Evidence map›Paper›PMID 41553682›Full record

ReviewAdvances in experimental medicine and biology2026

Transcriptional Control of Lipid Metabolism.

Laura Palanker Musselman, Huy G Truong, Justin R DiAngelo

Abstract readReview
PubMed Publisher
In one paragraph

Review in Advances in experimental medicine and 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

3 authors.

Laura Palanker Musselman *Department of Biological Sciences, Binghamton University, State University of New York, Binghamton, NY, USA.
Huy G TruongDivision of Science, Pennsylvania State University, Berks Campus, Reading, PA, USA.
Justin R DiAngelo *Division of Science, Pennsylvania State University, Berks Campus, Reading, PA, USA. Jrd5671@psu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transcriptional control of lipid metabolism uses a framework that parallels the control of lipid metabolism at the protein or enzyme level, via feedback and feed-forward mechanisms. Increasing the substrates for an enzyme often increases enzyme gene expression, for example. A paucity of product can likewise potentiate transcription or stability of the mRNA encoding the enzyme or enzymes needed to produce it. In addition, changes in second messengers or cellular energy charge can act as on/off switches for transcriptional regulators to control transcript (and protein) abundance. Insects use a wide range of DNA-binding transcription factors (TFs) that sense changes in the cell and its environment to produce the appropriate change in transcription at gene promoters. These TFs work together with histones, spliceosomes, and additional RNA processing factors to ultimately regulate lipid metabolism. In this chapter, we will first focus on the important TFs that control lipid metabolism in insects. Next, we will describe non-TF regulators of insect lipid metabolism such as enzymes that modify acetylation and methylation status, transcriptional coactivators, splicing factors, and microRNAs. To conclude, we consider future goals for studying the mechanisms underlying the control of lipid metabolism in insects.

Indexed as

Gene Expression RegulationInsectaLipid MetabolismTranscription, GeneticAnimalsMicroRNAsTranscription FactorsMicroRNAsTranscription FactorsInsectLipidLipogenesisLipolysisMetabolismmicroRNASplicingTranscriptional coactivatorTranscription factorTriglyceride

Identifiers

PMID41553682

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.