Evidence map›Paper›PMID 31609600›Full record

ArticleBiomacromolecules2019

Modular Polymer Antigens To Optimize Immunity.

Nitasha R Bennett, Cassie M Jarvis, Mohammad Murshid Alam, Daniel B Zwick, Jake M Olson, Hung V-T Nguyen, Jeremiah A Johnson, Mark E Cook, Laura L Kiessling

Abstract read
In one paragraph

Article in Biomacromolecules, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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  4. Review
  5. Article
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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

9 authors.

Nitasha R Bennett
Mohammad Murshid AlamORCID 0000-0002-5646-3309
Daniel B Zwick
Jake M Olson
Jeremiah A JohnsonORCID 0000-0001-9157-6491
Mark E Cook
Laura L KiesslingORCID 0000-0001-6829-1500

Funding

BIOTECHNOLOGY TRAINING PROGRAMT32GM008349 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI FOX, BRIAN G · 1989 to 2019
$22.5M
Synthetic Ligands for Modulating Immune Cell ResponsesR01AI055258 · NIAID · UNIVERSITY OF WISCONSIN-MADISON · PI KIESSLING, LAURA L · 2003 to 2024
$7.8M
Organic nanoparticles for dual MRI-guided therapeutic selection and ovarian cancer drug deliveryR01CA220468 · NCI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Jeremiah Allen Johnson · 2017 to 2026
$4.9M
NCI NIH HHS R01 CA220468NIAID NIH HHS R01 AI055258NIGMS NIH HHS T32 GM008349
6 · The paper itself

Abstract

Subunit vaccines can have excellent safety profiles, but their ability to give rise to robust immune responses is often compromised. For glycan-based vaccines, insufficient understanding of B and T cell epitope combinations that yield optimal immune activation hinders optimization. To determine which antigen features promote desired IgG responses, we synthesized epitope-functionalized polymers using ring-opening metathesis polymerization (ROMP) and assessed the effect of B and T cell epitope loading. The most robust responses were induced by polymers with a high valency of B and T cell epitopes. Additionally, IgG responses were greater for polymers with T cell epitopes that are readily liberated upon endosomal processing. Combining these criteria, we used ROMP to generate a nontoxic, polymeric antigen that elicited stronger antibody responses than a comparable protein conjugate. These findings highlight principles for designing synthetic antigens that elicit strong IgG responses against inherently weak immune targets such as glycans.

Indexed as

AntigensEpitopes, B-LymphocyteEpitopes, T-LymphocytePolymerizationAnimalsFemaleImmunoglobulin GMiceMice, Inbred BALB CVaccines, SubunitAntigensEpitopes, B-LymphocyteEpitopes, T-LymphocyteImmunoglobulin GVaccines, Subunit

Identifiers

PMID31609600
PMCPMC7382022

What OpenQuestion holds

Textmetadata
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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.