Anthropogenic activities impose multiple concurrent pressures on soils globally, but responses of soil microbes to multiple global change factors are poorly understood. Here, we apply 10 treatments (warming, drought, nitrogen deposition, salinity, heavy metal, microplastics, antibiotics, fungicides, herbicides and insecticides) individually and in combinations of 8 factors to soil samples, and monitor their bacterial and viral composition by metagenomic analysis. We recover 742 mostly unknown bacterial and 1865 viral Metagenome-Assembled Genomes (MAGs), and leverage them to describe microbial populations under different treatment conditions. The application of multiple factors selects for prokaryotic and viral communities different from any individual factor, favouring the proliferation of potentially pathogenic mycobacteria and novel phages, which apparently play a role in shaping prokaryote communities. We also build a 25 M gene catalog to show that multiple factors select for metabolically diverse, sessile and non-biofilm-forming bacteria with a high load of antibiotic resistance genes. Finally, we show that novel genes are relevant for understanding microbial response to global change. Our study indicates that multiple factors impose selective pressures on soil prokaryotes and viruses not observed at the individual factor level, and emphasizes the need of studying the effect of concurrent global change treatments.

Summary: We present draft genome assemblies of Beta patula, a critically endangered wild beet endemic to the Madeira archipelago, and of the closely related Beta vulgaris ssp. maritima (sea beet). Evidence‐based reference gene sets for B. patula and sea beet were generated, consisting of 25 127 and 27 662 genes, respectively. The genomes and gene sets of the two wild beets were compared with their cultivated sister taxon B. vulgaris ssp. vulgaris (sugar beet). Large syntenic regions were identified, and a display tool for automatic genome‐wide synteny image generation was developed. Phylogenetic analysis based on 9861 genes showing 1:1:1 orthology supported the close relationship of B. patula to sea beet and sugar beet. A comparative analysis of the Rz2 locus, responsible for rhizomania resistance, suggested that the sequenced B. patula accession was rhizomania susceptible. Reference karyotypes for the two wild beets were established, and genomic rearrangements were detected. We consider our data as highly valuable and comprehensive resources for wild beet studies, B. patula conservation management, and sugar beet breeding research.

Synteny conservation analysis is a well-established methodology to investigate the potential functional role of unknown prokaryotic genes. However, bioinformatic tools to reconstruct and visualise genomic contexts usually depend on slow computations, are restricted to narrow taxonomic ranges, and/or do not allow for the functional and interactive exploration of neighbouring genes across different species. Here, we present GeCoViz, an online resource built upon 12 221 reference prokaryotic genomes that provides fast and interactive visualisation of custom genomic regions anchored by any target gene, which can be sought by either name, orthologous group (KEGGs, eggNOGs), protein domain (PFAM) or sequence. To facilitate functional and evolutionary interpretation, GeCoViz allows to customise the taxonomic scope of each analysis and provides comprehensive annotations of the neighbouring genes. Interactive visualisation options include, among others, the scaled representations of gene lengths and genomic distances, and on the fly calculation of synteny conservation of neighbouring genes, which can be highlighted based on custom thresholds. The resulting plots can be downloaded as high-quality images for publishing purposes. Overall, GeCoViz offers an easy-to-use, comprehensive, fast and interactive web-based tool for investigating the genomic context of prokaryotic genes, and is freely available at https://gecoviz.cgmlab.org

Summary: Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict prokaryotic antimicrobial peptides (AMPs) by leveraging a vast dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown. We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident. To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.