Exploring Camelina sativa lipid metabolism regulation by combining gene co-expression and DNA affinity purification analyses.
Fabio Gomez CanoYi-Hsuan ChuMariel Cruz-GomezHesham M AbdullahYun Sun LeeDanny J SchnellErich GrotewoldPublished in: The Plant journal : for cell and molecular biology (2022)
Camelina (Camelina sativa) is an annual oilseed plant that is gaining momentum as a biofuel cover crop. Understanding gene regulatory networks is essential to deciphering plant metabolic pathways, including lipid metabolism. Here, we take advantage of a growing collection of gene expression datasets to predict transcription factors (TFs) associated with the control of Camelina lipid metabolism. We identified approximately 350 TFs highly co-expressed with lipid-related genes (LRGs). These TFs are highly represented in the MYB, AP2/ERF, bZIP, and bHLH families, including a significant number of homologs of well-known Arabidopsis lipid and seed developmental regulators. After prioritizing the top 22 TFs for further validation, we identified DNA-binding sites and predicted target genes for 16 out of the 22 TFs tested using DNA affinity purification followed by sequencing (DAP-seq). Enrichment analyses of targets supported the co-expression prediction for most TF candidates, and the comparison to Arabidopsis revealed some common themes, but also aspects unique to Camelina. Within the top potential lipid regulators, we identified CsaMYB1, CsaABI3AVP1-2, CsaHB1, CsaNAC2, CsaMYB3, and CsaNAC1 as likely involved in the control of seed fatty acid elongation and CsaABI3AVP1-2 and CsabZIP1 as potential regulators of the synthesis and degradation of triacylglycerols (TAGs), respectively. Altogether, the integration of co-expression data and DNA-binding assays permitted us to generate a high-confidence and short list of Camelina TFs involved in the control of lipid metabolism during seed development.
Keyphrases
- transcription factor
- dna binding
- fatty acid
- genome wide identification
- poor prognosis
- gene expression
- circulating tumor
- genome wide
- cell free
- single cell
- single molecule
- rna seq
- long non coding rna
- nucleic acid
- high throughput
- climate change
- risk assessment
- mass spectrometry
- artificial intelligence
- big data
- copy number
- human health
- circulating tumor cells
- plant growth
- genome wide analysis