Genomic annotation for vaccine target identification and immunoinformatics-guided multi-epitope-based vaccine design against Songling virus through screening its whole genome encoded proteins

Abstract

Songling virus (SGLV), a newly discovered tick-borne orthonairovirus, was recently identified in human spleen tissue. It exhibits cytopathic effects in human hepatoma cells and is associated with clinical symptoms including headache, fever, depression, fatigue, and dizziness, but no treatments or vaccines exist for this pathogenic virus. In the current study, immunoinformatics techniques were employed to identify potential vaccine targets within SGLV by comprehensively analyzing SGLV proteins. Four proteins were chosen based on specific thresholds to identify B-cell and T-cell epitopes, validated through IFN-γ epitopes. Six overlap MHC-I, MHC-II, and B cell epitopes were chosen to design a comprehensive vaccine candidate, ensuring 100% global coverage. These structures were paired with different adjuvants for broader protection against international strains. Vaccine constructions’ 3D models were high-quality and validated by structural analysis. After molecular docking, SGLV-V4 was selected for further research due to its lowest binding energy (-66.26 kcal/mol) and its suitable immunological and physiochemical properties. The vaccine gene is expressed significantly in E. coli bacteria through in silico cloning. Immunological research and MD simulations supported its molecular stability and robust immune response within the host cell. These findings can potentially be used in designing safer and more effective experimental SGLV-V4 vaccines.

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis

“…Though the associations between GC and T2D have been widely reported, the underlying genetic processes linking GC and T2D are still unknown. Fortunately, genetic exploration of disease-disease interaction has been possible through advances in sequencing and bioinformatics (4,(13)(14)(15)(16). Meanwhile, with the continuous development of machine learning algorithms in medicine, numerous studies have applied them to the screening of feature biomarkers (17)(18)(19).…”

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“…Though the associations between GC and T2D have been widely reported, the underlying genetic processes linking GC and T2D are still unknown. Fortunately, genetic exploration of disease-disease interaction has been possible through advances in sequencing and bioinformatics (4,(13)(14)(15)(16). Meanwhile, with the continuous development of machine learning algorithms in medicine, numerous studies have applied them to the screening of feature biomarkers (17)(18)(19).…”