Ott, M.-C. | Search bioRxiv

bioRxiv2023

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 hazard of chemicals in the environment. These tests are often time-consuming, 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 the regulatory risk assessment. Currently, lacking genomic information slows the process of NAM development. Here, we present a phylogenetically resolved overview of missing genomic resources for surrogate species within 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 non-animal reliant ecotoxicological risk assessment in the systems ecotoxicology era. Synopsis StatementWe identify missing genomic resources needed for the development and regulatory acceptance of new approach methodologies in environmental risk assessment.

pharmacology and toxicology↗

bioRxiv2020

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

Generalization of transcriptomics results can be achieved by comparison across experiments, which is based on integration of interrelated transcriptomics studies into a compendium. Both characterization of the fate of the organism as well as distinguishing between generic and specific responses can be gained in such a broader context. There are numerous methods for analyzing such data sets, most focusing on gene-wise dimension reduction to obtain marker genes and gene sets, e.g. for pathway analysis. Relying only on isolated biological modules might lead to missing of important confounders and relevant context. We have developed a novel method called Plant PhysioSpace, which provides the ability to compute experimental conditions across species and platforms without a priori reducing the reference information to specific gene-sets. It extracts physiologically relevant signatures from a reference data set, a collection of public data sets, 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 which provide quantitative likeness of the new experiment to the a priori compendium. Because of its robustness against noise and platform bias, Plant PhysioSpace can be used 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 also report the implementation of two R packages, one software and one data package, and a shiny web application, which provides plant biologists convenient ways to access our method and precomputed models from four different species.

plant biology↗