Vorinostat differentially alters 3D nuclear structure of cancer and non-cancerous esophageal cells

Vorinostat is genotoxic and epigenotoxic in the mouse bone marrow cells at the human equivalent doses

Vorinostat was approved as the first histone deacetylase inhibitor for the management of cutaneous T cell lymphoma. However, it's in vivo genetic and epigenetic effects on non-cancerous cells remain poorly understood. As genetic and epigenetic changes play a critical role in the pathogenesis of carcinogenesis, we investigated whether vorinostat induces genetic and epigenetic alterations in mouse bone marrow cells. Bone marrow cells were isolated 24 h following the last oral administration of vorinostat at the doses of 25, 50, or 100 mg/kg/day for five days (approximately equal to the recommended human doses). The cells were then used to assess clastogenicity and aneugenicity by the micronucleus test complemented by fluorescence in situ hybridization assay; DNA strand breaks, oxidative DNA strand breaks, and DNA methylation by the modified comet assay; apoptosis by annexin V/PI staining analysis and the occurrence of the hypodiploid DNA content; and DNA damage/repair gene expression by polymerase chain reaction (PCR) Array. The expression of the mRNA transcripts were also confirmed by real-time PCR and western blot analysis. Vorinostat caused structural chromosomal damage, numerical chromosomal abnormalities, DNA strand breaks, oxidative DNA strand breaks, DNA hypomethylation, and programed cell death in a dose-dependent manner. Furthermore, the expression of numerous genes implicated in DNA damage/repair were altered after vorinostat treatment. Accordingly, the genetic/epigenetic mechanism(s) of action of vorinostat may play a role in its carcinogenicity and support the continued study and development of new compounds with lower toxicity.

High-Content Image-Based Single-Cell Phenotypic Analysis for the Testicular Toxicity Prediction Induced by Bisphenol A and Its Analogs Bisphenol S, Bisphenol AF, and Tetrabromobisphenol A in a Three-Dimensional Testicular Cell Co-culture Model

Emerging data indicate that structural analogs of bisphenol A (BPA) such as bisphenol S (BPS), tetrabromobisphenol A (TBBPA), and bisphenol AF (BPAF) have been introduced into the market as substitutes for BPA. Our previous study compared in vitro testicular toxicity using murine C18-4 spermatogonial cells and found that BPAF and TBBPA exhibited higher spermatogonial toxicities as compared with BPA and BPS. Recently, we developed a novel in vitro three-dimensional (3D) testicular cell co-culture model, enabling the classification of reproductive toxic substances. In this study, we applied the testicular cell co-culture model and employed a high-content image (HCA)-based single-cell analysis to further compare the testicular toxicities of BPA and its analogs. We also developed a machine learning (ML)-based HCA pipeline to examine the complex phenotypic changes associated with testicular toxicities. We found dose- and time-dependent changes in a wide spectrum of adverse endpoints, including nuclear morphology, DNA synthesis, DNA damage, and cytoskeletal structure in a single-cell-based analysis. The co-cultured testicular cells were more sensitive than the C18 spermatogonial cells in response to BPA and its analogs. Unlike conventional population-averaged assays, single-cell-based assays not only showed the levels of the averaged population, but also revealed changes in the sub-population. Machine learning-based phenotypic analysis revealed that treatment of BPA and its analogs resulted in the loss of spatial cytoskeletal structure, and an accumulation of M phase cells in a dose- and time-dependent manner. Furthermore, treatment of BPAF-induced multinucleated cells, which were associated with altered DNA damage response and impaired cellular F-actin filaments. Overall, we demonstrated a new and effective means to evaluate multiple toxic endpoints in the testicular co-culture model through the combination of ML and high-content image-based single-cell analysis. This approach provided an in-depth analysis of the multi-dimensional HCA data and provided an unbiased quantitative analysis of the phenotypes of interest.