Finding microbial composition and biological processes as predictive signature to access the ongoing status of mangrove preservation.

Mangroves are complex land-sea transition ecosystems whose microbiota are essential for their nutrient recycling and conservation. Brazil is the third-largest estuarine area in the world and "Baía de Todos os Santos" (BTS) is one of the largest bays of the country, with wide anthropogenic exploration. Using a metagenomic approach, we investigated composition and functional adaptability as signatures of the microbiome of pristine and anthropized areas of BTS, including those under petroleum refinery influence. The taxonomic analysis showed dominance of sulfate-reducing Desulfobacteraceae, Rhodobacteraceae, and Flavobacteriaceae. Taxa were significantly diverse between pristine and disturbed areas. Disturbed mangroves showed a notary increase in abundance of halophilic, sulfur-related, and hydrocarbon-degrading genera and a decrease in diatoms compared to pristine area. The metabolic profile of BTS mangroves was correlated with the differentially abundant microbiota. Two ecological scenarios were observed: one marked by functions of central metabolism associated with biomass degradation and another by mechanisms of microbial adaptability to pollution conditions and environmental degradation. Part of the microbiome was distinct and not abundant in Brazilian estuarine soils. The microbiome signature observed in each BTS mangrove reflects how human actions impact the diversity of these ecosystems and also emphasize their role in attempting to restore disturbed mangroves. The microbiome may act as a potential biological indicator of the preservation status of these soils, despite the limitation of soil property conditions. Additionally, our data pointed to metagenomics as an additional tool for environmental assessment and reinforced the need for protective measures for the mangroves under study.

Exploring the Bacteriome and Resistome of Humans and Food-Producing Animals in Brazil.

The epidemiology of antimicrobial resistance (AMR) is complex, with multiple interfaces (human-animal-environment). In this context, One Health surveillance is essential for understanding the distribution of microorganisms and antimicrobial resistance genes (ARGs). This report describes a multicentric study undertaken to evaluate the bacterial communities and resistomes of food-producing animals (cattle, poultry, and swine) and healthy humans sampled simultaneously from five Brazilian regions. Metagenomic analysis showed that a total of 21,029 unique species were identified in 107 rectal swabs collected from distinct hosts, the highest numbers of which belonged to the domain Bacteria, mainly Ruminiclostridium spp. and Bacteroides spp., and the order Enterobacterales. We detected 405 ARGs for 12 distinct antimicrobial classes. Genes encoding antibiotic-modifying enzymes were the most frequent, followed by genes related to target alteration and efflux systems. Interestingly, carbapenemase-encoding genes such as blaAIM-1, blaCAM-1, blaGIM-2, and blaHMB-1 were identified in distinct hosts. Our results revealed that, in general, the bacterial communities from humans were present in isolated clusters, except for the Northeastern region, where an overlap of the bacterial species from humans and food-producing animals was observed. Additionally, a large resistome was observed among all analyzed hosts, with emphasis on the presence of carbapenemase-encoding genes not previously reported in Latin America. IMPORTANCE Humans and food production animals have been reported to be important reservoirs of antimicrobial resistance (AMR) genes (ARGs). The frequency of these multidrug-resistant (MDR) bacteria tends to be higher in low- and middle-income countries (LMICs), due mainly to a lack of public health policies. Although studies on AMR in humans or animals have been carried out in Brazil, this is the first multicenter study that simultaneously collected rectal swabs from humans and food-producing animals for metagenomics. Our results indicate high microbial diversity among all analyzed hosts, and several ARGs for different antimicrobial classes were also found. As far as we know, we have detected for the first time ARGs encoding carbapenemases, such as blaAIM-1, blaCAM-1, blaGIM-2, and blaHMB-1, in Latin America. Thus, our results support the importance of metagenomics as a tool to track the colonization of food-producing animals and humans by antimicrobial-resistant bacteria. In addition, a network surveillance system called GUARANI, created for this study, is ready to be expanded and to collect additional data.

Large Scale Genome-Centric Metagenomic Data from the Gut Microbiome of Food-Producing Animals and Humans.

The One Health concept is a global strategy to study the relationship between human and animal health and the transfer of pathogenic and non-pathogenic species between these systems. However, to the best of our knowledge, no data based on One Health genome-centric metagenomics are available in public repositories. Here, we present a dataset based on a pilot-study of 2,915 metagenome-assembled genomes (MAGs) of 107 samples from the human (N = 34), cattle (N = 28), swine (N = 15) and poultry (N = 30) gut microbiomes. Samples were collected from the five Brazilian geographical regions. Of the draft genomes, 1,273 were high-quality drafts (≥90% of completeness and ≤5% of contamination), and 1,642 were medium-quality drafts (≥50% of completeness and ≤10% of contamination). Taxonomic predictions were based on the alignment and concatenation of single-marker genes, and the most representative phyla were Bacteroidota, Firmicutes, and Proteobacteria. Many of these species represent potential pathogens that have already been described or potential new families, genera, and species with potential biotechnological applications. Analyses of this dataset will highlight discoveries about the ecology and functional role of pathogens and uncultivated Archaea and Bacteria from food-producing animals and humans. Furthermore, it also represents an opportunity to describe new species from underrepresented taxonomic groups.

Genome-centric metagenomics reveals insights into the evolution and metabolism of a new free-living group in Rhizobiales.

BACKGROUND: The Rhizobiales (Proteobacteria) order is an abundant and diverse group of microorganisms, being extensively studied for its lifestyle based on the association with plants, animals, and humans. New studies have demonstrated that the last common ancestor (LCA) of Rhizobiales had a free-living lifestyle, but the phylogenetic and metabolism characterization of basal lineages remains unclear. Here, we used a high-resolution phylogenomic approach to test the monophyly of the Aestuariivirgaceae family, a new taxonomic group of Rhizobiales. Furthermore, a deep metabolic investigation provided an overview of the main functional traits that can be associated with its lifestyle. We hypothesized that the presence of pathways (e.g., Glycolysis/Gluconeogenesis) and the absence of pathogenic genes would be associated with a free-living lifestyle in Aestuariivirgaceae. RESULTS: Using high-resolution phylogenomics approaches, our results revealed a clear separation of Aestuariivirgaceae into a distinct clade of other Rhizobiales family, suggesting a basal split early group and corroborate the monophyly of this group. A deep functional annotation indicated a metabolic versatility, which includes putative genes related to sugar degradation and aerobic respiration. Furthermore, many of these traits could reflect a basal metabolism and adaptations of Rhizobiales, as such the presence of Glycolysis/Gluconeogenesis pathway and the absence of pathogenicity genes, suggesting a free-living lifestyle in the Aestuariivirgaceae members. CONCLUSIONS: Aestuariivirgaceae (Rhizobiales) family is a monophyletic taxon of the Rhizobiales with a free-living lifestyle and a versatile metabolism that allows these microorganisms to survive in the most diverse microbiomes, demonstrating their adaptability to living in systems with different conditions, such as extremely cold environments to tropical rivers.

Analysis of the protease sequences of HIV-1 infected individuals after Indinavir monotherapy.

BACKGROUND: Protease inhibitors (PI) are an important HIV-1 treatment tool. The HIV-1 genetic diversity as a result of antiretroviral exposure is a potential barrier to successful antiretroviral therapy. OBJECTIVES: To describe the impact of the selective pressure of the PI Indinavir in the protease region of the pol gene of HIV-1. STUDY DESIGN: We have examined the extent of protease sequence heterogeneity in previously antiretroviral drug naive HIV-1 infected individuals receiving Indinavir as monotherapy for at least 48 weeks. RESULTS: Analysis based on the consensus of this group of sequences showed regions with higher and lower polymorphism. The degree of genetic variation was greater in regions less critical for the structure and function of the enzyme. To investigate the selective pressure imposed by drug therapy, we have analyzed the rate of synonymous (ds) and nonsynonymous (dn) substitutions. The three critical regions for enzyme activity showed ds/dn ratio >1. whereas other regions had ds/dn ratio <1. The detected amino acid mutations had a trend to be conservative, thus maintaining the physical chemical amino acid characteristics. Phylogenetic analysis established the presence of subtype B (n=38), subtype F (n=9), and B/F recombinants within the protease region of pol gene (n=3). More prevalent detected mutations, thought to contribute to antiretroviral resistance, were L63P (42%), L10I (35%), M36I (30%), V82A/T/F (26%). CONCLUSIONS: A great deal of predicted cross-resistance between PIs was observed. Out of the 50 individuals, 34% were considered to have major mutations to Indinavir, and 66% had minor or no mutations to Indinavir. Viral loads were significantly higher among patients with major mutations, compared with patients minor/no mutations, although no differences in the CD4 counts were found. The viral load at baseline and nadir (week 4) was able to predict the group of individuals with greater chances of selecting drug resistance related mutations.