Polymorphism of the N-acetyltransferase 2 gene as a susceptibility risk factor for antituberculosis drug-induced hepatotoxicity in Tunisian patients with tuberculosis
ABSTRACT Antituberculosis drug-induced hepatitis attributed to isoniazide (INH) is one of the most prevalent drug-induced liver injuries. INH is metabolized by hepatic N-acetyltransferase 2 (NAT2) to form hepatotoxins.
To evaluate whether polymorphism of the NAT2 gene was associated with antituberculosis drug-induced hepatotoxicity in Tunisian patients.
A total of 66 patients with tuberculosis (TB) who received anti-TB treatment were followed prospectively. Their NAT2 genotype was determined using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). We identified three single nucleotide polymorphisms (SNPs); 481C to T (NAT2*5B), 590G to A (NAT2*6A) and 857G to A (NAT2*7B). Univariate analysis and logistic regression analysis were used to evaluate the risk factors of isoniazid-induced hepatitis.
Fourteen patients (21.2%) were diagnosed with anti-TB drug-induced hepatitis. None of the rapid acetylators-type patients have expressed serum aminotransferase elevation. Among patients with hepatotoxicity, slow acetylators-type patients had a higher risk of hepatotoxicity than intermediate acetylators (21.4% vs. 78.6%, P=0.01). Statistical analysis revealed that the frequency of a variant diplotypes, NAT2*5B/5B and NAT2*6A/6A, were significantly increased in TB patients with hepatotoxicity, compared with those without hepatotoxicity (P=0.01, odds ratio [OR]=7.6 and P=0.029, OR=15, respectively). By contrast, the frequency of the rapid acetylation NAT2*4 allele was significantly lower in TB patients with hepatotoxicity than those without hepatotoxicity (P=0.02, OR=0.18). Moreover, 590G/G genotype was associated with decreased hepatotoxicity (P=0.01); by contrast, homozygous point mutation at position 481 and 590 were associated with a higher risk of hepatotoxicity (P=0.01).
Our results suggest that the slow-acetylator status of NAT2 is risk factor for INH-induced hepatotoxicity. Moreover, diplotypes, NAT2*5B/5B, NAT2*6A/6A, 481T/T and 590A/A, are useful new biomarkers for predicting anti-TB drug-induced hepatotoxicity.
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ABSTRACT: Genomic analysis of high-altitude populations residing in the Andes and Tibet has revealed several candidate loci for involvement in high-altitude adaptation, a subset of which have also been shown to be associated with hemoglobin levels, including EPAS1, EGLN1, and PPARA, which play a role in the HIF-1 pathway. Here, we have extended this work to high- and low-altitude populations living in Ethiopia, for which we have measured hemoglobin levels. We genotyped the Illumina 1M SNP array and employed several genome-wide scans for selection and targeted association with hemoglobin levels to identify genes that play a role in adaptation to high altitude. We have identified a set of candidate genes for positive selection in our high-altitude population sample, demonstrated significantly different hemoglobin levels between high- and low-altitude Ethiopians and have identified a subset of candidate genes for selection, several of which also show suggestive associations with hemoglobin levels. We highlight several candidate genes for involvement in high-altitude adaptation in Ethiopia, including CBARA1, VAV3, ARNT2 and THRB. Although most of these genes have not been identified in previous studies of high-altitude Tibetan or Andean population samples, two of these genes (THRB and ARNT2) play a role in the HIF-1 pathway, a pathway implicated in previous work reported in Tibetan and Andean studies. These combined results suggest that adaptation to high altitude arose independently due to convergent evolution in high-altitude Amhara populations in Ethiopia.Genome biology 01/2012; 13(1):R1. DOI:10.1186/gb-2012-13-1-r1 · 10.47 Impact Factor
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ABSTRACT: Mycobacterial diseases are a group of illnesses that cause a considerable number of deaths throughout the world, regardless of years of public health control efforts. Personalized medicine is a new but rapidly advancing field of healthcare. Personalized medicine in the field of mycobacteriology may be applied in the different levels of management such as prevention, diagnosis, treatment and prognosis. A genetic predisposition and a protein dysfunction study are recommended to tailor an individual approach in mycobacterial diseases.06/2012; 1(2-2):59-64. DOI:10.1016/j.ijmyco.2012.03.001
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ABSTRACT: Tuberculosis is one of the major public health problems worldwide. Modern antituberculous treatment can cure most patients; cure rates > 95% are achieved with standard short-course chemotherapy regimens containing isoniazid, rifampicin, pyrazinamide, and ethambutol among patients with drug-susceptible strains of tuberculosis; however, a small proportion do not respond to treatment or develop serious adverse events. Pharmacogenomic studies of drugs used in the treatment of tuberculosis could help us understand intersubject variations in treatment response. In this review, we compiled pharmacogenomic data on antituberculous drugs that were available from different settings that would give a better insight into the role of pharmacogenomics in the treatment of tuberculosis, thereby enhancing the efficacy and limiting the toxicity of existing antituberculosis medications. The PubMed database was searched from 1960 to the present using the keywords "tuberculosis", "antituberculosis treatment", "isoniazid", "rifampicin", "pyrazinamide", "ethambutol", "pharmacogenomics", and "polymorphism". Abstracts from meetings and review articles were included. Studies conducted in different settings suggest that pharmacogenomics plays a significant role in isoniazid metabolism, and impacts both treatment efficacy and frequency of adverse reactions. Single nucleotide polymorphisms influencing plasma rifampicin concentrations are also reported. No data are available regarding other first-line drugs, ie, ethambutol and pyrazinamide. There is a need to incorporate pharmacogenomics into clinical trials of tuberculosis in order to understand the factors impacting therapeutic success and occurrence of adverse drug effects.Pharmacogenomics and Personalized Medicine 09/2012; 5:89-98. DOI:10.2147/PGPM.S15454