Célio Dias Santos Júnior

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

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.

Several genetic breeding programs contributed to the development of soybean cultivars with high yield and adapted to different Brazilian edaphoclimatic conditions. However, the continuous progress of genetic breeding of this specie depends on the genetic variability and application of genetic parameters informations which helps a more efficient selection process.There are many multivariated technical approaches to study the variability among soybean groups, such as dissimilarity measures, cluster analysis, principal components and canonical variables. The heritability estimation, genetic gain and genetic correlations are important parameters which permit the breeder to choose the best improvement strategy. Parâmetros genéticos e variabilidade em genótipos de sojaVários programas de melhoramento genético contribuíram para o desenvolvimento de cultivaresde soja com alto rendimento e adaptados às diferentes condições edafoclimáticas Brazileiras. Noentanto, o progresso contínuo de melhoramento genético desta espécie depende da variabilidadegenética e da aplicação de informações sobre parâmetros genéticos que corroborem com oprocesso. Há muitas abordagens técnicas multivariadas para estudar a variabilidade entre osgrupos de soja, tais como as medidas de dissimilaridade, análise de agrupamento, componentesprincipais e variáveis canônicas. A estimativa de herdabilidade, ganho genético e correlaçõesgenéticas são importantes parâmetros que permitem ao criador a escolhada melhor estratégia demelhoramento.

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 the Amazon 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 possible priming 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. Video abstract.

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.