Missing Genomic Resources for the Next Generation of Environmental Risk Assessment.

Environmental risk assessment traditionally relies on a wide range of in vivo testing to assess the potential hazards of chemicals in the environment. These tests are often time-consuming and costly and can cause test organisms' suffering. Recent developments of reliable low-cost alternatives, both in vivo- and in silico-based, opened the door to reconsider current toxicity assessment. However, many of these new approach methodologies (NAMs) rely on high-quality annotated genomes for surrogate species of regulatory risk assessment. Currently, a lack of genomic information slows the process of NAM development. Here, we present a phylogenetically resolved overview of missing genomic resources for surrogate species within a regulatory ecotoxicological risk assessment. We call for an organized and systematic effort within the (regulatory) ecotoxicological community to provide these missing genomic resources. Further, we discuss the potential of a standardized genomic surrogate species landscape to enable a robust and nonanimal-reliant ecotoxicological risk assessment in the systems ecotoxicology era.

Plant PhysioSpace: a robust tool to compare stress response across plant species.

Generalization of transcriptomics results can be achieved by comparison across experiments. This generalization is based on integration of interrelated transcriptomics studies into a compendium. Such a focus on the bigger picture enables both characterizations of the fate of an organism and distinction between generic and specific responses. Numerous methods for analyzing transcriptomics datasets exist. Yet, most of these methods focus on gene-wise dimension reduction to obtain marker genes and gene sets for, for example, pathway analysis. Relying only on isolated biological modules might result in missing important confounders and relevant contexts. We developed a method called Plant PhysioSpace, which enables researchers to compute experimental conditions across species and platforms without a priori reducing the reference information to specific gene sets. Plant PhysioSpace extracts physiologically relevant signatures from a reference dataset (i.e. a collection of public datasets) by integrating and transforming heterogeneous reference gene expression data into a set of physiology-specific patterns. New experimental data can be mapped to these patterns, resulting in similarity scores between the acquired data and the extracted compendium. Because of its robustness against platform bias and noise, Plant PhysioSpace can function as an inter-species or cross-platform similarity measure. We have demonstrated its success in translating stress responses between different species and platforms, including single-cell technologies. We have also implemented two R packages, one software and one data package, and a Shiny web application to facilitate access to our method and precomputed models.

Unique features of a global human ectoparasite identified through sequencing of the bed bug genome.

The bed bug, Cimex lectularius, has re-established itself as a ubiquitous human ectoparasite throughout much of the world during the past two decades. This global resurgence is likely linked to increased international travel and commerce in addition to widespread insecticide resistance. Analyses of the C. lectularius sequenced genome (650 Mb) and 14,220 predicted protein-coding genes provide a comprehensive representation of genes that are linked to traumatic insemination, a reduced chemosensory repertoire of genes related to obligate hematophagy, host-symbiont interactions, and several mechanisms of insecticide resistance. In addition, we document the presence of multiple putative lateral gene transfer events. Genome sequencing and annotation establish a solid foundation for future research on mechanisms of insecticide resistance, human-bed bug and symbiont-bed bug associations, and unique features of bed bug biology that contribute to the unprecedented success of C. lectularius as a human ectoparasite.

RNA-Seq transcriptome analysis to identify genes involved in metabolism-based diclofop resistance in Lolium rigidum.

Weed control failures due to herbicide resistance are an increasing and worldwide problem that significantly affect crop yields. Metabolism-based herbicide resistance (referred to as metabolic resistance) in weeds is not well characterized at the genetic level. An RNA-Seq transcriptome analysis was used to find candidate genes that conferred metabolic resistance to the herbicide diclofop in a diclofop-resistant population (R) of the major global weed Lolium rigidum. A reference cDNA transcriptome (19 623 contigs) was assembled and assigned putative annotations. Global gene expression was measured using Illumina reads from untreated control, adjuvant-only control, and diclofop treatment of R and susceptible (S). Contigs that showed constitutive expression differences between untreated R and untreated S were selected for further validation analysis, including 11 contigs putatively annotated as cytochrome P450 (CytP450), glutathione transferase (GST), or glucosyltransferase (GT), and 17 additional contigs with annotations related to metabolism or signal transduction. In a forward genetics validation experiment, nine contigs had constitutive up-regulation in R individuals from a segregating F2 population, including three CytP450, one nitronate monooxygenase (NMO), three GST, and one GT. Principal component analysis using these nine contigs differentiated F2 -R from F2 -S individuals. In a physiological validation experiment in which 2,4-D pre-treatment induced diclofop protection in S individuals due to increased metabolism, seven of the nine genetically validated contigs were induced significantly. Four contigs (two CytP450, NMO, and GT) were consistently highly expressed in nine field-evolved metabolic resistant L. rigidum populations. These four contigs were strongly associated with the resistance phenotype and are major candidates for contributing to metabolic diclofop resistance.