Evidence map›Paper›PMID 39687606›Full record

ArticleFrontiers in immunology2024

Applying phylogenetic methods for species delimitation to distinguish B-cell clonal families.

Katalin Voss, Katrina M Kaur, Rituparna Banerjee, Felix Breden, Matt Pennell

Abstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Katalin VossDepartment of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, United States.
Katrina M KaurDepartment of Zoology, University of British Columbia, Vancouver, BC, Canada.
Rituparna BanerjeeBioinformatics Graduate Program, Faculty of Science, University of British Columbia, Vancouver, BC, Canada.
Felix BredenDepartment of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada.
Matt PennellDepartment of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The adaptive immune system generates a diverse array of B-cell receptors through the processes of V(D)J recombination and somatic hypermutation. B-cell receptors that bind to an antigen will undergo clonal expansion, creating a Darwinian evolutionary dynamic within individuals. A key step in studying these dynamics is to identify sequences derived from the same ancestral V(D)J recombination event (i.e. a clonal family). There are a number of widely used methods for accomplishing this task but a major limitation of all of them is that they rely, at least in part, on the ability to map sequences to a germline reference set. This requirement is particularly problematic in non-model systems where we often know little about the germline allelic diversity in the study population. Recognizing that delimiting B-cell clonal families is analogous to delimiting species from single locus data, we propose a novel strategy of reconstructing the phylogenetic tree of all B-cell sequences in a sample and using a popular species delimitation method, multi-rate Poisson Tree Processes (mPTP), to delimit clonal families. Using extensive simulations, we show that not only does this phylogenetically explicit approach perform well for the purpose of delimiting clonal families when no reference allele set is available, it performs similarly to state-of-the-art techniques developed specifically for B-cell data even when we have a complete reference allele set. Additionally, our analysis of an empirical dataset shows that mPTP performs similarly to leading methods in the field. These findings demonstrate the utility of using off-the-shelf phylogenetic techniques for analyzing B-cell clonal dynamics in non-model systems, and suggests that phylogenetic inference techniques may be potentially combined with mapping based approaches for even more robust inferences, even in model systems.

Indexed as

B-LymphocytesPhylogenyAnimalsClone CellsHumansReceptors, Antigen, B-CellV(D)J RecombinationReceptors, Antigen, B-CellAIRR-seqB-cell clonal family delimitationB-cell receptor repertoirebenchmarkingsomatic hypermutationspecies delimitation

Identifiers

PMID39687606
PMCPMC11646844

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.