Allelic and genotypic frequencies of NAT2, CYP2E1, and AADAC genes in a cohort of Peruvian tuberculosis patients.
Allelic and genotypic frequencies of NAT2, CYP2E1, and AADAC genes in a cohort of Peruvian tuberculosis patients.
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DOI:
10.1002/mgg3.1764
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发表时间:
2021-10
影响因子:
2
通讯作者:
Guio H
中科院分区:
文献类型:
--
作者:
Levano KS;Jaramillo-Valverde L;Tarazona DD;Sanchez C;Capristano S;Vásquez-Loarte T;Solari L;Mendoza-Ticona A;Soto A;Rojas C;Zegarra-Chapoñan R;Guio H
We determined the frequency of genetic polymorphisms in three anti‐TB drug metabolic proteins previously reported: N‐acetyltransferase 2 (NAT2), cytochrome P450 2E1 (CYP2E1), and arylacetamide deacetylase (AADAC) within a Peruvian population in a cohort of TB patients. We genotyped SNPs rs1041983, rs1801280, rs1799929, rs1799930, rs1208, and rs1799931 for NAT2; rs3813867 and rs2031920 for CYP2E1; and rs1803155 for AADAC in 395 participants completed their antituberculosis treatment. Seventy‐four percent of the participants are carriers of slow metabolizer genotypes: NAT2*5, NAT2*6, and NAT2*7, which increase the sensitivity of INH at low doses and increase the risk of drug‐induced liver injuries. Sixty‐four percent are homozygous for the wild‐type CYP2E1*1A allele, which could increase the risk of hepatotoxicity. However, 16% had a NAT2 fast metabolizer phenotype which could increase the risk of acquiring resistance to INH, thereby increasing the risk of multidrug‐resistant (MDR) or treatment failure. The frequency of rs1803155 (AADAC*2 allele) was higher (99.9%) in Peruvians than in European American, African American, Japanese, and Korean populations. This high prevalence of slow metabolizers for isoniazid in the Peruvian population should be further studied and considered to help individualize drug regimens, especially in countries with a great genetic diversity like Peru. These data will help the Peruvian National Tuberculosis Control Program develop new strategies for therapies. High prevalence of slow metabolizers for isoniazid in the Peruvian population should be further studied and considered to help individualize drug regimens, especially in countries with a great genetic diversity like Peru.
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影响因子:
6
作者:
Pontual, Yasmin;Pacheco, Vanessa S. S.;de Castro, Liane
通讯作者:
de Castro, Liane
影响因子:
2.9
作者:
Azuma, Junichi;Ohno, Masako;Kubota, Ryuji;Yokota, Soichiro;Nagai, Takayuki;Tsuyuguchi, Kazunari;Okuda, Yasuhisa;Takashima, Tetsuya;Kamimura, Sayaka;Fujio, Yasushi;Kawase, Ichiro
通讯作者:
Kawase, Ichiro
影响因子:
3.7
作者:
Ganachari, Malathesha;Ruiz-Morales, Jorge A.;Flores-Villanueva, Pedro O.
通讯作者:
Flores-Villanueva, Pedro O.
影响因子:
4.9
作者:
Sloan DJ;McCallum AD;Schipani A;Egan D;Mwandumba HC;Ward SA;Waterhouse D;Banda G;Allain TJ;Owen A;Khoo SH;Davies GR
通讯作者:
Davies GR
DOI:
10.1093/bioinformatics/btp121
发表时间:
2009-05-01
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
作者:
Kuznetsov IB;McDuffie M;Moslehi R
通讯作者:
Moslehi R