While colocalization within a bacterial operon enables coexpression of the constituent genes, the mechanistic logic of clustering of nonhomologous monocistronic genes in eukaryotes is not immediately obvious. Biosynthetic gene clusters that encode pathways for specialized metabolites are an exception to the classical eukaryote rule of random gene location and provide paradigmatic exemplars with which to understand eukaryotic cluster dynamics and regulation. Here, using 3C, Hi-C, and Capture Hi-C (CHi-C) organ-specific chromosome conformation capture techniques along with high-resolution microscopy, we investigate how chromosome topology relates to transcriptional activity of clustered biosynthetic pathway genes inArabidopsis thaliana. Our analyses reveal that biosynthetic gene clusters are embedded in local hot spots of 3D contacts that segregate cluster regions from the surrounding chromosome environment. The spatial conformation of these cluster-associated domains differs between transcriptionally active and silenced clusters. We further show that silenced clusters associate with heterochromatic chromosomal domains toward the periphery of the nucleus, while transcriptionally active clusters relocate away from the nuclear periphery. Examination of chromosome structure at unrelated clusters in maize, rice, and tomato indicates that integration of clustered pathway genes into distinct topological domains is a common feature in plant genomes. Our results shed light on the potential mechanisms that constrain coexpression within clusters of nonhomologous eukaryotic genes and suggest that gene clustering in the one-dimensional chromosome is accompanied by compartmentalization of the 3D chromosome.

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RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. Availability and Implementation: regutools is an R package available through Bioconductor at bioconductor.org/packages/regutools. Contact: github.com/ComunidadBioInfo/regutools,

Summary RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. Availability and Implementation regutools is an R package available through Bioconductor at bioconductor.org/packages/regutools.