Evidence map›Paper›PMID 21209849›Full record

ArticleJournal of drug delivery science and technology2010

Biomolecular engineering of intracellular switches in eukaryotes.

M K Pastuszka, J A Mackay

Abstract read
In one paragraph

Article in Journal of drug delivery science and technology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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

2 authors.

M K PastuszkaDepartment of Pharmacology and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA 90033-9121, United States.
J A Mackay

Funding

USC/NORRIS COMPREHENSIVE CANCER CENTER (CORE) SUPPORTP30CA014089 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Fumito Ito · 1985 to 2026
$181.4M
USC RESEARCH CENTER FOR LIVER DISEASES: PILOT PROGRAMSP30DK048522 · NIDDK · UNIVERSITY OF SOUTHERN CALIFORNIA · PI KAPLOWITZ, NEIL · 1995 to 2020
$21.8M
A rapid, reversible switch for controlling intracellular traffickingR21EB012281 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI MACKAY, JOHN ANDREW · 2010 to 2011
$438k
NCI NIH HHS P30 CA014089NIBIB NIH HHS R21 EB012281NIDDK NIH HHS P30 DK048522
6 · The paper itself

Abstract

Tools to selectively and reversibly control gene expression are useful to study and model cellular functions. When optimized, these cellular switches can turn a protein's function "on" and "off" based on cues designated by the researcher. These cues include small molecules, drugs, hormones, and even temperature variations. Here we review three distinct areas in gene expression that are commonly targeted when designing cellular switches. Transcriptional switches target gene expression at the level of mRNA polymerization, with examples including the tetracycline gene induction system as well as nuclear receptors. Translational switches target the process of turning the mRNA signal into protein, with examples including riboswitches and RNA interference. Post-translational switches control how proteins interact with one another to attenuate or relay signals. Examples of post-translational modification include dimerization and intein splicing. In general, the delay times between switch and effect decreases from transcription to translation to post-translation; furthermore, the fastest switches may offer the most elegant opportunities to influence and study cell behavior. We discuss the pros and cons of these strategies, which directly influence their usefulness to study and implement drug targeting at the tissue and cellular level.

Identifiers

PMID21209849
PMCPMC3013508

What OpenQuestion holds

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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.