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/.

Metagenomics has become a prominent technology to study the functional potential of all organisms in a microbial community. Most studies focus on the bacterial content of these communities, while ignoring eukaryotic microbes. Indeed, many metagenomics analysis pipelines silently assume that all contigs in a metagenome are prokaryotic, likely resulting in less accurate annotation of eukaryotes in metagenomes. Early detection of eukaryotic contigs allows for eukaryote-specific gene prediction and functional annotation. Here, we developed a classifier that distinguishes eukaryotic from prokaryotic contigs based on foundational differences between these taxa in terms of gene structure. We first developed Whokaryote, a random forest classifier that uses intergenic distance, gene density and gene length as the most important features. We show that, with an estimated recall, precision and accuracy of 94, 96 and 95 %, respectively, this classifier with features grounded in biology can perform almost as well as the classifiers EukRep and Tiara, which use k-mer frequencies as features. By retraining our classifier with Tiara predictions as an additional feature, the weaknesses of both types of classifiers are compensated; the result is Whokaryote+Tiara, an enhanced classifier that outperforms all individual classifiers, with an F1 score of 0.99 for both eukaryotes and prokaryotes, while still being fast. In a reanalysis of metagenome data from a disease-suppressive plant endospheric microbial community, we show how using Whokaryote+Tiara to select contigs for eukaryotic gene prediction facilitates the discovery of several biosynthetic gene clusters that were missed in the original study. Whokaryote (+Tiara) is wrapped in an easily installable package and is freely available from https://github.com/LottePronk/whokaryote.

Microbiomes play a pivotal role in plant growth and health, but the genetic factors involved in microbiome assembly remain largely elusive. Here, we map the molecular features of the rhizosphere microbiome as quantitative traits of a diverse hybrid population of wild and domesticated tomato. Gene content analysis of prioritized tomato quantitative trait loci suggests a genetic basis for differential recruitment of various rhizobacterial lineages, including a Streptomyces-associated 6.31 Mbp region harboring tomato domestication sweeps and encoding, among others, the iron regulator FIT and the water channel aquaporin SlTIP2.3. Within metagenome-assembled genomes of root-associated Streptomyces and Cellvibrio, we identify bacterial genes involved in metabolism of plant polysaccharides, iron, sulfur, trehalose, and vitamins, whose genetic variation associates with specific tomato QTLs. By integrating ‘microbiomics’ and quantitative plant genetics, we pinpoint putative plant and reciprocal rhizobacterial traits underlying microbiome assembly, thereby providing a first step towards plant-microbiome breeding programs.

Summary Root‐colonizing bacteria have been intensively investigated for their intimate relationship with plants and their manifold plant‐beneficial activities. They can inhibit growth and activity of pathogens or induce defence responses. In recent years, evidence has emerged that several plant‐beneficial rhizosphere bacteria do not only associate with plants but also with insects. Their relationships with insects range from pathogenic to mutualistic and some rhizobacteria can use insects as vectors for dispersal to new host plants. Thus, the interactions of these bacteria with their environment are even more complex than previously thought and can extend far beyond the rhizosphere. The discovery of this secret life of rhizobacteria represents an exciting new field of research that should link the fields of plant–microbe and insect–microbe interactions. In this review, we provide examples of plant‐beneficial rhizosphere bacteria that use insects as alternative hosts, and of potentially rhizosphere‐competent insect symbionts. We discuss the bacterial traits that may enable a host‐switch between plants and insects and further set the multi‐host lifestyle of rhizobacteria into an evolutionary and ecological context. Finally, we identify important open research questions and discuss perspectives on the use of these rhizobacteria in agriculture.

Microbiomes play a pivotal role in plant growth and health, but the genetic factors involved in microbiome assembly remain largely elusive. Here, 16S amplicon and metagenomic features of the rhizosphere microbiome were mapped as quantitative traits of a recombinant inbred line population of a cross between wild and domesticated tomato. Gene content analysis of prioritized tomato QTLs suggested a genetic basis for differential recruitment of various rhizobacterial lineages, including a Streptomyces-associated 6.31-Mbp region harboring tomato domestication sweeps and encoding, among others, the iron regulator FIT and the aquaporin SlTIP2.3. Within metagenome-assembled genomes of the rhizobacterial lineages Streptomyces and Cellvibrio, we identified microbial genes involved in metabolism of plant polysaccharides, iron, sulfur, trehalose, and vitamins, whose genetic variation associated with either modern or wild tomato QTLs. Integrating 'microbiomics' and quantitative plant genetics pinpointed putative plant and reciprocal microbial traits underlying microbiome assembly, thereby providing the first step towards plant-microbiome breeding programs.