Construction of a murine model of latent infection and reactivation induced by Talaromyces marneffei.

OBJECTIVE: To establish a murine model of Talaromyces marneffei (T. marneffei) latent infection and reactivation, providing a foundation for exploring the molecular mechanisms underlying disease relapse. METHODS: BALB/c mice were tail vein injected with T. marneffei at 0 days post-infection (dpi) and treated with cyclophosphamide (CTX) intraperitoneally every four days, starting from 21 dpi or 42 dpi. Mice were observed for body weight changes, liver and spleen indices, histological characteristics of liver and spleen, fungal load detection in liver and spleen, and Mp1p qualitation in liver and spleen to assess T. marneffei infection severity. RESULTS: T. marneffei-infected mice exhibited a trend of initial weight loss followed by recovery and a subsequent decrease in weight after CTX injection throughout the observation period. Liver and spleen indices, as well as tissue damage, significantly increased during infection but later returned to normal levels, with a gradual rise observed after immunosuppression. Fungal load analysis revealed positive T. marneffei cultures in the liver and spleen at 7 dpi and 14 dpi, followed by negative T. marneffei cultures from 21 dpi until day 21 post-immunosuppression (42 dpi or 63 dpi); however, the spleen remained T. marneffei-cultured negative, consistent with the trend observed in Mp1p detection results. CONCLUSION: A latent infection and reactivation model of T. marneffei in mice was successfully established, with the liver likely serving as a key site for latent T. marneffei.

Application of deep learning algorithm in the recognition of cryptococcosis and talaromycosis skin lesions.

BACKGROUND: Cryptococcosis and talaromycosis are known as 'neglected epidemics' due to their high case fatality rates and low concern. Clinically, the skin lesions of the two fungal diseases are similar and easily misdiagnosed. Therefore, this study aims to develop an algorithm to identify cryptococcosis/talaromycosis skin lesions. METHODS: Skin images of tararomiasis and cryptococcosis were collected from published articles and augmented using the Python Imaging Library (PIL). Then, five deep artificial intelligence models, VGG19, MobileNet, InceptionV3, Incept ResNetV2 and DenseNet201, were developed based on the collected datasets using transfer learning technology. Finally, the performance of the models was evaluated using sensitivity, specificity, F1 score, accuracy, AUC and ROC curve. RESULTS: In total, 159 articles (79 for cryptococcosis and 80 for talaromycosis), including 101 cryptococcosis skin lesion images and 133 talaromycosis skin lesion images, were collected for further mode construction. Five methods showed good performance for prediction but did not yield satisfactory results for all cases. Among them, DenseNet201 performed best in the validation set, followed by InceptionV3. However, InceptionV3 showed the highest sensitivity, accuracy, F1 score and AUC values in the training set, followed by DenseNet201. The specificity of DenseNet201 in the training set is better than that of InceptionV3. CONCLUSIONS: DenseNet201 and InceptionV3 are equivalent to the optimal model in these conditions and can be used in clinical settings as decision support tools for the identification and classification of skin lesions of cryptococcus/talaromycosis.

The role and mechanisms of PD-L1 in immune evasion during Talaromyces marneffei infection.

Talaromycosis, caused by Talaromyces marneffei (T. marneffei), is a systemic fungal disease that involves dissemination throughout the body. The ability of T. marneffei to evade the immune system is considered a crucial factor in its persistent infection, although the specific mechanisms are not yet fully understood. This study aims to investigate the molecular mechanisms underlying the occurrence of latent T. marneffei infection and immune evasion. The gene expression profile analysis in T. marneffei-infected mouse revealed that Pd-l1 exhibited the highest correlation strength with other hub genes, with a median of 0.60 (IQR: 0.50-0.69). T. marneffei infection upregulated the expression of PD-1 and PD-L1 in PBMCs from HIV patients, which was also observed in the T. marneffei-infected mouse and macrophage models. Treatment with a PD-L1 inhibitor significantly reduced fungal burden in the liver and spleen tissues of infected mice and in the kupffer-CTLL-2 co-culture system. PD-L1 inhibitor treatment increased CTLL-2 cell proliferation and downregulated the expression of PD-1, SHP-2, and p-SHP-2, indicating the activation of T cell viability and T cell receptor signaling pathway. Additionally, treatment with a PI3K inhibitor downregulated PD-L1 in T. marneffei-infected kupffer cells. Similar results were observed with treatment using the T. marneffei cell wall virulence factor ß-glucan. Overall, T. marneffei infection upregulated PD-L1 expression in HIV / T. marneffei patients, mice, and kupffer cells. Treatment with a PD-L1 inhibitor significantly reduced fungal burden, while activating T cell activity and proliferation, thereby promoting fungal clearance. Furthermore, the PI3K signaling pathway may be involved in the regulation of PD-L1 by T. marneffei.