Skip to content
#protein folding Open access

Autotaxin induces fibrosis in lamina cribrosa cells in glaucoma

Oct 2026 · Scientific Reports
Glaucoma and retinal disorders

Abstract

Lysophosphatidic acid (LPA) and the enzyme that generates it, autotaxin, have been shown to play a role in fibrosis in the trabecular meshwork in glaucoma, contributing to increased intraocular pressure. We wished to investigate the role of LPA and autotaxin in fibrosis of the lamina cribrosa (LC) of the optic nerve head in glaucoma. LC cells from glaucoma patient donors (GLC) and normal controls (NLC) were cultured, and expression of autotaxin and LPA receptors were determined through real-time quantitative PCR (RT-qPCR), western blotting and immunofluorescence. Autotaxin activity in cell media was measured via a phospholipase assay. GLC cells were treated with an autotaxin inhibitor S32826, and the rate of cell proliferation and ECM gene expression were assessed. GLC cells showed significantly increased autotaxin mRNA (3.84 ± 1.71 fold p < 0.05) and protein expression (1.54 ± 0.41 fold p < 0.05) compared to NLC. The LPA receptor LPA 3 was also significantly overexpressed (2.73 ± 0.23 fold p < 0.001). Treatment of GLC cells with S32826 significantly reduced autotaxin activity, cell proliferation and expression of ECM genes COL1A (0.51±0.06, p < 0.05) and FN1 (0.64±0.12, p < 0.05). Inhibition of autotaxin decreased cell proliferation and ECM production in LC cells, and therefore could be a potential target for fibrosis in the LC in glaucoma.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.