Arabidopsis thaliana TCP15 interacts with the MIXTA-like transcription factor MYB106/NOECK

MYB106 and MYB16 are MIXTA-like transcription factors that control trichome maturation and cuticle formation in Arabidopsis. In a recent study, we found that the TEOSINTE BRANCHED 1, CYCLOIDEA and PROLIFERATING CELL FACTORS (TCP) transcription factor TCP15 also acts as an important regulator of aerial epidermis specialization in Arabidopsis through the control of trichome development and cuticle formation. TCP15 and MYB106 regulate the expression of common groups of genes, including genes coding for transcription factors and enzymes of the cuticle biosynthesis pathway. In this study, we report that TCP15 physically interacts with MYB106 when both proteins are expressed in yeast cells or Nicotiana bentamiana leaves. Furthermore, we also observed interaction in leaves of Arabidopsis thaliana. Altogether, our findings raise the possibility that TCP15 and MYB106 bind together to the promoters of target genes to exert their action. Our data provide a base to investigate the role of TCP-MIXTA complexes in the context of cuticle development in Arabidopsis thaliana.Fil: Camoirano, Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Alem, Antonela Lucía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Gonzalez, Daniel Hector. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Viola, Ivana Lorena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentin

Class I TCP transcription factors regulate trichome branching and cuticle development in Arabidopsis

Trichomes and the cuticle are two specialized structures of the aerial epidermis that are important for plant organ development and interaction with the environment. In this study, we report that Arabidopsis thaliana plants affected in the function of the class I TEOSINTE BRANCHED 1, CYCLOIDEA, PCF (TCP) transcription factors TCP14 and TCP15 show overbranched trichomes in leaves and stems and increased cuticle permeability. We found that TCP15 regulates the expression of MYB106, a MIXTA-like transcription factor involved in epidermal cell and cuticle development, and overexpression of MYB106 in a tcp14 tcp15 mutant reduces trichome branch number. TCP14 and TCP15 are also required for the expression of the cuticle biosynthesis genes CYP86A4, GPAT6, and CUS2, and of SHN1 and SHN2, two AP2/EREBP transcription factors required for cutin and wax biosynthesis. SHN1 and CUS2 are also targets of TCP15, indicating that class I TCPs influence cuticle formation acting at different levels, through the regulation of MIXTA-like and SHN transcription factors and of cuticle biosynthesis genes. Our study indicates that class I TCPs are coordinators of the regulatory network involved in trichome and cuticle development.Fil: Camoirano, Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Arce, Agustín Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Ariel, Federico Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Alem, Antonela Lucía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Gonzalez, Daniel Hector. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Viola, Ivana Lorena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentin

ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture

Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system which combines 3D printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. We developed a novel deep learning based root extraction method which leverages the latest advances in convolutional neural networks for image segmentation, and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. Altogether, our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies as well as the screening of clock-related mutants, revealing novel root traits.Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system which combines 3D printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. We developed a novel deep learning based root extraction method which leverages the latest advances in convolutional neural networks for image segmentation, and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. Altogether, our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies as well as the screening of clock-related mutants, revealing novel root traits