Mathematical Characterization of Private and Public Immune Receptor Sequences.


Journal

Bulletin of mathematical biology
ISSN: 1522-9602
Titre abrégé: Bull Math Biol
Pays: United States
ID NLM: 0401404

Informations de publication

Date de publication:
14 09 2023
Historique:
received: 11 04 2023
accepted: 26 07 2023
medline: 15 9 2023
pubmed: 14 9 2023
entrez: 14 9 2023
Statut: epublish

Résumé

Diverse T and B cell repertoires play an important role in mounting effective immune responses against a wide range of pathogens and malignant cells. The number of unique T and B cell clones is characterized by T and B cell receptors (TCRs and BCRs), respectively. Although receptor sequences are generated probabilistically by recombination processes, clinical studies found a high degree of sharing of TCRs and BCRs among different individuals. In this work, we use a general probabilistic model for T/B cell receptor clone abundances to define "publicness" or "privateness" and information-theoretic measures for comparing the frequency of sampled sequences observed across different individuals. We derive mathematical formulae to quantify the mean and the variances of clone richness and overlap. Our results can be used to evaluate the effect of different sampling protocols on abundances of clones within an individual as well as the commonality of clones across individuals. Using synthetic and empirical TCR amino acid sequence data, we perform simulations to study expected clonal commonalities across multiple individuals. Based on our formulae, we compare these simulated results with the analytically predicted mean and variances of the repertoire overlap. Complementing the results on simulated repertoires, we derive explicit expressions for the richness and its uncertainty for specific, single-parameter truncated power-law probability distributions. Finally, the information loss associated with grouping together certain receptor sequences, as is done in spectratyping, is also evaluated. Our approach can be, in principle, applied under more general and mechanistically realistic clone generation models.

Identifiants

pubmed: 37707621
doi: 10.1007/s11538-023-01190-z
pii: 10.1007/s11538-023-01190-z
pmc: PMC10501991
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

102

Subventions

Organisme : NHLBI NIH HHS
ID : R01 HL146552
Pays : United States

Informations de copyright

© 2023. The Author(s).

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Auteurs

Lucas Böttcher (L)

Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, 60322, Frankfurt am Main, Germany. l.boettcher@fs.de.
Department of Computational Medicine, University of California, Los Angeles, 621 Charles E. Young Dr. S., Los Angeles, 90095-1766, CA, USA. l.boettcher@fs.de.
Department of Medicine, University of Florida, Gainesville, 32610, FL, USA. l.boettcher@fs.de.

Sascha Wald (S)

Statistical Physics Group, Centre for Fluid and Complex Systems, Coventry University, Priory Street, Coventry, CV1 5FB, UK.

Tom Chou (T)

Department of Computational Medicine, University of California, Los Angeles, 621 Charles E. Young Dr. S., Los Angeles, 90095-1766, CA, USA.
Department of Mathematics, University of California, Los Angeles, 520 Portola Plaza, Los Angeles, 90095-1555, CA, USA.

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