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Aneugen Molecular Mechanism Assay: Proof-of-Concept With 27 Reference Chemicals
Derek T Bernacki1, Steven M Bryce1, Jeffrey C Bemis1
1Litron Laboratories, Rochester, New York, 14623.
Abstract:
A tiered bioassay and data analysis scheme is described for elucidating the most common molecular targets responsible for chemical-induced in vitro aneugenicity: tubulin destabilization, tubulin stabilization, and inhibition of mitotic kinase(s). To evaluate this strategy, TK6 cells were first exposed to each of 27 presumed aneugens over a range of concentrations. After 4 and 24 h of treatment, γH2AX, p53, phospho-histone H3 (p-H3), and polyploidization biomarkers were evaluated using the MultiFlow DNA Damage Assay Kit. The assay identified 27 of 27 chemicals as genotoxic, with 25 exhibiting aneugenic signatures, 1 aneugenic and clastogenic, and 1 clastogenic. Subsequently, a newly described follow-up assay was employed to investigate the aneugenic agents' molecular targets. For these experiments, TK6 cells were exposed to each of 26 chemicals in the presence of 488 Taxol. After 4 h, cells were lysed and the liberated nuclei and mitotic chromosomes were stained with a nucleic acid dye and labeled with fluorescent antibodies against p-H3 and Ki-67. Flow cytometric analyses revealed that alterations to 488 Taxol-associated fluorescence were only observed with tubulin binders-increases in the case of tubulin stabilizers, decreases with destabilizers. Mitotic kinase inhibitors with known Aurora kinase B inhibiting activity were the only aneugens that dramatically decreased the ratio of p-H3-positive to Ki-67-positive nuclei. Unsupervised hierarchical clustering based on 488 Taxol fluorescence and p-H3: Ki-67 ratios clearly distinguished compounds with these disparate molecular mechanisms. Furthermore, a classification algorithm based on an artificial neural network was found to effectively predict molecular target, as leave-one-out cross-validation resulted in 25/26 agreement with a priori expectations. These results are encouraging, as they suggest that an adequate number of training set chemicals, in conjunction with a machine learning algorithm based on 488 Taxol, p-H3, and Ki-67 responses, can reliably elucidate the most commonly encountered aneugenic molecular targets.
Insights
This study developed a tiered bioassay to identify molecular targets of chemical-induced aneugenicity. A machine learning approach using specific biomarkers reliably predicted these targets in TK6 cells.
Area of Science:
- Toxicology and Pharmacology
- Molecular Biology
- Computational Biology
Background:
- Chemical-induced aneugenicity poses a significant risk, yet its molecular targets are not always clear.
- Understanding these targets is crucial for accurate risk assessment and the development of safer chemicals.
- Existing methods may not comprehensively identify the primary molecular mechanisms of aneugenicity.
Purpose of the Study:
- To establish and evaluate a tiered bioassay strategy for identifying molecular targets of chemical-induced aneugenicity.
- To investigate tubulin dynamics and mitotic kinase inhibition as key mechanisms.
- To develop a predictive model using machine learning for classifying aneugenic molecular targets.
Main Methods:
- TK6 cells were exposed to 27 presumed aneugens, with biomarkers like γH2AX, p53, phospho-histone H3 (p-H3), and polyploidization assessed.
- A follow-up assay involved exposing cells to 26 chemicals with 488 Taxol, analyzing p-H3 and Ki-67 ratios via flow cytometry.
- Unsupervised hierarchical clustering and an artificial neural network classification algorithm were used for data analysis and prediction.
Main Results:
- The initial assay identified 26 of 27 chemicals as genotoxic, with 25 showing aneugenic signatures.
- Follow-up analyses distinguished tubulin binders (stabilizers/destabilizers) and mitotic kinase inhibitors based on 488 Taxol fluorescence and p-H3:Ki-67 ratios.
- The artificial neural network achieved 25/26 accuracy in predicting molecular targets using leave-one-out cross-validation.
Conclusions:
- The tiered bioassay effectively identifies common molecular targets of chemical-induced aneugenicity.
- The combination of 488 Taxol, p-H3, and Ki-67 responses, analyzed by machine learning, reliably predicts these targets.
- This strategy offers a promising approach for elucidating aneugenic mechanisms and improving chemical safety assessments.
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