Evidence map›Paper›PMID 42619708›Full record

ArticlebioRxiv : the preprint server for biology2026

Quantitative Modeling of TLR Signaling Reveals Missing Negative Feedback Guiding Identification of TANK-IKKε Checkpoint.

Nathan P Manes, Fengkai Zhang, Bin Lin, Jing Sun, Sergio A Hassan, Anthony A Armstrong, Yuting Shao, Jessica M Calzola, Pauline R Kaplan-Stafford, Rachel A Gottschalk and 6 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

16 authors.

Nathan P ManesFunctional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Fengkai ZhangComputational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Bin LinSignaling Systems Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Jing SunSignaling Systems Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Sergio A HassanBioinformatics and Computational Biosciences Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Anthony A ArmstrongBioinformatics and Computational Biosciences Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Yuting ShaoComputational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Jessica M CalzolaFunctional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Pauline R Kaplan-StaffordFunctional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Rachel A GottschalkDept. of Immunology, University of Pittsburgh, Pittsburgh, PA, USA.
Matthew J MarinoFunctional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Doeun KimFunctional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Ronald N GermainLymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Martin Meier-SchellersheimComputational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Iain D C FraserSignaling Systems Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Aleksandra Nita-LazarFunctional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0002-8523-605X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sensing of tissue injury or infection by Toll-like receptors (TLRs) must rapidly mobilize host defense, but that activation must also be terminated on an appropriate time scale to avoid sustained, tissue-damaging inflammation. The full network of molecular interactions that governs both the rapid onset and the properly timed shutoff of TLR signaling remains incompletely characterized at a deep mechanistic level. To this end, we built a rule-based model of mouse macrophage TLR4 signaling at the molecular-interaction level, parameterized with measured protein copy numbers, RNA-seq-based abundance estimates, literature- and structure-informed reaction rates, and 979 dynamic experimental constraints, achieving a level of granularity beyond that of prior TLR models. The trained model reproduced much of the TLR4-induced NF-κB and MAP kinase response, but it consistently failed to capture deactivation of MyD88, TRAF6-associated species, and IKKα/β. Rather than treating this as simple model error, we used the recurrent failure as a biological signal that localized missing regulation to the proximalMyD88-IRAK-TRAF6 module and motivated experimental evaluation of IKKε and its scaffold TANK. Loss of IKKε enhanced transcriptional, cytokine, MAP kinase, and NF-κB responses to MyD88-specific TLR ligands, and TANK deficiency produced a similar cellular phenotype while abolishing stimulus-induced IKKε phosphorylation. Deficiency of either protein increased IRAK1and TRAF6 ubiquitination without increasing MyD88 ubiquitination, placing the inhibitory checkpoint at or immediately downstream of the IRAK1-TRAF6 ubiquitin-signaling node. Overlapping but non-identical in vivo phenotypes further supported a shared regulatory axis with additional protein-specific functions. Together, these findings illustrate a model-experiment discovery cycle in which quantitative pathway discordance identifies missing biology, revealing a TANK-dependent IKKε checkpoint that restrains MyD88-driven inflammation.

Indexed as

IKKεIRAK1macrophagepathway deactivationrule-based modelingTANKToll-like receptor 4TRAF6

Identifiers

PMID42619708
PMCPMC13482443

What OpenQuestion holds

Textmetadata
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.