A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies

Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation

Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants and the cell type context in which regulatory variants operate are typically unknown. Cell-type-specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offer a powerful framework for examining the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of cell types. Furthermore, identifying specific gene subnetworks or pathways that are targeted by a set of variants is a significant challenge. We have developed L-HiC-Reg, a Random Forests regression method to predict high-resolution contact counts in new cell types, and a network-based framework to identify candidate cell-type-specific gene networks targeted by a set of variants from a genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenomics Mapping Consortium cell types, which we used to interpret regulatory single nucleotide polymorphisms (SNPs) in the NHGRI-EBI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including schizophrenia, coronary artery disease (CAD) and Crohn's disease. We found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and the associated network-based analysis pipeline leverages long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes.

A genome resource for Acacia, Australia's largest plant genus

Acacia (Leguminosae, Caesalpinioideae, mimosoid clade) is the largest and most widespread genus of plants in the Australian flora, occupying and dominating a diverse range of environments, with an equally diverse range of forms. For a genus of its size and importance, Acacia currently has surprisingly few genomic resources. Acacia pycnantha, the golden wattle, is a woody shrub or tree occurring in south-eastern Australia and is the country's floral emblem. To assemble a genome for A. pycnantha, we generated long-read sequences using Oxford Nanopore Technology, 10x Genomics Chromium linked reads, and short-read Illumina sequences, and produced an assembly spanning 814 Mb, with a scaffold N50 of 2.8 Mb, and 98.3% of complete Embryophyta BUSCOs. Genome annotation predicted 47,624 protein-coding genes, with 62.3% of the genome predicted to comprise transposable elements. Evolutionary analyses indicated a shared genome duplication event in the Caesalpinioideae, and conflict in the relationships between Cercis (subfamily Cercidoideae) and subfamilies Caesalpinioideae and Papilionoideae (pea-flowered legumes). Comparative genomics identified a suite of expanded and contracted gene families in A. pycnantha, and these were annotated with both GO terms and KEGG functional categories. One expanded gene family of particular interest is involved in flowering time and may be associated with the characteristic synchronous flowering of Acacia. This genome assembly and annotation will be a valuable resource for all studies involving Acacia, including the evolution, conservation, breeding, invasiveness, and physiology of the genus, and for comparative studies of legumes.

BioNERO: an all-in-one R/Bioconductor package for comprehensive and easy biological network reconstruction

Currently, standard network analysis workflows rely on many different packages, often requiring users to have a solid statistics and programming background. Here, we present BioNERO, an R package that aims to integrate all aspects of network analysis workflows, including expression data preprocessing, gene coexpression and regulatory network inference, functional analyses, and intraspecies and interspecies network comparisons. The state-of-the-art methods implemented in BioNERO ensure that users can perform all analyses with a single package in a simple pipeline, without needing to learn a myriad of package-specific syntaxes. BioNERO offers a user-friendly framework that can be easily incorporated in systems biology pipelines.

Proteomics and plant biology: contributions to date and a look towards the next decade

INTRODUCTION

This review presents the view of the author, that is opinionable and even speculative, on the field of proteomics, its application to plant biology knowledge, and translation to biotechnology. Written in a more academic than scientific style, it is based on past original and review articles by the author´s group, and those published by leading scientists in the last two years.

AREAS COVERED

Starting with a general definition and references to historical milestones, it covers sections devoted to the different platforms employed, the plant biology discourse in the protein language, challenges and future prospects, ending with the author opinion.

EXPERT OPINION

In 25 years, five proteomics platform generations have appeared. We are now moving from proteomics to Systems Biology. While feasible with model organisms, proteomics of orphan species remains challenging. Proteomics, even in its simplest approach, sheds light on plant biological processes, central dogma, and molecular bases of phenotypes of interest, and it can be translated to areas such as food traceability and allergen detection. Proteomics should be validated and optimized to each experimental system, objectives, and hypothesis. It has limitations, artifacts, and biases. We should not blindly accept proteomics data and just create a list of proteins, networks, and avoid speculative biological interpretations. From the hundred to thousand proteins identified and quantified, it is important to obtain a focus and validate some of them, otherwise it is merely. We are starting to have the protein pieces, so let, from now, build the proteomics and biological puzzle.