Leveraging explainable machine learning models to assess forest health: A case study in Hainan, China.

Global forest area has declined over the past few years, forest quality has declined, and ecological and environmental events have increased with climate change and human activity. In the context of ecological civilization, forest health issues have received unprecedented attention. By improving forest health, forests can better perform their ecosystem service functions and promote green development. This study was carried out in the WuZhi Shan area of Hainan Tropical Rainforest National Park. We employed a decision tree algorithm, a machine learning technique, for our modeling due to its high accuracy and interpretability. The objective weighted method using criteria of importance through intercriteria correlation (CRITIC) was used to determine forest health classes based on survey and experimental data from 132 forest samples. The results showed that species diversity is the most important metric to measure forest health. An interpretable decision tree machine learning model was proposed to incorporate forest health indicators, providing up to 90% accuracy in the classification of forest health conditions. The model demonstrated a high degree of effectiveness, achieving an average precision of 90%, a recall of 67%, and an F1 score of 70.2% in predicting forest health. The interpretable decision tree classification results showed that breast height diameter is the most important variable in classifying the health status of both primary and secondary forests. This study highlights the importance of using interpretable machine learning methods for the decision-making process. Our work contributes to the scientific underpinnings of sustainable forest development and effective conservation planning.

Oncolytic viruses engineered to enforce cholesterol efflux restore tumor-associated macrophage phagocytosis and anti-tumor immunity in glioblastoma.

The codependency of cholesterol metabolism sustains the malignant progression of glioblastoma (GBM) and effective therapeutics remain scarce. In orthotopic GBM models in male mice, we identify that codependent cholesterol metabolism in tumors induces phagocytic dysfunction in monocyte-derived tumor-associated macrophages (TAMs), resulting in disease progression. Manipulating cholesterol efflux with apolipoprotein A1 (ApoA1), a cholesterol reverse transporter, restores TAM phagocytosis and reactivates TAM-T cell antitumor immunity. Cholesterol metabolomics analysis of in vivo-sorted TAMs further reveals that ApoA1 mediates lipid-related metabolic remodeling and lowers 7-ketocholesterol levels, which directly inhibits tumor necrosis factor signaling in TAMs through mitochondrial translation inhibition. An ApoA1-armed oncolytic adenovirus is also developed, which restores antitumor immunity and elicits long-term tumor-specific immune surveillance. Our findings provide insight into the mechanisms by which cholesterol metabolism impairs antitumor immunity in GBM and offer an immunometabolic approach to target cholesterol disturbances in GBM.

An engineered oncolytic vaccinia virus encoding a single-chain variable fragment against TIGIT induces effective antitumor immunity and synergizes with PD-1 or LAG-3 blockade.

BACKGROUND: In addition to directly lysing tumors, oncolytic viruses also induce antitumor immunity by recruiting and activating immune cells in the local tumor microenvironment. However, the activation of the immune cells induced by oncolytic viruses is always accompanied by high-level expression of immune checkpoints in these cells, which may reduce the efficacy of the oncolytic viruses. The aim of this study is to arm the oncolytic vaccinia virus (VV) with immune checkpoint blockade to enhance its antitumor efficacy. METHODS: Through homologous recombination with the parental VV, an engineered VV-scFv-TIGIT was produced, which encodes a single-chain variable fragment (scFv) targeting T-cell immunoglobulin and ITIM domain (TIGIT). The antitumor efficacy of the VV-scFv-TIGIT was explored in several subcutaneous and ascites tumor models. The antitumor efficacy of VV-scFv-TIGIT combined with programmed cell death 1 (PD-1) or lymphocyte-activation gene 3 (LAG-3) blockade was also investigated. RESULTS: The VV-scFv-TIGIT effectively replicated in tumor cells and lysed them, and prompt the infected tumor cells to secret the functional scFv-TIGIT. Compared with control VV, intratumoral injection of VV-scFv-TIGIT in several mouse subcutaneous tumor models showed superior antitumor efficacy, accompanied by more T cell infiltration and a higher degree of CD8+ T cells activation. Intraperitoneal injection of VV-scFv-TIGIT in a mouse model of malignant ascites also significantly improved T cell infiltration and CD8+ T cell activation, resulting in more than 90% of the tumor-bearing mice being cured. Furthermore, the antitumor immune response induced by VV-scFv-TIGIT was dependent on CD8+ T cells which mediated a long-term immunological memory and a systemic antitumor immunity against the same tumor. Finally, the additional combination of PD-1 or LAG-3 blockade further enhanced the antitumor efficacy of VV-scFv-TIGIT, increasing the complete response rate of tumor-bearing mice. CONCLUSIONS: Oncolytic virotherapy using engineered VV-scFv-TIGIT was an effective strategy for cancer immunotherapy. Administration of VV-scFv-TIGIT caused a profound reshaping of the suppressive tumor microenvironment from 'cold' to 'hot' status. VV-scFv-TIGIT also synergized with PD-1 or LAG-3 blockade to achieve a complete response to tumors with poor response to VV or immune checkpoint blockade monotherapy.

Enhanced antitumor efficacy of a novel oncolytic vaccinia virus encoding a fully monoclonal antibody against T-cell immunoglobulin and ITIM domain (TIGIT).

BACKGROUND: Oncolytic virotherapy with vaccinia virus (VV) can lead to effective anti-tumor immunity by turning "cold" tumors into "hot" tumors. However, its therapeutic potential is affected by the tumor's local immunosuppressive tumor microenvironment (TME). Therefore, it is necessary to explore the use of immune checkpoint inhibitors to arm oncolytic VVs to enhance their anti-tumor efficacy. METHODS: A novel recombinant oncolytic VV, VV-α-TIGIT, which encoded a fully monoclonal antibody against T-cell immunoglobulin and ITIM domain (TIGIT) was generated by homologous recombination with a shuttle plasmid. The anti-tumor efficacy of the VV-α-TIGIT was investigated in several subcutaneous and ascites tumor models. FINDINGS: The functional α-TIGIT was sufficiently produced and secreted by tumor cells infected with VV-α-TIGIT, which effectively replicated in tumor cells leading to significant oncolysis. Intratumoral injection of VV-α-TIGIT improved anti-tumor efficacy in several murine subcutaneous tumor models compared to VV-Control (without α-TIGIT insertion). Intraperitoneal injection of VV-α-TIGIT achieved approximately 70% of complete tumor regression in an ascites tumor model. At the same time, treatment with VV-α-TIGIT significantly increased the recruitment and activation of T cells in TME. Moreover, the in vivo anti-tumor activity of VV-α-TIGIT was largely dependent on CD8+ T cell-mediated immunity. Finally, the tumor-bearing mice cured of VV-α-TIGIT treatment resisted rechallenge with the same tumor cells, suggesting a long-term persistence of tumor-specific immunological memory. INTERPRETATION: The recombinant oncolytic virus VV-α-TIGIT successfully combines the advantages of oncolytic virotherapy and intratumorally expression of immune checkpoint inhibitor against TIGIT. This novel strategy can provide information on the optimal design of novel antibody-armed oncolytic viruses for cancer immunotherapy. FUNDING: This work was supported by the National Natural Science Foundation of China (81773255, 81472820, and 81700037), the Science and Technology Innovation Foundation of Nanjing University (14913414), and the Natural Science Foundation of Jiangsu Province of China (BK20171098).