Genome-Wide Analysis of DNA Methylation and Antituberculosis Drug-Induced Liver Injury in the Han Chinese Population

Longitudinal metabolomics of human plasma reveal metabolic dynamics and predictive markers of antituberculosis drug-induced liver injury

Tuberculosis (TB) remains the second leading cause of death from a single infectious agent and long-term medication could lead to antituberculosis drug-induced liver injury (ATB-DILI). We established a prospective longitudinal cohort of ATB-DILI with multiple timepoint blood sampling and used untargeted metabolomics to analyze the metabolic profiles of 107 plasma samples from healthy controls and newly diagnosed TB patients who either developed ATB-DILI within 2 months of anti-TB treatment (ATB-DILI subjects) or completed their treatment without any adverse drug reaction (ATB-Ctrl subjects). The untargeted metabolome revealed that 77 metabolites (of 895 total) were significantly changed with ATB-DILI progression. Among them, levels of multiple fatty acids and bile acids significantly increased over time in ATB-DILI subjects. Meanwhile, metabolites of the same class were highly correlated with each other and pathway analysis indicated both fatty acids metabolism and bile acids metabolism were up-regulated with ATB-DILI progression. The targeted metabolome further validated that 5 fatty acids had prediction capability at the early stage of the disease and 6 bile acids had a better diagnostic performance when ATB-DILI occurred. These findings provide evidence indicating that fatty acids metabolism and bile acids metabolism play a vital role during ATB-DILI progression. Our report adds a dynamic perspective better to understand the pathological process of ATB-DILI in clinical settings.

Advancing tuberculosis management: the role of predictive, preventive, and personalized medicine

Tuberculosis is a major global health issue, with approximately 10 million people falling ill and 1.4 million dying yearly. One of the most significant challenges to public health is the emergence of drug-resistant tuberculosis. For the last half-century, treating tuberculosis has adhered to a uniform management strategy in most patients. However, treatment ineffectiveness in some individuals with pulmonary tuberculosis presents a major challenge to the global tuberculosis control initiative. Unfavorable outcomes of tuberculosis treatment (including mortality, treatment failure, loss of follow-up, and unevaluated cases) may result in increased transmission of tuberculosis and the emergence of drug-resistant strains. Treatment failure may occur due to drug-resistant strains, non-adherence to medication, inadequate absorption of drugs, or low-quality healthcare. Identifying the underlying cause and adjusting the treatment accordingly to address treatment failure is important. This is where approaches such as artificial intelligence, genetic screening, and whole genome sequencing can play a critical role. In this review, we suggest a set of particular clinical applications of these approaches, which might have the potential to influence decisions regarding the clinical management of tuberculosis patients.

Genetic and Epigenetic Basis of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a rare but severe adverse drug reaction seen in pharmacotherapy and a major cause of postmarketing drug withdrawals. Advances in genome-wide studies indicate that genetic and epigenetic diversity can lead to inter-individual differences in drug response and toxicity. It is necessary to identify how the genetic variations, in the presence of environmental factors, can contribute to development and progression of DILI. Studies on microRNA, histone modification, DNA methylation, and single nucleotide polymorphisms related to DILI were retrieved from databases and were analyzed for the current research and updated to develop this narrative review. We have compiled some of the major genetic, epigenetic, and pharmacogenetic factors leading to DILI. Many validated genetic risk factors of DILI, such as variants of drug-metabolizing enzymes, HLA alleles, and some transporters were identified. In conclusion, these studies provide useful information in risk alleles identification and on implementation of personalized medicine.

Methylome-wide association study of different responses to risperidone in schizophrenia

Background: Accumulating evidence shows that DNA methylation plays a role in antipsychotic response. However, the mechanisms by which DNA methylation changes are associated with antipsychotic responses remain largely unknown. Methods: We performed a methylome-wide association study (MWAS) to evaluate the association between DNA methylation and the response to risperidone in schizophrenia. Genomic DNA methylation patterns were assessed using the Agilent Human DNA Methylation Microarray. Results: We identified numerous differentially methylated positions (DMPs) and regions (DMRs) associated with antipsychotic response. CYP46A1, SPATS2, and ATP6V1E1 had the most significant DMPs, with p values of 2.50 × 10^-6, 3.53 × 10^-6, and 5.71 × 10^-6, respectively. The top-ranked DMR was located on chromosome 7, corresponding to the PTPRN2 gene with a Šidák-corrected p-value of 9.04 × 10^-13. Additionally, a significant enrichment of synaptic function and neurotransmitters was found in the differentially methylated genes after gene ontology and pathway analysis. Conclusion: The identified DMP- and DMR-overlapping genes associated with antipsychotic response are related to synaptic function and neurotransmitters. These findings may improve understanding of the mechanisms underlying antipsychotic response and guide the choice of antipsychotic in schizophrenia.

Omics technologies to understand drug toxicity mechanisms

Drug side effects are an important study subject in pharmacology. Recent omics technologies provide a range of omics data and help to understand the biological mechanisms involved in drug effects. These modern technologies provide significant support to all biological disciplines, including drug toxicology. In this review, we provide an overview the use of omics applications to understand drug side effects at the molecular level. We discuss by available omics technologies, their possible uses, as well as their advantages and limitations.

AK2 is an AMP-sensing negative regulator of BRAF in tumorigenesis

The RAS-BRAF signaling is a major pathway of cell proliferation and their mutations are frequently found in human cancers. Adenylate kinase 2 (AK2), which modulates balance of adenine nucleotide pool, has been implicated in cell death and cell proliferation independently of its enzyme activity. Recently, the role of AK2 in tumorigenesis was in part elucidated in some cancer types including lung adenocarcinoma and breast cancer, but the underlying mechanism is not clear. Here, we show that AK2 is a BRAF-suppressor. In in vitro assays and cell model, AK2 interacted with BRAF and inhibited BRAF activity and downstream ERK phosphorylation. Energy-deprived conditions in cell model and the addition of AMP to cell lysates strengthened the AK2-BRAF interaction, suggesting that AK2 is involved in the regulation of BRAF activity in response to cell metabolic state. AMP facilitated the AK2-BRAF complex formation through binding to AK2. In a panel of HCC cell lines, AK2 expression was inversely correlated with ERK/MAPK activation, and AK2-knockdown or -knockout increased BRAF activity and promoted cell proliferation. Tumors from HCC patients showed low-AK2 protein expression and increased ERK activation compared to non-tumor tissues and the downregulation of AK2 was also verified by two microarray datasets (TCGA-LIHC and GSE14520). Moreover, AK2/BRAF interaction was abrogated by RAS activation in in vitro assay and cell model and in a mouse model of HRAS^G12V-driven HCC, and AK2 ablation promoted tumor growth and BRAF activity. AK2 also bound to BRAF inhibitor-insensitive BRAF mutants and attenuated their activities. These findings indicate that AK2 monitoring cellular AMP levels is indeed a negative regulator of BRAF, linking the metabolic status to tumor growth.

An Investigation of the Risk Factors Associated With Anti-Tuberculosis Drug-Induced Liver Injury or Abnormal Liver Functioning in 757 Patients With Pulmonary Tuberculosis

Objectives: To identify the risk factors associated with anti-tuberculosis drug-induced liver injury (AT-DILI) or abnormal living functioning from 757 patients with pulmonary tuberculosis (TB) registered at Nanshan Center for Chronic Disease Control (Nanshan CCDC), Shenzhen, Guangdong Province, China. Design and methods: We identified 757 TB patients who met our inclusion criteria by screening the Hospital Information System (HIS) at Nanshan CCDC. Next, we identified positive cases of AT-DILI or abnormal liver functioning based on results of the first-time liver function tests (LFTs) after taking anti-TB drugs. The χ2 test was used to relate the positive rate with a variety of factors. A logistic regression model was also used to identify statistically significant risk factors. Results: Of the 757 patients, the positive rate of AT-DILI or abnormal liver functioning was 37.9% (287/757). Univariate analysis revealed that the positive rate was 42.91% (212/494) for males and 28.52% (75/263) for females. The positive rate was significantly higher in males (p <0.001). Patients with an annual income of 9,231-13,845 USD had a significantly higher positive rate (67.35%; 33/49) than those with an income of 1,540-4616 USD (37.97%; 30/79) (p = 0.022). The most frequent prescription regime among positive cases was a 2 months supply of fixed dose combination Ethambutol Hydrochloride, Pyrazinamide, Rifampicin and Isoniazid Tablets (Ⅱ) 450 mg) followed by a 4 months supply of fixed dose combination Rifampin and Isoniazid Capsules (2FDC-HRZE half/4FDC-HR) at 56.03% (144/257). The least frequent prescription regime was a 2 months supply of fixed dose combination Rifampin, Isoniazid and Pyrazinamide Capsules with Ethambutol independently followed by a 4 months supply of fixed dose combination Rifampin and Isoniazid Capsules (2FDC-HRZ + EMB/4FDC-HR) at 24.27% (25/103). The difference between these two different regimes was significant (p = 0.022). With an increase in the duration of medication, patients under various prescription regimes all showed a gradual increase in the positive rate of AT-DILI or abnormal liver functioning. Conclusion: We identified several risk factors for the occurrence of AT-DILI or abnormal liver functioning, including gender, annual income, prescription regime, dosage, and treatment time.

Research Progress of Pharmacogenomics in Drug-Induced Liver Injury

Background: Drug-induced liver injury (DILI) is a common and serious adverse drug reaction with insufficient clinical diagnostic strategies and treatment methods. The only clinically well-received method is the Roussel UCLAF Causality Assessment Method scale, which can be applied to both individuals and prospective or retrospective studies. However, in severe cases, patients with DILI still would develop acute liver failure or even death. Pharmacogenomics, a powerful tool to achieve precision medicine, has been used to study the polymorphism of DILI related genes.

Summary: We summarized the pathogenesis of DILI and findings on associated genes and variations with DILI, including but not limited to HLA genes, drug metabolizing enzymes, and transporters genes, and pointed out further fields for DILI related pharmacogenomics study to provide references for DILI clinical diagnosis and treatment.

Key Messages: At present, most of the studies are mainly limited to CGS and GWAS, and there is still a long way to achieve clinical transformation. DNA methylation could be a new consideration, and ethnic differences and special populations also deserve attention.

Global DNA hypomethylation of Alu and LINE-1 transposable elements as an epigenetic biomarker of anti-tuberculosis drug-induced liver injury

Despite being highly effective, anti-tuberculosis (TB) drugs often induce adverse liver injury, anti-TB drug-induced liver injury (ATDILI), leading to treatment failure given no sensitive and selective ATDILI markers. Herein, we conducted a case–control association study to determine whether global DNA methylation of Alu and LINE-1 transposable elements responsible for genomic stability and transcriptional regulation was correlated with clinical parameters indicating ATDILI in TB patients and might serve as an ATDILI biomarker. Alu and LINE-1 methylation levels in blood leukocyte of 130 TB patients (80 ATDILI cases and 50 non-ATDILI cases) and 100 healthy controls were quantified using quantitative combined bisulfite restriction analysis. Both TB patients with and without ATDILI had significantly lower methylation levels of Alu and LINE-1 elements than healthy controls. Compared with non-ATDILI patients, Alu methylation levels were significantly decreased in ATDILI patients, commensurate with LINE-1 methylation analysis. Hypomethylation of Alu and LINE-1 measured within 1–7 days of TB treatment was independently associated with raised levels of serum aminotransferases assessed within 8–60 days of TB treatment. Receiver operating characteristic curve analysis uncovered that Alu and LINE-1 methylation levels were both more sensitive and specific for differentiating ATDILI cases from non-ATDILI cases than serum aminotransferases after starting TB treatment within 1–7 days. Kaplan-Meier analysis displayed a significant association between hypomethylation of Alu and LINE-1 elements and an increased rate of ATDILI occurrence in TB patients. Collectively, global DNA hypomethylation of Alu and LINE-1 elements would reflect ATDILI progression and might serve as novel sensitive and specific ATDILI biomarkers.

Predicting Antituberculosis Drug-Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study

Background

Tuberculosis (TB) is a pandemic, being one of the top 10 causes of death and the main cause of death from a single source of infection. Drug-induced liver injury (DILI) is the most common and serious side effect during the treatment of TB.

Objective

We aim to predict the status of liver injury in patients with TB at the clinical treatment stage.

Methods

We designed an interpretable prediction model based on the XGBoost algorithm and identified the most robust and meaningful predictors of the risk of TB-DILI on the basis of clinical data extracted from the Hospital Information System of Shenzhen Nanshan Center for Chronic Disease Control from 2014 to 2019.

Results

In total, 757 patients were included, and 287 (38%) had developed TB-DILI. Based on values of relative importance and area under the receiver operating characteristic curve, machine learning tools selected patients’ most recent alanine transaminase levels, average rate of change of patients’ last 2 measures of alanine transaminase levels, cumulative dose of pyrazinamide, and cumulative dose of ethambutol as the best predictors for assessing the risk of TB-DILI. In the validation data set, the model had a precision of 90%, recall of 74%, classification accuracy of 76%, and balanced error rate of 77% in predicting cases of TB-DILI. The area under the receiver operating characteristic curve score upon 10-fold cross-validation was 0.912 (95% CI 0.890-0.935). In addition, the model provided warnings of high risk for patients in advance of DILI onset for a median of 15 (IQR 7.3-27.5) days.

Conclusions

Our model shows high accuracy and interpretability in predicting cases of TB-DILI, which can provide useful information to clinicians to adjust the medication regimen and avoid more serious liver injury in patients.

CYP2E1, GSTM1, and GSTT1 genetic polymorphisms and their associations with susceptibility to antituberculosis drug-induced liver injury in Thai tuberculosis patients

Antituberculosis drug-induced liver injury (ATDILI) is the common adverse reaction of antituberculosis drugs. Glutathione S-transferases (GSTs), which are phase II metabolizing enzymes for detoxification, are recognized as potential mediators of hepatotoxicity. However, role of GSTs polymorphisms in ATDILI pathogenesis has never been observed in Thais. This study aimed to investigate associations between GSTs and ATDILI susceptibility. This retrospective case-control multicentered study was conducted by the collaboration from ten secondary and tertiary care hospitals across Thailand, including Northern, Central, and Southern parts of Thailand. We enrolled 80 tuberculosis (TB) patients with ATDILI and 174 those without ATDILI into the study. Polymerase chain reaction (PCR) was used to determine genetic polymorphisms of GSTM1 and GSTT1 genes. CYP2E1 genotyping data were derived from microarray data. We illustrated that GSTT1 null and GSTM1/GSTT1 dual null genotypes were correlated with an increased risk of ATDILI with odds ratio (OR) at 1.83 (95% confidence interval (CI), 1.00 to 3.35; P = 0.049) and 2.12 (95%CI, 1.02 to 4.38; P = 0.044), respectively. Interestingly, GSTT1 null and GSTM1/GSTT1 dual null genotypes were found to be correlated with an increased risk of ATDILI in Thai TB patients who carried CYP2E1 wild type phenotype with OR 2.99 (95%CI, 1.07 to 8.39; P = 0.037) and 3.44 (95%CI, 1.01 to 11.71; P = 0.048), respectively. Collectively, GSTT1 null and GSTM1/GSTT1 dual null genotypes were associated with a higher risk of ATDILI in Thai TB patients, which may serve as alternative genetic biomarkers for ATDILI.

Collagen type XVIII alpha 1 chain (COL18A1) variants affect the risk of anti-tuberculosis drug-induced hepatotoxicity: A prospective study

Background

The role of collagen type XVIII alpha 1 chain (COL18A1) in anti‐tuberculosis drug‐induced hepatotoxicity (ATDH) has not been reported. This study aimed to explore the association between of COL18A1 variants and ATDH susceptibility.

Methods

A total of 746 patients were enrolled in our study from December 2016 to April 2018, and all subjects in the study signed an informed consent form. The custom‐by‐design 2x48‐Plex SNPscanTM kit was used to genotype all selected 11 SNPs. Categorical variables were compared by chi‐square (χ²) or Fisher's exact test, while continuous variables were compared by Mann‐Whitney's U test. Plink was utilized to analyze allelic and genotypic frequencies, and genetic models. Multivariate logistic regression analyses were used to adjust potential factors. The odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were also calculated.

Results

Among patients with successfully genotyping, there were 114 cases and 612 controls. The mutant A allele of rs12483377 conferred the decreased risk of ATDH (OR = 0.13, 95%CI: 0.02–0.98, P = 0.020), and this significance still existed after adjusting age and gender (P = 0.024). The mutant homozygote AA genotype of rs12483377 was associated with decreased total protein levels (P = 0.018).

Conclusion

Our study first revealed that the A allele of COL18A1 rs12483377 was associated with the decreased risk of ATDH in the Western Chinese Han population, providing new perspective for the molecular prediction, precise diagnosis, and individual treatment of ATDH.

Combined 5-hydroxymethylcytosine content of human leucocyte antigen-B and human leucocyte antigen-DQB1 as novel biomarker for anti-tuberculosis drug-induced liver injury

This study investigated the diagnostic value of 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) contents of human leucocyte antigen (HLA)-B and HLA-DQB1 in anti-tuberculosis drug-induced liver injury (ADLI). In total, 110 ADLI patients and 120 patients without ADLI controls were enrolled. Enzyme-linked immunosorbent assay (ELISA) was used to detect the 5-mC and 5-hmC content in DNA from peripheral blood leucocytes. The univariate analysis showed that smoking, drinking, and 5-mC and 5-hmC content of HLA-B and HLA-DQB1 were significantly associated with ADLI. After adjusting for drinking and smoking, we found that 5-mC content of HLA-B and HLA-DQB1 were associated with ADLI (odds ratio [OR] = 0.251 and 0.347, respectively) and 5-hmC contents of HLA-B and HLA-DQB1 were also associated with ADLI (OR = 1.848 and 4.705, respectively). Receiver operating characteristic (ROC) analysis indicated that the 5-hmC contents of both HLA-B and HLA-DQB1 were more clinically significant than the 5-mC contents were. The combined 5-hmC level of HLA-B and HLA-DQB1 was the best diagnostic biomarker for ADLI, with the highest areas under the curve (AUC) for 0.953, sensitivity for 0.900 and specificity for 0.875. Therefore, combined 5-hmC levels of HLA-B and HLA-DQB1 could be significant evidence for diagnosis of ADLI.

The genetic variants in calcium signaling related genes influence anti-tuberculosis drug induced liver injury: A prospective study

Although many genetic variants related to anti-tuberculosis drug induced liver injury (ATDILI) have been identified, the prediction and personalized treatment of ATDILI have failed to achieve, indicating there remains an area for further exploration. This study aimed to explore the influence of single nucleotide polymorphisms (SNPs) in Bradykinin receptor B2 (BDKRB2), Teneurin transmembrane protein 2 (TENM2), transforming growth factor beta 2 (TGFB2), and solute carrier family 2 member 13 (SLC2A13) on the risk of ATDILI.The subjects comprised 746 Chinese tuberculosis (TB) patients. Custom-by-design 2x48-Plex SNPscanTM kit was employed to genotype 28 selected SNPs. The associations of SNPs with ATDILI risk and clinical phenotypes were analyzed according to the distributions of allelic and genotypic frequencies and different genetic models. The odds ratio (OR) with corresponding 95% confidence interval (CI) was calculated.Among subjects with successfully genotyped, 107 participants suffered from ATDILI during follow-up. In BDKRB2, patients with rs79280755 G allele or rs117806152 C allele were more vulnerable to ATDILI (PBonferronicorrection = .002 and .03, respectively). Rs79280755 increased the risk of ATDILI significantly whether in additive (OR = 3.218, 95% CI: 1.686-6.139, PBonferroni correction = .003) or dominant model (PBonferroni correction = .003), as well as rs117806152 (Additive model: PBonferroni correction = .05; dominant model: PBonferroni correction = .03). For TENM2, rs80003210 G allele contributed to the decreased risk of ATDILI (PBonferroni correction = .02), while rs2617972 A allele conferred susceptibility to ATDILI (PBonferroni correction = .01). Regarding rs2617972, significant findings were also observed in both additive (OR = 3.203, 95% CI: 1.487-6.896, PBonferroni correction = .02) and dominant model (PBonferroni correction = .02). Moreover, rs79280755 and rs117806152 in BDKRB2 significantly affected some laboratory indicators. However, no meaningful SNPs were observed in TGFB2 and SLC2A13.Our study revealed that both BDKRB2 and TENM2 genetic polymorphisms were interrogated in relation to ATDILI susceptibility and some laboratory indicators in the Western Chinese Han population, shedding a new light on exploring novel biomarkers and targets for ATDILI.