Biological nitrogen fixation (BNF) is a key process for the N input in agriculture, with outstanding economic and environmental benefits from the replacement of chemical fertilizers. However, not all symbioses are equally effective in fixing N₂, and a major example relies on the high contribution associated with the soybean (Glycine max), contrasting with the low rates reported with the common bean (Phaseolus vulgaris) crop worldwide. Understanding these differences represents a major challenge that can help to design strategies to increase the contribution of BNF, and next-generation sequencing (NGS) analyses of the nodule and root microbiomes may bring new insights to explain differential symbiotic performances. In this study, three treatments evaluated in non-sterile soil conditions were investigated in both legumes: (i) non-inoculated control; (ii) inoculated with host-compatible rhizobia; and (iii) co-inoculated with host-compatible rhizobia and Azospirillum brasilense. In the more efficient and specific symbiosis with soybean, Bradyrhizobium presented a high abundance in nodules, with further increases with inoculation. Contrarily, the abundance of the main Rhizobium symbiont was lower in common bean nodules and did not increase with inoculation, which may explain the often-reported lack of response of this legume to inoculation with elite strains. Co-inoculation with Azospirillum decreased the abundance of the host-compatible rhizobia in nodules, probably because of competitiveness among the species at the rhizosphere, but increased in root microbiomes. The results showed that several other bacteria compose the nodule microbiomes of both legumes, including nitrogen-fixing, growth-promoters, and biocontrol agents, whose contribution to plant growth deserves further investigation. Several genera of bacteria were detected in root microbiomes, and this microbial community might contribute to plant growth through a variety of microbial processes. However, massive inoculation with elite strains should be better investigated, as it may affect the root microbiome, verified by both relative abundance and diversity indices, that might impact the contribution of microbial processes to plant growth.

The global consumption of mineral fertilizers has increased in the last 60 years. However, these fertilizers can negatively affect the soil and the atmosphere. The application of soil amendments focusing on sustainable farming practices can reduce the effects of mineral fertilization. In this study, we investigated the effects of increasing the dose of a commercial amendment using poultry litter enriched with Bacillus (SMARTGRAN) in different types of soils in Brazil under microcosm conditions. These different types of soils were classified as Nitossolo, Argissolo arênico, Argissolo alumínico, Latossolo distroférrico, and Latossolo distrófico. The biological activity in the soil was quantified by measuring the enzymes arylsulfatase, beta-glucosidase, and acid phosphatase. Additionally, bacterial diversity was evaluated by amplicon sequencing of the 16S rRNA gene and conducting phylogenetic analyses of three types of soils, which were selected because of their occurrence and fertility profiles. The results showed an increase in those enzyme activities under all conditions. The results of the analysis of the bacterial community in Nitossolo, Argissolo arênico, and Latossolo distrófico soil types showed a direct relationship between the bacterial composition in the soil and the increase in the amendment dosage. The alpha diversity indices decreased considerably because some plant-growth-promoting bacteria, such as Bacillus, Massilia, Paenibacillus, and Rhizobium, increased in relative abundance. The results indicated that an organic amendment enriched with Bacillus had a beneficial effect on different types of soil in Brazil.

Background: Despite the surge in microbiome data acquisition, there is a limited availability of tools capable of effectively analyzing it and identifying correlations between taxonomic compositions and continuous environmental factors. Furthermore, existing tools also do not predict the environmental factors in new samples, underscoring the pressing need for innovative solutions to enhance our understanding of microbiome dynamics and fulfill the prediction gap. Here we introduce CODARFE, a novel tool for sparse compositional microbiome predictor selection and prediction of continuous environmental factors. Results: We tested CODARFE against 4 state-of-the-art tools in 2 experiments. First, CODARFE outperformed predictor selection in 21 of 24 databases in terms of correlation. Second, among all the tools, CODARFE achieved the highest number of previously identified bacteria linked to environmental factors for human data—that is, at least 7% more. We also tested CODARFE in a cross-study, using the same biome but under different external effects, using a model trained on 1 dataset to predict environmental factors on another dataset, achieving 11% of mean absolute percentage error. Finally, CODARFE is available in 5 formats, including a Windows version with a graphical interface, to installable source code for Linux servers and an embedded Jupyter notebook available at MGnify. Conclusions: Our findings underscore the robustness and broad applicability of CODARFE across diverse fields, even under varying experimental conditions. Additionally, the ability to predict outcomes in new samples allows for the generation of new insights in previously unexplored contexts, providing researchers with a versatile tool.

Despite the surge in data acquisition, there is a limited availability of tools capable of effectively analyzing microbiome data that identify correlations between taxonomic compositions and continuous environmental factors. Furthermore, existing tools also do not predict the environmental factors in new samples, underscoring the pressing need for innovative solutions to enhance our understanding of microbiome dynamics and fulfill the prediction gap. Here, we introduce CODARFE, a novel tool for sparse compositional microbiome-predictors selection and prediction of continuous environmental factors. We tested CODARFE against four state-of-the-art tools in two experiments. First, CODARFE outperformed predictor selection in 21 out of 24 databases in terms of correlation. Second, among all the tools, CODARFE achieved the highest number of previously identified bacteria linked to environmental factors for human data—that is, at least 7% more. We also tested CODARFE in a cross-study, using the same biome but under different external effects (e.g., ginseng field and cattle for arable soil, and HIV and crohn’s disease for human gut), using a model trained on one dataset to predict environmental factors on another dataset, achieving 11% of mean absolute percentage error. Finally, CODARFE is available in five formats, including a Windows version with a graphical interface, to installable source code for Linux servers and an embedded Jupyter notebook available at MGnify - https://github.com/alerpaschoal/CODARFE .

Complete and draft genome sequences of four phyllosphere-associated bacterial strains ( Microbacterium radiodurans , Brachybacterium rhamnosum , Sphingomonas citri , and Curtobacterium sp.) isolated from Mangifera indica leaves in polluted Medellín, Colombia, are presented. These resources enable future studies on plant-microbe interactions and phyllosphere microbial mediation of atmospheric pollutants under urban stress.