Ying Jin, Cynthia Rider, Ruili Huang, Menghang Xia, Helene Langevin, Shanshan Zhao, Daniel Zilber
Our discovery of hormetic curves is in line with estimates of hormetic behaviors in dose-response repositories like Tox21. Our proposed method has been effective in screening for hormesis from large-scale dose-response data, and the visualization method effectively facilitates the interpretation of test results.
INTRODUCTION: Botanical supplements are consumed to improve health despite the mystery of their mechanisms. One possible effect is hormesis, defined as a beneficial and stimulatory health effect at a low dose and detrimental effect at a high dose, or vice versa. Hormesis is usually discovered by manually examining the shape of dose-response curves, which is not scalable to the large number of chemical-assay combinations found in high-throughput screening (HTS) results.
METHODS: We proposed a semi-automatic statistical testing procedure to quickly evaluate large-scale dose-response data, accompanied by an R package shorm for easy implementation. The nonparametric shape tests are more flexible with few assumptions. We also developed a new visualization method to represent the test conclusions and uncertainties simultaneously. This representation highlights both significant and ambiguous findings for efficient examination.
RESULTS: We applied this method to a data set of 4860 chemical-assay pairs and identified 561 (12%) dose-response curves showing statistically significant hormesis.
CONCLUSION: Our discovery of hormetic curves is in line with estimates of hormetic behaviors in dose-response repositories like Tox21. Our proposed method has been effective in screening for hormesis from large-scale dose-response data, and the visualization method effectively facilitates the interpretation of test results.