Transcriptional profiling of the response to starvation and fattening reveals differential regulation of autophagy genes in mammals.
Margarita GalvesMichal SperberFatima Amer-SarsourRan ElkonAvraham AshkenaziPublished in: Proceedings. Biological sciences (2023)
Nutrient deprivation (starvation) induced by fasting and hypercaloric regimens are stress factors that can influence cell and tissue homeostasis in mammals. One of the key cellular responses to changes in nutrient availability is the cell survival pathway autophagy. While there has been much research into the protein networks regulating autophagy, less is known about the gene expression networks involved in this fundamental process. Here, we applied a network algorithm designed to analyse omics datasets, to identify sub-networks that are enriched for induced genes in response to starvation. This enabled us to identify two prominent active modules, one composed of key stress-induced transcription factors, including members of the Jun, Fos and ATF families, and the other comprising autophagosome sub-network genes, including ULK1. The results were validated in the brain, liver and muscle of fasting mice. Moreover, differential expression analysis of autophagy genes in the brain, liver and muscle of high-fat diet-exposed mice showed significant suppression of GABARAPL1 in the liver. Finally, our data provide a resource that may facilitate the future identification of regulators of autophagy.
Keyphrases
- endoplasmic reticulum stress
- cell death
- high fat diet
- gene expression
- stress induced
- transcription factor
- bioinformatics analysis
- genome wide
- insulin resistance
- genome wide identification
- signaling pathway
- oxidative stress
- single cell
- adipose tissue
- skeletal muscle
- dna methylation
- high fat diet induced
- white matter
- deep learning
- type diabetes
- stem cells
- resting state
- blood glucose
- machine learning
- functional connectivity
- metabolic syndrome
- blood pressure
- genome wide analysis
- small molecule
- weight loss
- drug induced
- big data