Influence of HNF4α and HNF4α-AS1 gene variants on the risk of anti-tuberculosis drugs-induced hepatotoxicity

Introduction

Tuberculosis (TB) has remained a leading cause of morbidity and mortality worldwide, with a markedly pronounced severity in China, where the costs of TB have been reported as catastrophic to patients’ families (1-3). Isoniazid, rifamycin, pyrazinamide, and ethambutol comprise the major regimen recommend for use by the World Health Organization (WHO) for patients with drug-susceptible TB (4). Although effective cure rate has reached about 95% under optimal conditions, the side effects of these regimens are not negligible, among which anti-tuberculosis drugs-induced hepatotoxicity (ATDH) is the most common and serious (5). The incidence of ATDH varies from 2% to 28% depending on different doses, schedules, and route of administration (5,6). The metabolites of isoniazid alone can induce hepatotoxicity; however, the incidence of hepatotoxicity increases significantly when isoniazid is co-administered with rifamycin (7). Although the mechanism of combination medication leading to enhanced hepatotoxicity has not been clearly addressed, a potential explanation is that rifamycin is a human-specific activator of pregnane X receptor (PXR)/nuclear receptor subfamily group I member 2 (NR1I2) (8,9). A nuclear receptor, PXR has been considered a master xenobiotic receptor coordinately regulating genes encoding drug-metabolizing enzymes (DMEs), such as cytochrome P450 enzymes (CYPs), conjugation enzymes, and transporters to essentially detoxify and eliminate xenobiotics (10). Due to its role in drug metabolism and transport, PXR was labeled a key factor in enhanced liver toxicity caused by rifamycin and isoniazid co-therapy (11). Unfortunately, Rifampicin and isoniazid co-therapy recommended by the WHO for TB patients was the main reason for ATDH, due to the effectiveness and economy of the co-therapy (4). With highly sensitive next-generation sequencing technology, transcriptional networks and upstream regulator analyses found that rifamycin is a stimulant of PXR and hepatocyte nuclear factor 4α (HNF4α). Hepatocyte nuclear factor 1 alpha-antisense-1 (HNF1α-AS1) and HNF4α-AS1 also regulate the expression and function of several drug-metabolizing cytochrome P450 (P450) enzymes, which also further impact P450-mediated drug metabolism and drug toxicity (12). Since both are involved in liver drug metabolism and drug toxicity by regulating cytochrome enzymes, these transcription factors (TFs) might be key regulators of a complex network of metabolism-associated pathways and result in unexpected drug-drug interactions (13).

The orphan nuclear receptor HNF4α is known as a master regulator of liver function (14,15). It is involved in many pathophysiological processes related to the development, proliferation, damage, and repair of hepatocytes, including hepatocellular carcinoma, non-alcoholic fatty liver disease, and drug-induced liver damage (16-18). Compared with the well identified functions of HNF4α, the underlying molecular mechanisms of HNF4α in drug-induced liver damage is still controversial. Overexpression of HNF4α sensitizes mice or primary hepatocytes to acetaminophen-induced liver injury (19). Meanwhile, degradation of HNF4α has been shown to aggravate steatosis and tumorigenesis in human livers induced by perfluorooctanoic acid and perfluorooctanesulfonic acid (20). Several TFs and co-regulators have been identified as potential specific partners for HNF4α (21). As a result, a possible mechanism of HNF4α in drug-induced hepatotoxicity is that it acts as an important regulator of coordinate nuclear-receptor-mediated response to xenobiotics (22,23). Transcriptional networks and upstream regulator analyses have shown HNF4α, PXR, and other TFs (NR1I3, RXRα, NF-κB) are hub regulators of the complex network of rifamycin relating drug-metabolizing enzymes (DMEs) and drug transporters (13,24). We speculate whether HNF4α participates in ATDH as a key gene in rifamycin-related drug metabolism pathways, similar to PXR.

Recent studies have suggested that long non-coding RNAs (lncRNAs) are also highly involved in physiological functions and diseases (25). It is worth mentioning that lncRNAs, especially neighborhood antisense lncRNAs, are involved in the expression or functions of TFs in gene regulation (26). HNF4α-AS1 is named based on its genomic location, which is the neighborhood region of HNF4α, and is transcribed in the opposite direction on the antisense strand. The HNF4α and HNF4α-AS1 genes form a typical pair of coding and neighborhood antisense noncoding genes. Both HNF1α-AS1 and HNF4α are highly expressed in liver. The similarity in tissue distribution might suggest functional connections between HNF4α and HNF4α-AS1 (27). A recent study found that HNF4α-AS1 expression can be strongly activated by HNF4α, suggesting the expression regulatory net between the TF-lncRNA pair (28). According to increasing evidence, HNF4α-AS1 has been found to regulate expression and function of several drug-metabolizing CYPs, which further impact CYP-mediated drug metabolism and drug toxicity (1,4). However, there is still no clear evidence showing the exact co-regulatory mechanism of the TF-lncRNA pair in ATDH. So far, it can only be confirmed that both of them have a crossover with PXR in regulation of CYPs (13,24,28). Since PXR is a key factor of ATDH, the question arises of whether the TF-lncRNA pair participates in ATDH through interaction with PXR.

Due to the atypical symptoms and signs of ATDH, its early diagnosis and prevention is still a challenge (29). If ATDH occurs, it may be recommended to temporarily discontinue the drug for medical observation. If the patient can tolerate it, the combination drug will still be recommended, but rifampicin may be replaced with a combination of rifapentin and isoniazid, or a more expensive second-line drug combination (1,30). Factors contributing to ATDH include genetic, epigenetic, physiological, pathological, and environmental (24). Among them, genetic predisposition plays an important role in occurrence and progress of ATDH (5,31,32). Pharmacogenetics can guide drug treatment according to patient genetics. It has been extensively applied to various fields of medicine to prevent serious adverse drug reactions (33,34). Pharmacogenetics has provided 27 pairs of annotated variant‐drug pairs for TB treatment, which are mainly associated with hepatotoxicity (35). The number of association studies between polymorphisms of key genes and susceptibility to ATDH is still on the rise (36-39). Meanwhile, pharmacogenetics researchers have explored the influence of HNF4α genetic variants on drug plasma concentration and susceptibility to adverse drug effects, including docetaxel-induced myelosuppression, imatinib-induced periorbital edema, and so on (40-44). Recently, a genome-wide association study (GWAS) identified that 4 single nucleotide polymorphisms (SNPs) of HNF4α were significantly associated with cytotoxicity in HepG2 cells after treatment with emodin (17). Accumulating evidence indicates that lncRNA polymorphisms may also be potential biomarkers used for early diagnosis, monitoring therapy response, and prognostic assessment including for TB (45,46). Since results of current pharmacogenomics studies on ATDH are still lacking consistency according to different races, drug dosages, and treatment protocol, the results may not be representative of a Chinese population infected with TB (31,32,47-49). Considering China’s heavy burden of TB, further pharmacogenetic studies aiming to identify novel potential targets are needed in order to provide a better understanding of the potential mechanism of ATDH and optimize treatment outcomes.

To our knowledge, HNF4α and HNF4α-AS1 are involved in liver functions. However, genetic polymorphisms of PXR have also been regarded to increase susceptibility to ATDH in different population, genetic polymorphisms of HNF4α and HNF4α-AS1 have also been regarded to related to some metabolic diseases in different population (50,51), the correlation between genetic polymorphisms of HNF4α and HNF4α-AS1 and predisposition of ATDH has not been elucidated yet (12,37-39,52). Pharmacogenetic study of HNF4α and HNF4α-AS1 may be an acceptable tool for treatment optimization of anti-tubercular drugs. Therefore, the purpose of this study was to clarify whether HNF4α-AS1 is involved in PXR regulation through bioinformatics, and to explore if the genetic variants contribute to the susceptibility of ATDH or clinical laboratory characteristics. We present the following article in accordance with the MDAR reporting checklist (available at https://dx.doi.org/10.21037/apm-21-2924).

Methods

Samples

The blood samples of this study were stored in the Bio-Bank of resources “Tuberculosis Researches” in the Department of Laboratory Medicine, West China Hospital, Sichuan University, China, as mentioned previously (37,53,54). According to the ATDH inclusion and exclusion criteria, there were a total of 118 samples in the ATDH group and 628 samples in the non-ATDH group (55,56). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Ethical approval for this study was granted by the Institutional Review Board of the West China Hospital of Sichuan University (2014-198). The subjects of the study had already signed an informed consent form at the beginning of the whole study.

Candidate SNP selection and genotyping

TagSNPs located in HNF4α and HNF4α-AS1 genomic regions were selected as mentioned previously with priority SNPs that may be related to the risk of ATDH or potential functional significance (37,57). The SNP genotyping work was conducted by the QIAamp^® DNA Blood Mini Kit (Qiagen, Hilden, Germany) and custom-by-design 2×48-Plex SNP Scan^TM Kit (Cat#: G0104, Gene Sky Biotechnologies Inc., Shanghai, China) as described previously (58). Thirty samples were selected for double-blind experiments randomly for repeatability and stability of genotyping.

Statistical analysis

The data in the ATDH and non-ATDH groups were compared using independent t-test (continuous variables) or chi-square test (categorical variable) by SPSS version 17.0 (IBM Corp., Chicago, IL, USA). Associations between SNPs and the risk of ATDH were evaluated by Plink version 1.07. The LD and haplotype analysis were conducted by Haploview version 4.2. The SNP-SNP interactions were analyzed by Multifactor Dimensionality Reduction software (MDR; version 3.0.1). Schematic diagram was conducted by Cytoscape (version 3.7.1; https://cytoscape.org/). Two-sided values of P<0.05 were considered statistically significant (59).

Results

Preliminary bioinformatics analysis

We first used lncRNA and protein interaction gene co-expression matrix [multi experiment matrix (MEM)] to predict the target genes regulated by HNF4α-AS1, and found HNF4α-AS1 and PXR may have potential interactions (https://biit.cs.ut.ee/mem/index.cgi), as shown in Figure S1. Using the online gene annotation and function enrichment website DAVID software (https://david.ncifcrf.gov), we performed Gene Ontology (GO) function annotation and function enrichment analysis on potential target genes of HNF4α-AS1, and found that some TFs (NR1I3, PXR, and HNF4Α, among others) had activity as transcriptional co-activator and/or specific binding ability with RNA polymerase II transcription regulatory region sequence (GO:0001228 and GO:0070653). A schematic diagram of HNF4α-AS1 regulation of related TFs is depicted in Figure 1.

Figure 1 The gene annotation and function enrichment analysis of target genes of HNF4α-AS1. Gene annotation and function enrichment analysis further indicated HNF4α-AS1 may have transcriptional co-activator activity and/or RNA polymerase II transcriptional regulatory region sequence specific binding ability for PXR/NR1I2 (GO:0001228) and HNF4α (GO:0070653). HNF4α-AS1, hepatocyte nuclear factor 4 alpha-antisense-1; PXR, pregnane X receptor; NR1I2, nuclear receptor subfamily group I member 2; GO, Gene Ontology; HNF4α, hepatocyte nuclear factor 4α.

Participant demographic characteristics

In total, 746 TB patients were consecutively included, 118 in the ATDH group and 628 in the non-ATDH group. There was no difference in age (42.85±18.44 vs. 40.92±15.72; P=0.284) and gender (proportion of male 58.47% vs. 59.71%; P=0.801) between ATDH group and non-ATDH group.

SNP allele, genotype, genetic model, and haplotype analysis

Based on criteria mentioned above, 15 SNPs were selected and genotyped successfully for 99.9% of participants. All genotype frequencies of selected SNPs in the non-ATDH group followed the Hardy-Weinberg Equilibrium (HWE) law (P>0.05) (Table S1). The allele distributions and genotype frequencies of all 15 SNPs are presented in Table 1. For the rs3212183 locus, the proportion of T allele was 18/218 (7.62%) in the ATDH group and 53/1,203 (4.22%) in the non-ATDH group, compared with C allele (OR: 1.874; 95% CI: 1.077 to 3.261, P=0.024). For the rs6130615 locus, the proportion of T allele was 85/151 (36.01%) in the ATDH group and 545/705 (43.60%) in the non-ATDH group, compared with C allele (OR: 0.728; 95% CI: 0.545 to 0.971, P=0.030). However, occurrence of genotype of these two loci showed no significant difference (both P>0.05). For other SNPs, no difference of allele or genotype was found between the two groups (all P>0.05). We also conducted multiple testing by Bonferroni correction which requires a more stringent significance level. When Bonferroni correction was applied, none of the 15 SNPs were statistically significant.

We constructed three genetic models (dominant, recessive, and additive patterns) to compare the significance of each SNP. As shown in Table 2, rs3212183 in dominant model (OR: 1.989; 95% CI: 1.102 to 3.591; P=0.022) and additive model (OR: 1.765; 95% CI: 1.042 to 2.991; P=0.034) showed statistical significance between these two groups; rs6130615 in dominant model (OR: 0.649; 95% CI: 0.434 to 0.970; P=0.034) and additive model (OR: 0.734; 95% CI: 0.551 to 0.976; P=0.033) showed statistical significance between the two groups; rs2868094 in the dominant model (OR: 0.631; 95% CI: 0.425 to 0.938; P=0.023) showed statistical significance between the two groups, but there was no statistical significance in the additive model. No genetic model was associated with susceptibility of ATDH in the SNPs after Bonferroni correction.

Haplotype was constructed to analyze additive association among selected SNPs with a frequency >0.05 and in strong LD state with one another by calculating the pairwise r² coefficient (r²>0.80). As shown in Figure S2 and Table S2, no haplotype was associated with susceptibility of ATDH (all P>0.05).

SNP-SNP interactions associated with susceptibility to ATDH

A total of six models were established between all loci as shown in Table 3. A 3-point model composed of rs3212200, rs3212208, and rs3818247 was statistically different (P=0.010). The cross-validation consistency of this model was 9/10. The balance accuracy was 0.5876 and 0.5600 in training set and validation set, respectively. This result indicated that the joint factor obtained by this model was calculated through 9 crossover calculations, and the correct rate of distinguishing target population was about 57%. A 4-point model composed of rs3212200, rs6103731, rs3212208, and rs3818247 was also statistically different (P=0.010). In summary, there was a combined effect of these loci.

Relationship between genetic polymorphism and laboratory test indicators

Genetic polymorphism not only affects disease susceptibility, but also has a certain correlation with the clinical features of disease, which may affect the different clinical characteristics of individuals. In this study, the correlation between quantitative laboratory results of participants at baseline before treatment and genotype of the rs3212183 locus was analyzed. Patients with TT genotype had the highest erythrocyte sedimentation rate (ESR) 98.00 (50.00–120.00), and the ESR of patients with CC and CT genotype were 35.00 (21.00–58.75) and 46.00 (15.00–81.00), respectively (Table S3). No correlation between laboratory indicators and genotype of the rs6130615 locus was found (Table S4).

Discussion

This study was undertaken to investigate the genetic influence of regulatory nuclear receptor (HNF4α) and its chaperone lncRNA (HNF4α-AS1) on susceptibility and clinical profiles of ATDH in a cohort of 746 Western Chinese patients with TB. Firstly, we explored whether HNF4α-AS1 was involved in the transcriptional regulation network of PXR, which is a key factor in the mechanism of ATDH (11). Bioinformatics analysis suggested that PXR was a potential target gene of HNF4α-AS1. Gene annotation and function enrichment analysis further suggested HNF4α-AS1 may modulate PXR and HNF4a expression through RNA polymerase II (GO:0001228) and HNF4α (GO:0070653). Subsequently, we focused on analyzing the association between genetic polymorphism of both HNF4α-AS1 and HNF4α with susceptibility to ATDH. As far as we know, this study was the first attempt to determine the association between HNF4α and HNF4α-AS1 genetic variants with predisposition of ATDH.

In the study, all selected 15 SNPs were in HWE in the non-ATDH group. There were two SNPs nominally associated with ATDH, namely rs3212183 (P=0.024) and rs6130615 (P=0.030, adjusted for age and gender) (Table 1). As we tested the association for 15 SNPs, false positive was likely to occur merely by chance if we still adopted the nominal significance level of 0.05. Therefore, a more stringent significance level was required, we thus adopted Bonferroni correction. In the setting of this study, the significance level would be 0.05/15=0.0033. When Bonferroni correction was applied, none of the 15 SNPs were statistically significant, no matter in allele, genetic model, nor SNP-SNP interactions.

A member of steroid hormone receptor superfamily, HNF4α is a TF coded by the HNF4α gene. It is expressed in many tissues and is especially rich in liver. Consistent with the cell location, HNF4α has been suggested to have a role in several pathophysiological processes of hepatocytes, as well as some metabolic pathways such as glucose metabolism (16,17). Due to the physiological role of hepatocytes in xenobiotic detoxification and glycogen metabolism, mutations in coding and regulatory regions of HNF4α have been associated with drug side effect (periorbital edema caused by imatinib) and some metabolism-related diseases (type 2 diabetes) (60,61).

Previous studies have indicated that HNF4α is associated with type 2 diabetes, but the role of the variants of rs3212183, located in intron 3, in susceptibility was heterogeneous among different races (60,62). We investigated whether rs3212183 was associated with laboratory indicators related to liver function or metabolism of glucose. There was no association detected between genotypes and allele level of serum glucose in our study. It may be due to racial genetic differences between Finnish, Ashkenazi Jews, Pima Indians, and Chinese. As a result, its relative contribution to type 2 diabetes may differ between populations (63). It is also possible that we lacked the power to detect a subtle association due to the relatively small sample number (n=746). Although the sample size of the present study was fairly large in comparison with many other studies, it may nonetheless have low statistical power to detect variants with modest effects, especially allele (T) for rs3212183 had a frequency of 0.762 in ATDH group and 0.422 in non-ATDH group. Thus, relationships between HNF4α genetic polymorphisms and ATDH risk should be interpreted with caution. Polymorphisms in the intron may affect messenger RNA (mRNA) stability and degradation, gene expression, and alternative splicing resulting in different protein isoforms (64,65). To some extent, the biological significance of SNPs located in intron is relatively difficult to verify. The genome-wide expression quantitative trait loci (eQTL) data from multiple tissues of major Genotype-Tissue Expression (GTEx) project databases is widely used in the biological function annotation function of SNPs (66). As a remedy, we annotated that SNP rs3212183 was significantly correlated with the expression of C20orf111 in whole blood (P=0.0005) through database HaploReg version 4.1 (https://www.broadinstitute.org/mammals/haploreg) (67).

The SNP rs6130615 is located in 3' untranslated region (3'-UTR) of HNF4Α gene. Previous studies showed that patients with mechanical heart valves with CC genotype of rs6130615 had an 8.4-fold increased risk of bleeding during warfarin treatment (68). A reasonable explanation was that HNF4α mutation may result in vascular endothelial growth factor (VEGF) dysfunction. As VEGF is a well-known protein involved in vascular formation, VEGF dysfunction could lead to bleeding complications (69,70). Polymorphisms in the rs6130615 locus have also been reported in association with increased severity of anemia when treated with docetaxel. The biological effects of these variants may play a role in the interpatient variability in docetaxel pharmacokinetics (71). As stability and transport of mRNA transcripts are dependent on a properly configured 3'-UTR, we speculated that mutation at this locus would cause dysfunction of the protein by affecting gene expression and/or secondary structure of mRNA (64,65). We searched online StarBase database (https://starbase.sysu.edu.cn) to predict through bioinformatics and found miR-122 is a candidate miRNA for the 3'-UTR region mRNA of HNF4α. In multiple studies, it has been considered that miR-122 is related to drug-induced hepatotoxicity caused by isoniazid (72,73). Whether the mutation of HNF4α in the 3'-UTR region participates in ATDH by changing the interaction with miRNA is worth exploring. We also retrieved the genomic eQTL database and found rs6130615 was an eQTL for SERINC3 gene expression in whole blood (P=0.00007) (67). Unfortunately, our study did not find any correlation between the genotype of this locus and blood cell count.

A neighbor antisense lncRNA gene of the human HNF4α gene, HNF4α-AS1 is located at human chromosome level with a length of 17.96 kb, containing 4 exons and 3 introns (12). Clues have indicated that expression regulatory net between the TF-lncRNA pairs is elevating (27,28,71). A haplotype constructed by rs6130608-rs2425637 has been reported to be correlated with the risk of metabolic syndrome in French-Canadian youth (74). Another study reported that polymorphisms of rs2425637 were significantly associated with type 2 diabetes at either allele or genotype level in Chinese people (75). Thus, as well as rs3212183 in HNF4α, the risk of rs2425637 polymorphisms contributed to type 2 diabetes might also be population specific. From the perspective of HNF4α-AS1 and HNF4α synergistic regulation of ATDH, no pharmacogenetic effects has ever been reported. We retrieved online website (https://bioinfo.bjmu.edu.cn/mirsnp/search/) to search potential functional SNPs with strong LD with rs2425637, and found no valuable clue for the biological significance of the variant (data not shown).

There were several strengths to our study: (I) participants were recruited from the West China Hospital, which the highest quality medical center in western China, to ensure the surveillance of ATDH with strict criteria to avoid mis-classification. (II) The laboratory for testing is certified by the American Association of Pathologists to ensure all laboratory data had good quality and reliability. Our research also had some limitations. In future, to achieve significance, a larger sample size would be required.

We should also identify the association between the ATDH and performed functional verification tests in vitro and vivo.

We concluded that genetic polymorphisms of the HNF4α and HNF4α-AS1 genes showed no significant associations with susceptibility to ATDH in the present Chinese Han population. Therefore, they did not appear to be major determinants for ATDH.

Acknowledgments

Funding: This work was supported by The Science and Technology Project of the Health Planning Committee of Sichuan (19PJ163) and Chengdu Municipal Health Project (20190679).

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://dx.doi.org/10.21037/apm-21-2924

Data Sharing Statement: Available at https://dx.doi.org/10.21037/apm-21-2924

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/apm-21-2924). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Ethical approval for this study was granted by the Institutional Review Board of the West China Hospital of Sichuan University (2014-198). The subjects of the study had already signed an informed consent form at the beginning of the whole study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. WHO. Gobal Tuberculosis Report 2019. Available online: https://www.who.int/publications/i/item/9789241565714
2. WHO. Global tuberculosis report 2018. Available online: https://apps.who.int/iris/bitstream/handle/10665/274453/9789241565646-eng.pdf
3. Furin J, Cox H, Pai M. Tuberculosis. Lancet 2019;393:1642-56. [Crossref] [PubMed]
4. Floyd K, Glaziou P, Zumla A, et al. The global tuberculosis epidemic and progress in care, prevention, and research: an overview in year 3 of the End TB era. Lancet Respir Med 2018;6:299-314. [Crossref] [PubMed]
5. Motta I, Calcagno A, Bonora S. Pharmacokinetics and pharmacogenetics of anti-tubercular drugs: a tool for treatment optimization? Expert Opin Drug Metab Toxicol 2018;14:59-82. [Crossref] [PubMed]
6. Hu X, Zhang M, Bai H, et al. Antituberculosis Drug-Induced Adverse Events in the Liver, Kidneys, and Blood: Clinical Profiles and Pharmacogenetic Predictors. Clin Pharmacol Ther 2018;104:326-34. [Crossref] [PubMed]
7. Gangadharam PR. Isoniazid, rifampin, and hepatotoxicity. Am Rev Respir Dis 1986;133:963-5. [PubMed]
8. Chen R, Wang J, Zhang Y, et al. Key factors of susceptibility to anti-tuberculosis drug-induced hepatotoxicity. Arch Toxicol 2015;89:883-97. [Crossref] [PubMed]
9. Ramappa V, Aithal GP. Hepatotoxicity Related to Anti-tuberculosis Drugs: Mechanisms and Management. J Clin Exp Hepatol 2013;3:37-49. [Crossref] [PubMed]
10. Oladimeji PO, Chen T. PXR: More Than Just a Master Xenobiotic Receptor. Mol Pharmacol 2018;93:119-27. [Crossref] [PubMed]
11. Li F, Lu J, Cheng J, et al. Human PXR modulates hepatotoxicity associated with rifampicin and isoniazid co-therapy. Nat Med 2013;19:418-20. [Crossref] [PubMed]
12. Chen L, Bao Y, Jiang S, et al. The Roles of Long Noncoding RNAs HNF1α-AS1 and HNF4α-AS1 in Drug Metabolism and Human Diseases. Noncoding RNA 2020;6:24. [Crossref] [PubMed]
13. Dyavar SR, Mykris TM, Winchester LC, et al. Hepatocytic transcriptional signatures predict comparative drug interaction potential of rifamycin antibiotics. Sci Rep 2020;10:12565. [Crossref] [PubMed]
14. Wu H, Reizel T, Wang YJ, et al. A negative reciprocal regulatory axis between cyclin D1 and HNF4α modulates cell cycle progression and metabolism in the liver. Proc Natl Acad Sci U S A 2020;117:17177-86. [Crossref] [PubMed]
15. Sahoo S, Singh D, Chakraborty P, et al. Emergent Properties of the HNF4α-PPARγ Network May Drive Consequent Phenotypic Plasticity in NAFLD. J Clin Med 2020;9:870. [Crossref] [PubMed]
16. Chen L, Wang P, Manautou JE, et al. Knockdown of Long Noncoding RNAs Hepatocyte Nuclear Factor 1α Antisense RNA 1 and Hepatocyte Nuclear Factor 4α Antisense RNA 1 Alters Susceptibility of Acetaminophen-Induced Cytotoxicity in HepaRG Cells. Mol Pharmacol 2020;97:278-86. [Crossref] [PubMed]
17. Chen Y, Zhang T, Wu L, et al. Metabolism and Toxicity of Emodin: Genome-Wide Association Studies Reveal Hepatocyte Nuclear Factor 4α Regulates UGT2B7 and Emodin Glucuronidation. Chem Res Toxicol 2020;33:1798-808. [Crossref] [PubMed]
18. Bauzá G, Miller G, Kaseje N, et al. Injury-induced changes in liver specific transcription factors HNF-1α and HNF-4α. J Surg Res 2012;175:298-304. [Crossref] [PubMed]
19. An Y, Wang P, Xu P, et al. An Unexpected Role of Cholesterol Sulfotransferase and its Regulation in Sensitizing Mice to Acetaminophen-Induced Liver Injury. Mol Pharmacol 2019;95:597-605. [Crossref] [PubMed]
20. Beggs KM, McGreal SR, McCarthy A, et al. The role of hepatocyte nuclear factor 4-alpha in perfluorooctanoic acid- and perfluorooctanesulfonic acid-induced hepatocellular dysfunction. Toxicol Appl Pharmacol 2016;304:18-29. [Crossref] [PubMed]
21. Lambert É, Babeu JP, Simoneau J, et al. Human Hepatocyte Nuclear Factor 4-α Encodes Isoforms with Distinct Transcriptional Functions. Mol Cell Proteomics 2020;19:808-27. [Crossref] [PubMed]
22. Tirona RG, Lee W, Leake BF, et al. The orphan nuclear receptor HNF4alpha determines PXR- and CAR-mediated xenobiotic induction of CYP3A4. Nat Med 2003;9:220-4. [Crossref] [PubMed]
23. Kodama S, Yamazaki Y, Negishi M, Pregnane X. Receptor Represses HNF4α Gene to Induce Insulin-Like Growth Factor-Binding Protein IGFBP1 that Alters Morphology of and Migrates HepG2 Cells. Mol Pharmacol 2015;88:746-57. [Crossref] [PubMed]
24. Wang Y, Yan L, Liu J, et al. The HNF1α-Regulated LncRNA HNF1α-AS1 Is Involved in the Regulation of Cytochrome P450 Expression in Human Liver Tissues and Huh7 Cells. J Pharmacol Exp Ther 2019;368:353-62. [Crossref] [PubMed]
25. Zhou J, Li Y, Liu X, et al. LncRNA-Regulated Autophagy and its Potential Role in Drug-Induced Liver Injury. Ann Hepatol 2018;17:355-63. [Crossref] [PubMed]
26. Villegas VE, Zaphiropoulos PG. Neighboring gene regulation by antisense long non-coding RNAs. Int J Mol Sci 2015;16:3251-66. [Crossref] [PubMed]
27. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet 2013;45:580-5. [Crossref] [PubMed]
28. Chen L, Bao Y, Piekos SC, et al. A Transcriptional Regulatory Network Containing Nuclear Receptors and Long Noncoding RNAs Controls Basal and Drug-Induced Expression of Cytochrome P450s in HepaRG Cells. Mol Pharmacol 2018;94:749-59. [Crossref] [PubMed]
29. Hoofnagle JH, Björnsson ES. Drug-Induced Liver Injury - Types and Phenotypes. N Engl J Med 2019;381:264-73. [Crossref] [PubMed]
30. Chakaya J, Khan M, Ntoumi F, et al. Global Tuberculosis Report 2020 - Reflections on the Global TB burden, treatment and prevention efforts. Int J Infect Dis 2021; [Epub ahead of print]. [Crossref] [PubMed]
31. Bao Y, Ma X, Rasmussen TP, et al. Genetic Variations Associated with Anti-Tuberculosis Drug-Induced Liver Injury. Curr Pharmacol Rep 2018;4:171-81. [Crossref] [PubMed]
32. Richardson M, Kirkham J, Dwan K, et al. Influence of genetic variants on toxicity to anti-tubercular agents: a systematic review and meta-analysis (protocol). Syst Rev 2017;6:142. [Crossref] [PubMed]
33. Daniel LL, Dickson AL, Chung CP. Precision medicine for rheumatologists: lessons from the pharmacogenomics of azathioprine. Clin Rheumatol 2021;40:65-73. [Crossref] [PubMed]
34. Zarei S, Costas Y, Orozco G, et al. A Web-Based Pharmacogenomics Search Tool for Precision Medicine in Perioperative Care. J Pers Med 2020;10:65. [Crossref] [PubMed]
35. Whirl-Carrillo M, McDonagh EM, Hebert JM, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther 2012;92:414-7. [Crossref] [PubMed]
36. Yang M, Pan H, Chen H, et al. Association between NR1I2 polymorphisms and susceptibility to anti-tuberculosis drug-induced hepatotoxicity in an Eastern Chinese Han population: A case-control study. Infect Genet Evol 2020;83:104349 [Crossref] [PubMed]
37. Zhang J, Zhao Z, Bai H, et al. Genetic polymorphisms in PXR and NF-κB1 influence susceptibility to anti-tuberculosis drug-induced liver injury. PLoS One 2019;14:e0222033 [Crossref] [PubMed]
38. Wang JY, Tsai CH, Lee YL, et al. Gender-Dimorphic Impact of PXR Genotype and Haplotype on Hepatotoxicity During Antituberculosis Treatment. Medicine (Baltimore) 2015;94:e982 [Crossref] [PubMed]
39. Wang Y, Xiang X, Huang WW, et al. Association of PXR and CAR Polymorphisms and Antituberculosis Drug-Induced Hepatotoxicity. Sci Rep 2019;9:2217. [Crossref] [PubMed]
40. Cusato J, Tomasello C, Simiele M, et al. Efavirenz pharmacogenetics in a cohort of Italian patients. Int J Antimicrob Agents 2016;47:117-23. [Crossref] [PubMed]
41. Cusato J, De Nicolò A, Boglione L, et al. Influence of ABCB11 and HNF4α genes on daclatasvir plasma concentration: preliminary pharmacogenetic data from the Kineti-C study. J Antimicrob Chemother 2017;72:2846-9. [Crossref] [PubMed]
42. Shen C, Zhang B, Liu Z, et al. Effects of ABCB1, ABCC2, UGT2B7 and HNF4α genetic polymorphisms on oxcarbazepine concentrations and therapeutic efficacy in patients with epilepsy. Seizure 2017;51:102-6. [Crossref] [PubMed]
43. Chen H, Liu J, Zhou Y, et al. Association of Hepatic Nuclear Factor 4 Alpha Gene Polymorphisms With Free Imatinib Plasma Levels and Adverse Reactions in Chinese Gastrointestinal Stromal Tumor Patients. Ther Drug Monit 2019;41:582-90. [Crossref] [PubMed]
44. Ren W, Zhou C, Liu Y, et al. Genetic associations of docetaxel-based chemotherapy-induced myelosuppression in Chinese Han population. J Clin Pharm Ther 2020;45:354-64. [Crossref] [PubMed]
45. Bai H, Wu Q, Hu X, et al. Clinical significance of lnc-AC145676.2.1-6 and lnc-TGS1-1 and their variants in western Chinese tuberculosis patients. Int J Infect Dis 2019;84:8-14. [Crossref] [PubMed]
46. Wu Q, Zhong H, Bai H, et al. Clinical relevance of the lnc-HNF1B-3:1 genetic polymorphisms in Western Chinese tuberculosis patients. J Clin Lab Anal 2020;34:e23076 [Crossref] [PubMed]
47. Richardson M, Kirkham J, Dwan K, et al. CYP genetic variants and toxicity related to anti-tubercular agents: a systematic review and meta-analysis. Syst Rev 2018;7:204. [Crossref] [PubMed]
48. Choi R, Jeong BH, Koh WJ, et al. Recommendations for Optimizing Tuberculosis Treatment: Therapeutic Drug Monitoring, Pharmacogenetics, and Nutritional Status Considerations. Ann Lab Med 2017;37:97-107. [Crossref] [PubMed]
49. Petros Z, Lee MM, Takahashi A, et al. Genome-wide association and replication study of anti-tuberculosis drugs-induced liver toxicity. BMC Genomics 2016;17:755. [Crossref] [PubMed]
50. Marcil V, Sinnett D, Seidman E, et al. Association between genetic variants in the HNF4A gene and childhood-onset Crohn's disease. Genes Immun 2012;13:556-65. [Crossref] [PubMed]
51. Winckler W, Graham RR, de Bakker PI, et al. Association testing of variants in the hepatocyte nuclear factor 4alpha gene with risk of type 2 diabetes in 7,883 people. Diabetes 2005;54:886-92. [Crossref] [PubMed]
52. Zazuli Z, Barliana MI, Mulyani UA, et al. Polymorphism of PXR gene associated with the increased risk of drug-induced liver injury in Indonesian pulmonary tuberculosis patients. J Clin Pharm Ther 2015;40:680-4. [Crossref] [PubMed]
53. Zhang J, Zhao Z, Bai H, et al. The Variant at TGFBRAP1 but Not TGFBR2 Is Associated with Antituberculosis Drug-Induced Liver Injury. Evid Based Complement Alternat Med 2019;2019:1685128 [Crossref] [PubMed]
54. Zhang J, Jiao L, Bai H, et al. A Notch4 missense mutation is associated with susceptibility to tuberculosis in Chinese population. Infect Genet Evol 2020;78:104145 [Crossref] [PubMed]
55. WHO. Common Terminology Criteria for Adverse Events (CTCAE) Version 5.0. Uppsala: Uppsala Monitoring Centre, 2017:1-155.
56. Nahid P, Dorman SE, Alipanah N, et al. Executive Summary: Official American Thoracic Society/Centers for Disease Control and Prevention/Infectious Diseases Society of America Clinical Practice Guidelines: Treatment of Drug-Susceptible Tuberculosis. Clin Infect Dis 2016;63:853-67. [Crossref] [PubMed]
57. Zhao Z, Peng W, Hu X, et al. SFRP1 variations influence susceptibility and immune response to Mycobacterium tuberculosis in a Chinese Han population. Infect Genet Evol 2016;37:259-65. [Crossref] [PubMed]
58. Zhang J, Zhao Z, Zhong H, et al. Importance of common TLR2 genetic variants on clinical phenotypes and risk in tuberculosis disease in a Western Chinese population. Infect Genet Evol 2018;60:173-80. [Crossref] [PubMed]
59. Zhang J, Jiao L, Song J, et al. Genetic and Functional Evaluation of the Role of FOXO1 in Antituberculosis Drug-Induced Hepatotoxicity. Evid Based Complement Alternat Med 2021;2021:3185874 [Crossref] [PubMed]
60. Muller YL, Infante AM, Hanson RL, et al. Variants in hepatocyte nuclear factor 4alpha are modestly associated with type 2 diabetes in Pima Indians. Diabetes 2005;54:3035-9. [Crossref] [PubMed]
61. Andrulionyte L, Laukkanen O, Chiasson JL, et al. Single nucleotide polymorphisms of the HNF4alpha gene are associated with the conversion to type 2 diabetes mellitus: the STOP-NIDDM trial. J Mol Med (Berl) 2006;84:701-8. [Crossref] [PubMed]
62. Silander K, Mohlke KL, Scott LJ, et al. Genetic variation near the hepatocyte nuclear factor-4 alpha gene predicts susceptibility to type 2 diabetes. Diabetes 2004;53:1141-9. [Crossref] [PubMed]
63. Sookoian S, Gemma C, Pirola CJ. Influence of hepatocyte nuclear factor 4alpha (HNF4alpha) gene variants on the risk of type 2 diabetes: a meta-analysis in 49,577 individuals. Mol Genet Metab 2010;99:80-9. [Crossref] [PubMed]
64. Boelsterli UA, Lee KK. Mechanisms of isoniazid-induced idiosyncratic liver injury: emerging role of mitochondrial stress. J Gastroenterol Hepatol 2014;29:678-87. [Crossref] [PubMed]
65. Mbatchi LC, Brouillet JP, Evrard A. Genetic variations of the xenoreceptors NR1I2 and NR1I3 and their effect on drug disposition and response variability. Pharmacogenomics 2018;19:61-77. [Crossref] [PubMed]
66. Carithers LJ, Ardlie K, Barcus M, et al. A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project. Biopreserv Biobank 2015;13:311-9. [Crossref] [PubMed]
67. Lee JH, Bugarija B, Millan EJ, et al. Systematic identification of cis-silenced genes by trans complementation. Hum Mol Genet 2009;18:835-46. [Crossref] [PubMed]
68. Kim W, Yee J, Chang BC, et al. Association between HNF4A mutations and bleeding complications in patients with stable international normalized ratio. Pharmacogenet Genomics 2019;29:200-6. [Crossref] [PubMed]
69. Eisenach PA, Roghi C, Fogarasi M, et al. MT1-MMP regulates VEGF-A expression through a complex with VEGFR-2 and Src. J Cell Sci 2010;123:4182-93. [Crossref] [PubMed]
70. Deng YP, Li W, Li YL, et al. MT1-MMP up-regulates VEGF expression in human breast carcinoma MCF-7 cells and induces tumor angiogenesis. Zhonghua Zhong Liu Za Zhi 2009;31:727-31. [PubMed]
71. Chew SC, Lim J, Singh O, et al. Pharmacogenetic effects of regulatory nuclear receptors (PXR, CAR, RXRα and HNF4α) on docetaxel disposition in Chinese nasopharyngeal cancer patients. Eur J Clin Pharmacol 2014;70:155-66. [Crossref] [PubMed]
72. Song L, Zhang ZR, Zhang JL, et al. MicroRNA-122 is involved in oxidative stress in isoniazid-induced liver injury in mice. Genet Mol Res 2015;14:13258-65. [Crossref] [PubMed]
73. Song L, Zhang Z, Zhang J, et al. Ratio of microRNA-122/155 in isoniazid-induced acute liver injury in mice. Exp Ther Med 2016;12:889-94. [Crossref] [PubMed]
74. Marcil V, Amre D, Seidman EG, et al. Hepatocyte nuclear factor 4 alpha polymorphisms and the metabolic syndrome in French-Canadian youth. PLoS One 2015;10:e0117238 [Crossref] [PubMed]
75. Chen Z, Zhang D, Liu Y, et al. Variants in hepatocyte nuclear factor 4alpha gene promoter region and type 2 diabetes risk in Chinese. Exp Biol Med (Maywood) 2010;235:857-61. [Crossref] [PubMed]

(English Language Editor: J. Jones)