@article{68f840854b3c4465bdea13dabbc7ddcf,
title = "Mucin-mimetic glycan arrays integrating machine learning for analyzing receptor pattern recognition by influenza A viruses",
abstract = "Influenza A viruses (IAVs) exploit host glycans in airway mucosa for entry and infection. Detection of changes in IAV glycan-binding phenotype can provide early indication of transmissibility and infection potential. While zoonotic viruses are monitored for mutations, the influence of host glycan presentation on viral specificity remains obscured. Here, we describe an array platform that uses synthetic mimetics of mucin glycoproteins to model how receptor presentation and density in the mucinous glycocalyx may impact IAV recognition. H1N1 and H3N2 binding in arrays of α2,3- and α2,6-sialyllactose receptors confirmed their known sialic-acid-binding specificities and revealed their different sensitivities to receptor presentation. Further, the transition of H1N1 from avian to mammalian cell culture improved the ability of the virus to recognize mucin-like displays of α2,6-sialic acid receptors. Support vector machine (SVM) learning efficiently characterized this shift in binding preference and may prove useful to study viral evolution to a new host.",
keywords = "glycan array, hemagglutinin, influenza A, machine learning, mucin, receptor pattern, SDG3: Good health and well-being, support vector machines",
author = "Lucas, {Taryn M.} and Chitrak Gupta and Altman, {Meghan O.} and Emi Sanchez and Naticchia, {Matthew R.} and Pascal Gagneux and Abhishek Singharoy and Kamil Godula",
note = "Funding Information: We thank the UCSD Glycobiology Research and Training Center for access to tissue culture facilities and analytical instrumentation, Dr. Christopher Fisher for his help with virus culture and characterization methods, and the ASU Research Computing and their Agave computing cluster for the computational resources. The authors also wish to thank Prof. Mia Huang (TSRI) for her technical advice and valuable insights over the course of this research. This work was supported in part by the NIH Director{\textquoteright}s New Innovator Award ( NICHD : 1DP2HD087954-01 ), the NIH Director{\textquoteright}s Glycoscience Common Fund ( 1R21AI129894-01 ), and the Gordon and Betty Moore Foundation via a Scialog grant (# 9162.07 ). K.G. was supported by the Alfred P. Sloan Foundation ( FG-2017-9094 ) and the Research Corporation for Science Advancement via the Cottrell Scholar Award (grant # 24119 ). T.M.L. was supported by the Chemistry and Biology Interface training program ( NIGMS : 5T32GM112584-03 ). A.S. was supported by a CAREER award from the NSF ( MCB-1942763 ) and an RO1 grant from the NIH (GM095583). C.G. was supported by DoD National Defense Education Program Center (Contract #HQ00342110007: Singharoy PI). A.S. and C.G. also acknowledge funds from ASU-Mayo Foundation, AstraZenaca, and start-up grants from Arizona State University School of Molecular Sciences and Biodesign Institute's Center for Applied Structural Discovery. P.G. and M.O.A. are supported by the G. Harold and Leyla Y. Mathers Charitable Foundation . E.S. was supported by the UCSD Paths Program and the Undergraduate Summer Research Award through the UCSD Division of Physical Sciences. Funding Information: We thank the UCSD Glycobiology Research and Training Center for access to tissue culture facilities and analytical instrumentation, Dr. Christopher Fisher for his help with virus culture and characterization methods, and the ASU Research Computing and their Agave computing cluster for the computational resources. The authors also wish to thank Prof. Mia Huang (TSRI) for her technical advice and valuable insights over the course of this research. This work was supported in part by the NIH Director's New Innovator Award (NICHD: 1DP2HD087954-01), the NIH Director's Glycoscience Common Fund (1R21AI129894-01), and the Gordon and Betty Moore Foundation via a Scialog grant (#9162.07). K.G. was supported by the Alfred P. Sloan Foundation (FG-2017-9094) and the Research Corporation for Science Advancement via the Cottrell Scholar Award (grant #24119). T.M.L. was supported by the Chemistry and Biology Interface training program (NIGMS: 5T32GM112584-03). A.S. was supported by a CAREER award from the NSF (MCB-1942763) and an RO1 grant from the NIH (GM095583). C.G. was supported by DoD National Defense Education Program Center (Contract #HQ00342110007: Singharoy PI). A.S. and C.G. also acknowledge funds from ASU-Mayo Foundation, AstraZenaca, and start-up grants from Arizona State University School of Molecular Sciences and Biodesign Institute's Center for Applied Structural Discovery. P.G. and M.O.A. are supported by the G. Harold and Leyla Y. Mathers Charitable Foundation. E.S. was supported by the UCSD Paths Program and the Undergraduate Summer Research Award through the UCSD Division of Physical Sciences. Conceptualization, K.G. and A.S.; methodology, K.G. A.S. T.M.L. C.G. and M.O.A.; software, A.S. and C.G.; validation, T.M.L. and C.G.; formal analysis, T.M.L. C.G. and M.R.N.; investigation, T.M.L. and C.G.; data curation, T.M.L. C.G. E.S. and M.R.N.; writing ? original draft, K.G. A.S. T.M.L. and C.G.; writing ? review & editing, K.G. A.S. T.M.L. and C.G.; funding acquisition, K.G. and A.S.; resources, M.O.A. and P.G.; supervision, M.O.A. and P.G. The authors declare no competing interests. One or more of the authors of this paper self-identifies as an underrepresented ethnic minority in science. One or more of the authors of this paper self-identifies as a member of the LGBTQ+ community. Publisher Copyright: {\textcopyright} 2021 Elsevier Inc.",
year = "2021",
month = dec,
day = "9",
doi = "10.1016/j.chempr.2021.09.015",
language = "English (US)",
volume = "7",
pages = "3393--3411",
journal = "Chem",
issn = "2451-9294",
publisher = "Elsevier Inc.",
number = "12",
}