyaluronan synthase 2 levels in vascular endothelial cells

Chen, C. G.·Gubbiotti, M. A.·Han, X.·Yu, Y.·Linhardt, R. J.·Iozzo, R. V.
View original source ↗Export metadata ↓

Abstract

Hyaluronan is emerging as a key player regulating wound repair, inflammation, and angiogenesis. Of the three hyaluronan synthases (HAS1-3), HAS2 is the main driver of tumorigenicity by enhancing hyaluronan deposition. We discovered that HAS2 was degraded in vascular endothelial cells via autophagy, a catabolic process evoked by endorepellin and endostatin, two angiostatic and pro-autophagic effectors, and by Torin 1, a specific mTOR inhibitor. Protracted autophagy increased co-localization of HAS2 with three core components of the autophagosome, LC3, p62, and ATG9A, and binding of HAS2 to ATG9A. Importantly, autophagic induction led to an exclusive and marked suppression of secreted hyaluronan with no significant effects on either heparan or chondroitin sulfate levels. Thus, we have unveiled autophagy as a key catabolic mechanism regulating the production of hyaluronan in endothelial cells. Moreover, our study provides a biological link between autophagy and angiogenesis that could lead to potential targets for tumor neovascularization.\n\nSummaryHyaluronan is pro-angiogenic and pro-tumorigenic. We report a novel mechanism through which three autophagic inducers[-]endorepellin, endostatin, and Torin 1[-]regulate the levels of hyaluronan synthase-2. Protracted autophagy results in suppression of extracellular hyaluronan, thereby implicating autophagy in the regulation hyaluronan production.

Explore related subjects

cell biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chen, C. G., Gubbiotti, M. A., Han, X., Yu, Y., Linhardt, R. J., Iozzo, R. V.. 2018-09-10. Autophagy regulates hyaluronan synthase 2 levels in vascular endothelial cells. https://doi.org/10.1101/413948

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Cigarette smoke extract and palmitic acid exposed patient-derived iPS- RPE cells as a model for AMD

Purpose: Age-related macular degeneration (AMD) is a multifactorial disease in which dysfunction and loss of retinal pigment epithelial (RPE) cells contribute to disease progression. Besides genetics, cigarette smoking and high fat diet are important environmental and metabolic risk factors for AMD and are associated with RPE dysfunction. However, the effects of combined environmental and metabolic stress on AMD-derived RPE cells remain poorly understood. In this study, we used patient-derived induced pluripotent stem cell-derived RPE (iPSC-RPE) cells to examine the effects of cigarette smoke extract (CSE) and palmitic acid (PA) on RPE cell function. Methods: iPSCs derived from non-AMD and AMD donors were differentiated into RPE cells and characterized based on morphology, RPE-specific marker expression, polarity, gene expression, and barrier function. Mature iPSC-RPE cells were exposed to different concentrations of CSE and PA to determine experimental conditions. Cell death, epithelial-to-mesenchymal transition (EMT)-like morphological changes, stress-related gene expression, RPE polarity, TEER, VEGF-A and PEDF secretion, and intracellular lipid accumulation were then evaluated following CSE, PA, or combined CSE and PA exposure. Results: Differentiated iPSC-RPE cells developed typical pigmented, hexagonal morphology, expressed RPE-specific markers, and formed polarized monolayers. Both CSE and PA increased cell death in a dose-dependent manner in the control and AMD-iPSC-RPE, with AMD-iPSC-RPE exhibiting greater susceptibility, particularly those from patients with dry AMD. CSE and PA also induced also induced EMT-like morphological changes and increased the expression of ZEB1, NQO1, and IL8. Furthermore, CSE and PA disrupted the polarized distribution of ZO-1 and Best1, significantly reduced the polarity ratio, decreased PEDF and VEGF-A secretion and increased intracellular lipid accumulation. Combined CSE and PA treatment produced more pronounced effects than either treatment alone in AMD lines. Conclusions: AMD- iPSC-RPE cells, particularly those derived from dry AMD donors, exhibit increased susceptibility to environmental and metabolic stress. Exposure to CSE and PA revealed this vulnerability through alterations in cell survival, stress responses, epithelial morphology, polarity, secretory function, and intracellular lipid accumulation. These findings highlight the effects of CSE and lipid stress on RPE metabolism and underscore the utility of patient-derived iPSC-RPE cells as a model for investigating how environmental and metabolic factors contribute to RPE dysfunction and AMD pathogenesis.

cell biology↗
bioRxiv2026

Structural Determinants of Ligand-Mediated Hmg2 Mallostery

The sterol pathway is a conserved eukaryotic process that produces essential metabolites. Hmg-CoA reductase (HMGR) is a key rate limiting enzyme of the sterol pathway and is the most successful clinical target for managing cholesterol levels. However, determination of the structural features that are essential for its regulation remains elusive. Because of the conservation between mammals and yeast, for many years we have studied the structural features of a yeast Hmg2 isozyme of HMGR. Hmg2 is regulated by the mevalonate pathway isoprenoid GGPP. GGPP acts similarly to an allosteric ligand and appears to bind to a specific site on Hmg2, causing Hmg2 to undergo reversible misfolding. Misfolded Hmg2 is then recognized by the protein quality control mechanism known as the HRD pathway. Due to its similarity to allosteric regulation we have termed this ligand-dependent misfolding mallostery. In this work, we systematically probe each residue of the Hmg2 lumenal domain as well as assess the transmembrane domain and lumenal domain as independent domains and use in vivo and in vitro techniques to assess what regions are important for GGPP response. We identified stabilizing mutations within each loop of the lumenal domain but did not find mutants that were completely unresponsive to GGPP. When assessing the transmembrane domain and lumenal domain as two independent structures, we found that only the transmembrane domain responded to GGPP specifically in vitro. Our results suggest that GGPP binding occurs within the Hmg2 transmembrane domain and that the loops in the lumenal domain are required for transducing a mallosteric response to GGPP. Taken together, these data suggest a new model of Hmg2 regulation.

cell biology↗
bioRxiv2026

RELAY: Relational In-Context Learning for Cellular Perturbation Prediction

Cellular responses to perturbations vary across multiple experimental variables, such as perturbation identity, cell type, donor, and dose, defining a multidimensional condition space in which each measured population occupies one coordinate. Existing methods predict unmeasured conditions using explicit representations of these variable values, generalizing unseen values therefore depends on the availability and quality of such representations. We present RELAY, a relational in-context model that predicts unmeasured coordinates from measured responses at related coordinates. For each target, RELAY uses measured control--perturbed pairs from conditions that share one or more experimental variables with the target, such as the same perturbation in another cell type. The model receives only which variables are shared, not their values, and uses these demonstrations to predict the target response. We evaluate RELAY on chemical, genetic, and cytokine perturbation datasets under two settings: unseen combinations, where test values are individually observed during training but not in their target combinations, and unseen variable values, where test values are introduced only through context at inference. RELAY outperforms existing in-context methods on unseen variable values and supervised perturbation-prediction methods on unseen combinations. All resources will be open sourced, including code and data.

cell biology↗