Mycobacteria possess a uniquely complex cell envelope and rely on a diverse array of secretion systems to interact with their environment, ensure survival, and modulate host immune responses. This review provides a comprehensive overview of these secretion pathways, from the universally conserved Sec and Tat systems to the specialized ESX/type VII secretion systems, as well as lipid transporters of the MmpL family, with particular emphasis on Mycobacterium tuberculosis and other clinically relevant members of the M. tuberculosis complex and non-tuberculous mycobacteria. By integrating findings from historical literature and the most recent experimental and bioinformatic studies, we outline the genetic organization, structure, regulation, and functional interplay of these pathways. Emphasis is placed on how these systems are not isolated entities but form a highly interconnected network that coordinates protein and lipid export essential for virulence, immune modulation, and cell wall integrity. We also explore the translational potential of secreted effectors and their transport machineries, discussing their relevance as targets for therapeutic interventions, including novel inhibitors, diagnostic biomarkers, and vaccine candidates. We highlight critical knowledge gaps and propose avenues for future research, particularly those that leverage multidisciplinary approaches. By drawing connections across secretion systems and emphasizing their shared and distinct roles, this work aims to provide an integrated framework that supports both fundamental understanding and biomedical innovation in mycobacterial pathogenesis.

Rat hepatitis E virus genotype 1 (rHEV) is an emerging zoonotic pathogen found globally in commensal rodents and is a significant cause of hepatitis, especially in immunocompromised populations. We systematically analyzed 99 rHEV genomes and identified multiple insertions and deletions predominantly within the macro X domain of ORF1, including a recurrent deletion of a 7−39 amino acid region in a large cluster of subtype II.b. Significant homology to human gene fragments was detected in 14% of genomes, including sequences related to transcription factors and phosphatases. This marks the first evidence of host genome-derived gene insertions in rHEV, expanding the understanding of rHEV genome plasticity, and highlights the need for further functional studies to elucidate the role of these variants in viral pathogenesis and zoonotic adaptation.

Synthetic lethality (SL) is a genetic interaction that refers to the phenomenon of cell death caused by the simultaneous inactivation of two non-lethal genes. Due to high-cost constraints and time consumption of experimental screening, computational prediction methods have become the main research tool. Currently, methods based on machine learning have been widely used in SL research, and discovering effective features to enhance the accuracy of predictions remains the key challenge to overcome in current research. We propose an SL prediction method based on graph embedding. First, we transformed five types of raw omics data into graph structures to capture the complex associations among genes. Then, using the graph embedding technique, we extracted feature information for each gene and constructed the feature representation of SL pairs by mathematical operations. Finally, different from GNN, which infers a single graph, we used the machine learning classifiers to discriminate positive and negative samples. Our method achieved better AUC than GNN-based baseline methods. Overall, this study firstly proposed a prediction model for Escherichia coli (E. coli) SLs that integrates the advantages of graph embedding techniques and classifier ensembles, which significantly improves the accuracy and reliability of prediction, and also provides new perspectives and methods for this field.