Computational exploration of the global microbiome for antibiotic discovery.

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.

New globally distributed bacterial phyla within the FCB superphylum.

Microbes in marine sediments play crucial roles in global carbon and nutrient cycling. However, our understanding of microbial diversity and physiology on the ocean floor is limited. Here, we use phylogenomic analyses of thousands of metagenome-assembled genomes (MAGs) from coastal and deep-sea sediments to identify 55 MAGs that are phylogenetically distinct from previously described bacterial phyla. We propose that these MAGs belong to 4 novel bacterial phyla (Blakebacterota, Orphanbacterota, Arandabacterota, and Joyebacterota) and a previously proposed phylum (AABM5-125-24), all of them within the FCB superphylum. Comparison of their rRNA genes with public databases reveals that these phyla are globally distributed in different habitats, including marine, freshwater, and terrestrial environments. Genomic analyses suggest these organisms are capable of mediating key steps in sedimentary biogeochemistry, including anaerobic degradation of polysaccharides and proteins, and respiration of sulfur and nitrogen. Interestingly, these genomes code for an unusually high proportion (~9% on average, up to 20% per genome) of protein families lacking representatives in public databases. Genes encoding hundreds of these protein families colocalize with genes predicted to be involved in sulfur reduction, nitrogen cycling, energy conservation, and degradation of organic compounds. Our findings advance our understanding of bacterial diversity, the ecological roles of these bacteria, and potential links between novel gene families and metabolic processes in the oceans.

Pangenome Analysis of Plant Transcripts and Coding Sequences.

The pangenome of a species is the sum of the genomes of its individuals. As coding sequences often represent only a small fraction of each genome, analyzing the pangene set can be a cost-effective strategy for plants with large genomes or highly heterozygous species. Here, we describe a step-by-step protocol to analyze plant pangene sets with the software GET_HOMOLOGUES-EST . After a short introduction, where the main concepts are illustrated, the remaining sections cover the installation and typical operations required to analyze and annotate pantranscriptomes and gene sets of plants. The recipes include instructions on how to call core and accessory genes, how to compute a presence-absence pangenome matrix, and how to identify and analyze private genes, present only in some genotypes. Downstream phylogenetic analyses are also discussed.

Towards the biogeography of prokaryotic genes.

Microbial genes encode the majority of the functional repertoire of life on earth. However, despite increasing efforts in metagenomic sequencing of various habitats1-3, little is known about the distribution of genes across the global biosphere, with implications for human and planetary health. Here we constructed a non-redundant gene catalogue of 303 million species-level genes (clustered at 95% nucleotide identity) from 13,174 publicly available metagenomes across 14 major habitats and use it to show that most genes are specific to a single habitat. The small fraction of genes found in multiple habitats is enriched in antibiotic-resistance genes and markers for mobile genetic elements. By further clustering these species-level genes into 32 million protein families, we observed that a small fraction of these families contain the majority of the genes (0.6% of families account for 50% of the genes). The majority of species-level genes and protein families are rare. Furthermore, species-level genes, and in particular the rare ones, show low rates of positive (adaptive) selection, supporting a model in which most genetic variability observed within each protein family is neutral or nearly neutral.