Dataset Information

Genetic and Functional Evaluation of the Role of FOXO1 in Antituberculosis Drug-Induced Hepatotoxicity.

ABSTRACT:

Background

The accumulation of the hepatotoxic substance protoporphyrin IX (PPIX) induced by aminolevulinate synthase 1 (ALAS1) activation is one of the important mechanisms of antituberculosis drug-induced hepatotoxicity (ATDH). Forkhead box protein O1 (FOXO1) may activate ALAS1 transcription. However, little is known about their roles in ATDH; we performed a study to determine the association between polymorphisms in the two genes and ATDH susceptibility. Then, we verified this possible association by cellular functional experiments.

Materials and methods

Tag single-nucleotide polymorphisms (TagSNPs) in the two genes were genotyped in 746 tuberculosis patients. The frequencies of the alleles, genotypes, genetic models, and haplotype distribution of the variants were compared between the case and control groups. L-02 cells and HepG2 cells were incubated with the indicated concentration of isoniazid (INH) and rifampicin (RIF) for the desired times, and then the expression levels of ALAS1 and FOXO1 mRNAs and proteins were detected. HepG2 cells were transiently transfected with FOXO1 siRNA to observe the effect of changes in the FOXO1 expression on the cell survival rate and ALAS1 expression.

Results

The C allele at rs2755237 and the T allele at rs4435111 in the FOXO1 gene were associated with a decreased risk of ATDH. The expression of ALAS1 in both L-02 cells and HepG2 cells was increased by the coadministration of INH/RIF (600/200 μM) for 24 h. Although FOXO1 expression was reduced slightly by the same treatment, its content in the nucleus was significantly increased. However, the cell survival rate and ALAS1 expression level were not significantly altered by the downregulation of FOXO1 in HepG2 cells.

Conclusions

Variants of the rs4435111 and rs2755237 loci in the FOXO1 gene were associated with susceptibility to ATDH. Coadministration of INH/RIF promoted the transfer of FOXO1 from the cytoplasm to the nucleus, but the functional significance of its nuclear translocation requires further verification.

SUBMITTER: Zhang J

PROVIDER: S-EPMC8238576 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Quantitative structure-activity relationship (QSAR) modeling and toxicogenomics are typically used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely, their chemical descriptors and toxicogenomics profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs ( http://toxico.nibio.go.jp/datalist.html ). The model end point was hepatotoxicity in the rat following 28 days of continuous exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (correct classification rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomics data (24 h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomics descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomics data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of subchronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.