Diedrich, S. | bioRxiv Science

bioRxiv2023

MGX 2.0: Shotgun- and assembly-based metagenome and metatranscriptome analysis from a single source

Metagenomics studies have enabled scientists to analyze the genetic information of natural habitats or even complete ecosystems, including otherwise unculturable microbes. The processing of such datasets, however, remains a challenging task requiring extensive computational resources. MGX 2.0 is a versatile solution for the analysis and interpretation of microbial community sequence data. MGX 2.0 supports the processing of raw metagenomes and metatranscriptomes, but also enables assembly-based strategies, including downstream taxonomic binning, bin quality assessment, abundance quantification, and subsequent annotation coming from a single source. Due to the modular design of MGX, users are able to choose from a wide range of different methods for microbial community sequence data analysis, allowing them to directly compare between read-based and assembly-based approaches or to evaluate different strategies to analyze their data.

bioinformatics↗

bioRxiv2022

Chemical screening of food-related chemicals for human fatty liver risk: Combining high content imaging of cellular responses with in vitro to in vivo extrapolation

1Nonalcoholic fatty liver disease (NAFLD) is an increasingly prevalent human disease with accumulating evidence linking its pathophysiology and co-morbidities to chemical exposures. The complex pathophysiology of NAFLD has limited the elucidation of potential chemical etiologies. In this study we generated a high-content imaging analysis method for the simultaneous quantification of sentinel steatosis cellular markers in chemically exposed human liver cells in vitro combined with a computational model for the extrapolation of human oral equivalent doses (OED). First, the in vitro test method was generated using 14 reference chemicals with known capacities to induce cellular alterations in nuclear morphology, lipid accumulation, mitochondrial membrane potential and oxidative stress. These effects were quantified on a single cell- and population-level, and then, using physiologically based pharmacokinetic modelling and reverse dosimetry, OEDs were extrapolated from these in vitro data. The extrapolated OEDs were confirmed to be within biologically relevant exposure ranges for the reference chemicals. Next, we tested 14 chemicals found in food, selected from thousands of putative chemicals on the basis of structure-based prediction for nuclear receptor activation. Amongst these, orotic acid had an extrapolated OED overlapping with realistic exposure ranges. By the strategy developed in this study, we were able to characterize known NAFLD-inducing chemicals and translate to data scarce food-related chemicals, amongst which we identified orotic acid to induce steatosis. This strategy addresses needs of next generation risk assessment, and can be used as a first chemical prioritization hazard screening step in a tiered approach to identify chemical risk factors for NAFLD.

pharmacology and toxicology↗