Novel Pseudomonas Species Prevent the Growth of the Phytopathogenic Fungus Aspergillus flavus.

In response to the escalating demand for sustainable agricultural methodologies, the utilization of microbial volatile organic compounds (VOCs) as antagonists against phytopathogens has emerged as a viable eco-friendly alternative. Microbial volatiles exhibit rapid diffusion rates, facilitating prompt chemical interactions. Moreover, microorganisms possess the capacity to emit volatiles constitutively, as well as in response to biological interactions and environmental stimuli. In addition to volatile compounds, these bacteria demonstrate the ability to produce soluble metabolites with antifungal properties, such as APE Vf, pyoverdin, and fragin. In this study, we identified two Pseudomonas strains (BJa3 and MCal1) capable of inhibiting the in vitro mycelial growth of the phytopathogenic fungus Aspergillus flavus, which serves as the causal agent of diseases in sugarcane and maize. Utilizing GC/MS analysis, we detected 47 distinct VOCs which were produced by these bacterial strains. Notably, certain volatile compounds, including 1-heptoxydecane and tridecan-2-one, emerged as primary candidates for inhibiting fungal growth. These compounds belong to essential chemical classes previously documented for their antifungal activity, while others represent novel molecules. Furthermore, examination via confocal microscopy unveiled significant morphological alterations, particularly in the cell wall, of mycelia exposed to VOCs emitted by both Pseudomonas species. These findings underscore the potential of the identified BJa3 and MCal1 Pseudomonas strains as promising agents for fungal biocontrol in agricultural crops.

Secretome Analysis of Thermothelomyces thermophilus LMBC 162 Cultivated with Tamarindus indica Seeds Reveals CAZymes for Degradation of Lignocellulosic Biomass.

The analysis of the secretome allows us to identify the proteins, especially carbohydrate-active enzymes (CAZymes), secreted by different microorganisms cultivated under different conditions. The CAZymes are divided into five classes containing different protein families. Thermothelomyces thermophilus is a thermophilic ascomycete, a source of many glycoside hydrolases and oxidative enzymes that aid in the breakdown of lignocellulosic materials. The secretome analysis of T. thermophilus LMBC 162 cultivated with submerged fermentation using tamarind seeds as a carbon source revealed 79 proteins distributed between the five diverse classes of CAZymes: 5.55% auxiliary activity (AAs); 2.58% carbohydrate esterases (CEs); 20.58% polysaccharide lyases (PLs); and 71.29% glycoside hydrolases (GHs). In the identified GH families, 54.97% are cellulolytic, 16.27% are hemicellulolytic, and 0.05 are classified as other. Furthermore, 48.74% of CAZymes have carbohydrate-binding modules (CBMs). Observing the relative abundance, it is possible to state that only thirteen proteins comprise 92.19% of the identified proteins secreted and are probably the main proteins responsible for the efficient degradation of the bulk of the biomass: cellulose, hemicellulose, and pectin.

Improving annotation propagation on molecular networks through random walks: introducing ChemWalker.

MOTIVATION: Annotation of the mass signals is still the biggest bottleneck for the untargeted mass spectrometry analysis of complex mixtures. Molecular networks are being increasingly adopted by the mass spectrometry community as a tool to annotate large-scale experiments. We have previously shown that the process of propagating annotations from spectral library matches on molecular networks can be automated using Network Annotation Propagation (NAP). One of the limitations of NAP is that the information for the spectral matches is only propagated locally, to the first neighbor of a spectral match. Here, we show that annotation propagation can be expanded to nodes not directly connected to spectral matches using random walks on graphs, introducing the ChemWalker python library. RESULTS: Similarly to NAP, ChemWalker relies on combinatorial in silico fragmentation results, performed by MetFrag, searching biologically relevant databases. Departing from the combination of a spectral network and the structural similarity among candidate structures, we have used MetFusion Scoring function to create a weight function, producing a weighted graph. This graph was subsequently used by the random walk to calculate the probability of 'walking' through a set of candidates, departing from seed nodes (represented by spectral library matches). This approach allowed the information propagation to nodes not directly connected to the spectral library match. Compared with NAP, ChemWalker has a series of improvements, on running time, scalability and maintainability and is available as a standalone python package. AVAILABILITY AND IMPLEMENTATION: ChemWalker is freely available at https://github.com/computational-chemical-biology/ChemWalker. CONTACT: ridasilva@usp.br. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Combining Functional Genomics and Whole-Genome Sequencing to Detect Antibiotic Resistance Genes in Bacterial Strains Co-Occurring Simultaneously in a Brazilian Hospital.

(1) Background: The rise of multi-antibiotic resistant bacteria represents an emergent threat to human health. Here, we investigate antibiotic resistance mechanisms in bacteria of several species isolated from an intensive care unit in Brazil. (2) Methods: We used whole-genome analysis to identify antibiotic resistance genes (ARGs) and plasmids in 34 strains of Gram-negative and Gram-positive bacteria, providing the first genomic description of Morganella morganii and Ralstonia mannitolilytica clinical isolates from South America. (3) Results: We identified a high abundance of beta-lactamase genes in resistant organisms, including seven extended-spectrum beta-lactamases (OXA-1, OXA-10, CTX-M-1, KPC, TEM, HYDRO, BLP) shared between organisms from different species. Additionally, we identified several ARG-carrying plasmids indicating the potential for a fast transmission of resistance mechanism between bacterial strains. Furthermore, we uncovered two pairs of (near) identical plasmids exhibiting multi-drug resistance. Finally, since many highly resistant strains carry several different ARGs, we used functional genomics to investigate which of them were indeed functional. In this sense, for three bacterial strains (Escherichia coli, Klebsiella pneumoniae, and M. morganii), we identified six beta-lactamase genes out of 15 predicted in silico as those mainly responsible for the resistance mechanisms observed, corroborating the existence of redundant resistance mechanisms in these organisms. (4) Conclusions: Systematic studies similar to the one presented here should help to prevent outbreaks of novel multidrug-resistant bacteria in healthcare facilities.

Boundaries in metagenomic screenings using lacZα-based vectors.

Metagenomics approaches have been of high relevance for providing enzymes used in diverse industrial applications. In the current study, we have focused on the prospection of protease and glycosyl hydrolase activities from a soil sample by using the lacZα -based plasmid pSEVA232. For this, we used a functional screen based on skimmed milk agar and a pH indicator dye for detection of both enzymes, as previously reported in literature. Although we effectively identified positive clones in the screenings, subsequent experiments revealed that this phenotype was not because of the hydrolytic activity encoded in the metagenomic fragments, but rather due to the insertion of small metagenomic DNA fragments in frame within the coding region of the lacZ gene present in the original vector. Analyses of the thermodynamic stability of mRNA secondary structures indicated that recovering of positive clones was probably due to higher expression levels of the chimeric lacZα-genes in respect to the original from empty vector. We concluded that this method has a higher tendency for recovery false positive clones, when used in combination with a lacZα-based vector. As these vectors are massively used in functional metagenomic screenings, we highlight the importance of reporting boundaries in established metagenomic screenings methodologies.

Genetically Engineered Proteins to Improve Biomass Conversion: New Advances and Challenges for Tailoring Biocatalysts.

Protein engineering emerged as a powerful approach to generate more robust and efficient biocatalysts for bio-based economy applications, an alternative to ecologically toxic chemistries that rely on petroleum. On the quest for environmentally friendly technologies, sustainable and low-cost resources such as lignocellulosic plant-derived biomass are being used for the production of biofuels and fine chemicals. Since most of the enzymes used in the biorefinery industry act in suboptimal conditions, modification of their catalytic properties through protein rational design and in vitro evolution techniques allows the improvement of enzymatic parameters such as specificity, activity, efficiency, secretability, and stability, leading to better yields in the production lines. This review focuses on the current application of protein engineering techniques for improving the catalytic performance of enzymes used to break down lignocellulosic polymers. We discuss the use of both classical and modern methods reported in the literature in the last five years that allowed the boosting of biocatalysts for biomass degradation.

Metagenomic Approaches for Understanding New Concepts in Microbial Science.

Over the past thirty years, since the dawn of metagenomic studies, a completely new (micro) universe was revealed, with the potential to have profound impacts on many aspects of the society. Remarkably, the study of human microbiome provided a new perspective on a myriad of human traits previously regarded as solely (epi-) genetically encoded, such as disease susceptibility, immunological response, and social and nutritional behaviors. In this context, metagenomics has established a powerful framework for understanding the intricate connections between human societies and microbial communities, ultimately allowing for the optimization of both human health and productivity. Thus, we have shifted from the old concept of microbes as harmful organisms to a broader panorama, in which the signal of the relationship between humans and microbes is flexible and directly dependent on our own decisions and practices. In parallel, metagenomics has also been playing a major role in the prospection of "hidden" genetic features and the development of biotechnological applications, through the discovery of novel genes, enzymes, pathways, and bioactive molecules with completely new or improved biochemical functions. Therefore, this review highlights the major milestones over the last three decades of metagenomics, providing insights into both its potentialities and current challenges.