MIBiG 3.0 : a community-driven effort to annotate experimentally validated biosynthetic gene clusters

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/

PIKAChU: a Python-based informatics kit for analysing chemical units

As efforts to computationally describe and simulate the biochemical world become more commonplace, computer programs that are capable of in silico chemistry play an increasingly important role in biochemical research. While such programs exist, they are often dependency-heavy, difficult to navigate, or not written in Python, the programming language of choice for bioinformaticians. Here, we introduce PIKAChU (Python-based Informatics Kit for Analysing CHemical Units): a cheminformatics toolbox with few dependencies implemented in Python. PIKAChU builds comprehensive molecular graphs from SMILES strings, which allow for easy downstream analysis and visualisation of molecules. While the molecular graphs PIKAChU generates are extensive, storing and inferring information on aromaticity, chirality, charge, hybridisation and electron orbitals, PIKAChU limits itself to applications that will be sufficient for most casual users and downstream Python-based tools and databases, such as Morgan fingerprinting, similarity scoring, substructure matching and customisable visualisation. In addition, it comes with a set of functions that assists in the easy implementation of reaction mechanisms. Its minimalistic design makes PIKAChU straightforward to use and install, in stark contrast to many existing toolkits, which are more difficult to navigate and come with a plethora of dependencies that may cause compatibility issues with downstream tools. As such, PIKAChU provides an alternative for researchers for whom basic cheminformatic processing suffices, and can be easily integrated into downstream bioinformatics and cheminformatics tools. PIKAChU is available at https://github.com/BTheDragonMaster/pikachu. Graphical Abstract: [Figure not available: see fulltext.

Virus infection modulates male sexual behaviour in Caenorhabditis elegans

Mating dynamics follow from natural selection on mate choice and individuals maximizing their reproductive success. Mate discrimination reveals itself by a plethora of behaviours and morphological characteristics, each of which can be affected by pathogens. A key question is how pathogens affect mate choice and outcrossing behaviour. Here we investigated the effect of Orsay virus on the mating dynamics of the androdiecious (male and hermaphrodite) nematode Caenorhabditis elegans. We tested genetically distinct strains and found that viral susceptibility differed between sexes in a genotype-dependent manner with males of reference strain N2 being more resistant than hermaphrodites. Males displayed a constitutively higher expression of intracellular pathogen response (IPR) genes, whereas the antiviral RNAi response did not have increased activity in males. Subsequent monitoring of sex ratios over 10 generations revealed that viral presence can change mating dynamics in isogenic populations. Sexual attraction assays showed that males preferred mating with uninfected rather than infected hermaphrodites. Together our results illustrate for the first time that viral infection can significantly affect male mating choice and suggest altered mating dynamics as a novel cause benefitting outcrossing under pathogenic stress conditions in C. elegans