Microbial interactions, which regulate the dynamics of eco- and agrosystems, can be harnessed to enhance antagonism against phytopathogenic fungi in agriculture. This study tests the hypothesis that plant growth-promoting rhizobacteria (PGPR) can also be potential biological control agents (BCAs). Antifungal activity assays against potentially phytopathogenic fungi were caried out using cultures and cell-free filtrates of nine PGPR strains previously isolated from agricultural soils. Cultures of Bacillus sp. BS36 inhibited the growth of Alternaria sp. AF12 and Fusarium sp. AF68 by 74 and 65%, respectively. Cell-free filtrates of the same strain also inhibited the growth of both fungi by 54 and 14%, respectively. Furthermore, the co-cultivation of Bacillus sp. BS36 with Pseudomonas sp. BS95 and the target fungi improved their antifungal activity. A subsequent metabolomic analysis using Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) identified fengycin- and surfactin-like lipopeptides (LPs) in the Bacillus sp. BS36 cell-free filtrates, which could explain their antifungal activity. The co-production of multiple families of LPs by Bacillus sp. BS36 is an interesting feature with potential practical applications. These results highlight the potential of the PGPR strain Bacillus sp. BS36 to work as a BCA and the need for more integrative approaches to develop biocontrol tools more accessible and adoptable by farmers.

Metabolomics aims to perform a comprehensive identification and quantification of the small molecules present in a biological system. Due to metabolite diversity in concentration, structure, and chemical characteristics, the use of high-resolution methodologies, such as mass spectrometry (MS) or nuclear magnetic resonance (NMR), is required. In metabolomics data analysis, suitable data pre-processing, and pre-treatment procedures are fundamental, with subsequent steps aiming at highlighting the significant biological variation between samples over background noise. Traditional data analysis focuses primarily on the comparison of the features’ intensity values. However, intensity data are highly variable between experimental batches, instruments, and pre-processing methods or parameters. The aim of this work was to develop a new pre-treatment method for MS-based metabolomics data, in the context of sample profiling and discrimination, considering only the occurrence of spectral features, encoding feature presence as 1 and absence as 0. This “Binary Simplification” encoding (BinSim) was used to transform several benchmark datasets before the application of clustering and classification methods. The performance of these methods after the BinSim pre-treatment was consistently as good as and often better than after different combinations of traditional, intensity-based, pre-treatments. Binary Simplification is, therefore, a viable pre-treatment procedure that effectively simplifies metabolomics data-analysis pipelines.