Environmental interactions are regulated by temperature in Burkholderia seminalis TC3.4.2R3.

Burkholderia seminalis strain TC3.4.2R3 is an endophytic bacterium isolated from sugarcane roots that produces antimicrobial compounds, facilitating its ability to act as a biocontrol agent against phytopathogenic bacteria. In this study, we investigated the thermoregulation of B. seminalis TC3.4.2R3 at 28 °C (environmental stimulus) and 37 °C (host-associated stimulus) at the transcriptional and phenotypic levels. The production of biofilms and exopolysaccharides such as capsular polysaccharides and the biocontrol of phytopathogenic fungi were enhanced at 28 °C. At 37 °C, several metabolic pathways were activated, particularly those implicated in energy production, stress responses and the biosynthesis of transporters. Motility, growth and virulence in the Galleria mellonella larvae infection model were more significant at 37 °C. Our data suggest that the regulation of capsule expression could be important in virulence against G. mellonella larvae at 37 °C. In contrast, B. seminalis TC3.4.2R3 failed to cause death in infected BALB/c mice, even at an infective dose of 107 CFU.mL-1. We conclude that temperature drives the regulation of gene expression in B. seminalis during its interactions with the environment.

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.

Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.