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Bacterial type IV secretion systems (T4SSs) are versatile and membrane-spanning apparatuses, which mediate both genetic exchange and delivery of effector proteins to target eukaryotic cells. The secreted effectors (T4SEs) can affect gene expression and signal transduction of the host cells. As such, they often function as virulence factors and play an important role in bacterial pathogenesis. Nowadays, T4SE prediction tools have utilized various machine learning algorithms, but the accuracy and speed of these tools remain to be improved. In this study, we apply a sequence embedding strategy from a pre-trained language model of protein sequences (TAPE) to the classification task of T4SEs. The training dataset is mainly derived from our updated type IV secretion system database SecReT4 with newly experimentally verified T4SEs. An online web server termed T4SEfinder is developed using TAPE and a multi-layer perceptron (MLP) for T4SE prediction after a comprehensive performance comparison with several candidate models, which achieves a slightly higher level of accuracy than the existing prediction tools. It only takes about 3 minutes to make a classification for 5000 protein sequences by T4SEfinder so that the computational speed is qualified for whole genome-scale T4SEs detection in pathogenic bacteria. T4SEfinder might contribute to meet the increasing demands of re-annotating secretion systems and effector proteins in sequenced bacterial genomes. T4SEfinder is freely accessible at https://tool2-mml.sjtu.edu.cn/T4SEfinder_TAPE/. [ABSTRACT FROM AUTHOR]

Virulence plasmids of hypervirulent Klebsiella pneumoniae (hvKp) have the potential to transfer to drug-resistant strains or integrate with other plasmids, facilitating the genome evolution of threatening pathogens. We conducted an in-depth analysis of the publicly available 156 complete genome sequences of hvKp together with a multi-region clinical cohort of 171 hvKp strains from China to provide evidence for the virulence plasmid evolution. Virulence plasmids were frequently detected in the ST23 and ST11 K. pneumoniae strains. Multidrug-resistant hvKp (MDR-hvKp) occupied a large proportion of hvKp, and the coexistence of virulence and resistance plasmids may be the major cause. Virulence plasmids commonly possessed multiple replicons, of which IncFIBK was the most prevalent (84.6%). We identified 49 IncFIBK alleles among 583 IncFIBK plasmids, and they could be divided into Clades I, II, and III. We further observed that conjugative and non-conjugative virulence plasmids could be distinguished by IncFIBK genetic diversity, and IncFIBK subtyping could also indirectly indicate a chimeric preference of conjugative virulence plasmids. On this basis, we developed an open-access web tool called KpVR for IncFIBK subtyping. In conclusion, the genetic diversity of IncFIBK virulence plasmids could be used for tracking the evolution of virulence plasmids, and further preventing the emergence of MDR-hvKp strains. [ABSTRACT FROM AUTHOR]

Tn3 family transposons are a widespread group of replicative transposons, notorious for contributing to the dissemination of antibiotic resistance, particularly the global prevalence of carbapenem resistance. The transposase (TnpA) of these elements catalyzes DNA breakage and rejoining reactions required for transposition. However, the molecular mechanism for target site selection with these elements remains unclear. Here, we identify a QLxxLR motif in N-terminal of Tn3 TnpAs and demonstrate that this motif allows interaction between TnpA of Tn3 family transposon Tn1721 and the host β-sliding clamp (DnaN), the major processivity factor of the DNA replication machinery. The TnpA-DnaN interaction is essential for Tn1721 transposition. Our work unveils a mechanism whereby Tn3 family transposons can bias transposition into certain replisomes through an interaction with the host replication machinery. This study further expands the diversity of mobile elements that use interaction with the host replication machinery to bias integration., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Proteins secreted by Gram-negative bacteria are tightly linked to the virulence and adaptability of these microbes to environmental changes. Accurate identification of such secreted proteins can facilitate the investigations of infections and diseases caused by these bacterial pathogens. However, current bioinformatic methods for predicting bacterial secreted substrate proteins have limited computational efficiency and application scope on a genome-wide scale. Here, we propose a novel deep-learning-based framework-DeepSecE-for the simultaneous inference of multiple distinct groups of secreted proteins produced by Gram-negative bacteria. DeepSecE remarkably improves their classification from nonsecreted proteins using a pretrained protein language model and transformer, achieving a macro-average accuracy of 0.883 on 5-fold cross-validation. Performance benchmarking suggests that DeepSecE achieves competitive performance with the state-of-the-art binary predictors specialized for individual types of secreted substrates. The attention mechanism corroborates salient patterns and motifs at the N or C termini of the protein sequences. Using this pipeline, we further investigate the genome-wide prediction of novel secreted proteins and their taxonomic distribution across ~1,000 Gram-negative bacterial genomes. The present analysis demonstrates that DeepSecE has major potential for the discovery of disease-associated secreted proteins in a diverse range of Gram-negative bacteria. An online web server of DeepSecE is also publicly available to predict and explore various secreted substrate proteins via the input of bacterial genome sequences., Competing Interests: Competing Interests: The authors declare that they have no competing interests.