Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity

Modi Safra, Zvi Tamari, Pazit Polak, Shachaf Shiber, Moshe Matan, Hani Karameh, Yigal Helviz, Adva Levy-Barda, Vered Yahalom, Avi Peretz, Eli Ben-Chetrit, Baruch Brenner, Tamir Tuller, Meital Gal-Tanamy, Gur Yaari

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Article number1031914
JournalFrontiers in Immunology
StatePublished - 2023

Bibliographical note

Publisher Copyright:
Copyright © 2023 Safra, Tamari, Polak, Shiber, Matan, Karameh, Helviz, Levy-Barda, Yahalom, Peretz, Ben-Chetrit, Brenner, Tuller, Gal-Tanamy and Yaari.


  • AIRR-seq
  • B cell
  • BCR
  • COVID-19
  • machine learning
  • somatic hypermutation


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