Academic literature on the topic 'Eukaryotic clustering'

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.

MBF1 (multiprotein bridging factor 1) is a highly conserved protein in archaea and eukaryotes. It was originally identified as a mediator of the eukaryotic transcription regulator BmFTZ-F1 (Bombyx mori regulator of fushi tarazu). MBF1 was demonstrated to enhance transcription by forming a bridge between distinct regulatory DNA-binding proteins and the TATA-box-binding protein. MBF1 consists of two parts: a C-terminal part that contains a highly conserved helix–turn–helix, and an N-terminal part that shows a clear divergence: in eukaryotes, it is a weakly conserved flexible domain, whereas, in archaea, it is a conserved zinc-ribbon domain. Although its function in archaea remains elusive, its function as a transcriptional co-activator has been deduced from thorough studies of several eukaryotic proteins, often indicating a role in stress response. In addition, MBF1 was found to influence translation fidelity in yeast. Genome context analysis of mbf1 in archaea revealed conserved clustering in the crenarchaeal branch together with genes generally involved in gene expression. It points to a role of MBF1 in transcription and/or translation. Experimental data are required to allow comparison of the archaeal MBF1 with its eukaryotic counterpart.

Phylogenomic analyses of hundreds of protein-coding genes aimed at resolving phylogenetic relationships is now a common practice. However, no software currently exists that includes tools for dataset construction and subsequent analysis with diverse validation strategies to assess robustness. Furthermore, there are no publicly available high-quality curated databases designed to assess deep (>100 million years) relationships in the tree of eukaryotes. To address these issues, we developed an easy-to-use software package, PhyloFisher (https://github.com/TheBrownLab/PhyloFisher), written in Python 3. PhyloFisher includes a manually curated database of 240 protein-coding genes from 304 eukaryotic taxa covering known eukaryotic diversity, a novel tool for ortholog selection, and utilities that will perform diverse analyses required by state-of-the-art phylogenomic investigations. Through phylogenetic reconstructions of the tree of eukaryotes and of the Saccharomycetaceae clade of budding yeasts, we demonstrate the utility of the PhyloFisher workflow and the provided starting database to address phylogenetic questions across a large range of evolutionary time points for diverse groups of organisms. We also demonstrate that undetected paralogy can remain in phylogenomic “single-copy orthogroup” datasets constructed using widely accepted methods such as all vs. all BLAST searches followed by Markov Cluster Algorithm (MCL) clustering and application of automated tree pruning algorithms. Finally, we show how the PhyloFisher workflow helps detect inadvertent paralog inclusions, allowing the user to make more informed decisions regarding orthology assignments, leading to a more accurate final dataset.