Anti-TF polyclonal antibody [FITC]

Cross-family transcription factor (TF) interactions play critical roles in the regulation of plant developmental and metabolic pathways. WRINKLED1 (WRI1) is a key TF governing oil biosynthesis in plants. However, little is known about WRI1-interacting factors and their roles in oil biosynthesis. We screened a TF library using Arabidopsis (Arabidopsis thaliana) WRI1 (AtWRI1) as bait in yeast two-hybrid assays and identified three TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR (TCP) family TFs, namely TCP4, TCP10, and TCP24, as AtWRI1-interacting partners. The physical interaction between AtWRI1 and TCPs was further validated using bimolecular fluorescence complementation assays. TCPs play important roles in various plant developmental processes; however, their involvement in fatty acid biosynthesis was not previously known. Coexpression of TCP4, but not TCP10 or TCP24, with AtWRI1 reduced AtWRI1-mediated oil biosynthesis in Nicotiana benthamiana leaves. Transcriptomic analysis in transgenic Arabidopsis plants with enhanced TCP4 activity engineered by expressing rTCP4 (i.e. miR319-resistant TCP4) revealed that AtWRI1 target genes were significantly repressed. TCP4 expression is strongly correlated with AtWRI1 during embryo development. A tcp4 loss-of-function mutant, the jaw-D mutant with a strong reduction of TCP4 expression, and a tcp2 tcp4 tcp10 triple mutant accumulated more seed oil than wild-type Arabidopsis. In addition, TCP4 repressed the AtWRI1-mediated transactivation of the promoters of fatty acid biosynthetic genes. Collectively, our findings suggest that TCP4 represses fatty acid biosynthetic gene expression through interaction with AtWRI1, leading to a reduction of AtWRI1-mediated seed oil accumulation.

Background:Emerging evidence suggests that the immune system plays a crucial role in the regulation of the response to therapy and long-term outcomes of patients with breast cancer (BRCA). In this study, we aimed to identify a significant signature based on immune-related genes to predict the prognosis of BRCA patients. Methods:The expression data were downloaded from The Cancer Genome Atlas (TCGA). The immune-related gene list, the transcription factor (TF) gene list, and the immune infiltrate scores of samples in the TCGA database were acquired from the ImmPort database, the Cistrome Cancer database, and the TIMER database, respectively. Univariate Cox regression analysis was utilized to identify prognostic immune-related differentially expressed genes (DEGs) (PIRDEGs) in BRCA. A prognostic immune signature containing 15 PIRDEGs in BRCA was established using the least absolute shrinkage and selection operator (LASSO) model with 1,000 iterations followed by a stepwise Cox proportional hazards model with a training set of 508 samples in TCGA. An independent assessment of the prognostic prediction ability of the signature was conducted using Kaplan-Meier survival analysis with a testing set of 505 samples in TCGA. Results:We identified 466 PIRDEGs and 80 TFs among the DEGs. A gene signature containing 15 PIRDEGs was constructed. Risk scores of BRCA patients were calculated using this model, which showed a high accuracy of prognosis prediction in both the training set and testing set and could be an independent prognostic factor of BRCA patients. Conclusions:Our study revealed that a PIRDEG signature could be a candidate prognostic biomarker for predicting the overall survival (OS) of patients with BRCA.