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Item Extensive bacteriocin gene shuffling in the Streptococcus bovis/Streptococcus equinus complex reveals gallocin D with activity against vancomycin resistant enterococci.(Springer Nature Limited, 2020-08-10) Hill D; O'Connor PM; Altermann E; Day L; Hill C; Stanton C; Ross RPStreptococcus gallolyticus LL009 produces gallocin D, a narrow spectrum two component bacteriocin with potent activity against vancomycin-resistant enterococci. Gallocin D is distinct from gallocin A, a separate two component bacteriocin produced by S. gallolyticus. Although the gene clusters encoding gallocin A and gallocin D have a high degree of gene synteny, the structural genes are highly variable and appear to have undergone gene shuffling with other streptococcal species. Gallocin D was analysed in laboratory-based experiments. The mature peptides are 3,343 ± 1 Da and 3,019 ± 1 Da and could be readily synthesized and display activity against a vancomycin resistant Enterococcus strain EC300 with a MIC value of 1.56 µM. Importantly, these bacteriocins could contribute to the ability of S. gallolyticus to colonize the colon where they have been associated with colorectal cancer.Item DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning(BioMed Central Ltd, 2023-12) Wu J; Liu B; Zhang J; Wang Z; Li JPURPOSE: Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. RESULTS: In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.Item Mining the 99 Lives Cat Genome Sequencing Consortium database implicates genes and variants for the Ticked locus in domestic cats (Felis catus)(John Wiley and Sons Ltd on behalf of Stichting International Foundation for Animal Genetics, 2021-06) Lyons LA; Buckley RM; Harvey RJ; 99 Lives Cat Genome ConsortiumTabby patterns of fur coats are defining characteristics in wild and domestic felids. Historically, three autosomal alleles at one locus (Tabby): Abyssinian (Ta ; a.k.a. ticked), mackerel (Tm ; a.k.a. striped) and blotched (tb ; a.k.a. classic, blotched) were thought to control these patterns in domestic cats and their breeds. Currently, at least three loci influence cat tabby markings, two of which are designated Tabby and Ticked. The Tabby locus is laeverin (LVRN) and affects the mackerel and blotched patterns. The unidentified gene for the Ticked locus on cat chromosome B1 was suggested to control the presence or absence of the ticked pattern (Tabby - Abyssinian (Ta ; a.k.a. ticked). The cat reference genome (Cinnamon, the Abyssinian) has the ticked phenotype and the variant dataset and coat phenotypes from the 99 Lives Cat Genome Consortium (195 cats) were used to identify candidate genes and variants associated with the Ticked locus. Two strategies were used to find the Ticked allele(s), one considered Cinnamon with the reference allele or heterozygous (Strategy A) and the other considered Cinnamon as having the variant allele or heterozygous (Strategy B). For Strategy A, two variants in Dickkopf Wnt Signaling Pathway Inhibitor 4 (DKK4), a p.Cys63Tyr (B1:41621481, c.188G>A) and a less common p.Ala18Val (B1:42620835, c.53C>T) variant are suggested as two alleles influencing the Ticked phenotype. Bioinformatic and molecular modeling analysis suggests that these changes disrupt a key disulfide bond in the Dkk4 cysteine-rich domain 1 or Dkk4 signal peptide cleavage respectively. All coding variants were excluded as Ticked alleles using Strategy B.