Ubiquitin-fusion degradation pathway: A new strategy for inducing CD8 cells specific for mycobacterial HSP65

A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network

Background: Pulmonary tuberculosis (PTB) is a chronic infectious disease and is the most common type of TB. Although the sputum smear test is a gold standard for diagnosing PTB, the method has numerous limitations, including low sensitivity, low specificity, and insufficient samples.

Methods: The present study aimed to identify specific biomarkers of PTB and construct a model for diagnosing PTB by combining random forest (RF) and artificial neural network (ANN) algorithms. Two publicly available cohorts of TB, namely, the GSE83456 (training) and GSE42834 (validation) cohorts, were retrieved from the Gene Expression Omnibus (GEO) database. A total of 45 and 61 differentially expressed genes (DEGs) were identified between the PTB and control samples, respectively, by screening the GSE83456 cohort. An RF classifier was used for identifying specific biomarkers, following which an ANN-based classification model was constructed for identifying PTB samples. The accuracy of the ANN model was validated using the receiver operating characteristic (ROC) curve. The proportion of 22 types of immunocytes in the PTB samples was measured using the CIBERSORT algorithm, and the correlations between the immunocytes were determined.

Results: Differential analysis revealed that 11 and 22 DEGs were upregulated and downregulated, respectively, and 11 biomarkers specific to PTB were identified by the RF classifier. The weights of these biomarkers were determined and an ANN-based classification model was subsequently constructed. The model exhibited outstanding performance, as revealed by the area under the curve (AUC), which was 1.000 for the training cohort. The AUC of the validation cohort was 0.946, which further confirmed the accuracy of the model.

Conclusion: Altogether, the present study successfully identified specific genetic biomarkers of PTB and constructed a highly accurate model for the diagnosis of PTB based on blood samples. The model developed herein can serve as a reliable reference for the early detection of PTB and provide novel perspectives into the pathogenesis of PTB.

Dendritic cell-based vaccination with lentiviral vectors encoding ubiquitinated hepatitis B core antigen enhances hepatitis B virus-specific immune responses in vivo

The activity of hepatitis B virus (HBV)-specific cytotoxic T lymphocytes (CTLs) plays a predominant role in the clearance of HBV. Dendritic cells (DCs) are key antigen-presenting cells and play an important role in the initiation of immune responses. We previously verified that lentiviral vector encoding ubiquitinated hepatitis B core antigen (LV-Ub-HBcAg) effectively transduced DCs to induce maturation, and the mature DCs efficiently induced T cell polarization to Th1 and generated HBcAg-specific CTLs ex vivo. In this study, HBV-specific immune responses of LV-Ub-HBcAg in BALB/c mice (H-2Kd) were evaluated. It was shown that direct injection of LV-Ub-HBcAg increased the production of cytokines IL-2 and IFN-γ, elicited strong antibody responses, and remarkably generated a high percentage of IFN-γ+CD8+ T cells with HBV-specific CTL responses in BALB/c mice. In addition, direct injection of LV-Ub-HBcAg induced potent anti-HBV immune responses, similar to those elicited by in vitro-transduced DCs. In conclusion, the DC-based therapeutic vaccine LV-Ub-HBcAg elicited specific antibody immune responses and induced robust specific CTL activity in vivo.