Selecting genes for analysis using historically contingent progress: from RNA changes to protein–protein interactions

Progress in biology has generated numerous lists of genes that share some property. But advancing from these lists of genes to understanding their roles is slow and unsystematic. Here we use RNA silencing in Caenorhabditis elegans to illustrate an approach for prioritizing genes for detailed study given limited resources. The partially subjective relationships between genes forged by both deduced functional relatedness and biased progress in the field were captured as mutual information and used to cluster genes that were frequently identified yet remain understudied. Some proteins encoded by these understudied genes are predicted to physically interact with known regulators of RNA silencing, suggesting feedback regulation. Predicted interactions with proteins that act in other processes and the clustering of studied genes among the most frequently perturbed suggest regulatory links connecting RNA silencing to other processes like the cell cycle and asymmetric cell division. Thus, among the gene products altered when a process is perturbed could be regulators of that process acting to restore homeostasis, which provides a way to use RNA sequencing to identify candidate protein-protein interactions. Together, the analysis of perturbed transcripts and potential interactions of the proteins they encode could help prioritize candidate regulators of any process.

Authors

Institutions

Publication Details

Journal
Nucleic Acids Research
Published
2024-12-04
DOI
https://doi.org/10.1093/nar/gkae1246
Citations
1
Primary Topic
Genetics, Aging, and Longevity in Model Organisms
Type
article
Field-Weighted Citation Impact
0.23

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Selecting genes for analysis using historically contingent progress: from RNA changes to protein–protein interactions

Antony M Jose, Farhaan Lalit
1 citations
Nucleic Acids Research
Genetics, Aging, and Longevity in Model Organisms
0.23
article

Selecting genes for analysis using historically contingent progress: from RNA changes to protein–protein interactions

Antony M Jose, Farhaan Lalit
article en
1 citations

Abstract

Progress in biology has generated numerous lists of genes that share some property. But advancing from these lists of genes to understanding their roles is slow and unsystematic. Here we use RNA silencing in Caenorhabditis elegans to illustrate an approach for prioritizing genes for detailed study given limited resources. The partially subjective relationships between genes forged by both deduced functional relatedness and biased progress in the field were captured as mutual information and used to cluster genes that were frequently identified yet remain understudied. Some proteins encoded by these understudied genes are predicted to physically interact with known regulators of RNA silencing, suggesting feedback regulation. Predicted interactions with proteins that act in other processes and the clustering of studied genes among the most frequently perturbed suggest regulatory links connecting RNA silencing to other processes like the cell cycle and asymmetric cell division. Thus, among the gene products altered when a process is perturbed could be regulators of that process acting to restore homeostasis, which provides a way to use RNA sequencing to identify candidate protein-protein interactions. Together, the analysis of perturbed transcripts and potential interactions of the proteins they encode could help prioritize candidate regulators of any process.

Nucleic Acids ResearchVol. 53(1)
Université Bourgogne Franche-Comté (FR), University of Maryland, College Park (US)
National Science Foundation, National Institutes of Health, Graduate School, University of Maryland
Openalex Percentile: Top 44%
Genetics, Aging, and Longevity in Model Organisms
0.23
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.