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James T. Sorrentino

Researcher at University of California, San Diego

Publications -  12
Citations -  174

James T. Sorrentino is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Medicine & Heparan sulfate. The author has an hindex of 5, co-authored 9 publications receiving 71 citations. Previous affiliations of James T. Sorrentino include University of California, Berkeley.

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Journal ArticleDOI

Proteomic atlas of organ vasculopathies triggered by Staphylococcus aureus sepsis.

TL;DR: In vivo biotinylation and high-resolution mass spectrometry are combined to characterize organ-level changes of the murine vascular cell surface proteome induced by MRSA sepsis, indicating that MRSA-sepsis triggers extensive proteome remodeling of the vascular cell surfaces, in a tissue-specific manner.
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A Systems View of the Heparan Sulfate Interactome

TL;DR: Heparan sulfate proteoglycans consist of a small family of proteins decorated with one or more covalently attached heparan sulfate glycosaminoglycan chains as mentioned in this paper.
Posted ContentDOI

Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivity

TL;DR: It is shown that commensal host bacterial communities can modify HS and thereby modulate SARS-CoV-2 spike protein binding and that these communities change with host age and sex.
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A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering

TL;DR: This modeling approach enables rational glycoengineering and the elucidation of relationships between glycosyltransferases, thereby facilitating biopharmaceutical research and aiding the broader study of Glycosylation to elucidate the genetic basis of complex changes in glycosYLation.
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Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis.

TL;DR: GlyCompare as discussed by the authors uses shared biosynthetic steps for all measured glycans to correct for sparsity and non-independence in glycomics, which enables direct comparison of different glycoprofiles and increases statistical power.