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Towards pharmacogenomics-guided tuberculosis (TB) therapy: N-acetyltransferase-2 genotypes among TB-infected Kenyans of mixed ethnicity

Abstract

Background

Though persons of African descent have one of the widest genetic variability, genetic polymorphisms of drug-metabolising enzymes such as N-Acetyltransferase-2 (NAT2) are understudied. This study aimed to identify prevalent NAT2 single nucleotide polymorphisms (SNPs) and infer their potential effects on enzyme function among Kenyan volunteers with tuberculosis (TB) infection. Genotypic distribution at each SNP and non-random association of alleles were evaluated by testing for Hardy-Weinberg Equilibrium (HWE) and Linkage Disequilibrium (LD).

Methods

We isolated genomic DNA from cryopreserved Peripheral Blood Mononuclear Cells of 79 volunteers. We amplified the protein-coding region of the NAT2 gene by polymerase chain reaction (PCR) and sequenced PCR products using the Sanger sequencing method. Sequencing reads were mapped and aligned to the NAT2 reference using the Geneious software (Auckland, New Zealand). Statistical analyses were performed using RStudio version 4.3.2 (2023.09.1 + 494).

Results

The most frequent haplotype was the wild type NAT2*4 (37%). Five genetic variants: 282C > T (NAT2*13), 341 T > C (NAT2*5), 803A > G (NAT2*12), 590G > A (NAT2*6) and 481C > T (NAT2*11) were observed with allele frequencies of 29%, 18%, 6%, 6%, and 4% respectively. According to the bimodal distribution of acetylation activity, the predicted phenotype was 76% rapid (mainly consisting of the wildtype NAT2*4 and the NAT2*13A variant). A higher proportion of rapid acetylators were female, 72% vs 28% male (p = 0.022, odds ratio [OR] 3.48, 95% confidence interval [CI] 1.21 to 10.48). All variants were in HWE. NAT2 341 T > C was in strong complete LD with the 590G > A variant (D′ = 1.0, r2 = − 0.39) but not complete LD with the 282C > T variant (D′ = 0.94, r2 = − 0.54).

Conclusion

The rapid acetylation haplotypes predominated. Despite the LD observed, none of the SNPs could be termed tag SNP. This study adds to the genetic characterisation data of African populations at NAT2, which may be useful for developing relevant pharmacogenomic tools for TB therapy. To support optimised, pharmacogenomics-guided TB therapy, we recommend genotype-phenotype studies, including studies designed to explore gender-associated differences.

Peer Review reports

Background

Polymorphisms in genes encoding for drug metabolising enzymes can alter enzymatic activity and, as a result, alter therapeutic effectiveness and toxicity. The N-Acetyltransferase-2 (NAT2), a phase II xenobiotic enzyme, is responsible for the biotransformation of many aromatic and heterocyclic amines, such as Isoniazid (INH). The gene encoding the enzyme is located on chromosome 8 at p22. Genetic variability in N-acetylation capacity is a well-known phenomenon attributable to the polymorphisms in the NAT2 gene in humans [1, 2]. There are over 65 allelic variants to the reference NAT2*4, each possessing one or more single nucleotide polymorphisms (SNPs) in the NAT2 loci. The most frequent SNPs (presented by the reference SNP cluster identifiers (rsID) and nucleotide change) are rs1799929 (481C > T), rs1799930 (590G > A), rs1799931 (857G > A), rs1801279 (191G > A), rs1041983 (282C > T), rs1801280 (341 T > C) and rs1208 (803A > G), with varying proportions in different populations [3,4,5,6,7]. These allelic variants, determined from genotypic analyses, confer rapid/fast (2 rapid alleles) and slow (two slow alleles) enzymatic activity/phenotypic effects.

Genotypic analyses have implications for precision medicine, whereby pharmacogenomics-guided therapy has been shown to result in improved clinical effectiveness [8, 9]. Tuberculosis (TB) remains an infectious disease of global public health significance decades after the discovery of effective chemotherapy. INH, a core drug in treating TB, is subject to a bimodal distribution of metabolism, fast and slow, resulting from the variability in the NAT2 gene. People with fast enzymatic activity are prone to suboptimal drug levels, resulting in reduced efficacy, [10] and a possible contribution to the evolution of drug-resistant TB [9]. People with slow enzymatic activity are at an increased risk of drug toxicity [9]. This alteration of treatment effectiveness underscores the need for pharmacogenomics-guided TB therapy [1, 9,10,11].

Understanding population genetics is essential for pharmacogenomics-guided TB therapy, given that the NAT2 gene is a target for population-specific selection pressure. Extensive studies have demonstrated significant population-specific polymorphisms in the NAT2 gene. However, many of these were conducted among Asians and Caucasians [8, 12,13,14,15,16]. Extrapolating these data to the African population results in misrepresentation. Although pharmacogenomic studies among Africans are sparse, interethnic genetic diversity is prevalent [6, 7, 11, 12, 17,18,19,20,21,22,23]. This paper presents the results of a study that sequenced the NAT2 gene to identify prevalent SNPs and inferred their potential effects on enzyme function among people with TB infection in Kenya. The heterogeneity of this population and the possibility of recombination among the SNPs were also evaluated.

Materials and methods

Study setting and design

This cross-sectional study used data collected at enrolment into a prospective observational study of adults aged ≥18 years from three HIV care and prevention centres in Nairobi, Kenya. The clientele attending all three facilities is cosmopolitan. Laboratory procedures occurred at the Kenya Medical Research Institute, Center for Respiratory Disease Research (KEMRI CRDR) immunology laboratory and the Molecular Medicine and Infectious Diseases Laboratory of the University of Nairobi, Kenya.

Study period and population

Participants seeking HIV care and prevention services between December 2019 and December 2020 were eligible to enrol in the study. They were all of African descent from Kenya. A total of 79 participants with TB infection confirmed by a positive interferon-gamma release assay consented to genetic testing. The sample size was calculated to estimate the frequency of the least common alleles in the population reliably. Thus, it was determined based on a 0.4% allele frequency representing NAT2*7, [21, 24] one of the major known NAT2 genetic variants, [25] and one of the least common alleles in many African ethnic groups, [21, 24, 26] at a 95% confidence level.

Sample collection, preparation and DNA extraction

Blood samples for genotyping were obtained from 79 participants. Peripheral mononuclear cell samples (PBMCs) were extracted, cryopreserved at − 80 °C, and batched for genetic analysis. DNA extraction was done using the Qiagen DNA Mini Kit (Hilden, Germany) per the manufacturer’s instructions. Briefly, after thawing, 200ul of PBMCs were lysed using 200ul Lysis Buffer AL and 20ul of Proteinase K and incubated at 56 degrees Celsius for 10 minutes. For DNA precipitation, 200ul of 100% molecular-grade ethanol was added to the lysate. The lysate was then transferred to a sterile affinity spin column and spun at 6800 g for nucleic acid target capture, then washed with Wash Buffer A1 and A2, respectively, centrifuged at 6800 g and 17,000 g for 1 min, and eluted in 40ul of sterile RNAase-free water. DNA was stored at − 20 degrees for subsequent use.

N-acetyltransferase 2 gene amplification

The coding region (exon2) of the NAT2 gene was amplified by the Polymerase Chain Reaction (PCR) technique using the 96-Well Fast Thermal Cycler (Applied Biosystems, Waltham, Massachusetts, United States). Gene-specific primer set (Inqaba Biotec South Africa), forward 5’GGGATCATGGACATTGAAGCA3’ and reverse 5’ATGTTTTCTAGCATGAATCACTCTG3’ as described previously [27] were used, amplifying a total fragment length of about 1150 bp. The final PCR reaction volume (25ul) consisted of 3ul of genomic DNA, 2X Mastermix (2X Green GoTaq™ Reaction Buffer pH 8.5, 400 PM dATP, 400 PM dGTP, 400 PM dCTP, 400 PM dTTP and 3 mM MgCl2), and 10 picomoles of forward and reverse primers. The PCR conditions used are as outlined by Zahra et al. [27] Initial denaturation was set at 95 °C for 3 minutes, followed by 35 cycles consisting of a denaturation step at 95 °C for 30 seconds, an annealing step at 60 °C for 30 seconds, and an elongation step at 72 °C for 1 minute. This was completed with a final elongation cycle at 72 °C for 5 minutes [27].

Agarose gel electrophoresis and purification

To verify amplification of the NAT2 coding region, the PCR products were run on a 1% agarose gel stained with SYBR™ safe DNA gel stain (Thermo Fisher Scientific, San Francisco, USA) and visualised using UVTEC™ Gel Documentation System (Cleaver Scientific, United Kingdom). The amplified PCR fragment was purified using EXOSAP-IT™ (Applied Biosystems, Waltham, Massachusetts), whose function is the hydrolysis of excess primers and nucleotides. Briefly, PCR tubes were labelled appropriately, and 2 μl of EXOSAP-IT™ was added to each tube. To the corresponding tube, 5 μl of the initial PCR product was slowly added and incubated at 37 °C for 15 minutes following enzyme inactivation, which occurred at 80 °C for 15 minutes.

N-acetyltransferase 2 gene sequencing

The exon 2 coding region of the NAT2 gene was sequenced using Brilliant-Dye Terminator v3.1 Cycle Sequencing kit (Nimagen, Netherlands). Three microlitres (3ul) of purified PCR product was added to 7.0ul sequencing mix comprising 5X Sequencing Buffer, Brilliant Dye Terminator, 10 picomoles of forward primer 5’GGGATCATGGACATTGAAGCA3’ and a separate reaction consisting of a reverse primer 5’ATGTTTTCTAGCATGAATCACTCTG 3′ [27]. The cycling sequencing conditions were 25 cycles of 96 °C for 10 seconds, 50 °C for 5 seconds, and 60 °C for 4 minutes. Cycle Sequencing products were purified using BigDye-Xterminator Purification Kit (Applied Biosystems, Waltham, Massachusetts). Using Applied Biosystem’s 3730XLGenetic Analyzer, Sanger sequencing was performed, and AB1 files containing chromatogram data were generated.

Sequence data analyses

To screen for different SNPs, alignment of forward and reverse sequences was done separately and mapped to the Homo sapiens N-acetyltransferase 2 (NAT2) reference genome GenBank accession number NG012246 [28]. This is the NAT2*4 reference historically designated as wild type. Sequences of samples were evaluated for chromatogram peaks and overlapping bases to make consensus sequences. To ensure the accuracy of identifying homozygous and heterozygous SNPs, independent validation and interpretation of chromatogram data was conducted using bioinformatics data analysis software (Geneious Prime Dotmatics, Auckland, New Zealand). The homozygous and heterozygous SNPs were differentiated depending on electropherogram peaks and quality scores. Single clear peaks represented a homozygous SNP, whereas double peaks with different fluorescent peaks indicated a heterozygous SNP.

Acetylator phenotype classification

NAT2 enzymatic activity populations were classified into two acetylation phenotypes, slow and rapid, according to the Human NAT2 Alleles (Haplotypes) nomenclature [29]. The alleles considered rapid were the wild-type NAT24 and SNPs of 282C > T (NAT213), 481C > T (NAT211), and 803A > G (NAT212) [29, 30]. All other alleles containing SNPs of 341 T > C (NAT25), 590G > A (NAT26), 857G > A (NAT27) and 191G > A (NAT214) were considered slow [29,30,31].

Statistical analysis

Statistical analysis was performed using RStudio: Integrated Development Environment for R Version 4.3.2 (2023.09.1 + 494). Descriptive statistics were used to summarise allele frequencies and sociodemographic characteristics. A comparison of allele frequencies to published data from other African populations was done using the chi-square (χ2) test. Logistic regression analysis was used to evaluate the effects of gender, age, ethnicity and HIV status on NAT2 phenotype. The genotypic distributions at each SNP were assessed for Hardy-Weinberg equilibrium (HWE) using an exact test described by Fung T and Keenan K (eq. 9) [32] and the χ2 test. Linkage disequilibrium (LD) was measured using the squared correlation coefficient (r2) and Lewontin’s standardised disequilibrium coefficient (D′), an indicator of allelic segregation for two genetic variants. The probability level of 0.05 was considered the cut-off value for significance.

Results

Sociodemographic characteristics of participants

The sociodemographic characteristics of all the 79 participants included in the study are summarised in Table 1. Their median age (interquartile range) was 39 (12.5) years, with the majority (47%) being ≥40 years. Females were 65% (51/79), those living with HIV were 52% (41/79), and the most predominant ethnic group was the eastern Bantu at 63% (50/79).

Table 1 Sociodemographic characteristics of participants (N = 79)

NAT2 sequence analysis

The estimated amplification size of the NAT2 gene (Exon2) was 1150 bp. Seventy-nine (79) participants were analysed for their genotype at the seven common SNPs of the NAT2 gene. Five polymorphic sites located within the NAT2 coding exon were identified, namely 282C > T, 341 T > C, 481C > T, 590G > A and 803A > G.

Allele and haplotype frequencies

The detected NAT2 alleles with their frequencies are presented in Table 2. The wild-type NAT24 was the most frequent allele in 37% (29/79) of the participants. In order of predominance, the allele frequencies of known NAT2 genetic variants were 282C > T 29% (23/79), 341 T > C 18% (14/79), 803A > G 6% (5/79), 590G > A 6% (5/79) and 481C > T 4% (3/79). Including the reference NAT2*4, we inferred eleven haplotypes (Table 3). In comparison to other diverse African populations represented in published literature, [7, 19, 21, 26, 33, 34] the Kenyan population in this study had a significantly lower frequency of the NAT2*6 (590G > A) allele (χ2, p< 0.05) (Table 4). Similarly, the studied Kenyan population had significantly lower frequencies of the NAT2*5 (341 T > C) allele (compared to other Bantu and Nilotic populations of Kenya [21] and a Senegalese population [7]), the NAT2*11 (481 C > T) allele (compared to varied African populations represented in the 1000 genome project [34]), and the NAT2*12 (803A > G) allele (compared to the Zulu community [33] and African populations represented in the 1000 genome project [34]) (χ2, p < 0.05) (Table 4). Additionally, the studied population had significantly higher frequencies of the NAT2*13 (282C > T) allele (compared to a Senegalese population [7]) and the wildtype NAT2*4 allele (compared to a Senegalese population [7], the Zulu community [33] and other diverse African populations inclusive of the East African Community [19]) (χ2, p< 0.05) (Table 4). Together, these data suggest considerable diversity in the distribution of NAT2 polymorphisms in populations of African ancestry.

Table 2 Frequency of NAT2 alleles (N = 79)
Table 3 Frequency of NAT2 haplotypes and predicted phenotype (N = 79)
Table 4 Frequency of NAT2 alleles compared to published data of other African populations

Acetylation phenotype

We inferred that 60 (76%) and 19 (24%) of the 79 participants had fast and slow acetylation phenotypes, respectively (Tables 2 and 3). The rapid acetylation phenotype was primarily attributed to the wildtype NAT2*4 genotype (37%) and the NAT2*13 variant (29%), while the slow acetylation phenotype was attributed principally to the NAT2*5 variant (18%) (Tables 2 and 3). Acetylation phenotype differed significantly by gender, with a higher proportion of rapid acetylators being female, 72% vs 28% male, and vice versa (p = 0.022, odds ratio [OR] 3.48, 95% confidence interval [CI] 1.21 to 10.48) (Table 5). Our findings suggest a high prevalence of the rapid acetylation phenotype, with gender-associated differences.

Table 5 Comparison of the Predicted phenotype by sociodemographic characteristics

Genotype frequency

We found heterozygote SNPs to be more common than homozygote SNPs (154 versus 48, respectively), depicting a heterozygous excess. The highest frequency of observed heterozygote genotypes was NAT2*5 341 T > C and NAT2*13  282C > T, occurring equally at 42% (33/79), followed by NAT2*6 590G > A at a frequency of 39% (30/77). The lowest frequency of observed heterozygote genotypes was NAT2*12 803A > G and NAT2*11 481C > T, occurring equally at 37% (29/79).

Hardy-Weinberg equilibrium and linkage disequilibrium

We then examined the HWE for genotypic distribution at each SNP and the LD for the non-random association of alleles at different loci. All five variants were within HWE (based on confidence intervals and P-values > 5%; results not shown). Four of the five NAT2 variants (282C > T, 341 T > C, 590G > A, and 803 A > G) had allele frequencies > 5% and were included in the analysis for LD, as depicted in Fig. 1. NAT2 341 T > C variant was in weak negative correlation and strong complete LD with 590G > A (r2 = − 0.39, D′ = 1.0, p< 0.0001). Similarly, the NAT2 341 T > C variant was in weak negative correlation and in strong but not complete LD with the 282C > T variant (r2 = − 0.54, D′ = 0.94, p < 0.0001). NAT2 282C > T was in moderate to weak LD with 590 G > A (D′ = 0.67, r2 = 0.46, p < 0.0001), and 803A > G (D′ = 0.56, r2 = − 0.33, p < 0.0001). NAT 2803A > G displayed weak LD and positive correlation with 341 T > C (D′ = 0.29, r2 = 0.28, P< 0.001) and no LD with 590 G > A (Fig. 1). In summary, although significant pairwise LD was found between the 4 SNPs, the 803A > G displayed weak or no LD with the other SNPs, and 341 T > C was in strong LD with both 282C > T and 590G > A variants. These results suggest constant genetic variation (HWE) and the non-random association of NAT2 341 T > C with the 590G > A and 282C > T variants (strong LD).

Fig. 1
figure 1

Linkage disequilibrium heatmap (left) and pairwise disequilibrium plot (right). D, Linkage disequilibrium coefficient; D′, Lewontin’s standardised disequilibrium coefficient; r, squared correlation coefficient; X^2, chi-square; n (sample size) = 79, 1 participant had a deletion at mutation site 590G > A. Marker refers to the Single Nucleotide polymorphism (SNPs): 590GA refers to 590 G > A, 341TC refers to 341 T > C, 282CT refers to 282C > T and 803AG refers to 803A > G. Only genetic variants with a frequency higher than 5% were used to evaluate LD. On the left is the LD heatmap; the red squares represent LD, and the yellow square represents no LD. On the right is the pairwise disequilibrium plot. The plots (coloured lines which are numbered) represent the different markers (SNPs), marker 1 being 282C > T, marker 2 being 341 T > C, marker 3 being 590 G > A and marker 4 being 803A > G. The plots that are close together are in moderate to strong LD. For example, 341 T > C (2) and 590 G > A (3) are in strong complete LD and negatively correlated, while 282C > T (1) and 341 T > C (2) are in strong LD and negatively correlated. On the other hand, 803A > G is either in weak LD (with 341 T > C and 282C > T) or no LD (with 590 G > A) as specified

Discussion

Genetic variations in the NAT2 gene affect drug metabolism and modulate therapeutic effectiveness and toxicity. This is particularly important in high TB burden contexts where INH is widely used to treat TB infection (preventive therapy) and disease. INH is partly metabolized through acetylation by the NAT2 enzyme. Pharmacogenomics testing can potentially improve treatment outcomes with the progressive adoption of genetic analysis to individualize drug therapy [9, 10]. We sequenced the coding region of the NAT2 gene in 79 participants to identify prevalent SNPs and infer their potential effects on enzyme function. The heterogeneity of this population and the possibility of recombination among the SNPs were also evaluated. Multilocus sequence analysis is essential to deriving accurate genotype information in heterogenous populations such as those from Africa [19, 21, 24]. Therefore, we used the established panel of seven SNPs (rs1801279 (191G > A), rs1041983 (282C > T), rs1801280 (341 T > C), rs1799929 (481C > T), rs1799930 (590G > A), rs1208 (803A > G) and rs1799931 (857G > A)) to infer the NAT2 genotype and phenotype [35, 36].

We found the most frequent haplotype to be the wildtype NAT2*4 (37%), and five polymorphic sites were identified within the NAT2 coding exon. Together with their allelic groups, these include the 282C > T (NAT2*13) (29%), 341 T > C (NAT2*5) (18%), 803A > G (NAT2*12) (6%), 590G > A (NAT2*6) (6%) and 481C > T (NAT2*11) (4%). These haplotypes and other novel variants have previously been identified in varying proportions among diverse African populations [7, 19, 21, 26, 33, 34]. In keeping with our findings, the NAT2*13 haplotype was more predominant than the NAT2*5 and NAT2*6 haplotypes in African populations evaluated in the 1000 genome project. Contrary to our findings, the NAT2*5 and NAT2*6 haplotypes were more predominant overall than the rapid encoding wild-type NAT2*4 and NAT2*13 haplotypes in previous studies of varied African populations [7, 19, 21, 26, 33]. From two tribal groups of Kenya, the NAT2*12 (803A > G) variant (one of the least frequent in our study) was the most frequently identified [19]. This was followed by NAT2*11 (481C > T) in one of the tribal groups (the least frequent in our study) [19]. It has long been known that Sub-Saharan Africa has a high sequence variation and haplotype diversity at the NAT2 gene [7, 19, 21, 24]. Together, these data and prior studies confirm this diversity, thus underscoring the need for more population genetic studies to identify markers of therapeutic variability.

We also inferred the identified polymorphic sites’ potential effects on enzyme function. The rapid acetylation phenotype was prevalent at 76%, largely accounted for by the wild-type NAT2*4 (39%) and the variant NAT2*13A (29%). This contradicts previous findings from Sub-Saharan African populations where the slow acetylation phenotype has been reported as most prevalent, [19, 33] and similar to findings from Asian populations [7]. In keeping with findings from our study, a high frequency of the rapid acetylator phenotype has been reported amongst Nigerians, although the methods used differed [37]. Further, in a Senegalese population, an almost equal number of rapid and slow acetylation phenotypes was reported [22]. Similar to what has been found in many African populations, our study also identified the rapid acetylation haplotype NAT2*12A, which is rarely found in Asian populations [7]. Additionally, the NAT2*5 was the commonest contributor to the slow acetylator phenotype in our study, as is common in many African populations [19, 33]. Acetylation status has been suggested to vary by human adaptation to different exposures, such as dietary habits and varying environments, with agriculturalists depicting slow acetylation phenotype in contrast to hunter-gatherer communities [24, 38, 39]. Our study population was cosmopolitan, consisting of urban immigrants from different geographical settings in Kenya, which may explain the variability from previous reports. Previous data have reported no interaction between gender and NAT2 Acetylator phenotypes, [40, 41] although the populations studied were not of African ancestry. The small sample size that was predominantly female could be a source of bias in our study. Our findings of a high frequency of the rapid-encoding NAT2 haplotypes that differ from previous findings may translate to an altered therapeutic efficacy of INH during TB treatment, risking the development of drug resistance. Additionally, there may be gender interactions that need further exploration in context and with larger sample sizes designed to explore associations.

We observed HWE in all five variants. Without evolutionary influences, allele and genotype frequencies in a population will remain constant across generations, according to the Hardy-Weinberg principle [42]. Thus, HWE helps estimate the number of homozygotes and heterozygote variant carriers in genetically stable populations [43]. Deviation from HWE could signify natural selection, excessive inbreeding, mutation, genetic drift, or gene flow [44,45,46]. Deviation from HWE could also signify genotyping errors in cases of heterozygous excess, more so where no gene reference exists [43, 47]. In our study, sequencing reads were mapped and aligned to the Homo sapiens NAT2 reference, [28] reducing the likelihood of genotyping errors. Our results depict constant genetic variation in the studied population.

We observed strong and complete LD between 341 T > C and 590G > A NAT2 variants and a strong but incomplete LD between 341 T > C and 282C > T NAT2 variants. LD evaluates deviation from the random association of alleles [48] and is a key concept to designing ‘indirect’ association studies for complex diseases and identifying genes that may contribute to phenotypic variation [45]. The 341 T > C NAT2 variant is the main representative of the slow encoding NAT2*5 allele, with the 590G > A variant also indicated as a representative [29]. Our findings of strong and complete LD between 341 T > C and 590G > A suggest that detection of the 590G > A variant may also give an accurate estimation of the slow encoding NAT2*5 genotype in the studied population. On the other hand, the rapid encoding NAT2*13 allele is mainly represented by the presence of the 282C > T variant [29]. None of the other variants identified in our study are indicated as representatives of the NAT2*13 allele [29]. Although we found the 341 T > C variant to be in strong but not complete LD with the 282C > T variant, none of the volunteers with the 282C > T variant also had the homozygous 341 T > C variant. In that regard, we suggest that detection of the 282C > T variant may accurately estimate the rapid encoding NAT2*13 allele in this population. However, despite the high LD observed between the specified SNPs, none could be termed tag SNP (confirmed to be representative) as none had pairwise correlation ≥0.80.

Our study has several limitations. First, the sample size was small, which may create deviation from equilibrium and lead to biased results in association. However, the statistical power indicated that the sample size was sufficient to study the NAT2 variants in this population. Nevertheless, a larger sample size may be more suited to explore associations. Second, the acetylation phenotype was deduced from the genotype without correlating with phenotypic studies. The ongoing study plans to evaluate this aspect. Despite these limitations, several strengths exist. We used eq. 9, described by Fung T and Keenan K, to account for sampling uncertainty in assessing HWE [32]. We employed all seven signature SNPs within the NAT2 gene for genotyping. This panel is recognised for accurately typing an individual’s acetylator status. We included both HIV-negative and HIV-positive volunteers in this study. This is important for the generalisability of our study findings, given the wide use of INH in HIV populations for TB treatment and preventive therapy.

Conclusion

Using multilocus analysis, we identified five polymorphic sites within the NAT2 gene among Kenyan volunteers with TB infection. None of the SNPs could be termed tag SNP. We report a high frequency of rapid phenotype encoding NAT2 haplotypes, with gender-associated differences in acetylation phenotype. With these findings, we provide additional genetic characterisation of African populations at NAT2 that may be useful for developing relevant pharmacogenomic tools for TB therapy. We recommend continued genotyping of African populations at this locus and genotype-phenotype studies, including exploration of gender-associated differences in acetylation phenotypes towards optimised pharmacogenomics-guided TB therapy.

Availability of data and materials

Datasets supporting the conclusions of this article are available in the GenBank repository, with accession numbers OR138137 to OR138215, https://www.ncbi.nlm.nih.gov/nuccore/OR138137. Sociodemographic data are not publicly available due to privacy policy regulations but are available from the corresponding author upon reasonable request.

Abbreviations

CI:

Confidence interval

HIV:

Human immunodeficiency virus

HWE:

Hardy-Weinberg equilibrium

INH:

Isoniazid

LD:

Linkage Disequilibrium

NAT2:

N-Acetyltransferase-2

PCR:

Polymerase chain reaction

SD:

Standard deviation

SNP:

Single nucleotide polymorphisms

TB:

Tuberculosis.

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Acknowledgements

We thank the patients who volunteered for this study and their families.

LNN acknowledges the Consortium for Advanced Research Training in Africa (CARTA) for the support provided through doctoral training. CARTA is jointly led by the African Population and Health Research Centre and the University of the Witwatersrand and funded by the Carnegie Corporation of New York (Grant No. G-19–57145), Sida (Grant No:54100113), Uppsala Monitoring Centre, Norwegian Agency for Development Cooperation (Norad), the Wellcome Trust (reference no. 107768/Z/15/Z), and the UK Foreign, Commonwealth & Development Office, with support from the Developing Excellence in Leadership, Training and Science in Africa (DELTAS Africa) programme. The statements made and views expressed are solely the responsibility of the Fellow. For open access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

LNN acknowledges the NIH/Fogarty HIV Research Training Program (D43 TW011817 Tuberculosis & HIV Co-Infection Training Program in Kenya) for the support provided through advanced research training while writing this manuscript.

LNN, JOM, VN, LO and MM acknowledge Patrick Njage and Erick Ouko of the Institute of Statistics and Data Science (www.isads.org) for their support in data analysis.

LNN, JOM, VN, LO and MM thank the research team involved in data collection, particularly Mercy Kawira and Elizabeth Ndegwa, and the staff and administration of the Kenyatta National Hospital’s comprehensive care clinic, KEMRI Center for Respiratory Disease and Pumwani Maternity Hospital, for supporting this work. We thank the University of Nairobi Faculty of Health Sciences for providing the research infrastructure for this study.

Funding

This work was supported by funding from the Consortium for Advanced Research Training in Africa (CARTA). CARTA is jointly led by the African Population and Health Research Centre and the University of the Witwatersrand and funded by the Carnegie Corporation of New York (Grant No. G-19–57145), Sida (Grant No:54100113), Uppsala Monitoring Centre, Norwegian Agency for Development Cooperation (Norad), the Wellcome Trust (reference no. 107768/Z/15/Z), and the UK Foreign, Commonwealth & Development Office, with support from the Developing Excellence in Leadership, Training and Science in Africa (DELTAS Africa) programme. Funding for this work was also obtained from the Africa Centre of Excellence, Materials, Products, and Nano Technology (ACE-MAPRONANO) project. The funders had no role in the study design, data collection and analysis, publication decisions, or manuscript preparation.

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Authors and Affiliations

Authors

Contributions

LNN, JOM, and VN developed the concept and study design. LNN, JOM, VN, and MM supervised the Study. LNN and LO did the laboratory analysis. LNN, VN and LO did the data analysis. LNN wrote the main manuscript text. LNN, JOM, VN, LO and MM critically revised the manuscript. All authors have read and approved the final draft for publication.

Corresponding author

Correspondence to Lilian N. Njagi.

Ethics declarations

Ethics approval and consent to participate

This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Human Ethics Committee of the Affiliated institutions, Kenyatta National Hospital/University of Nairobi Institutional Review Board (Approval reference KNH-ERC/A/375). Verbal and written informed consent, including consent for genetic testing, was obtained from all participants.

Consent for publication

No individual personal data are included in this manuscript. All data were anonymised and extracted after obtaining informed consent and ethical approval to conduct the study.

Competing interests

The authors declare no competing interests.

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Njagi, L.N., Mecha, J.O., Mureithi, M.W. et al. Towards pharmacogenomics-guided tuberculosis (TB) therapy: N-acetyltransferase-2 genotypes among TB-infected Kenyans of mixed ethnicity. BMC Med Genomics 17, 14 (2024). https://doi.org/10.1186/s12920-023-01788-1

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