TCP transcription factors play a key role in regulating various developmental processes, particularly in shoot branching, flower development, and leaf development, and these factors are exclusively found in plants. However, comprehensive studies investigating TCP transcription factors in pepper (Capsicum annuum L.) are lacking. In this study, we identified 27 CaTCP members in the pepper genome, which were classified into Class I and Class II through phylogenetic analysis. The motif analysis revealed that CaTCPs in the same class exhibit similar numbers and distributions of motifs. We predicted that 37 previously reported miRNAs target 19 CaTCPs. The expression levels of CaTCPs varied in various tissues and growth stages. Specifically, CaTCP16, a member of Class II (CIN), exhibited significantly high expression in flowers. Class I CaTCPs exhibited high expression levels in leaves, while Class II CaTCPs showed high expression in lateral branches, especially in the CYC/TB1 subclass. The expression profile suggests that CaTCPs play specific roles in the developmental processes of pepper. We provide a theoretical basis that will assist in further functional validation of the CaTCPs.

Iron deficiency caused by high pH of saline–alkali soil is a major source of abiotic stress affecting plant growth. However, the molecular mechanism underlying the iron deficiency response in cotton (Gossypium hirsutum) is poorly understood. In this study, we investigated the impacts of iron deficiency at the cotton seedling stage and elucidated the corresponding molecular regulation network, which centered on a hub gene GhbHLH121. Iron deficiency induced the expression of genes with roles in the response to iron deficiency, especially GhbHLH121. The suppression of GhbHLH121 with virus-induced gene silence technology reduced seedlings’ tolerance to iron deficiency, with low photosynthetic efficiency and severe damage to the structure of the chloroplast. Contrarily, ectopic expression of GhbHLH121 in Arabidopsis enhanced tolerance to iron deficiency. Further analysis of protein/protein interactions revealed that GhbHLH121 can interact with GhbHLH IVc and GhPYE. In addition, GhbHLH121 can directly activate the expression of GhbHLH38, GhFIT, and GhPYE independent of GhbHLH IVc. All told, GhbHLH121 is a positive regulator of the response to iron deficiency in cotton, directly regulating iron uptake as the upstream gene of GhFIT. Our results provide insight into the complex network of the iron deficiency response in cotton.

Expression quantitative trait loci (eQTL) provide a powerful means of investigating the biological basis of genome-wide association study (GWAS) results and exploring complex traits or phenotypes. In addition to identifying the causal gene in cis , eQTL analysis also reveals a large number of trans-regulated genes located on different chromosomes, which form a gene regulatory network (GRN) that complements the GWAS locus. However, the dissection of a GRN and the crosstalk underlying multiple agronomical traits, along with prioritizing important genes in eQTL-derived GRNs, remains a major challenge. In this study, we generated 558 transcriptional profiles of lint-bearing ovules at one day post-anthesis (DPA) from a selective core cotton germplasm, from which we identified 12,207 eQTLs. By integrating with a GWAS catalog, we found that 66 out of 187 (35.29%) known phenotypic GWAS loci are colocalized with 1,090 eQTLs, forming 38 major functional GRNs predominantly (30 out of 38) associated with seed size-related phenotypes. Of the eGenes, 34 were shared between at least two functional GRNs, exhibiting pleiotropic effects, such as NF-YB3 , GRDP1 , and IDD7 . Narrow-sense heritability analysis showed that the heritability increased with combining the eQTLs with GRNs compared to those with previous yield trait GWAS loci. The extreme gradient boosting (XGBoost) machine learning approach was then applied to predict seed cotton yield phenotypes based on gene expression. Top-ranking eGenes ( NF-YB3 , FLA2 , and GRDP1 ) derived by XGBoost with pleiotropic effects on yield traits were validated, along with their potential roles by correlation analysis, domestication selection analysis, and transgenic plants. This study provides insights into the mining of GRNs in relation to the pleiotropy of phenotype. The combination of eQTL and machine learning approaches is efficient in improving the genetic dissection of agricultural traits.