Evidence map›Paper›PMID 37748140›Full record

ArticleJournal of the American Chemical Society2023

Autophagy Receptor-Inspired Antibody-Fusion Proteins for Targeted Intracellular Degradation.

Ziwen Jiang, Yu-Hsuan Kuo, Michelle R Arkin

Open access · hybridAbstract read
In one paragraph

Article in Journal of the American Chemical Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
5.4field-weighted citation impact, top 3% 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

18 citing papers in PubMed, 25 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

University of California, San Francisco · US

Funding

Protein and small-molecule tools to probe the conformational dependence of the VCP/p97 protein-protein interaction network--Equipment SupplementR01GM130145 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ARKIN, MICHELLE · 2018 to 2021
$1.6M
Understanding the effects of p97 protein-protein interactions on cellular functions using antibody-based inhibitorsF32GM139242 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI JIANG, ZIWEN · 2021 to 2023
$205k
NIGMS NIH HHS F32 GM139242NIGMS NIH HHS R01 GM130145
6 · The paper itself

Abstract

Autophagy is responsible for the degradation of large intracellular contents, such as unwanted protein aggregates and organelles. Impaired autophagy can therefore lead to the accumulation of pathological aggregates, correlating with aging and neurodegenerative diseases. However, a broadly applicable methodology is not available for the targeted degradation of protein aggregates or organelles in mammalian cells. Herein, we developed a series of autophagy receptor-inspired targeting chimeras (AceTACs) that can induce the targeted degradation of aggregation-prone proteins and protein aggregates (e.g., huntingtin, TDP-43, and FUS mutants), as well as organelles (e.g., mitochondria, peroxisomes, and endoplasmic reticulum). These antibody-fusion-based AceTAC degraders were designed to mimic the function of autophagy receptors, simultaneously binding with the cellular targets and the LC3 proteins on the autophagosomal membrane, eventually transporting the target to the autophagy-lysosomal process for degradation. The AceTAC degradation system provides design principles for antibody-based degradation through autophagy, largely expanding the scope of intracellular targeted degradation technologies.

Indexed as

AutophagyProtein AggregatesAnimalsEndoplasmic ReticulumLysosomesMammalsPeroxisomesProtein Aggregates

Identifiers

PMID37748140
PMCPMC10636752
OpenAlexW4387008550

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.