Motivation: Antimicrobial peptides (AMPs) have the potential to tackle multidrug-resistant pathogens in both clinical and non-clinical contexts. The recent growth in the availability of genomes and metagenomes provides an opportunity for in silico prediction of novel AMP molecules. However, due to the small size of these peptides, standard gene prospection methods cannot be applied in this domain and alternative approaches are necessary. In particular, standard gene prediction methods have low precision for short peptides, and functional classification by homology results in low recall. Results: Here, we present Macrel (for metagenomic AMP classification and retrieval), which is an end-to-end pipeline for the prospection of high-quality AMP candidates from (meta)genomes. For this, we introduce a novel set of 22 peptide features. These were used to build classifiers which perform similarly to the state-of-the-art in the prediction of both antimicrobial and hemolytic activity of peptides, but with enhanced precision (using standard benchmarks as well as a stricter testing regime). We demonstrate that Macrel recovers high-quality AMP candidates using realistic simulations and real data. Availability: Macrel is implemented in Python 3. It is available as open source at https://github.com/BigDataBiology/macrel and through bioconda. Classification of peptides or prediction of AMPs in contigs can also be performed on the webserver: https://big-data-biology.org/software/macrel.

Background: The Amazon River is one of the largest in the world and receives huge amounts of terrestrial organic matter (TeOM) from the surrounding rainforest. Despite this TeOM is typically recalcitrant (i.e. resistant to degradation), only a small fraction of it reaches the ocean, pointing to a substantial TeOM degradation by the river microbiome. Yet, microbial genes involved in TeOM degradation in the Amazon River were barely known. Here, we examined the Amazon River microbiome by analysing 106 metagenomes from 30 sampling points distributed along the river.Results: We constructed theAmazon River basin Microbial non-redundant Gene Catalogue(AMnrGC) that includes ~ 3.7 million non-redundant genes, affiliating mostly to bacteria. We found that the Amazon River microbiome contains a substantial gene-novelty compared to other relevant known environments (rivers and rainforest soil). Genes encoding for proteins potentially involved in lignin degradation pathways were correlated to tripartite tricarboxylates transporters and hemicellulose degradation machinery, pointing to a possiblepriming effect. Based on this, we propose a model on how the degradation of recalcitrant TeOM could be modulated by labile compounds in the Amazon River waters. Our results also suggest changes of the microbial community and its genomic potential along the river course.Conclusions: Our work contributes to expand significantly our comprehension of the world’s largest river microbiome and its potential metabolism related to TeOM degradation. Furthermore, the produced gene catalogue (AMnrGC) represents an important resource for future research in tropical rivers.

Small open reading frames (smORFs) shorter than 100 codons are widespread and perform essential roles in microorganisms, where they encode proteins active in several cell functions, including signal pathways, stress response, and antibacterial activities. However, the ecology, distribution and role of small proteins in the global microbiome remain unknown. Here, we construct a global microbial smORFs catalog (GMSC) derived from 63,410 publicly available metagenomes across 75 distinct habitats and 87,920 high-quality isolate genomes. GMSC contains 965 million non-redundant smORFs with comprehensive annotations. We find that archaea harbor more smORFs proportionally than bacteria. We moreover provide a tool called GMSC-mapper to identify and annotate small proteins from microbial (meta)genomes. Overall, this publicly-available resource demonstrates the immense and underexplored diversity of small proteins.

The Amazon Basin is the largest hydrographic basin on the planet, and the dynamics of its aquatic microorganisms strongly impact global biogeochemical cycles. However, it remains poorly studied. This metagenome project was performed to obtain a snapshot of prokaryotic microbiota from four important lakes in the Amazon Basin.

Stingless bees of the genus Melipona, have long been considered an enigmatic case among social insects for their mode of caste determination, where in addition to larval food type and quantity, the genotype also has a saying, as proposed over 50 years ago by Warwick E. Kerr. Several attempts have since tried to test his Mendelian two-loci/two-alleles segregation hypothesis, but only recently a single gene crucial for sex determination in bees was evidenced to be sex-specifically spliced and also caste-specifically expressed in a Melipona species. Since alternative splicing is frequently associated with epigenetic marks, and the epigenetic status plays a major role in setting the caste phenotype in the honey bee, we investigated here epigenetic chromatin modification in the stingless bee Melipona scutellaris. We used an ELISA-based methodology to quantify global methylation status and western blot assays to reveal histone modifications. The results evidenced DNA methylation/demethylation events in larvae and pupae, and significant differences in histone methylation and phosphorylation between newly emerged adult queens and workers. The epigenetic dynamics seen in this stingless bee species represent a new facet in the caste determination process in Melipona bees and suggest a possible mechanism that is likely to link a genotype component to the larval diet and adult social behavior of these bees.