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Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti-Candida albicans
Xiaowei Chai1, Yuanying Jiang2, Hui Lu2
1Department of Dermatology, Hair Medical Center of Shanghai Tongji Hospital, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Introduction:
Candidiasis, mainly caused by Candida albicans, poses a serious threat to human health. The escalating drug resistance in C. albicans and the limited antifungal options highlight the critical need for novel therapeutic strategies.
Methods:
We evaluated 12 machine learning models on a self-constructed dataset with known anti-C. albicans activity. Based on their performance, the optimal model was selected to screen our separate in-house compound library with unknown anti-C. albicans activity for potential antifungal agents. The anti-C. albicans activity of the selected compounds was confirmed through in vitro drug susceptibility assays, hyphal growth assays, and biofilm formation assays. Through transcriptomics, proteomics, iron rescue experiments, CTC staining, JC-1 staining, DAPI staining, molecular docking, and molecular dynamics simulations, we elucidated the mechanism underlying the anti-C. albicans activity of the compound.
Result:
Among the evaluated machine learning models, the best predictive model was an ensemble learning model constructed from Random Forests and Categorical Boosting using soft voting. It predicts that Dp44mT exhibits potent anti-C. albicans activity. The in vitro tests further verified this finding that Dp44mT can inhibit planktonic growth, hyphal formation, and biofilm formation of C. albicans. Mechanistically, Dp44mT exerts antifungal activity by disrupting cellular iron homeostasis, leading to a collapse of mitochondrial membrane potential and ultimately causing apoptosis.
Conclusion:
This study presents a practical approach for predicting the antifungal activity of com-pounds using machine learning models and provides new insights into the development of antifungal compounds by disrupting iron homeostasis in C. albicans.
Insights
Machine learning identified Dp44mT as a potent antifungal agent against Candida albicans. This compound disrupts iron homeostasis, leading to fungal cell death and offering a novel therapeutic strategy for candidiasis.
Area of Science:
- Mycology
- Computational Biology
- Drug Discovery
Background:
- Candidiasis, caused by Candida albicans, is a significant health concern due to increasing antifungal drug resistance.
- Limited therapeutic options necessitate the development of novel antifungal strategies.
Purpose of the Study:
- To develop and apply machine learning models for predicting anti-Candida albicans activity.
- To identify novel compounds with antifungal properties against Candida albicans.
- To elucidate the mechanism of action of identified antifungal compounds.
Main Methods:
- Evaluated 12 machine learning models for predicting anti-Candida albicans activity.
- Selected an optimal ensemble model (Random Forests and Categorical Boosting) for screening a compound library.
- Validated identified compounds using in vitro assays (drug susceptibility, hyphal growth, biofilm formation).
- Elucidated the mechanism of action through transcriptomics, proteomics, and various cellular assays.
Main Results:
- An ensemble machine learning model accurately predicted Dp44mT as a potent inhibitor of Candida albicans.
- Dp44mT demonstrated efficacy in inhibiting planktonic growth, hyphal formation, and biofilm development in vitro.
- Mechanistic studies revealed Dp44mT disrupts iron homeostasis, collapses mitochondrial membrane potential, and induces apoptosis in Candida albicans.
Conclusions:
- Machine learning provides a viable approach for predicting antifungal compound activity.
- Dp44mT represents a promising candidate for candidiasis treatment by targeting iron homeostasis.
- This study offers new insights into developing antifungal agents through iron metabolism disruption.
