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Here, we report the draft genomic sequences of Bacillus amyloliquefaciens strain CBMAI 1301, isolated from soybean seeds, and Bacillus subtilis strain CBMAI 1302, isolated from soil. These strains have potential applications for the biological control of phytopathogens, and the sequencing of these two genomes could greatly benefit soybean cultivation.

Biosurfactants are surface-active molecules originated from renewable resources, which are produced by microbial fermentation or chemical/enzymatic catalysis. These molecules present important advantages as compared to petrochemical surfactants, given their resistance to extreme conditions, biodegradability, specificity, and environmental compatibility. Besides that, the high production costs hinder its commercialization. In this way, this article aimed to analyze microbial biosurfactants production, focusing on the optimization of metabolic pathways and production processes, to identify key aspects and provide alternatives to allow a cost-effective production at industrial scale. This was achieved by a broad analysis of biosurfactants properties, applications, and biosynthetic pathways (in terms of yield, cofactors, and energy), in addition to an assessment of production-associated costs. As a result of the present extensive data survey and analysis, key production aspects are disclosed. The metabolic pathway yield analysis demonstrated that production of biosurfactants can be significantly improved (highest theoretical yield was 0.47 gbiosurfactant /gsubstrate ) by the use of biomolecular engineering techniques to generate optimized synthetic pathways. With an alternative proposed pathway for surfactin, yield was improved and imbalance in cofactors and ATP was reduced. Analysis of productive costs indicated that to make rhamnolipids commercial production feasible, the main efforts should focus on lowering substrate costs as well as the identification of energy-efficient unit operations to lower electricity cost, since these parameters accounted for 19.36 and 78.22%, respectively, of the production costs. The data generated by this analysis highlight the need for multidisciplinary collaboration to make rhamnolipids economically feasible, including biomolecular engineering and process intensification.

The golden mussel (Limnoperna fortunei) is a freshwater bivalve that was introduced in South America almost 30 years ago, likely through ballast water of Asian ships. Since then, it has spread across the continent, causing both economic and environmental impacts. The study of the population structure of an invasive species may bring valuable insights towards understanding its pattern of dispersion, which in turn will help to create more effective management plans. Here, we have compared mussel populations from 5 different Brazilian reservoirs and tested for the presence of geographic genetic structure. In order to obtain a high number of single nucleotide variants (SNVs) at good cost-benefit, we have for the first time applied the double digest restriction-site associated DNA sequencing (ddRAD-seq) protocol for the golden mussel. The ddRAD-seq protocol allowed us to obtain 2046 SNVs, which were then used to assess population structure by applying three independent methodologies: Principal Component Analysis (PCA), Bayesian Clustering and Phylogenetic Tree. All methodologies have indicated absent geographic structure.

Natural compounds and derivatives play an essential role in the pharmaceutical industry, however, the difficulty in resynthesizing natural products or isolate them from the native host, often limit their availability, elevate costs and slow down the pharmaceutical manufacturing process. In this context, application of synthetic biology could enable the efficient production of large amounts of drugs or drug precursors in heterologous microorganisms aiming to accelerate the entire manufacturing process. Considering this perspective, here we developed a pipeline to automatically search for metabolites available in the metabolic space that are structurally similar to worldwide approved drugs. This pipeline involved the in silico screening of metabolites from a metabolic pathway meta-database using both Tanimoto coefficients based on Daylight like fingerprints and Maximum Common Substructure algorithm. The method was successfully applied to identify metabolites sharing essential scaffolds with one or more drugs as potential candidates for metabolic engineering. Three of these metabolites (Festuclavine, Scopolamine, and Baccatin III) were identified as similar to many drugs like Cabergoline, Oxitropium, Paclitaxel and had their metabolic pathways computationally mapped for their production in Saccharomyces cerevisiae with our proprietary pathway design software. These compounds are examples of new opportunities for the application of synthetic biology in pharmaceutical production.

Sugarcane is currently the economic basis of more than 100 countries, planted on over 25 million hectares worldwide. It has been considered one of the most promising crops for generating clean and renewable energy and is expected to become the second largest energy source in the world by 2030. The global production of this crop is continuously growing, mostly because of the rising consumption of sugar and ethanol. Increases in both crop area plantations and yield are occurring to meet the growing demand. As sugarcane plants establish associations with a great diversity of microorganisms during their life cycle, the use of beneficial fungi and bacteria as a complementary tool to improve plant yield has arisen as a powerful option to meet the current needs to increase productivity and sustainability. However, despite the economic importance of sugarcane, knowledge regarding the microbial community associated with this crop is still limited, as there is still a lack of information on the real diversity and roles of fungal and bacterial species. Current knowledge on sugarcane mycobiota has revealed Epicoccum, Trichoderma, and arbuscular mycorrhizae fungi as the main beneficial fungal agents with high potential for use as microbial-based biostimulants, leading to positive effects that include growth promotion, plant protection, stress resistance, and improved nutrient acquisition. The most studied plant growth-promoting bacteria (PGPB) associated with sugarcane include representatives of the genera Beijerinckia, Gluconacetobacter, Herbaspirillum, Burkholderia, and Azospirillum. The most significant effect obtained from the interaction of sugarcane with these PGPBs is the reduction of chemical nitrogen fertilizers, as these bacteria are able to convert atmospheric nitrogen into an available source, ammonium. Therefore, the use of microbial inoculants should be maximized in crop production, as there is strong evidence that sugarcane plants are able to grow more efficiently by establishing interactions with beneficial microorganisms. This chapter presents an overview on sugarcane production worldwide and gathers the main information about fungi and bacteria described as beneficial to sugarcane, as well as recent data on its complex microbiome.

This study aimed to perform a comparative analysis of the diversity of endophytic fungal communities isolated from the leaves and branches of Rhizophora mangle, Avicennia schaueriana and Laguncularia racemosa trees inhabiting two mangroves in the state of Sao Paulo, Brazil [Cananeia and Bertioga (oil spill-affected and unaffected)] in the summer and winter. Three hundred and forty-three fungi were identified by sequencing the ITS1-5.8S-ITS2 region of rDNA. Differences were observed in the frequencies of fungi isolated from the leaves and branches of these three different plant species sampled from the Bertioga oil spill-affected and the oil-unaffected mangrove sites in the summer and winter; these differences indicate a potential impact on fungal diversity in the study area due to the oil spill. The molecular identification of the fungi showed that the fungal community associated with these mangroves is composed of at least 34 different genera, the most frequent of which were Diaporthe, Colletotrichum, Fusarium, Trichoderma and Xylaria. The Shannon and the Chao1 indices [H'(95 %) = 4.00, H'(97 %) = 4.22, Chao1(95 %) = 204 and Chao1(97 %) = 603] indicated that the mangrove fungal community possesses a vast diversity and richness of endophytic fungi. The data generated in this study revealed a large reservoir of fungal genetic diversity inhabiting these Brazilian mangrove forests and highlighted substantial differences between the fungal communities associated with distinct plant tissues, plant species, impacted sites and sampling seasons.

The sugarcane root endophyte Trichoderma virens 223 holds enormous potential as a sustainable alternative to chemical pesticides in the control of sugarcane diseases. Its efficacy as a biocontrol agent is thought to be associated with its production of chitinase enzymes, including N-acetyl-s-D-glucosaminidases, chitobiosidases and endochitinases. We used targeted gene deletion and RNA-dependent gene silencing strategies to disrupt N-acetyl-s-D-glucosaminidase and endochitinase activities of the fungus, and to determine their roles in the biocontrol of soil-borne plant pathogens. The loss of N-acetyl-s-D-glucosaminidase activities was dispensable for biocontrol of the plurivorous damping-off pathogens Rhizoctonia solani and Sclerotinia sclerotiorum, and of the sugarcane pathogen Ceratocystis paradoxa, the causal agent of pineapple disease. Similarly, suppression of endochitinase activities had no effect on R. solani and S. sclerotiorum disease control, but had a pronounced effect on the ability of T. virens 223 to control pineapple disease. Our work demonstrates a critical requirement for T. virens 223 endochitinase activity in the biocontrol of C. paradoxa sugarcane disease, but not for general antagonism of other soil pathogens. This may reflect its lifestyle as a sugarcane root endophyte.

Several genome-scale metabolic reconstruction tools have been developed in the last decades. They have helped to reconstruct various metabolic models, which have contributed to a variety of fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a high level of bioinformatic skills, and most of them are not scalable for multiple genomes. Moreover, the functionalities required to build models are generally scattered through multiple tools, requiring knowledge of their utilization.Here, we present ChiMera, which combines the most efficient tools in model reconstruction, prediction, and visualization. ChiMera uses CarveMe top-down approach, based on genomic evidence, to prune a global model with a high level of curation, generating a draft genome able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules for metabolic network visualization. The first module generates maps for the most important pathways, e.g., core-metabolism, fatty acid oxidation and biosynthesis, nucleotides and amino acids biosynthesis, and glycolysis. The second module produces a genome-wide metabolic map, which can be used to retrieve KEGG pathway information for each compound in the model. A module to investigate gene essentiality and knockout is also present.Overall, ChiMera combines model creation, gap-filling, flux balance analysis (FBA), and metabolic network visualization to create a simulation-ready genome-scale model, helping genetic engineering projects, prediction of phenotypes, and other model-driven discoveries without requiring high-level of bioinformatic skills. Uma série de ferramentas de reconstrução metabólica em escala genômica foram desenvolvidas nas últimas décadas. Essas ferramentas auxiliaram na reconstrução de modelos metabólicos, que contribuíram para uma variedade de campos, por exemplo, engenharia genética, descoberta de drogas e previsão de fenótipos. No entanto, o uso desses programas requer um alto nível de habilidades em bioinformática, e a maioria deles não é escalável para múltiplos genomas. Além disso, as funcionalidades necessárias para a construção de modelos geralmente estão espalhadas por várias ferramentas, exigindo conhecimento de sua utilização. Aqui, apresentamos a ChiMera, que combina as ferramentas mais eficientes em reconstrução, predição e visualização de modelos. A ChiMera usa a abordagem top-down da ferramenta CarveMe, baseada em evidências genômicas, para editar um modelo global com alto nível de curadoria, gerando uma reconstrução preliminar capaz de produzir previsões de crescimento usando análise de fluxo de balanço de metabólitos, tanto para para bactérias gram-positivas como gram-negativas. A ChiMera também contém dois módulos para visualização da rede metabólica. O primeiro módulo gera mapas para as vias mais importantes, por exemplo, metabolismo central, oxidação e biossíntese de ácidos graxos, biossíntese de nucleotídeos e aminoácidos e glicólise. O segundo módulo produz um mapa metabólico de todo o genoma, que pode ser usado para recuperar informações das vias metabólicas usando informações do banco de dados KEGG para cada composto no modelo. Um módulo para investigar a essencialidade e nocaute gênico também está presente.No geral, a ChiMera combina criação de modelo, preenchimento de etapas nas vias metabólicas (gap-fill), análise de balanço de fluxo (FBA) e visualização de rede metabólica para criar um modelo em escala de genoma pronto para simulação, ajudando projetos de engenharia genética, predição de fenótipos e outras descobertas orientadas por modelos. Tudo isso sem exigir alto nível de habilidades de bioinformática.

Dentre os mecanismos relacionados à interação bactéria-planta, a biossíntese bacteriana de ácido indol acético (AIA) exerce um papel fundamental na promoção do crescimento vegetal, uma vez que é capaz de influenciar inúmeros processos fisiológicos nas plantas. Diferentes vias metabólicas são utilizadas pelas bactérias para a biossíntese do AIA, sendo a via do ácido indol-3-pirúvico (IPyA) a mais comumente descrita. Nesta via encontra-se o gene indol-3-piruvato descarboxilase (ipdC) com vital função na produção de AIA utilizando como precursor o aminoácido L-triptofano. Nesse contexto, estudos moleculares acerca das vias metabólicas e dos genes envolvidos nesse processo são preponderantes para o entendimento da inter-relação das vias regulatórias com a síntese do fitormônio. A rizobactéria Bacillus sp. (RZ2MS9) vem apresentando satisfatória atividade na promoção de crescimento vegetal. O sequenciamento do seu genoma apontou a presença de uma vasta gama de genes relacionados à promoção do crescimento, com destaque para genes codificadores de auxinas. Assim, o estudo teve por objetivo comprovar a função do gene ipdC na biossíntese do AIA pela via dpendente do L-triptofano através do nocaute sítio dirigido do gene ipdC na Rizobactéria Promotora do Crescimento em Plantas (RPCP) Bacillus sp. (RZ2MS9). Para tanto, foi realizado o nocaute sítio dirigido por meio da técnica de CRISPR-Cas9. O nocaute do gene ipdC foi eficiente, gerando mutantes disruptivos para o referido gene. A biossíntese do AIA pela linhagem ΔipdC apresentou reduções nas concentrações do fitormônio, de acordo com o tempo de crescimento, sendo 87,96% em 24 horas, 88,25% em 48 horas e 58,27% em 72 horas do crescimento em comparação à linhagem selvagem (WT). Além disso, a biossíntese do AIA na ausência do aminoácido L-triptofano também foi avaliada, não sendo constatada síntese do fitormônio em nenhum dos tempos crescimento, tanto na linhagem selvagem, quanto na linhagem ΔipdC. O presente estudo foi pioneiro no nocaute do gene ipdC em uma linhagem de Bacillus utilizando a técnica de CRISPR-Cas9. Os resultados obtidos contribuem para um melhor entendimento da influência do gene ipdC e da via IPyA na biossíntese do AIA pela linhagem RZ2MS9 e futuramente sera comprovado seu papel na promoção de crescimento vegetal. Among the mechanisms related to the bacterium-plant interaction, the bacterial biosynthesis of indole acetic acid (AIA) plays a fundamental role in the promotion of plant growth, since it is capable of influencing innumerable physiological processes in plants. Different metabolic pathways are used by bacteria for the biosynthesis of IAA, with the indole-3-pyruvic acid (IPyA) pathway being the most commonly described. In this pathway, the indole-3-pyruvate decarboxylase (ipdC) gene has a vital role in the production of IAA using the amino acid L-tryptophan as a precursor. In this context, molecular studies about the metabolic pathways and the genes involved in this process are preponderant for the understanding of the interrelationship of the regulatory pathways with the phytormonium synthesis. The rhizobacterium Bacillus sp. (RZ2MS9) has been showing satisfactory activity in promoting plant growth. The sequencing of its genome pointed to the presence of a wide range of genes related to growth promotion, especially genes encoding auxins. Thus, the objective of the present study was to verify the function of the ipdC gene in the IAA biosynthesis L-tryptophan dependent through the knockout of the ipdC in the plant growth-promoting rhizobateria (PGPR) Bacillus sp. (RZ2MS9). Therefore, the knockout was realized using the CRISPR-Cas9. The knockout of the ipdC gene was efficient, generating disruptive mutants for the said gene. IAA biosynthesis by the ΔipdC strain showed reductions in phytormonium concentrations, according to the growth time, being 87.96% in 24 hours, 88.25% in 48 hours and 58.27% in 72 hours of growth compared to the Wild Type (WT). In addition, the biosynthesis of IAA in the absence of the amino acid L-tryptophan was also evaluated, with no phytormonium synthesis being observed at any growth time, both in the wild type and ΔipdC strain. The present study pioneered the knockout of the ipdC gene in a Bacillus strain using the CRISPR-Cas9. The results obtained contribute to a better understanding of the influence of the ipdC gene and the IPyA pathway in the IAA biosynthesis through the RZ2MS9 strain, and its role in plant growth promoting will be demonstrated in the future.