Clustered organization of biosynthetic non-homologous genes is emerging as a characteristic feature of plant genomes. The co-regulation of clustered genes seems to largely depend on epigenetic reprogramming and three-dimensional chromatin conformation. Here we identified the long noncoding RNA (lncRNA) MARneral Silencing (MARS), localized inside the Arabidopsis marneral cluster, and which controls the local epigenetic activation of its surrounding region in response to ABA. MARS modulates the POLYCOMB REPRESSIVE COMPLEX 1 (PRC1) component LIKE-HETEROCHROMATIN PROTEIN 1 (LHP1) binding throughout the cluster in a dose-dependent manner, determining H3K27me3 deposition and chromatin condensation. In response to ABA, MARS decoys LHP1 away from the cluster and promotes the formation of a chromatin loop bringing together the MARNERAL SYNTHASE 1 (MRN1) locus and a distal ABA-responsive enhancer. The enrichment of co-regulated lncRNAs in clustered metabolic genes suggests that the acquisition of noncoding transcriptional units constitute an additional regulatory layer driving the evolution of biosynthetic pathways.

Background 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 that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. Results We developed a novel deep learning–based root extraction method that 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. Conclusions 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.

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