Evidence map›Paper›PMID 34566920›Full record

ArticleFrontiers in microbiology2021

Autophagy as a Mechanism for Adaptive Prediction-Mediated Emergence of Drug Resistance.

Nivedita Nivedita, John D Aitchison, Nitin S Baliga

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. bioRxiv : the preprint server for biology · 2024
    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

3 authors.

Nivedita NiveditaInstitute for Systems Biology, Seattle, WA, United States.
John D AitchisonSeattle Children's Research Institute, Seattle, WA, United States.
Nitin S BaligaInstitute for Systems Biology, Seattle, WA, United States.

Funding

A systems analysis of drug tolerance in Mycobacterium tuberculosisR01AI128215 · NIAID · INSTITUTE FOR SYSTEMS BIOLOGY · PI Nitin S Baliga · 2017 to 2026
$8.8M
Site Specific Immunophenotyping Assays of COVID19 Patients to Align with NIAID National StudyR01AI141953 · NIAID · INSTITUTE FOR SYSTEMS BIOLOGY · PI BALIGA, NITIN S · 2019 to 2023
$5.7M
NIAID NIH HHS R01 AI128215NIAID NIH HHS R01 AI141953
6 · The paper itself

Abstract

Drug resistance is a major problem in treatment of microbial infections and cancers. There is growing evidence that a transient drug tolerant state may precede and potentiate the emergence of drug resistance. Therefore, understanding the mechanisms leading to tolerance is critical for combating drug resistance and for the development of effective therapeutic strategy. Through laboratory evolution of yeast, we recently demonstrated that adaptive prediction (AP), a strategy employed by organisms to anticipate and prepare for a future stressful environment, can emerge within 100 generations by linking the response triggered by a neutral cue (caffeine) to a mechanism of protection against a lethal agent (5-fluoroorotic acid, 5-FOA). Here, we demonstrate that mutations selected across multiple laboratory-evolved lines had linked the neutral cue response to core genes of autophagy. Across these evolved lines, conditional activation of autophagy through AP conferred tolerance, and potentiated subsequent selection of mutations in genes specific to overcoming the toxicity of 5-FOA. These results offer a new perspective on how extensive genome-wide genetic interactions of autophagy could have facilitated the emergence of AP over short evolutionary timescales to potentiate selection of 5-FOA resistance-conferring mutations.

Indexed as

adaptive predictionautophagyconditional fitnessdrug resistancestructured environments

Identifiers

PMID34566920
PMCPMC8461305

What OpenQuestion holds

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LicenceCC BY
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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.