Memory CD4+ T cell receptor repertoire data mining as a tool for identifying cytomegalovirus serostatus

Nicolas De Neuter, Esther Bartholomeus, George Elias, Nina Keersmaekers, Arvid Suls, Hilde Jansens, Evelien Smits, Niel Hens, Philippe Beutels, Pierre Van Damme, Geert Mortier, Viggo Van Tendeloo, Kris Laukens, Pieter Meysman, Benson Ogunjimi

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Pathogens of past and current infections have been identified directly by means of PCR or indirectly by measuring a specific immune response (e.g., antibody titration). Using a novel approach, Emerson and colleagues showed that the cytomegalovirus serostatus can also be accurately determined by using a T cell receptor repertoire data mining approach. In this study, we have sequenced the CD4+ memory T cell receptor repertoire of a Belgian cohort with known cytomegalovirus serostatus. A random forest classifier was trained on the CMV specific T cell receptor repertoire signature and used to classify individuals in the Belgian cohort. This study shows that the novel approach can be reliably replicated with an equivalent performance as that reported by Emerson and colleagues. Additionally, it provides evidence that the T cell receptor repertoire signature is to a large extent present in the CD4+ memory repertoire.

Original languageEnglish
Pages (from-to)255-260
Number of pages6
JournalGenes and Immunity
Volume20
Issue number3
DOIs
Publication statusPublished - Mar 2019

Keywords

  • Adult
  • CD4-Positive T-Lymphocytes/immunology
  • Cytomegalovirus Infections/blood
  • Data Mining/methods
  • Humans
  • Immunologic Memory
  • Receptors, Antigen, T-Cell/genetics
  • Serologic Tests/methods

Fingerprint

Dive into the research topics of 'Memory CD4+ T cell receptor repertoire data mining as a tool for identifying cytomegalovirus serostatus'. Together they form a unique fingerprint.

Cite this