MalariaFlow: A comprehensive deep learning platform for multistage phenotypic antimalarial drug discovery.

Malaria remains a significant global health challenge due to the growing drug resistance of Plasmodium parasites and the failure to block transmission within human host. While machine learning (ML) and deep learning (DL) methods have shown promise in accelerating antimalarial drug discovery, the performance of deep learning models based on molecular graph and other co-representation approaches warrants further exploration. Current research has overlooked mutant strains of the malaria parasite with varying degrees of sensitivity or resistance, and has not covered the prediction of inhibitory activities across the three major life cycle stages (liver, asexual blood, and gametocyte) within the human host, which is crucial for both treatment and transmission blocking. In this study, we manually curated a benchmark antimalarial activity dataset comprising 407,404 unique compounds and 410,654 bioactivity data points across ten Plasmodium phenotypes and three stages. The performance was systematically compared among two fingerprint-based ML models (RF::Morgan and XGBoost:Morgan), four graph-based DL models (GCN, GAT, MPNN, and Attentive FP), and three co-representations DL models (FP-GNN, HiGNN, and FG-BERT), which reveal that: 1) The FP-GNN model achieved the best predictive performance, outperforming the other methods in distinguishing active and inactive compounds across balanced, more positive, and more negative datasets, with an overall AUROC of 0.900; 2) Fingerprint-based ML models outperformed graph-based DL models on large datasets (>1000 compounds), but the three co-representations DL models were able to incorporate domain-specific chemical knowledge to bridge this gap, achieving better predictive performance. These findings provide valuable guidance for selecting appropriate ML and DL methods for antimalarial activity prediction tasks. The interpretability analysis of the FP-GNN model revealed its ability to accurately capture the key structural features responsible for the liver- and blood-stage activities of the known antimalarial drug atovaquone. Finally, we developed a web server, MalariaFlow, incorporating these high-quality models for antimalarial activity prediction, virtual screening, and similarity search, successfully predicting novel triple-stage antimalarial hits validated through experimental testing, demonstrating its effectiveness and value in discovering potential multistage antimalarial drug candidates.

A Novel ASCT2 Inhibitor, C118P, Blocks Glutamine Transport and Exhibits Antitumour Efficacy in Breast Cancer.

BACKGROUND: The microtubule protein inhibitor C118P shows excellent anti-breast cancer effects. However, the potential targets and mechanisms of C118P in breast cancer remain unknown. METHODS: Real-time cellular analysis (RTCA) was used to detect cell viability. Apoptosis and the cell cycle were detected by flow cytometry. Computer docking simulations, surface plasmon resonance (SPR) technology, and microscale thermophoresis (MST) were conducted to study the interaction between C118P and alanine-serine-cysteine transporter 2 (ASCT2). Seahorse XF technology was used to measure the basal oxygen consumption rate (OCR). The effect of C118P in the adipose microenvironment was explored using a co-culture model of adipocytes and breast cancer cells and mouse cytokine chip. RESULTS: C118P inhibited proliferation, potentiated apoptosis, and induced G2/M cell cycle arrest in breast cancer cells. Notably, ASCT2 was validated as a C118P target through reverse docking, SPR, and MST. C118P suppressed glutamine metabolism and mediated autophagy via ASCT2. Similar results were obtained in the adipocyte-breast cancer microenvironment. Adipose-derived interleukin-6 (IL-6) promoted the proliferation of breast cancer cells by enhancing glutamine metabolism via ASCT2. C118P inhibited the upregulation of ASCT2 by inhibiting the effect of IL-6 in co-cultures. CONCLUSION: C118P exerts an antitumour effect against breast cancer via the glutamine transporter ASCT2.

Phylogenetic evidence reveals early Kra-Dai divergence and dispersal in the late Holocene.

Studying language evolution brings a crucial perspective to bear on questions of human prehistory. As the most linguistically diverse region on earth, East and Southeast Asia have witnessed extensive sociocultural and ethnic contacts among different language communities. Especially, the Kra-Dai language family exhibits tremendous socio-cultural importance in these regions. Due to limited historical accounts, however, there are several controversies on their linguistic relatedness, ambiguities regarding the divergence time, and uncertainties on the dispersal patterns. To address these issues, here we apply Bayesian phylogenetic methods to analyze the largest lexical dataset containing 646 cognate sets compiled for 100 Kra-Dai languages. Our dated phylogenetic tree showed their initial divergence occurring approximately 4000 years BP. Phylogeographic results supported the early Kra-Dai language dispersal from the Guangxi-Guangdong area of South China towards Mainland Southeast Asia. Coupled with genetic, archaeological, paleoecologic, and paleoclimatic data, we demonstrated that the Kra-Dai language diversification could have coincided with their demic diffusion and agricultural spread shaped by the global climate change in the late Holocene. The interdisciplinary alignments shed light on reconstructing the prehistory of Kra-Dai languages and provide an indispensable piece of the puzzle for further studying prehistoric human activities in East and Southeast Asia.

Targeting Mitochondrial Metabolic Reprogramming as a Potential Approach for Cancer Therapy.

Abnormal energy metabolism is a characteristic of tumor cells, and mitochondria are important components of tumor metabolic reprogramming. Mitochondria have gradually received the attention of scientists due to their important functions, such as providing chemical energy, producing substrates for tumor anabolism, controlling REDOX and calcium homeostasis, participating in the regulation of transcription, and controlling cell death. Based on the concept of reprogramming mitochondrial metabolism, a range of drugs have been developed to target the mitochondria. In this review, we discuss the current progress in mitochondrial metabolic reprogramming and summarized the corresponding treatment options. Finally, we propose mitochondrial inner membrane transporters as new and feasible therapeutic targets.