Hybrid allele-specific ChIP-seq analysis identifies variation in brassinosteroid-responsive transcription factor binding linked to traits in maize.

BACKGROUND: Genetic variation in regulatory sequences that alter transcription factor (TF) binding is a major cause of phenotypic diversity. Brassinosteroid is a growth hormone that has major effects on plant phenotypes. Genetic variation in brassinosteroid-responsive cis-elements likely contributes to trait variation. Pinpointing such regulatory variations and quantitative genomic analysis of the variation in TF-target binding, however, remains challenging. How variation in transcriptional targets of signaling pathways such as the brassinosteroid pathway contributes to phenotypic variation is an important question to be investigated with innovative approaches. RESULTS: Here, we use a hybrid allele-specific chromatin binding sequencing (HASCh-seq) approach and identify variations in target binding of the brassinosteroid-responsive TF ZmBZR1 in maize. HASCh-seq in the B73xMo17 F1s identifies thousands of target genes of ZmBZR1. Allele-specific ZmBZR1 binding (ASB) has been observed for 18.3% of target genes and is enriched in promoter and enhancer regions. About a quarter of the ASB sites correlate with sequence variation in BZR1-binding motifs and another quarter correlate with haplotype-specific DNA methylation, suggesting that both genetic and epigenetic variations contribute to the high level of variation in ZmBZR1 occupancy. Comparison with GWAS data shows linkage of hundreds of ASB loci to important yield and disease-related traits. CONCLUSION: Our study provides a robust method for analyzing genome-wide variations of TF occupancy and identifies genetic and epigenetic variations of the brassinosteroid response transcription network in maize.

ARSK1 activates TORC1 signaling to adjust growth to phosphate availability in Arabidopsis.

Nutrient sensing and signaling are essential for adjusting growth and development to available resources. Deprivation of the essential mineral phosphorus (P) inhibits root growth.1 The molecular processes that sense P limitation to trigger early root growth inhibition are not known yet. Target of rapamycin (TOR) kinase is a central regulatory hub in eukaryotes to adapt growth to internal and external nutritional cues.2,3 How nutritional signals are transduced to TOR to control plant growth remains unclear. Here, we identify Arabidopsis-root-specific kinase 1 (ARSK1), which attenuates initial root growth inhibition in response to P limitation. We demonstrate that ARSK1 phosphorylates and stabilizes the regulatory-associated protein of TOR 1B (RAPTOR1B), a component of the TOR complex 1, to adjust root growth to P availability. These findings uncover signaling components acting upstream of TOR to balance growth to P availability.

METACLUSTER-an R package for context-specific expression analysis of metabolic gene clusters.

SUMMARY: Plants and microbes produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have yet to be elucidated. Some biosynthetic pathways are encoded by enzymes collocated in the chromosome. To facilitate a more comprehensive condition and tissue-specific expression analysis of metabolic gene clusters, we developed METACLUSTER, a probabilistic framework for characterizing metabolic gene clusters using context-specific gene expression information. AVAILABILITY AND IMPLEMENTATION: METACLUSTER is freely available at https://github.com/mbanf/METACLUSTER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomic diversity of a nectar yeast clusters into metabolically, but not geographically, distinct lineages.

Both dispersal limitation and environmental sorting can affect genetic variation in populations, but their contribution remains unclear, particularly in microbes. We sought to determine the contribution of geographic distance (as a proxy for dispersal limitation) and phenotypic traits (as a proxy for environmental sorting), including morphology, metabolic ability and interspecific competitiveness, to the genotypic diversity in a nectar yeast species, Metschnikowia reukaufii. To measure genotypic diversity, we sequenced the genomes of 102 strains of M. reukaufii isolated from the floral nectar of hummingbird-pollinated shrub, Mimulus aurantiacus, along a 200-km coastline in California. Intraspecific genetic variation showed no detectable relationship with geographic distance, but could be grouped into three distinct lineages that correlated with metabolic ability and interspecific competitiveness. Despite ample evidence for strong competitive interactions within and among nectar yeasts, a full spectrum of the genotypic and phenotypic diversity observed across the 200-km coastline was represented even at a scale as small as 200 m. Further, more competitive strains were not necessarily more abundant. These results suggest that dispersal limitation and environmental sorting might not fully explain intraspecific diversity in this microbe and highlight the need to also consider other ecological factors such as trade-offs, source-sink dynamics and niche modification.

Enhancing gene regulatory network inference through data integration with markov random fields.

A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE's potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants.

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.

Computational inference of gene regulatory networks: Approaches, limitations and opportunities.

Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

PictureSensation - a mobile application to help the blind explore the visual world through touch and sound.

We present PictureSensation, a mobile application for the hapto-acoustic exploration of images. It is designed to allow for the visually impaired to gain direct perceptual access to images via an acoustic signal. PictureSensation introduces a swipe-gesture based, speech-guided, barrier free user interface to guarantee autonomous usage by a blind user. It implements a recently proposed exploration and audification principle, which harnesses exploration methods that the visually impaired are used to from everyday life. In brief, a user explores an image actively on a touch screen and receives auditory feedback about its content at his current finger position. PictureSensation provides an extensive tutorial and training mode, to allow for a blind user to become familiar with the use of the application itself as well as the principles of image content to sound transformations, without any assistance from a normal-sighted person. We show our application's potential to help visually impaired individuals explore, interpret and understand entire scenes, even on small smartphone screens. Providing more than just verbal scene descriptions, PictureSensation presents a valuable mobile tool to grant the blind access to the visual world through exploration, anywhere.

microProtein Prediction Program (miP3): A Software for Predicting microProteins and Their Target Transcription Factors.

An emerging concept in transcriptional regulation is that a class of truncated transcription factors (TFs), called microProteins (miPs), engages in protein-protein interactions with TF complexes and provides feedback controls. A handful of miP examples have been described in the literature but the extent of their prevalence is unclear. Here we present an algorithm that predicts miPs and their target TFs from a sequenced genome. The algorithm is called miP prediction program (miP3), which is implemented in Python. The software will help shed light on the prevalence, biological roles, and evolution of miPs. Moreover, miP3 can be used to predict other types of miP-like proteins that may have evolved from other functional classes such as kinases and receptors. The program is freely available and can be applied to any sequenced genome.