Search bioRxiv⌕ Search

Biology subjects

Gagnon, J. R.

Publications and source records attributed to Gagnon, J. R..

2 recordsLinked to original sources

Bioinformatic process for the identification and characterization of bacterial repeat-in-toxin adhesins

Gram-negative bacteria attach to host surfaces using ligand-binding domains (LBDs) at the distal tips of fibrillar RTX adhesins. Blocking the initial binding interaction(s) can potentially prevent colonization and subsequent biofilm formation and infection. To this end, adhesins must be identified, and it is also essential to determine the dominant adhesin type for those species that have more than one. RTX adhesins are frequently the largest proteins within each species (ranging from 1500 to 15,000 aa) and are often misannotated as incomplete/pseudogene products because their highly repetitive nature confounds genome assemblies from short-read technologies. Our bioinformatic process collates predicted proteins from long-read assemblies, which are then clustered based on the similarity of their C-terminal regions where the LBDs are typically located. RTX adhesins are identified by their length and domain structure and are modelled using AlphaFold3. An exhaustive search of multiple strains from seven species revealed a total of 35 different RTX adhesins that map to 16 different loci, with differing arrangements of LBDs that include putative carbohydrate-binding modules and von Willebrand Factor A-like domains. Notably, similar adhesins are sometimes found in multiple species, either by descent or through DNA uptake, and three species have an RTX adhesin of uncertain function because it lacks an obvious LBD. ImportanceMany bacteria initiate infection by reaching out with large, complex proteins called adhesins to attach themselves to a host cell. The DNA sequences of adhesins are difficult to read, due to their length and repetitiveness. This study leverages recent technological advances like "long-read sequencing" and structural modelling to identify and characterize the adhesins in seven species of harmful bacteria: Acinetobacter baumannii, Aeromonas hydrophila, Aeromonas salmonicida, Bordetella parapertussis, Legionella pneumophila, Vibrio parahaemolyticus, and Vibrio vulnificus. We identified thirty-five unique versions of adhesins and demonstrated their mix-and-match architecture. This research provides a foundation for strategies to block bacteria from binding surfaces, offering a vital alternative treatment as antibiotic resistance continues to rise.

biochemistry↗

Texture-Analysis Classification of Intraductal Carcinoma of the Prostate Using Label-Free Multimodal Nonlinear Optical Imaging

Intraductal carcinoma of the prostate (IDC-P) is a very aggressive histopathological subtype of prostate cancer (PCa) for which no accurate biomarkers exist. In our work, we apply a multimodal nonlinear optical imaging approach that uses second-harmonic generation (SHG) and stimulated Raman scattering (SRS) imaging to distinguish IDC-P from regular PCa and benign prostate tissue. Images from each tissue type were classified using support vector machine (SVM). The technique classified the images from each region based on first-order statistics and texture-based second-order statistics derived from the gray-level co-occurrence matrix (GLCM) of the images. Our results demonstrate that SVM models trained on either SHG or SRS images accurately classify IDC-P as well as high-grade PCa, low-grade PCa, and benign tissue with a mean classification accuracy of over 89%. Furthermore, a classification model combining both SHG and SRS imaging modalities can accurately classify all tissue types with a mean classification accuracy of 98%.

pathology↗