Evidence map›Paper›PMID 26990103›Full record

ArticlePLoS computational biology2016

Persistence and Adaptation in Immunity: T Cells Balance the Extent and Thoroughness of Search.

G Matthew Fricke, Kenneth A Letendre, Melanie E Moses, Judy L Cannon

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
3.5field-weighted citation impact, top 7% 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

39 citing papers in PubMed, 79 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Review
  20. 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 1 institution in 1 country.

G Matthew FrickeDepartment of Computer Science, The University of New Mexico, Albuquerque, New Mexico, United States of America.
Kenneth A LetendreDepartment of Biology, The University of New Mexico, Albuquerque, New Mexico, United States of America.
Melanie E MosesDepartment of Computer Science, The University of New Mexico, Albuquerque, New Mexico, United States of America.
Judy L CannonDepartment of Molecular Genetics and Microbiology, The University of New Mexico School of Medicine, Albuquerque, New Mexico, United States of America.
University of New Mexico · US

Funding

WOMEN'S CANCERS RESEARCH PROGRAMP30CA118100 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Yolanda Sanchez · 2005 to 2026
$57.1M
UNM TRAININGP50GM085273 · NIGMS · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI WILSON, BRIDGET S · 2009 to 2018
$26.1M
Integrative Program in CNS Pathophysiology ResearchP30GM103400 · NIGMS · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI LIU, KE JIAN · 2012 to 2016
$4.7M
COBRE Center for Evolutionary and Theoretical ImmunologyP20GM103452 · NIGMS · UNIVERSITY OF NEW MEXICO · PI LOKER, ERIC SAMUEL · 2012 to 2013
$4.2M
The role of PKCtheta in T cell and T-ALL migrationR01AI097202 · NIAID · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI CANNON, JUDY LIN · 2012 to 2016
$1.9M
NCI NIH HHS P30 CA118100NCI NIH HHS P30-CA118100NIAID NIH HHS 1R01AI097202NIAID NIH HHS R01 AI097202NIGMS NIH HHS 5P20GM103452NIGMS NIH HHS P20 GM103452NIGMS NIH HHS P30 GM103400NIGMS NIH HHS P30GM103400NIGMS NIH HHS P50 GM085273
6 · The paper itself

Abstract

Effective search strategies have evolved in many biological systems, including the immune system. T cells are key effectors of the immune response, required for clearance of pathogenic infection. T cell activation requires that T cells encounter antigen-bearing dendritic cells within lymph nodes, thus, T cell search patterns within lymph nodes may be a crucial determinant of how quickly a T cell immune response can be initiated. Previous work suggests that T cell motion in the lymph node is similar to a Brownian random walk, however, no detailed analysis has definitively shown whether T cell movement is consistent with Brownian motion. Here, we provide a precise description of T cell motility in lymph nodes and a computational model that demonstrates how motility impacts T cell search efficiency. We find that both Brownian and Lévy walks fail to capture the complexity of T cell motion. Instead, T cell movement is better described as a correlated random walk with a heavy-tailed distribution of step lengths. Using computer simulations, we identify three distinct factors that contribute to increasing T cell search efficiency: 1) a lognormal distribution of step lengths, 2) motion that is directionally persistent over short time scales, and 3) heterogeneity in movement patterns. Furthermore, we show that T cells move differently in specific frequently visited locations that we call "hotspots" within lymph nodes, suggesting that T cells change their movement in response to the lymph node environment. Our results show that like foraging animals, T cells adapt to environmental cues, suggesting that adaption is a fundamental feature of biological search.

Indexed as

Models, ImmunologicalModels, StatisticalAdaptation, PsychologicalAdaptive ImmunityAnimalsCell MovementComputer SimulationHumansImmunity, InnateLymph NodesT-Lymphocytes

Identifiers

PMID26990103
PMCPMC4798282
OpenAlexW2301269841

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.