Evidence map›Paper›PMID 37227256›Full record

ArticleeLife2023

Statistical inference reveals the role of length, GC content, and local sequence in V(D)J nucleotide trimming.

Magdalena L Russell, Noah Simon, Philip Bradley, Frederick A Matsen

Open access · goldAbstract read
In one paragraph

Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

4 authors at 2 institutions in 2 countries.

Magdalena L RussellComputational Biology Program, Fred Hutchinson Cancer Center, Seattle, United States.ORCID 0000-0002-1068-1968
Noah SimonDepartment of Biostatistics, University of Washington, Seattle, United States.
Philip Bradley *Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, United States.ORCID 0000-0002-0224-6464
Frederick A Matsen *Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, United States.ORCID 0000-0003-0607-6025
Cape Town HVTN Immunology Laboratory / Hutchinson Centre Research Institute of South Africa · ZAUniversity of Washington · US

Funding

DECODING THE INTERACTIONS BETWEEN T CELL RECEPTORS AND PEPTIDE-MHCR01AI136514 · NIAID · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI Paul G. Thomas · 2018 to 2026
$6.9M
Blending deep learning with probabilistic mechanistic models to predict and understand the evolution and function of adaptive immune receptorsR01AI146028 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MATSEN, FREDERICK ALBERT · 2019 to 2024
$3.4M
Molecular modeling and machine learning for protein structures and interactionsR35GM141457 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BRADLEY, PHILIP · 2021 to 2025
$2.1M
High-Performance Compute Cluster for Comprehensive Cancer and Infectious Diseases ResearchS10OD028685 · OD · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BRADLEY, PHILIP · 2020 to 2020
$2.0M
Howard Hughes Medical InstituteNIAID NIH HHS R01 AI136514NIAID NIH HHS R01 AI146028NIGMS NIH HHS R35 GM141457NIH HHS S10 OD028685
6 · The paper itself

Abstract

To appropriately defend against a wide array of pathogens, humans somatically generate highly diverse repertoires of B cell and T cell receptors (BCRs and TCRs) through a random process called V(D)J recombination. Receptor diversity is achieved during this process through both the combinatorial assembly of V(D)J-genes and the junctional deletion and insertion of nucleotides. While the Artemis protein is often regarded as the main nuclease involved in V(D)J recombination, the exact mechanism of nucleotide trimming is not understood. Using a previously published TCRβ repertoire sequencing data set, we have designed a flexible probabilistic model of nucleotide trimming that allows us to explore various mechanistically interpretable sequence-level features. We show that local sequence context, length, and GC nucleotide content in both directions of the wider sequence, together, can most accurately predict the trimming probabilities of a given V-gene sequence. Because GC nucleotide content is predictive of sequence-breathing, this model provides quantitative statistical evidence regarding the extent to which double-stranded DNA may need to be able to breathe for trimming to occur. We also see evidence of a sequence motif that appears to get preferentially trimmed, independent of GC-content-related effects. Further, we find that the inferred coefficients from this model provide accurate prediction for V- and J-gene sequences from other adaptive immune receptor loci. These results refine our understanding of how the Artemis nuclease may function to trim nucleotides during V(D)J recombination and provide another step toward understanding how V(D)J recombination generates diverse receptors and supports a powerful, unique immune response in healthy humans.

Indexed as

NucleotidesV(D)J RecombinationBase CompositionHumansNucleotidesadaptive immune receptor repertoiresArtemiscomputational biologyhumanimmunologyinflammationsystems biologytrimming profilesV(D)J recombination

Identifiers

PMID37227256
PMCPMC10212571
OpenAlexW4378172609

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.