Predictive features of gene expression variation reveal mechanistic link with differential expression
Abstract: For most biological processes, organisms must respond to extrinsic cues, while maintaining essential gene expression programmes. Although studied extensively in single cells, it is still unclear how variation is controlled in multicellular organisms. Here, we used a machine‐learning approach to identify genomic features that are predictive of genes with high versus low variation in their expression across individuals, using bulk data to remove stochastic cell‐to‐cell variation. Using embryonic gene expression across 75 Drosophila isogenic lines, we identify features predictive of expression variation (controlling for expression level), many of which are promoter‐related. Genes with low variation fall into two classes reflecting different mechanisms to maintain robust expression, while genes with high variation seem to lack both types of stabilizing mechanisms. Applying this framework to humans revealed similar predictive features, indicating that promoter architecture is an ancient mechanism to control expression variation. Remarkably, expression variation features could also partially predict differential expression after diverse perturbations in both Drosophila and humans. Differential gene expression signatures may therefore be partially explained by genetically encoded gene‐specific features, unrelated to the studied treatment.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Predictive features of gene expression variation reveal mechanistic link with differential expression ; volume:16 ; number:8 ; year:2020 ; extent:24
Molecular systems biology ; 16, Heft 8 (2020) (gesamt 24)
- Creator
- DOI
-
10.15252/msb.20209539
- URN
-
urn:nbn:de:101:1-2022062407364028380970
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:28 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Sigalova, Olga M.
- Shaeiri, Amirreza
- Forneris, Mattia
- Furlong, Eileen E. M.
- Zaugg, Judith B.
Other Objects (12)
Epigenomic signatures reveal mechanistic clues and predictive markers for autism spectrum disorder
Predictive musculoskeletal simulations reveal the mechanistic link between speed, posture and energetics among extant mammals
Predictive modeling of gene expression regulation
Modeling relaxation experiments with a mechanistic model of gene expression
Machine learning analyses reveal circadian clock features predictive of anxiety among UK biobank participants
The predictive nature of transcript expression levels on protein expression in adult human brain
Gene-expression patterns reveal underlying biological processes in Kawasaki disease
Perturbation-response genes reveal signaling footprints in cancer gene expression
Instagram photos reveal predictive markers of depression
Expression patterns of SLIT/ROBO mRNAs reveal a characteristic feature in the entorhinal-hippocampal area of macaque monkeys
Differential expression and co-expression gene networks reveal candidate biomarkers of boar taint in non-castrated pigs
INSM1 Expression in Breast Neoplasms with Neuroedocrine Features
Epigenomic signatures reveal mechanistic clues and predictive markers for autism spectrum disorder
Predictive musculoskeletal simulations reveal the mechanistic link between speed, posture and energetics among extant mammals
Predictive modeling of gene expression regulation
Modeling relaxation experiments with a mechanistic model of gene expression
Machine learning analyses reveal circadian clock features predictive of anxiety among UK biobank participants
The predictive nature of transcript expression levels on protein expression in adult human brain
Gene-expression patterns reveal underlying biological processes in Kawasaki disease
Perturbation-response genes reveal signaling footprints in cancer gene expression
Instagram photos reveal predictive markers of depression
Expression patterns of SLIT/ROBO mRNAs reveal a characteristic feature in the entorhinal-hippocampal area of macaque monkeys
Differential expression and co-expression gene networks reveal candidate biomarkers of boar taint in non-castrated pigs
INSM1 Expression in Breast Neoplasms with Neuroedocrine Features
Epigenomic signatures reveal mechanistic clues and predictive markers for autism spectrum disorder
Predictive musculoskeletal simulations reveal the mechanistic link between speed, posture and energetics among extant mammals
Predictive modeling of gene expression regulation
Modeling relaxation experiments with a mechanistic model of gene expression
Machine learning analyses reveal circadian clock features predictive of anxiety among UK biobank participants
The predictive nature of transcript expression levels on protein expression in adult human brain
Gene-expression patterns reveal underlying biological processes in Kawasaki disease
Perturbation-response genes reveal signaling footprints in cancer gene expression
Instagram photos reveal predictive markers of depression
Expression patterns of SLIT/ROBO mRNAs reveal a characteristic feature in the entorhinal-hippocampal area of macaque monkeys
Differential expression and co-expression gene networks reveal candidate biomarkers of boar taint in non-castrated pigs