Biology subjects

da Silva, A. M.

Publications and source records attributed to da Silva, A. M..

2 recordsLinked to original sources

bioRxiv2021

Controlled spatial organization of bacterial clusters reveals cell filamentation is vital for Xylella fastidiosa biofilm formation

The morphological plasticity of bacteria to form filamentous cells commonly represents an adaptive strategy induced by stresses. In contrast, for diverse pathogens filamentous cells have been observed during biofilm formation, with function yet to be elucidated. To identify prior hypothesized quorum sensing as trigger of such cell morphogenesis, spatially controlled cell adhesion is pivotal. Here, we demonstrate highly-selective cell adhesion of the biofilm-forming phytopathogen Xylella fastidiosa to gold-patterned SiO2 substrates with well-defined geometries and dimensions. The consequent control of both cell density and distances between cell clusters using these patterns provided evidence of quorum sensing governing filamentous cell formation. While cell morphogenesis is induced by cell cluster density, filamentous cell growth is oriented towards neighboring cell clusters and distance-dependent; large interconnected cell clusters create the early biofilm structural framework. Together, our findings and investigative platform could facilitate therapeutic developments targeting biofilm formation mechanisms of X. fastidiosa and other pathogens.

microbiology↗

bioRxiv2020

vHULK, a new tool for bacteriophage host prediction based on annotated genomic features and deep neural networks

The experimental determination of a bacteriophage host is a laborious procedure. For this reason, there is a pressing need for reliable computational predictions of bacteriophage hosts in phage research in general and in phage therapy in particular. Here, we present a new program called vHULK for phage host prediction based on 9,504 phage genome features. These features take into account alignment significance scores between predicted-protein sequences in the phage genomes and a curated database of viral protein families. The features were fed to a deep neural network, and four distinct models were trained to predict 61 different host genera and 52 host species. In random controlled test sets, the program obtained 99% and 98% accuracy values at the genus and species levels, respectively. On a validation dataset with 2,178 phage genomes, mean accuracies were 82% and 52% at the genus and species levels, respectively. When compared against other phage host prediction programs on the same validation dataset, vHULK achieved substantially better performance, therefore demonstrating that the program is an advance on the state-of-art in phage host prediction. vHULK is freely available at https://github.com/LaboratorioBioinformatica/vHULK.

bioinformatics↗