Noncovalent hyaluronan crosslinking by TSG-6: Modulation by heparin, heparan sulfate, and PRG4

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Abstract

The size, conformation, and organization of the glycosaminoglycan hyaluronan (HA) affect its interactions with soluble and cell surface-bound proteins. HA that is induced to form stable networks has unique biological properties relative to unmodified soluble HA. AlphaLISA assay technology offers a facile and general experimental approach to assay protein-mediated networking of HA in solution. Connections formed between two end-biotinylated 50 kDa HA (bHA) chains can be detected by signal arising from streptavidin-coated donor and acceptor beads being brought into close proximity when the bHA chains are bridged by proteins. We observed that incubation of bHA with the protein TSG-6 (tumor necrosis factor alpha stimulated gene/protein 6, TNFAIP/TSG-6) leads to dimerization or higher order multimerization of HA chains in solution. We compared two different heparin (HP) samples and two heparan sulfate (HS) samples for the ability to disrupt HA crosslinking by TSG-6. Both HP samples had approximately three sulfates per disaccharide, and both were effective in inhibiting HA crosslinking by TSG-6. HS with a relatively high degree of sulfation (1.75 per disaccharide) also inhibited TSG-6 mediated HA networking, while HS with a lower degree of sulfation (0.75 per disaccharide) was less effective. We further identified Proteoglycan 4 (PRG4, lubricin) as a TSG-6 ligand, and found it to inhibit TSG-6-mediated HA crosslinking. The effects of HP, HS, and PRG4 on HA crosslinking by TSG-6 were shown to be due to HP/HS/PRG4 inhibition of HA binding to the Link domain of TSG-6. Using the AlphaLISA platform, we also tested other HA-binding proteins for ability to create HA networks. The G1 domain of versican (VG1) effectively networked bHA in solution but required a higher concentration than TSG-6. Cartilage link protein (HAPLN1) and the HA binding protein segment of aggrecan (HABP, G1-IGD-G2) showed only low and variable magnitude HA networking effects. This study unambiguously demonstrates HA crosslinking in solution by TSG-6 and VG1 proteins, and establishes PRG4, HP and highly sulfated HS as modulators of TSG-6 mediated HA crosslinking.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

Mihaly Varadi, Stephen Anyango, Mandar Deshpande … (2021)

The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk ) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.

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Localization of matrix metalloproteinase 9 to the cell surface provides a mechanism for CD44-mediated tumor invasion.

Qi Yu, I Stamenkovic (1999)

The cell surface hyaluronan receptor CD44 promotes tumor growth and metastasis by mechanisms that remain poorly understood. We show here that CD44 associates with a proteolytic form of the matrix metalloproteinase-9 (MMP-9) on the surface of mouse mammary carcinoma and human melanoma cells. CD44-associated cell surface MMP-9 promotes cell-mediated collagen IV degradation in vitro and mediates tumor cell invasion of G8 myoblast monolayers. Several distinct CD44 isoforms coprecipitate with MMP-9 and CD44/MMP-9 coclustering is observed to be dependent on the ability of CD44 to form hyaluronan-induced aggregates. Disruption of CD44/MMP-9 cluster formation, by overexpression of soluble or truncated cell surface CD44, is shown to inhibit tumor invasiveness in vivo. Our observations indicate that CD44 serves to anchor MMP-9 on the cell surface and define a mechanism for CD44-mediated tumor invasion.

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Author and article information

Contributors

Adam P. Tanguay: URI : https://loop.frontiersin.org/people/1909587/overview

Teraesa M. Vitelli: URI : https://loop.frontiersin.org/people/1955631/overview

Tannin A. Schmidt: URI : https://loop.frontiersin.org/people/1996401/overview

Mary K. Cowman: URI : https://loop.frontiersin.org/people/153645/overview

Journal

Journal ID (nlm-ta): Front Mol Biosci

Journal ID (iso-abbrev): Front Mol Biosci

Journal ID (publisher-id): Front. Mol. Biosci.

Title: Frontiers in Molecular Biosciences

Publisher: Frontiers Media S.A.

ISSN (Electronic): 2296-889X

Publication date (Electronic): 05 October 2022

Publication date Collection: 2022

Volume: 9

Electronic Location Identifier: 990861

Affiliations

[1] ¹ Department of Biomedical Engineering , Tandon School of Engineering , New York University , New York, NY, United States

[2] ² Department of Biomedical Engineering , School of Dental Medicine , UConn Health , Farmington, CT, United States

[3] ³ New York Medical College , Valhalla, NY, United States

[4] ⁴ Department of Emergency Medicine , Warren Alpert Medical School and School of Engineering , Brown University , Providence, RI, United States

[5] ⁵ Department of Orthopedic Surgery, Grossman School of Medicine, New York University , New York, NY, United States

Author notes

Edited by: Maren Roman, Virginia Tech, United States

Reviewed by: Preethi Chandran, Howard University, United States

Yen-Liang Liu, China Medical University (Taiwan), Taiwan

*Correspondence: Mary K. Cowman, mary.cowman@ 123456nyu.edu ; Tannin A. Schmidt, tschmidt@ 123456uchc.edu

^†These authors have contributed equally to this work and share first authorship

This article was submitted to Glycoscience, a section of the journal Frontiers in Molecular Biosciences

Article

Publisher ID: 990861

DOI: 10.3389/fmolb.2022.990861

PMC ID: 9579337

SO-VID: 2a951261-d37f-4de1-9eea-a710da196327

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 10 July 2022

Date accepted : 20 September 2022

Funding

Funded by: New York University , doi 10.13039/100006732;

Funded by: National Institutes of Health , doi 10.13039/100000002;

Funded by: Ines Mandl Research Foundation , doi 10.13039/100017628;

Funded by: University of Connecticut Health Center , doi 10.13039/100017834;

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