Virulencefactors (VFs) are key molecules that enable pathogens to evade the immune systems of the host. These factors are crucial for revealing the pathogenic processes of microbes and drug discovery. Identification of virulence factors in microbes become an important problem in the field of bioinformatics. To address this problem, this study proposes a novel model DTVF (Deep Transfer Learning for Virulence Factor Prediction), which integrates the ProtT5 protein sequence extraction model with a dual-channel deep learning model. In the dual-channel deep learning model, we innovatively integrate long short-term memory (LSTM) with convolutional neural networks (CNNs), creating a novel integrated architecture. Furthermore, by incorporating the attention mechanism, the accuracy of VF detection was significantly enhanced. We evaluated the DTVF model against other excellent-performing models in the field. DTVF demonstrates superior performance, achieving an accuracy rate of 84.55% and an AUROC of 92.08% on the benchmark dataset. DTVF shows state-of-the-art performance in this field, surpassing the existing models in nearly all metrics. To facilitate the use of biologists, we have also developed an interactive web-based user interface version of DTVF based on Gradio.

Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures. Currently, the associations among skin microbiota, circulating metabolites, and epilepsy are still not well studied. In this study, we applied univariate and two-step Mendelian randomization analysis using single nucleotide polymorphisms as instrumental variables to analyze the possible associations. Five skin microbiota (asv022, asv008, bacillales, clostridiale, and micrococcaceae) and four circulating metabolites were found to be associated with epilepsy. Our results also showed that leucine level (p-value = 0.0487, OR = 1.067) and glutamine level (p-value = 0.0372, OR = 1.065) show a slight increased association with epilepsy, while phospholipids in medium LDL (p-value = 0.0302, OR = 0.9308) and ratio of saturated fatty acids (p-value = 0.0309, OR = 0.9017) appear to show a slight inverse correlation with epilepsy. In addition, the heterogeneity test and horizontal pleiotropy analysis indicated these results are robust in different situations. By mapping analysis, 54 genes were associated with at least two metabolites. By functional analysis, these genes were enriched in the immune-related pathways, which may have systemic effects on brain function. Our results provide a novel insight for understanding possible mechanisms of some epilepsy associations, which by further study might provide avenues for therapy research.

Wuchereria bancrofti is a parasite transmitted by mosquitoes and can cause a neglected tropical disease called Lymphatic filariasis. However, the genome of W. bancrofti was not well studied, making novel drug development difficult. This study aims to identify microRNA, annotate protein function, and explore the pathogenic mechanism of W. bancrofti by genome-wide analysis. Novel miRNAs were identified by analysis of expressed sequence tags (ESTs) from this parasite. Protein homology was obtained by a bidirectional best-hit strategy using BLAST. By an EST-based method, we identified 20 novel miRNAs in the genome. The AU content of these miRNAs ranged from 39.7% to 80.0%, with a mean of 52.9%. Among them, 14 miRNA homologs were present in mammal genomes, while six miRNA homologs were present in non-mammal genomes. By conducting a detailed sequence alignment using BLAST, we have successfully annotated the functions of 75 previously unannotated proteins, enhancing our understanding of the proteome and potentially revealing new targets for therapy. Homology distribution analysis indicated that a set of critical proteins were present in parasites and mosquitoes, but not present in mammals. By searching the literature, ten proteins were found to be involved in the pathogenic infection process of W. bancrofti. In addition, the miRNA–gene network analysis indicated that two pathogenic genes (CALR and HMGB2) are regulated by newly identified miRNAs. These genes were supposed to play key roles in the infection mechanism of W. bancrofti. In conclusion, our genome-wide analysis provided new clues for the prevention and treatment of W. bancrofti infection.