@article{129463359725435c8e3abf58f8e79d63,
title = "Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity",
abstract = "Introduction: The success of the human body in fighting SARS-CoV2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance. Methods: We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV2 compared with uninfected controls. Results: In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients. Discussion: These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.",
keywords = "AIRR-seq, B cell, BCR, COVID-19, machine learning, somatic hypermutation",
author = "Modi Safra and Zvi Tamari and Pazit Polak and Shachaf Shiber and Moshe Matan and Hani Karameh and Yigal Helviz and Adva Levy-Barda and Vered Yahalom and Avi Peretz and Eli Ben-Chetrit and Baruch Brenner and Tamir Tuller and Meital Gal-Tanamy and Gur Yaari",
note = "Publisher Copyright: Copyright {\textcopyright} 2023 Safra, Tamari, Polak, Shiber, Matan, Karameh, Helviz, Levy-Barda, Yahalom, Peretz, Ben-Chetrit, Brenner, Tuller, Gal-Tanamy and Yaari.",
year = "2023",
doi = "10.3389/fimmu.2023.1031914",
language = "אנגלית",
volume = "14",
journal = "Frontiers in Immunology",
issn = "1664-3224",
publisher = "Frontiers Media SA",
}
