Phenotypic drug resistance and genetic mutations linked to resistance among extrapulmonary tuberculosis patients in Ethiopia: Insights from Whole Genome Sequencing

Phenotypic drug resistance and genetic mutations linked to resistance among extrapulmonary tuberculosis patients in Ethiopia: Insights from Whole Genome Sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Phenotypic drug resistance and genetic mutations linked to resistance among extrapulmonary tuberculosis patients in Ethiopia: Insights from Whole Genome Sequencing Hilina Mollalign, Dawit Hailu Alemayehu, Dereje Beyene, Kalkidan Melaku, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5302564/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 15 You are reading this latest preprint version Abstract Globally, drug-resistant tuberculosis (DR-TB) is responsible for 13% of mortality attributable to antimicrobial resistance. In Ethiopia, extrapulmonary tuberculosis (EPTB) is a significant public health challenge, and drug resistance (DR) in EPTB is often overlooked. In a cross-sectional study conducted between August 2022 and October 2023, we aimed to explore the magnitude of phenotypic drug resistance and identify genetic mutations linked to resistance using 189 Mycobacterium tuberculosis (MTB) isolates cultured from extrapulmonary clinical specimens. Additionally, we assessed the agreement of the phenotypic and whole genome sequencing (WGS) based genotypic drug resistance detection. We performed phenotypic drug sensitivity testing (pDST) using liquid culture BD BACTECTM MGITTM 960 system and WGS using Illumina NextSeq500/550. The genomic data analysis pipelines MTBSeq and TBProfiler were used to predict drug resistance-conferring mutations. The agreement between the pDST and WGS was analyzed using SPSS version 29.0 software. Our result demonstrated phenotypic resistance to at least one anti-TB drug was detected in 16.9% (32/189) of the study participants. Isoniazid-resistant rifampicin-susceptible-TB (Hr-TB) and multi-drug-resistant TB (MDR-TB) phenotypes accounted for 2.6% (5/189) and 4.2% (8/189) respectively. Prevalence of MDR-TB was 2.4% (4/170) among newly diagnosed and 21.1% (4/19) among previously treated cases. WGS identified more (14/160, 8.75%) rifampicin-resistant genotypes (RR-TB) compared to pDST (8/189, 4.2%). We have identified a putative compensatory mutation for rifampicin (rpoBSer450Leu, rpoCAsp747Ala) for the first time from an EPTB clinical specimen in Ethiopia. Overall, there was a 3.75% rifampicin mono-resistant-TB(RMR-TB) genotype, which remains undetected using the conventional pDST and represented 42.9% (6/14) of the identified RR-TB genotypes. Mutations conferring rifampicin resistance-interim (rpoB.Ser450Ala) represented the majority (83.3%) of RMR-TB. Changes in ethA genes associated with ethionamide resistance were the most common resistance (n=7, 87.5%) in MDR-TB cases. There was a strong agreement between the pDST and WGS-TB Profiler pipeline to detect RR-TB (kappa=0.8) compared to the MTBSeq pipeline (k=0.58). In conclusion, MDR-TB, Hr-TB, and interim-RMR-TB are equally important public health challenges in the realm of EPTB in Ethiopia. The role of WGS is tremendous in detecting borderline/interim RMR-TB, which will help for tailored, personalized treatment strategies. Biological sciences/Microbiology Health sciences/Diseases Extrapulmonary tuberculosis Whole genome sequencing Ethiopia Figures Figure 1 Introduction Tuberculosis (TB) is a leading cause of morbidity and mortality globally where in 2022, an estimated 10.6 million people fell ill with TB, and more than 1.3 million died 1 . The continued emergence of drug-resistant TB (DR-TB) remains a major hurdle to the global TB control efforts. Globally, 13% of mortality attributable to antimicrobial resistance is due to DR-TB 2 . In 2022, an estimated 410,000 multidrug resistant TB (MDR-TB) cases were diagnosed, and only two in five infected persons began treatment, with only 63% treatment success rate 1 . Ethiopia is among the high TB/TB-HIV burden countries 3 , with an annual TB incidence of 126 per 100,000 population 1 . Extrapulmonary TB (EPTB), defined as a TB disease affecting any organ system other than the lung, has remained a public health challenge in Ethiopia, representing 29–31% of the notified TB cases over the last decade 1 , 4 , 5 . TB lymphadenitis is the most predominant (80%) form of EPTB, and the contributing factors related to its high prevalence are not yet fully explained 6 . Hence, determinants of EPTB have been outlined as a top research priority in the national TB research road map 5 . Ultrasound-guided cytology is the most widely used diagnostic method, and poor treatment outcome remains a major concern 5 . Baseline or follow-up drug sensitivity test (DST) is often overlooked to guide treatment, because clinical specimens from EPTB patients are often pauci-bacillary and involve invasive sample collection procedures. Thus, treatment options are often extrapolated from pulmonary TB (PTB), and there is a paucity of information about drug resistances and genetic mutation profiles linked to resistance in EPTB. Genotypic drug sensitivity test (gDST) can potentially underpin the phenotypic drug sensitivity test (pDST) due to the strong agreement and with rapid detection of resistance to rifampicin (RIF) and isoniazid (INH) 7 . However, discordances have been also reported for the detection of other anti TB regimens 7 , 8 . Whole genome sequencing (WGS), an advanced genotyping method, can predict drug resistances including resistances occurring outside the hotspot resistance determining regions. Furthermore, WGS can detect mutations associated with borderline and interim resistances that remain undetected using the phenotypic and other conventional molecular methods. Thus WGS-based resistance detection in different settings assist to develop and/or update the rapid molecular assays for detecting resistances in clinical specimens 1 . Furthermore, it could also help to complete the World Health Organization’s (WHO) mutation catalogue of Mycobacterium tuberculosis (MTB) 9 . Most clinical Mycobacterium tuberculosis (MTB) examined for drug resistance prediction using WGS have been isolated from patients with PTB 7 , 10 . Despite the significant contribution of EPTB to the global and national TB case load, WGS data from these patients and their association with phenotypic resistance are not well established. In this study, we aimed to explore phenotypic drug resistance in MTB isolates from EPTB patients and identify genetic mutations linked to resistance using WGS. Additionally, we assessed the agreement between the pDST and WGS. Results Characteristics of study participants MTB isolates from 189 study participants were tested for phenotypic drug resistance. The overall mean age of the study participants was 32 years (95% CI: 30-34 years). More than half (102/189, 54%) of the study participants were males. Contact history to active TB index case was reported by 22.8%, while the remaining either had no known contact history (66.7%) or were uncertain to report about their contact history (10.5%). Previous treatment history was reported only by 10.1%, while the remaining 89.9% were newly diagnosed at the time of enrollment. Previous treatment history was associated with drug resistance (p<0.001). Table 1 describes participants’ characteristics across drug sensitivity profile. Table 1 :Demographic characteristics, clinical history and phenotypic drug resistance profiles of EPTB patients, from Aug 2022-Oct 2023. Participant characteristics Resistance to at least one drug Hr-TB MDR/RR-TB Pan susceptible Total n p-value Age Mean age in years 33 40 28 32 0.158 Sex Female Male 8 (9.2%) 11 (10.8%) 3 (3.4%) 2 (2%) 4 (4.6%) 4 (3.9%) 72 (82.8%) 85 (83.3%) 87 102 0.905 HIV status Reactive Non-reactive Unknown 1 (5.3%) 14 (9.9%) 4 (13.8%) 1 (5.3%) 4 (2.8%) - 1 (5.3%) 4 (2.8%) 3 (10.3%) 16 (84.2%) 119 (84.4%) 22 (75.9%) 19 141 29 0.350 Treatment history New Retreatment 15 (8.8%) 4 (21.1%) 3 (1.8%) 2 (10.5%) 4 (2.4%) 4 (21.1%) 148 (87.1%) 9 (47.4%) 170 19 <0.001 Contact history Yes No Unknown 4 (9.3%) 12 (9.5%) 3 (15%) 1 (2.3%) 3 (2.4%) 1 (5%) 4 (9.3%) 3 (2.4%) 1 (5%) 34 (79.1%) 108 (85.7%) 15 (75%) 43 126 20 0.339 First and second line phenotypic drug resistance detection Phenotypic resistance to at least one anti-TB drug was detected in 16.9% (32/189) isolates. Isoniazid resistant-rifampicin susceptible TB (Hr-TB) and MDR-TB/RR-TB phenotypes accounted for 2.6% (5/189) and 4.2% (8/189), respectively. Prevalence of MDR-TB among newly diagnosed and previously treated cases were 2.4% (4/170) and 21.1% (4/19), respectively, whereas Hr-TB accounted for 1.8% (3/170) and 10.5% (2/19) in newly diagnosed and previously treated cases, respectively. There was a statistically significant association between previous TB treatment history and drug resistance ( p<0.001 ). Resistance to delamanid was detected in one (0.5%) MDR-TB patient and WGS identified a missense variant (fbiC. Ser706Pro) at 18.31% allele frequency. However, this mutation has not been described in the catalogue of mutations 9 . Resistance to second line injectables, capreomycin (3.7%) and kanamycin (2.7%) were detected mostly in non MDR -TB cases, but no resistance to amikacin was detected. Only one MDR-TB and one Hr-TB isolates showed resistance to capreomycin. Table 2 shows the proportion of resistant isolates detected for each anti-TB drug. Table 2 : Proportion of drug resistance among phenotypic DST and WGS-tested isolates. Drug name Phenotypic resistance Genotypic resistance MTBSeq (n=160) Genotypic resistance TBProfiler (n=160) New (n=170) Retreatment (n=19) p-value New (n=144) Retreatment (n=16) p-value New (n=144) Retreatment (n=16) p-value STR 4(2.4%) 6(31.6%) <0.001 3(2.1%) 4(25%) 0.002 8(5.6%) 6(37.5%) <0.001 INH 7(4.1%) 6(31.6%) <0.001 5(3.5%) 5(31.3%) <0.001 5(3.5%) 5(31.3%) <0.001 RIF 4(2.4%) 4(21.1%) 0.024 10(6.9%) 4(25%) 0.036 4(2.8%) 4(25%) 0.004 EMB 2(1.2%) 1(5.3%) 0.275 3(2.1%) 3(18.8%) 0.014 3(2.1%) 3(18.8%) 0.014 PZA 9(5.3%) 5(26.3%) 0.007 3(2.1%) 2(12.5%) 0.079 5(3.5%) 4(25%) 0.006 CAP 6(3.6%) 1(5.3%) 0.534 - 1(1.3%) 0.100 4(2.8%) - 0.653 KAM 4(2.4%) 1(5.3%) 0.418 - - - - - DEL 1(0.6%) - 1.000 - - 1(0.7%) - 0.9 ETO Not tested - - 1(6.3%) 0.100 4(2.8%) 3(18.8%) 0.023 WGS based drug resistance detection Whole genome sequencing-based drug resistance results were available for 160 isolates. The frequency of genetic drug resistance conferring mutations as depicted by TB profiler and MTBSeq for each drug is presented in Figure 1. WGS detected 87.5% of phenotypically identified MDR-TB. Phenotypic DST, on the other hand, failed to detect one MDR-TB genotype, which has a missense variant (rpoB.His445Ser). This mutation is a group-2 WHO confidence grade mutation associated with borderline rifampicin resistance 9 . MTBSeq detected more rifampicin resistant-TB (RR-TB) cases; 8.75% (n=14/160) than TB Profiler; 5% (n=8/160) and pDST; 4.2% (8/189). Nearly half (6/14, 42.9%) of these RR-TB appeared interim rifampicin mono resistant-TB (RMR-TB) (Table 3). WGS detected MDR-TB in 5%, all with high-level isoniazid (INH) resistance mutation (KatG.Ser315Thr). Moreover, one MDR-TB isolate had a putative compensatory mutation for rifampicin (rpoBSer450Leu, rpoCAsp747Ala). This MDR-TB isolate had a double point mutation and one phylogenetic single nucleotide polymorphism (SNP) at INH resistance determining region (inhASer94Ala, katGSer140Asn phylo SNP, katGSer315Asn). This isolate was identified from a male, 19-year-old TB retreatment patient from a rural part of Ethiopia. The patient has reported a history of contact with an active TB case. The patient was a TB treatment defaulter with a history of treatment interruption for more than two months. A putative compensatory mutation is defined based on a previously reported criteria 21 in which the isolate should carry a secondary RNA polymerase mutation (rpoC/A mutation) with a primary mutation on hotspot rifampicin resistance determining region (RRDR), mostly rpoBSer450Leu, the slow growth rate of the primary culture and the mutation shall never happen in drug susceptible isolate. Based on this, our study isolate harbors a mutation that has been reported as a compensatory mutation 22-24 . To our knowledge, this is the first compensatory mutation report from Ethiopia identified from clinical EPTB specimen. Furthermore, an upstream gene variant of (ahpC_c.-88G>A c.-77delT) was detected among MDR-TB and Hr-TB carrying KatG mutations. A mutation on ahpC gene is known to compensate for the katG deficit of isoniazid resistance 25 . Table 3 : Mutation profiles of phenotypically susceptible RMR-TB as predicted by MTBSeq pipeline. ID Nucleotide change Resistant/Susceptible Allele frequency WHO- confidence grading ETB_26 Ser450Ala R 5.98 Assoc. w R-interim ETB_52 Asn438Thr R 6.03 Unreported ETB_102 Ser450Ala R 13.72 Assoc. w R-interim ETB_105 Ser450Ala R 15.63 Assoc. w R-interim ETB_108 Ser450Ala R 14.31 Assoc. w R-interim ETB_130 Ser450Ala R 19.74 Assoc. w R-interim Hetero-resistance detection and prevalence Hetero resistance, which is a precursor to the development of fully resistant populations, was identified based on the frequency of alleles for a specific variant using WGS. In this study hetero resistance was detected in RMR-TB only. At a 10% threshold (4/6, 66.7%) and at a 5% threshold, two (2/6, 33.3%) of the identified RMR-TB showed hetero-resistance (Table 3). In these genotypes, variants conferring drug resistance appeared at an average allele frequency of 12.6%. Level of agreement between WGS and pDST A comparative analysis of MTBSeq and TBProfiler pipelines using the phenotypic method as a reference showed that both pipelines detected 76.9% of pINH resistances with 23.1% phenotype-genotype discordances. Of the RR-TB phenotypes, both pipelines detected 87.5% with 12.5% discordance. MTBSeq identified six other additional RR-TB genotypes that have not been detected by the phenotypic method. There was a strong agreement between the pDST and WGS-TBProfiler pipeline for detecting RR-TB cases, kappa coefficient ( k=0.8 ) as compared to MTBSeq pipeline ( k=0. 58 ) (Table 4). A start lost codon on fbiB gene conferring resistance for delamanid /protionamide was detected by TBProfiler in a single non-MDR-TB isolate. Polydrug resistance in MDR-TB cases was detected for ethambutol (EMB, 75%), streptomycin (STR, 100%), pyrazinamide (PZA, 75%) and ethionamide (ETO, 75%) using TBProfiler. MTBSeq, on the other hand, detected more EMB (87.5%) than streptomycin (50%) and pyrazinamide (37.5%) among MDR-TB cases. Table 4 : Agreement between pDST and WGS for the detection of drug resistance and coefficient of agreement. gDST/MTBSeq Vs. Phenotypic DST gDST/TBProfiler Vs. Phenotypic DST Drug % R within phenotypes* % R within genotypes** Kappa coefficient p-value % R within phenotypes* % R within genotypes** Kappa coefficient p-value RIF 87.5% 50% 0.582 <0.001 87.5% 87.5% 0.814 <0.001 INH 76.9% 100% 0.865 <0.001 76.9% 100% 0.865 <0.001 STR 40% 57.1% 0.414 0.003 100% 71.4% 0.784 <0.001 EMB 100% 50% 0.590 <0.001 100% 50% 0.590 <0.001 PZA 50% 100% 0.693 <0.001 80% 88.9% 0.848 <0.001 *% Phenotypically detected R also appeared resistant in gDST; **Genotypically detected R also appeared resistant in pDST Assessment of genomic variants in phenotypic resistant and WGS susceptible isolatesPhenotypic resistances that are not detected by WGS are summarized in Table 5. In these phenotypes, the group 3 mutations of “uncertain significance” according to the WHO mutation catalogue 9 were identified. Manual correlation of these phenotypes with additional variants with genotypic frameshift and upstream gene variants increases the concordance of the two methods by 3 isolates for STR and PZA, where most pDST- WGS discrepancies were observed. Table 5 : List of phenotypically identified resistances with genotypic non-resistance variants Drug Change Confidence grading Type Total-n STR Gly69Asp Uncertain significance Missense variant 3 c.386delG Uncertain significance Frameshift variant 1 c.115delC Uncertain significance Frameshift variant 1 c.351delG Uncertain significance Frameshift variant 1 INH Arg463Leu + Thr203Thr + Pro29Pro Uncertain significance Phylogenetic-SNP 2 RIF c.-218G>A Uncertain significance Up-stream gene variant 1 PZA c.390_391dupGG Uncertain significance Frameshift variant 1 c.-125delC Uncertain significance Upstream gene variant 2 p. Tyr41* Uncertain significance Stop gained 1 AMK/CAP/KAN -187C>T Uncertain significance Upstream gene variant 9 Evaluation of resistance conferring mutations in susceptible phenotypes All rpoB mutations conferring RIF resistance detected in susceptible phenotypes listed below were associated with interim genotypic resistance. The identified mutations in KatG and embB for INH and EMB respectively had also appeared in the hotspot region with no hetero resistance in these regions (Table 6). Table 6 : List of resistance conferring mutations in phenotypically susceptible new and previously treated patients Drug Mutation Other pDST resistances Confidence grading New Retreatment HIV status Total STR c.102delG - Uncertain significance 3 - NR 3 RIF His445Tyr + His445Pro INH+PZA Assoc w R-Interim - 1 NR 1 Ser450Ala - Assoc w R-Interim 5 - NR 5 Asn438Thr - Unreported 1 - NR 1 Val170Phe STR+INH Assoc w R 1 - NR 1 EMB Met306Val STR+INH +RIF+PZA Assoc w R 1 1 NR 2 Met306Ile +CAP Assoc w R 1 - Unknown 1 Discussion In this study, we have described the phenotypic drug sensitivity and genetic mutations conferring drug resistance in MTB isolates collected from EPTB patients in Ethiopia. To our knowledge, this study is the first to report phenotypic and WGS-based genotypic data on large numbers (n=189 and 160 isolates, respectively) of EPTB clinical specimens collected from different regions of Ethiopia. In this study, there was a high rate (3.75%) of RMR-TB overall which remains undetected using the conventional DST approach. All were detected among newly diagnosed people with TB and without reported TB contact history and were thus classified as primary RMR-TB. This figure accounts for nearly half (42.8%) of RMR among RR-TB/MDR-TB cases in our study population. Mutations at rpoBSer450Ala were detected in 83.3% of these RMR-TB cases and are classified as the group-2 rifampicin resistance associated-interim in the WHO mutation catalogue 9 . These mutations appeared as a minority variant of rifampicin hetero-resistance-interim, and hence usually result in phenotypic susceptibility and poor treatment outcome 26 . Similarly a study from South Africa reported a high rate (22.7%) of RMR-TB among routinely diagnosed MDR/RR-TB patients 27 . A study from Antwerp reference laboratory also identified a borderline rpoB mutation in a proportion of 20%-30% RMR-TB among new cases of random drug resistance surveys (DRS) 28 . Such interim resistances are missed on the conventional pDST and the rapid molecular diagnostics, which have a direct clinical impact such as underdiagnosis, inadequate treatment and re-occurrence of secondary cases 29 . Given the high occurrence of such phenotypically undetected interim primary RMR in our study population, we conclude that the presence of minority variants of interim RMR-TB could contribute to poor treatment outcomes or re-occurrence of EPTB in the study setting. It has been well understood that resistance-associated mutations result in high fitness cost, slowing the in vitro growth rates and transmission compared to the susceptible phenotypes 30 . Despite the increased fitness cost due to resistance mutations, rifampicin resistant phenotypes continued spreading globally, challenging the TB control effort. The potential spreading capability regardless of fitness cost in these resistant phenotypes was explained by the presence of a compensatory mutation 31 . We have reported an MDR-TB isolate carrying a compensatory mutation in the rpoC gene (rpoBSer450Leu, rpoCAsp747Ala) as defined previously 21,32 . After an intensive literature search, we report this mutation as the fourth of its type next to the first report by Casali et al followed by two isolates, reported by Alame et.al and Liu et al 22-24 . A study from South Africa explored the clinical significance of compensatory mutations and reported that most compensatory evolution in MTB was associated with smear positive PTB, increased transmission and increased mutational burden 33 . In line with this, the clinical strain of (ETB-162) had also carried multiple mutations conferring resistances to INH (inhA p. Ser94Ala katG p. Ser140Asn katG p. Ser315Asn), EMB (embB p. Met06Val), PZA (pncA p. Trp68Gly), injectable drugs (rrs n.514A>C), and ETO (ethA c.1054delG), in addition to the identified RIF resistance conferring mutations. Furthermore, the patient with this strain has a history of treatment default and reported contact history to active TB index. The clinical specimen (a 2x2cm mass lesion with actively draining sinus tracts) of this patient was initially diagnosed as acute suppurative inflammation at the time of enrollment into this study. The primary culture also showed a slow growth rate (18 days and 6 hrs for 253 growth units). The cost of slow growth rate could explain the multiple resistance-associated mutations 32 . Most compensatory mutations occurring in rpoABC genes were documented to decrease the fitness cost of rpoBSer450Leu mutation 32 . Isoniazid, another potent first-line anti-TB drug used in the treatment of DS-TB, potentiates the effect of rifampicin and also prevents mycolic acid synthesis 34 . Because the Xpert MTB/RIF (Cepheid, Sunnyvale, CA, USA) assay only detects rifampicin resistance, and the use of Xpert XDR is recommended when rifampicin resistance is identified, diagnosis of Hr-TB has been overlooked. In this study, Hr-TB was observed in 1.8% of newly diagnosed and 10.5% of previously treated EPTB cases. Globally, Hr-TB is estimated to occur in 8% of all forms of TB 35 . Another multicountry analysis of cross-sectional data reported Hr-TB prevalence of 7.4% among new and 11.4% among previously treated patients 36 . In this study, RMR-TB is mainly identified in newly diagnosed EPTB cases whereas the proportion of Hr-TB is higher in previously treated EPTB patients. A similar finding has also been reported from PTB patients 37 . Resistance to isoniazid also comes with increased fitness cost. To overcome this resistance-associated fitness cost, MTB restores the loss of function through co-evolution of compensatory mutation 25 . Our WGS gDST identified a compensatory mutation at the locus of the ahpC gene (ahpC_c.-88G>A c.-77delT) for a katG deficit (katGSer315Thr). Similar mutations at the oxyR-ahpC (-88g>a) intergenic region were reported among majority of Hr-TB and MDR-TB isolates 38 . Another study has also reported deletion of the upstream gene locus (-77del) as a novel compensatory effects marker with strong evidence for convergent evolution, co-occurrence with loss of function mutations in katG as well as association with INH resistant isolates 25 . Based on this evidence, our study identified KatG deficit (ahpC_c.-88G>A c.-77delT) co-occurring with katG Ser315Thr in one MDR-TB and one Hr-TB case. Patients with Hr-TB are at an increased risk of developing MDR-TB 39 . Similarly, 90% of rifampicin resistant TB are also resistant to isoniazid, thus MDR-TB 40 . Resistance to either of these drugs results in unsuccessful treatment outcome and fuels acquisition of resistance to the other 36 . In our study, we have reported a high prevalence (4.2%) of MDR-TB which accounts for 2.4% and 21.1% among newly diagnosed and previously treated cases, respectively. In 2022, the global MDR/RR-TB prevalence was 3.3% among new and 17% among previously treated cases 1 . Though drug resistances in EPTB in our setting have not been explicitly addressed using WGS, previous studies have also reported a higher rate of MDR-TB in EPTB 41 . The prevalence of hetero-resistance among RMR-TB was 42.9%. Hetero resistance has a clinical implication of treatment failure and progression to a fully resistant strain. A recent study from India reported 64.8% rifampicin hetero resistance 42 . Another systematic review reported a pooled prevalence of 7% rifampicin hetero-resistance with a varying prevalence in different settings 43 . In Ethiopia, a study of clinical isolates collected through the nation-wide drug resistance survey reported low prevalence of rifampicin hetero-resistance (1.6%) among MDR-TB patients, even though the authors noted that the overall prevalence was not rare 44 . Low hetero-resistance prevalence was reported among MDR-TB strains from Pakistan (3.9%), with nearly half of the studied strains harboring compensatory mutations 45 . The high rate of rifampicin mono-resistance, accompanied by hetero-resistance, identified in this study warrants post-treatment close monitoring of EPTB for treatment failure or re-occurrence of the disease. Evaluation of the agreement level for drug resistance detection between pDST and WGS in this study revealed a moderate ( kappa: 0.41-0.6 ) to great ( kappa: 0.81-1 ) agreement across a spectrum of the analyzed first and second line anti-TB drugs. There was a moderate agreement ( kappa: 0.582 ) between the MTBSeq pipeline and pDST to detect rifampicin resistance. TBProfiler identified 57.1% of RR-TB detected by MTBSeq ( kappa=0.73 ). As all RMR-TB were detected only through MTBSeq, the reduced agreement between the two pipelines may be explained by the incomplete repertoire of the characterized mutations in the TBProfiler databases 25 . In this study, we observed a high rate of interim RMR-TB that remains undetected by the currently recommended critical concentration for pDST. This signifies the role of WGS in detecting interim and hetero resistances, which will aid personalized treatment strategies. Lowering the critical concentration of rifampicin from 1µg/ml to 0.5 µg/ml for pDST may benefit the detection of interim rifampicin resistance using pDST approaches 46,47 . Overall, MDR-TB, Hr-TB and interim-RMR-TB are equally important public health challenges in the realm of EPTB in Ethiopia. This study lacks data on treatment outcomes to follow through anti-TB drug resistance patterns with clinical conditions of affected patients, which is worth addressing in future studies. While we have not characterized the protein structure of the reported compensatory mutations, further studies on the role of compensatory mutations and restored fitness in resistant strains are warranted. Methods Study design and setting A prospective cross-sectional study was conducted on MTB isolates grown on Mycobacterium Growth Indicator Tube (MGIT) and Lowenstein Jensen (LJ) media cultured from extrapulmonary clinical specimen. The clinical samples were obtained from 542 prospectively enrolled presumptive EPTB study participants between August 2022 and October 2023(unpublished data). A total of 189 study participants were bacteriologically confirmed for EPTB. The study participants were enrolled from six high EPTB hotspot regions of central and northern part of Ethiopia, within a one-year time frame, which reflects the national representativeness of the sampling and shows a snapshot of the current EPTB status in Ethiopia. Clinical specimens were retrieved from lymph node aspirates 81% (153/189) and other clinical specimens 19% (36/189) such as pleural fluid, ascitic fluid, pericardial fluid, synovial fluid and urine. All MTB culture positive isolates were checked for purity and subjected to pDST and WGS. The study protocol was approved by the institutional ethics review boards (IRB) of the Ethiopian Public Health Institute (# EPHI-IRB-433-2022) and Addis Ababa University, College of Natural and Computational Sciences (# CNS-IRB/06/14/2022). The research was performed in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants and/or their legal guardians. Laboratory investigations Phenotypic drug sensitivity test (pDST) Phenotypic DST was performed using BACTEC MGIT 960 at a predefined critical drug concentrations 11 of first-line anti-TB drugs: rifampicin [RIF (1.0 µg/ml)], isoniazid [INH (0.1 µg/ml)], streptomycin [STR (1.0 µg/ml)], ethambutol [EMB (5.0 µg/ml)], and pyrazinamide [PZA (100 µg/ml)]. All isolates identified as rifampicin resistant (RR), multidrug resistant (MDR) or isoniazid resistant, rifampicin susceptible TB (Hr-TB) were further tested for second-line drug resistance at predefined critical concentrations 12 of bedaquiline [BDQ (1 µg/ml)], clofazimine [CFZ (1 µg/ml)], delamanid [DLM (0.06 µl/ml)], linezolid [LZD (1 µg/ml), levofloxacin [(LFX (1.0 µg/ml)], moxifloxacin [MFX (0.25 µg/ml)], and ofloxacin [OFX (2 µg/ml)]. Nucleic acid (DNA) extraction and library preparation MTBC isolates grown on LJ media with confluent growth of 3-4 weeks were used for sequencing. The genomic DNA extraction using the N -acetyl- N , N , N -trimethyl ammonium bromide (CTAB) method, precipitation, purification and elution was performed following the standard protocol 13 . Briefly, two to three loop-full of MTB colonies were scrubbed from LJ culture and transferred into a tube containing 400 𝜇l of Tris-EDTA (TE) buffer. The cells were heat killed with a pre-warmed heat block at 80 o C for 1hr and lysed using 50 𝜇l of lysozyme at 37 o C for 1h. The concentration and purity of the extracted genomic DNA was measured using the fluorometric Qubit4 14 and spectrophotometric Nano Drop. Library preparation was done using Illumina DNA prep kit following the standard protocol 15 . Whole genome sequencing (WGS) and quality control Library preparation was performed at the Armauer Hansen Research Institute (AHRI), and whole genome sequencing was performed at the Ethiopian Public Health Institute (EPHI) using an Illumina NextSeq550 (Illumina San Diego, CA, USA) instrument. WGS based drug resistance detection To detect drug resistance associated mutations, the sequence reads were aligned to a reference genome M. tuberculosis H37Rv ATCC 27294 (NC_000962.3). SNPs calling was made using Sam tools v1.6 16 at thresholds of minimum mapping quality of 20, minimum base quality at a position of 20, minimum read depth at a position of 8X, and maximum strand bias for a position of 90%. To detect hetero resistance, defined as the occurrence of mixed wild type and mutant sub population in an organism 17 , the variant calling was performed using the minimum mapping quality of 20, minimum base quality at a position of 20, minimum read depth at a position of 2X, and maximum strand bias at a position of 10%. Resistance conferring mutation was predicted using two bioinformatics pipelines, MTBseq pipeline 18 and TB Profiler 19 , by application of the MEM algorithm of the Burrows-Wheeler alignment tool v0.7.17 20 . Statistical analysis All data were double entered into EpiData version 4.6.0.6 and exported to SPSS version 29.0 software (SPSS Inc., Chicago, Illinois, USA). Descriptive statistics and binary and multinomial logistic regression models were used to describe variables as appropriate. The probability level of <0.05 was considered statistically significant. Kappa statistics were used to evaluate the strength of agreement between pDST and WGS drug resistance prediction. We interpreted a kappa coefficient value as low agreement if (k=<0.4), moderate agreement if (k=0.41-0.6), substantial agreement if (k=0.61-0.8) and great agreement if (k=0.81-1.0), as previously described 8 . Abbreviations STR: streptomycin INH: isoniazid RIF: rifampicin EMB: ethambutol PZA: pyrazinamide AMK: amikacin CAP: capreomycin KAN: kanamycin ETO: ethionamide DEL: delamanid Declarations Data availability All data associated with the main finding is provided in tables and figures. The raw sequence data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) under BioProject number PRJNA1174701. Acknowledgements This work was supported, in part by the NIH Fogarty International Center Global Infectious Diseases grant D43TW009127, the Ethiopian Public Health Institute (EPHI), the core support from the Armauer Hansen Research Institute (AHRI), and Addis Ababa University. The supporting institutes had no role in the study design, data collection and analyses, decision for publication, or manuscript preparation. We thank Ashleigh Nicole Cox from Georgia State University for providing us with an English Language edit to this research paper. Author contributions HM designed the study, analyzed data and wrote the manuscript including comments from all authors. BY and GD conducted phenotypic drug sensitivity tests. DHA, KM and DC conducted molecular characterization. AG, AA and BA performed bioinformatics analysis. SM, JMC, MG, LW, KB and AG reviewed the manuscript. DB, KB and LW supervised the study. All authors read and approved of the final manuscript. Competing interests The authors declared no competing interests. References World Health Organization. Global Tuberculosis Report;2023. Farhat, M. et al. Drug-resistant tuberculosis: a persistent global health concern. Nat. Rev. Microbiol. 22 (10), 617–635 (2024). World Health Organization, Global lists of high-burden countries for TB, multi-drug / rifampicin-resistant TB(MDR/RR-TB), and TB/HIV, 2021–2025. (2021). World Health Organization. Global Tuberculosis Report;2022. National Tuberculosis Research. Roadmap 2022–2026 3rd edition, M.o.H., Ethiopia. Berg, S. et al. Investigation of the high rates of extrapulmonary tuberculosis in Ethiopia reveals no single driving factor and minimal evidence for zoonotic transmission of Mycobacterium bovis infection. BMC Infect. Dis. 15 , 112 (2015). Vīksna, A. et al. Genotypic and phenotypic comparison of drug resistance profiles of clinical multidrug-resistant Mycobacterium tuberculosis isolates using whole genome sequencing in Latvia. BMC Infect. Dis. 23 , 638 (2023). Ahmad, S., Mokaddas, E., Al-Mutairi, N., Eldeen, H. S. & Mohammadi, S. Discordance across Phenotypic and Molecular Methods for Drug Susceptibility Testing of Drug-Resistant Mycobacterium tuberculosis Isolates in a Low TB Incidence Country. PLOS ONE . 11 , e0153563 (2016). Catalogue of mutations in. Mycobacterium tuberculosis complex and their association with drug resistance, second edition (World Health Organization, 2023). Licence: CC BY-NC-SA 3.0 IGO. Katale, B. Z. et al. Whole genome sequencing of Mycobacterium tuberculosis isolates and clinical outcomes of patients treated for multidrug-resistant tuberculosis in Tanzania. BMC Genom. 21 , 174 (2020). Technical report on critical concentrations for drug susceptibility. testing of isoniazid and the rifamycins (rifampicin, rifabutin and rifapentine). Geneva: World Health Organization; (2021). World Health Organization. Technical Report on critical concentrations for drug susceptibility testing of medicines used in the treatment of drug-resistant tuberculosis, (2018). de Almeida, I. N., da Silva Carvalho, W., Rossetti, M. L., Costa, E. R. D. & de Miranda, S. S. Evaluation of six different DNA extraction methods for detection of Mycobacterium tuberculosis by means of PCR-IS6110: preliminary study. BMC Res. Notes . 6 , 561 (2013). Qubit 4. 0 Fluorometer User Guide. Catalog Number Q33226. Publication Number MAN0017209. Revision D.0. Illumina, D. N. A. Prep Reference Guide, Document#1000000025416v09,2020. Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics . 27 , 2987–2993 (2011). European Respiratory Journal 33, 368 (2009). Kohl, T. A. et al. MTBseq: a comprehensive pipeline for whole genome sequence analysis of Mycobacterium tuberculosis complex isolates. PeerJ . 6 , e5895 (2018). Phelan, J. E. et al. Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs. Genome Med. 11 , 41 (2019). Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics . 25 , 1754–1760 (2009). Comas, I. et al. Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes. Nat. Genet. 44 , 106–110 (2012). Liu, Q. et al. Have compensatory mutations facilitated the current epidemic of multidrug-resistant tuberculosis? Emerg. Microbes Infect. 7 , 98 (2018). Casali, N. et al. Microevolution of extensively drug-resistant tuberculosis in Russia. Genome Res 22, 735 – 45 (2012). Alame Emane, A. K., Guo, X., Takiff, H. E. & Liu, S. Drug resistance, fitness and compensatory mutations in Mycobacterium tuberculosis. Tuberculosis . 129 , 102091 (2021). Napier, G., Campino, S., Phelan, J. E. & Clark, T. G. Large-scale genomic analysis of Mycobacterium tuberculosis reveals extent of target and compensatory mutations linked to multi-drug resistant tuberculosis. Sci. Rep. 13 , 623 (2023). Zhang, X. et al. Genomic markers of drug resistance in Mycobacterium tuberculosis populations with minority variants. J. Clin. Microbiol. 61 , e0048523 (2023). Salaam-Dreyer, Z. et al. Rifampicin-Monoresistant Tuberculosis Is Not the Same as Multidrug-Resistant Tuberculosis: a Descriptive Study from Khayelitsha, South Africa. Antimicrob. Agents Chemother. 65 10.1128/aac.00364 – 21 (2021). Van Deun, A. et al. Mycobacterium tuberculosis borderline rpoB mutations: emerging from the unknown. Eur. Respir. J. 58 , 2100783 (2021). Van Armand, T. D. Kya Jai Maug Aung, Mohamed Anwar Hossain, Mourad Gumusboga, Willem Bram De Rijk, Sabira Tahseen, Bouke Catherine de Jong, Leen Rigouts. Mycobacterium tuberculosis borderline rpoB mutations: emerging from the unknown. Eur. Respir J. 58 , 2100783 (2021). Mariam, D. H., Mengistu, Y., Hoffner, S. E. & Andersson, D. I. Effect of rpoB mutations conferring rifampin resistance on fitness of Mycobacterium tuberculosis. Antimicrob. Agents Chemother. 48 , 1289–1294 (2004). Gagneux, S. et al. The competitive cost of antibiotic resistance in Mycobacterium tuberculosis. Science . 312 , 1944–1946 (2006). Conkle-Gutierrez, D. et al. Novel and reported compensatory mutations in rpoABC genes found in drug resistant tuberculosis outbreaks. Front. Microbiol. 14 (2024). Goig, G. A. et al. Effect of compensatory evolution in the emergence and transmission of rifampicin-resistant Mycobacterium tuberculosis in Cape Town, South Africa: a genomic epidemiology study. Lancet Microbe . 4 , e506–e515 (2023). Unissa, A. N., Subbian, S., Hanna, L. E. & Selvakumar, N. Overview on mechanisms of isoniazid action and resistance in Mycobacterium tuberculosis. Infect. Genet. Evol. 45 , 474–492 (2016). World Health Organization, Global Tuberculosis Report. (2017). Dean, A. S. et al. Prevalence and genetic profiles of isoniazid resistance in tuberculosis patients: A multicountry analysis of cross-sectional data. PLoS Med. 17 , e1003008 (2020). Zhang, L. et al. Treatment outcomes of retreated patients with isoniazid/rifampicin resistant pulmonary tuberculosis. BMC Infect. Dis. 24 , 7 (2024). Norouzi, F. et al. Significance of the coexistence of non-codon 315 katG, inhA, and oxyR-ahpC intergenic gene mutations among isoniazid-resistant and multidrug-resistant isolates of Mycobacterium tuberculosis: a report of novel mutations. Pathog Glob Health . 116 , 22–29 (2022). Srinivasan, V. et al. Sources of Multidrug Resistance in Patients With Previous Isoniazid-Resistant Tuberculosis Identified Using Whole Genome Sequencing: A Longitudinal Cohort Study. Clin. Infect. Dis. 71 , e532–e539 (2020). Gibson, J., Donnan, E. & Eather, G. Management of rifampicin mono-resistant tuberculosis in Queensland, Australia: a retrospective case series. Respirol. Case Rep. 6 , e00366 (2018). Diriba, G. et al. Drug resistance and its risk factors among extrapulmonary tuberculosis in Ethiopia: A systematic review and meta-analysis. PLOS ONE . 16 , e0258295 (2021). Desikan, P. et al. Heteroresistance to rifampicin & isoniazid in clinical samples of patients with presumptive drug-resistant tuberculosis in Central India. Indian J. Med. Res. 157 , 174–182 (2023). Ye, M. et al. Antibiotic heteroresistance in Mycobacterium tuberculosis isolates: a systematic review and meta-analysis. Ann. Clin. Microbiol. Antimicrob. 20 , 73 (2021). Getahun, M., Ameni, G., Mollalign, H., Diriba, G. & Beyene, D. Genotypic and phenotypic drug-resistance detection and prevalence of heteroresistance in patients with isoniazid- and multidrug-resistant tuberculosis in Ethiopia. IJID Reg. 2 , 149–153 (2022). Khan, A. S. et al. Characterization of rifampicin-resistant Mycobacterium tuberculosis in Khyber Pakhtunkhwa, Pakistan. Sci. Rep. 11 , 14194 (2021). Wang, W. et al. Reevaluating Rifampicin Breakpoint Concentrations for Mycobacterium tuberculosis Isolates with Disputed rpoB Mutations and Discordant Susceptibility Phenotypes. Microbiol. Spectr. 10 , e0208721 (2022). Xia, H. et al. Detection of Mycobacterium tuberculosis Rifampicin Resistance Conferred by Borderline rpoB Mutations: Xpert MTB/RIF is Superior to Phenotypic Drug Susceptibility Testing. Infect. Drug Resist. 15 , 1345–1352 (2022). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5302564","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":380060565,"identity":"4a267f12-e347-4306-9a46-f37014e88cea","order_by":0,"name":"Hilina 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Institute","correspondingAuthor":false,"prefix":"","firstName":"Kidist","middleName":"","lastName":"Bobosha","suffix":""},{"id":380060579,"identity":"147af550-b8e2-4174-a727-d4acb343ae33","order_by":14,"name":"Liya Wassie","email":"","orcid":"","institution":"Armauer Hansen Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Liya","middleName":"","lastName":"Wassie","suffix":""}],"badges":[],"createdAt":"2024-10-21 08:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5302564/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5302564/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-40253-8","type":"published","date":"2026-02-15T15:57:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71635190,"identity":"49e52711-0faa-4d59-9445-a0aacc33ddf7","added_by":"auto","created_at":"2024-12-17 10:07:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79551,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of resistance conferring mutations detected by TB profiler and MTBSeq among EPTB isolates\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5302564/v1/2e6f4b60936a6dceeb4efd4f.png"},{"id":102785205,"identity":"d94b816c-5681-4706-aad9-40b5313db02e","added_by":"auto","created_at":"2026-02-16 16:02:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1691246,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5302564/v1/ba4ade17-976c-45e3-81f4-81403c40e021.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Phenotypic drug resistance and genetic mutations linked to resistance among extrapulmonary tuberculosis patients in Ethiopia: Insights from Whole Genome Sequencing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) is a leading cause of morbidity and mortality globally where in 2022, an estimated 10.6\u0026nbsp;million people fell ill with TB, and more than 1.3\u0026nbsp;million died\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The continued emergence of drug-resistant TB (DR-TB) remains a major hurdle to the global TB control efforts. Globally, 13% of mortality attributable to antimicrobial resistance is due to DR-TB\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In 2022, an estimated 410,000 multidrug resistant TB (MDR-TB) cases were diagnosed, and only two in five infected persons began treatment, with only 63% treatment success rate\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEthiopia is among the high TB/TB-HIV burden countries\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, with an annual TB incidence of 126 per 100,000 population\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Extrapulmonary TB (EPTB), defined as a TB disease affecting any organ system other than the lung, has remained a public health challenge in Ethiopia, representing 29\u0026ndash;31% of the notified TB cases over the last decade\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. TB lymphadenitis is the most predominant (80%) form of EPTB, and the contributing factors related to its high prevalence are not yet fully explained\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Hence, determinants of EPTB have been outlined as a top research priority in the national TB research road map\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Ultrasound-guided cytology is the most widely used diagnostic method, and poor treatment outcome remains a major concern\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Baseline or follow-up drug sensitivity test (DST) is often overlooked to guide treatment, because clinical specimens from EPTB patients are often pauci-bacillary and involve invasive sample collection procedures. Thus, treatment options are often extrapolated from pulmonary TB (PTB), and there is a paucity of information about drug resistances and genetic mutation profiles linked to resistance in EPTB.\u003c/p\u003e \u003cp\u003eGenotypic drug sensitivity test (gDST) can potentially underpin the phenotypic drug sensitivity test (pDST) due to the strong agreement and with rapid detection of resistance to rifampicin (RIF) and isoniazid (INH)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, discordances have been also reported for the detection of other anti TB regimens\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Whole genome sequencing (WGS), an advanced genotyping method, can predict drug resistances including resistances occurring outside the hotspot resistance determining regions. Furthermore, WGS can detect mutations associated with borderline and interim resistances that remain undetected using the phenotypic and other conventional molecular methods. Thus WGS-based resistance detection in different settings assist to develop and/or update the rapid molecular assays for detecting resistances in clinical specimens\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Furthermore, it could also help to complete the World Health Organization\u0026rsquo;s (WHO) mutation catalogue of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMost clinical \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB) examined for drug resistance prediction using WGS have been isolated from patients with PTB\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Despite the significant contribution of EPTB to the global and national TB case load, WGS data from these patients and their association with phenotypic resistance are not well established. In this study, we aimed to explore phenotypic drug resistance in MTB isolates from EPTB patients and identify genetic mutations linked to resistance using WGS. Additionally, we assessed the agreement between the pDST and WGS.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMTB isolates from 189 study participants were tested for phenotypic drug resistance. The overall mean age of the study participants was 32 years (95% CI: 30-34 years). More than half (102/189, 54%) of the study participants were males. Contact history to active TB index case was reported by 22.8%, while the remaining either had no known contact history (66.7%) or were uncertain to report about their contact history (10.5%). Previous treatment history was reported only by 10.1%, while the remaining 89.9% were newly diagnosed at the time of enrollment. Previous treatment history was associated with drug resistance (p\u0026lt;0.001). Table 1 describes participants\u0026rsquo; characteristics across drug sensitivity profile.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e:Demographic characteristics, clinical history and phenotypic drug resistance profiles\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eof EPTB patients, from Aug 2022-Oct 2023.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipant characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResistance to at least one drug\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHr-TB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDR/RR-TB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePan susceptible\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal \u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean age in years\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8 (9.2%)\u003c/p\u003e\n \u003cp\u003e11 (10.8%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (3.4%)\u003c/p\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (4.6%)\u003c/p\u003e\n \u003cp\u003e4 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e72 (82.8%)\u003c/p\u003e\n \u003cp\u003e85 (83.3%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eReactive\u003c/p\u003e\n \u003cp\u003eNon-reactive\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 (5.3%)\u003c/p\u003e\n \u003cp\u003e14 (9.9%)\u003c/p\u003e\n \u003cp\u003e4 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 (5.3%)\u003c/p\u003e\n \u003cp\u003e4 (2.8%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 (5.3%)\u003c/p\u003e\n \u003cp\u003e4 (2.8%)\u003c/p\u003e\n \u003cp\u003e3 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16 (84.2%)\u003c/p\u003e\n \u003cp\u003e119 (84.4%)\u003c/p\u003e\n \u003cp\u003e22 (75.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment history\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNew\u003c/p\u003e\n \u003cp\u003eRetreatment\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15 (8.8%)\u003c/p\u003e\n \u003cp\u003e4 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (1.8%)\u003c/p\u003e\n \u003cp\u003e2 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (2.4%)\u003c/p\u003e\n \u003cp\u003e4 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e148 (87.1%)\u003c/p\u003e\n \u003cp\u003e9 (47.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eContact history\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (9.3%)\u003c/p\u003e\n \u003cp\u003e12 (9.5%)\u003c/p\u003e\n \u003cp\u003e3 (15%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003cp\u003e3 (2.4%) \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 (5%) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (9.3%)\u003c/p\u003e\n \u003cp\u003e3 (2.4%)\u003c/p\u003e\n \u003cp\u003e1 (5%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e34 (79.1%)\u003c/p\u003e\n \u003cp\u003e108 (85.7%)\u003c/p\u003e\n \u003cp\u003e15 (75%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFirst and second line phenotypic drug resistance detection\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePhenotypic resistance to at least one anti-TB drug was detected in 16.9% (32/189) isolates. Isoniazid resistant-rifampicin susceptible TB (Hr-TB) and MDR-TB/RR-TB phenotypes accounted for 2.6% (5/189) and 4.2% (8/189), respectively. Prevalence of MDR-TB among newly diagnosed and previously treated cases were 2.4% (4/170) and 21.1% (4/19), respectively, whereas Hr-TB accounted for 1.8% (3/170) and 10.5% (2/19) in newly diagnosed and previously treated cases, respectively. There was a statistically significant association between previous TB treatment history and drug resistance (\u003cem\u003ep\u0026lt;0.001\u003c/em\u003e). Resistance to delamanid was detected in one (0.5%) MDR-TB patient and WGS identified a missense variant (fbiC. Ser706Pro) at 18.31% allele frequency. However, this mutation has not been described in the catalogue of mutations\u003csup\u003e9\u003c/sup\u003e. Resistance to second line injectables, capreomycin (3.7%) and kanamycin (2.7%) were detected mostly in non MDR -TB cases, but no resistance to amikacin was detected. Only one MDR-TB and one Hr-TB isolates showed resistance to capreomycin. Table 2 shows the proportion of resistant isolates detected for each anti-TB drug.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e: Proportion of drug resistance among phenotypic DST and WGS-tested isolates.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"749\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhenotypic resistance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotypic resistance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMTBSeq (n=160)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotypic resistance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTBProfiler (n=160)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNew (n=170)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRetreatment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNew\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=144)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRetreatment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=16)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNew\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=144)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRetreatment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=16)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4(2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6(31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3(2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4(25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e8(5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eINH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e7(4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6(31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e5(3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5(31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e5(3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5(31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eRIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4(2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4(21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e10(6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4(25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4(2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4(25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eEMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2(1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1(5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3(2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e3(2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003ePZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e9(5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5(26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3(2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e5(3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4(25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eCAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e6(3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1(5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1(1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4(2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eKAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4(2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1(5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eDEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1(0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1(0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eETO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eNot tested\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4(2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eWGS based drug resistance detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole genome sequencing-based drug resistance results were available for 160 isolates. The frequency of genetic drug resistance conferring mutations as depicted by TB profiler and MTBSeq for each drug is presented in Figure 1. WGS detected 87.5% of phenotypically identified MDR-TB. Phenotypic DST, on the other hand, failed to detect one MDR-TB genotype, which has a missense variant (rpoB.His445Ser). This mutation is a group-2 WHO confidence grade mutation associated with borderline rifampicin resistance\u003csup\u003e9\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMTBSeq detected more rifampicin resistant-TB (RR-TB) cases; 8.75% (n=14/160) than TB Profiler; 5% (n=8/160) and pDST; 4.2% (8/189). Nearly half (6/14, 42.9%) of these RR-TB appeared interim rifampicin mono resistant-TB (RMR-TB) (Table 3). WGS detected MDR-TB in 5%, all with high-level isoniazid (INH) resistance mutation (KatG.Ser315Thr). Moreover, one MDR-TB isolate had a putative compensatory mutation for rifampicin (rpoBSer450Leu, rpoCAsp747Ala). This MDR-TB isolate had a double point mutation and one phylogenetic single nucleotide polymorphism (SNP) at INH resistance determining region (inhASer94Ala, katGSer140Asn phylo SNP, katGSer315Asn). This isolate was identified from a male, 19-year-old TB retreatment patient from a rural part of Ethiopia. The patient has reported a history of contact with an active TB case. The patient was a TB treatment defaulter with a history of treatment interruption for more than two months. A putative compensatory mutation is defined based on a previously reported criteria\u003csup\u003e21\u003c/sup\u003e in which the isolate should carry a secondary RNA polymerase mutation (rpoC/A mutation) with a primary mutation on hotspot rifampicin resistance determining region (RRDR), mostly rpoBSer450Leu, the slow growth rate of the primary culture and the mutation shall never happen in drug susceptible isolate. Based on this, our study isolate harbors a mutation that has been reported as a compensatory mutation\u003csup\u003e22-24\u003c/sup\u003e. To our knowledge, this is the first compensatory mutation report from Ethiopia identified from clinical EPTB specimen. Furthermore, an upstream gene variant of (ahpC_c.-88G\u0026gt;A c.-77delT) was detected among MDR-TB and Hr-TB carrying KatG mutations. A mutation on ahpC gene is known to compensate for the katG deficit of isoniazid resistance\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e: Mutation profiles of phenotypically susceptible RMR-TB as predicted by MTBSeq pipeline.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNucleotide change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResistant/Susceptible\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHO- confidence grading\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eETB_26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eSer450Ala\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eAssoc. w R-interim\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eETB_52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsn438Thr\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnreported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eETB_102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eSer450Ala \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e13.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eAssoc. w R-interim\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eETB_105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eSer450Ala\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e15.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eAssoc. w R-interim\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eETB_108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eSer450Ala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e14.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eAssoc. w R-interim\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eETB_130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eSer450Ala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e19.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eAssoc. w R-interim\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eHetero-resistance detection and prevalence \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHetero resistance,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewhich is a precursor to the development of fully resistant populations, was identified based on the frequency of alleles for a specific variant using WGS. In this study hetero resistance was detected in RMR-TB only. At a 10% threshold (4/6, 66.7%) and at a 5% threshold, two (2/6, 33.3%) of the identified RMR-TB showed hetero-resistance (Table 3). In these genotypes, variants conferring drug resistance appeared at an average allele frequency of 12.6%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLevel of agreement between WGS and pDST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comparative analysis of MTBSeq and TBProfiler pipelines using the phenotypic method as a reference showed that both pipelines detected 76.9% of pINH resistances with 23.1% phenotype-genotype discordances. Of the RR-TB phenotypes, both pipelines detected 87.5% with 12.5% discordance. MTBSeq identified six other additional RR-TB genotypes that have not been detected by the phenotypic method. There was a strong agreement between the pDST and WGS-TBProfiler pipeline for detecting RR-TB cases, kappa coefficient (\u003cem\u003ek=0.8\u003c/em\u003e) as compared to MTBSeq pipeline (\u003cem\u003ek=0.\u003c/em\u003e\u003cem\u003e58\u003c/em\u003e) (Table 4). A start lost codon on fbiB gene conferring resistance for delamanid /protionamide was detected by TBProfiler in a single non-MDR-TB isolate. Polydrug resistance in MDR-TB cases was detected for ethambutol (EMB, 75%), streptomycin (STR, 100%), pyrazinamide (PZA, 75%) and ethionamide (ETO, 75%) using TBProfiler. MTBSeq, on the other hand, detected more EMB (87.5%) than streptomycin (50%) and pyrazinamide (37.5%) among MDR-TB cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e: Agreement between pDST and WGS for the detection of drug resistance and coefficient of agreement.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 333px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cstrong\u003egDST/MTBSeq Vs. Phenotypic DST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 333px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u003cstrong\u003egDST/TBProfiler Vs. Phenotypic DST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDrug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e% R within phenotypes*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e% R within genotypes**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eKappa coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e% R within phenotypes*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e% R within genotypes**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eKappa coefficient\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eRIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e87.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e87.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e87.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eINH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e76.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e76.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e57.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e71.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eEMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ePZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e88.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*% Phenotypically detected R also appeared resistant in gDST; **Genotypically detected R also appeared resistant in pDST\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAssessment of genomic variants in phenotypic resistant and WGS susceptible isolatesPhenotypic resistances that are not detected by WGS are summarized in Table 5. In these phenotypes, the group 3 mutations of \u0026ldquo;uncertain significance\u0026rdquo; according to the WHO mutation catalogue\u003csup\u003e9\u003c/sup\u003e were identified. Manual correlation of these phenotypes with additional variants with genotypic frameshift and upstream gene variants increases the concordance of the two methods by 3 isolates for STR and PZA, where most pDST- WGS discrepancies were observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e: List of phenotypically identified resistances with genotypic non-resistance variants\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"691\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConfidence grading\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal-n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eGly69Asp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eUncertain significance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eMissense variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ec.386delG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eUncertain significance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eFrameshift variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ec.115delC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eUncertain significance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eFrameshift variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ec.351delG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eUncertain significance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eFrameshift variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eINH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eArg463Leu + Thr203Thr + Pro29Pro\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eUncertain significance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003ePhylogenetic-SNP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eRIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ec.-218G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eUncertain significance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eUp-stream gene variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003ePZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ec.390_391dupGG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eUncertain significance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eFrameshift variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ec.-125delC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eUncertain significance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eUpstream gene variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ep. Tyr41*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eUncertain significance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eStop gained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eAMK/CAP/KAN\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-187C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eUncertain significance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eUpstream gene variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of resistance conferring mutations in susceptible phenotypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll rpoB mutations conferring RIF resistance detected in susceptible phenotypes listed below were associated with interim genotypic resistance. The identified mutations in KatG and embB for INH and EMB respectively had also appeared in the hotspot region with no hetero resistance in these regions (Table 6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e: List of resistance conferring mutations in phenotypically susceptible new and previously treated patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"747\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther pDST resistances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConfidence grading\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNew\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRetreatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003ec.102delG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eUncertain significance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eRIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003eHis445Tyr + His445Pro\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eINH+PZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eAssoc w R-Interim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003eSer450Ala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eAssoc w R-Interim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsn438Thr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnreported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003eVal170Phe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eSTR+INH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eAssoc w R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eEMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003eMet306Val\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eSTR+INH\u003c/p\u003e\n \u003cp\u003e+RIF+PZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eAssoc w R\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003eMet306Ile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e+CAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eAssoc w R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we have described the phenotypic drug sensitivity and genetic mutations conferring drug resistance in MTB isolates collected from EPTB patients in Ethiopia. To our knowledge, this study is the first to report phenotypic and WGS-based genotypic data on large numbers (n=189 and 160 isolates, respectively) of EPTB clinical specimens collected from different regions of Ethiopia. In this study, there was a high rate (3.75%) of RMR-TB overall which remains undetected using the conventional DST approach. All were detected among newly diagnosed people with TB and without reported TB contact history and were thus classified as primary RMR-TB. This figure accounts for nearly half (42.8%) of RMR among RR-TB/MDR-TB cases in our study population. Mutations at rpoBSer450Ala were detected in 83.3% of these RMR-TB cases and are classified as the group-2 rifampicin resistance associated-interim in the WHO mutation catalogue\u003csup\u003e9\u003c/sup\u003e. These mutations appeared as a minority variant of rifampicin hetero-resistance-interim, and hence usually result in phenotypic susceptibility and poor treatment outcome\u003csup\u003e26\u003c/sup\u003e. Similarly a study from South Africa reported a high rate (22.7%) of RMR-TB among routinely diagnosed MDR/RR-TB patients\u003csup\u003e27\u003c/sup\u003e. A study from Antwerp reference laboratory also identified a borderline rpoB mutation in a proportion of 20%-30% RMR-TB among new cases of random drug resistance surveys (DRS)\u003csup\u003e28\u003c/sup\u003e. Such interim resistances are missed on the conventional pDST and the rapid molecular diagnostics, which have a direct clinical impact such as underdiagnosis, inadequate treatment and re-occurrence of secondary cases\u003csup\u003e29\u003c/sup\u003e. Given the high occurrence of such phenotypically undetected interim primary RMR in our study population, we conclude that the presence of minority variants of interim RMR-TB could contribute to poor treatment outcomes or re-occurrence of EPTB in the study setting. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt has been well understood that resistance-associated mutations result in high fitness cost, slowing the \u003cem\u003ein vitro\u003c/em\u003e growth rates and transmission compared to the susceptible phenotypes\u003csup\u003e30\u003c/sup\u003e. Despite the increased fitness cost due to resistance mutations, rifampicin resistant phenotypes continued spreading globally, challenging the TB control effort. The potential spreading capability regardless of fitness cost in these resistant phenotypes was explained by the presence of a compensatory mutation\u003csup\u003e31\u003c/sup\u003e. We have reported an MDR-TB isolate carrying a compensatory mutation in the rpoC gene (rpoBSer450Leu, rpoCAsp747Ala) as defined previously\u003csup\u003e21,32\u003c/sup\u003e. After an intensive literature search, we report this mutation as the fourth of its type next to the first report by Casali et al followed by two isolates, reported by Alame et.al and Liu et al\u003csup\u003e22-24\u003c/sup\u003e. A study from South Africa explored the clinical significance of compensatory mutations and reported that most compensatory evolution in MTB was associated with smear positive PTB, increased transmission and increased mutational burden\u003csup\u003e33\u003c/sup\u003e. In line with this, the clinical strain of (ETB-162) had also carried multiple mutations conferring resistances to INH (inhA p. Ser94Ala katG p. Ser140Asn katG p. Ser315Asn), EMB (embB p. Met06Val), PZA (pncA p. Trp68Gly), injectable drugs (rrs n.514A\u0026gt;C), and ETO (ethA c.1054delG), in addition to the identified RIF resistance conferring mutations. Furthermore, the patient with this strain has a history of treatment default and reported contact history to active TB index. The clinical specimen (a 2x2cm mass lesion with actively draining sinus tracts) of this patient was initially diagnosed as acute suppurative inflammation at the time of enrollment into this study. The primary culture also showed a slow growth rate (18 days and 6 hrs for 253 growth units). The cost of slow growth rate could explain the multiple resistance-associated mutations\u003csup\u003e32\u003c/sup\u003e. Most compensatory mutations occurring in rpoABC genes were documented to decrease the fitness cost of rpoBSer450Leu mutation\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIsoniazid, another potent first-line anti-TB drug used in the treatment of DS-TB, potentiates the effect of rifampicin and also prevents mycolic acid synthesis\u003csup\u003e34\u003c/sup\u003e. Because the Xpert MTB/RIF (Cepheid, Sunnyvale, CA, USA) assay only detects rifampicin resistance, and the use of Xpert XDR is recommended when rifampicin resistance is identified, diagnosis of Hr-TB has been overlooked. In this study, Hr-TB was observed in 1.8% of newly diagnosed and 10.5% of previously treated EPTB cases. Globally, Hr-TB is estimated to occur in 8% of all forms of TB\u003csup\u003e35\u003c/sup\u003e. Another multicountry analysis of \u0026nbsp;cross-sectional data reported Hr-TB prevalence of 7.4% among new and 11.4% among previously treated patients\u003csup\u003e36\u003c/sup\u003e. In this study, RMR-TB is mainly identified in newly diagnosed EPTB cases whereas the proportion of Hr-TB is higher in previously treated EPTB patients. A similar finding has also been reported from PTB patients\u003csup\u003e37\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResistance to isoniazid also comes with increased fitness cost. To overcome this resistance-associated fitness cost, MTB restores the loss of function through co-evolution of compensatory mutation\u003csup\u003e25\u003c/sup\u003e. Our WGS gDST identified a compensatory mutation at the locus of the ahpC gene (ahpC_c.-88G\u0026gt;A c.-77delT) for a katG deficit (katGSer315Thr). Similar mutations at the oxyR-ahpC (-88g\u0026gt;a)\u0026nbsp;intergenic region were reported among majority of Hr-TB and MDR-TB isolates\u003csup\u003e38\u003c/sup\u003e. Another study has also reported deletion of the upstream gene locus (-77del) as a novel compensatory effects marker with strong evidence for convergent evolution, co-occurrence with loss of function mutations in katG as well as association with INH resistant isolates\u003csup\u003e25\u003c/sup\u003e. Based on this evidence, our study identified KatG deficit (ahpC_c.-88G\u0026gt;A c.-77delT) co-occurring with katG Ser315Thr in one MDR-TB and one Hr-TB case.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients with Hr-TB are at an increased risk of developing MDR-TB\u003csup\u003e39\u003c/sup\u003e. Similarly, 90% of rifampicin resistant TB are also resistant to isoniazid, thus MDR-TB\u003csup\u003e40\u003c/sup\u003e. Resistance to either of these drugs results in unsuccessful treatment outcome and fuels acquisition of resistance to the other\u003csup\u003e36\u003c/sup\u003e. In our study, we have reported a high prevalence (4.2%) of MDR-TB which accounts for 2.4% and 21.1% among newly diagnosed and previously treated cases, respectively. In 2022, the global MDR/RR-TB prevalence was 3.3% among new and 17% among previously treated cases\u003csup\u003e1\u003c/sup\u003e. Though drug resistances in EPTB in our setting have not been explicitly addressed using WGS, previous studies have also reported a higher rate of MDR-TB in EPTB\u003csup\u003e41\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe prevalence of hetero-resistance among RMR-TB was 42.9%. Hetero resistance has a clinical implication of treatment failure and progression to a fully resistant strain. A recent study from India reported 64.8% rifampicin hetero resistance\u003csup\u003e42\u003c/sup\u003e. Another systematic review reported a pooled prevalence of 7% rifampicin hetero-resistance with a varying prevalence in different settings\u003csup\u003e43\u003c/sup\u003e. In Ethiopia, a study of clinical isolates collected through the nation-wide drug resistance survey reported low prevalence of rifampicin hetero-resistance (1.6%) among MDR-TB patients, even though the authors noted that the overall prevalence was not rare\u003csup\u003e44\u003c/sup\u003e. Low hetero-resistance prevalence was reported among MDR-TB strains from Pakistan (3.9%), with nearly half of the studied strains harboring compensatory mutations\u003csup\u003e45\u003c/sup\u003e. The high rate of rifampicin mono-resistance, accompanied by hetero-resistance, identified in this study warrants post-treatment close monitoring of EPTB for treatment failure or re-occurrence of the disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvaluation of the agreement level for drug resistance detection between pDST and WGS in this study revealed a moderate (\u003cem\u003ekappa: 0.41-0.6\u003c/em\u003e) to great (\u003cem\u003ekappa: 0.81-1\u003c/em\u003e) agreement across a spectrum of the analyzed first and second line anti-TB drugs. There was a moderate agreement (\u003cem\u003ekappa: 0.582\u003c/em\u003e) between the MTBSeq pipeline and pDST to detect rifampicin resistance. TBProfiler identified 57.1% of RR-TB detected by MTBSeq (\u003cem\u003ekappa=0.73\u003c/em\u003e). As all RMR-TB were detected only through MTBSeq, the reduced agreement between the two pipelines may be explained by the incomplete repertoire of the characterized mutations in the TBProfiler databases\u003csup\u003e25\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we observed a high rate of interim RMR-TB that remains undetected by the currently recommended critical concentration for pDST. This signifies the role of WGS in detecting interim and hetero resistances, which will aid personalized treatment strategies. Lowering the critical concentration of rifampicin from 1\u0026micro;g/ml to 0.5 \u0026micro;g/ml for pDST may benefit the detection of interim rifampicin resistance using pDST approaches\u003csup\u003e46,47\u003c/sup\u003e. Overall, MDR-TB, Hr-TB and interim-RMR-TB are equally important public health challenges in the realm of EPTB in Ethiopia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study lacks data on treatment outcomes to follow through anti-TB drug resistance patterns with clinical conditions of affected patients, which is worth addressing in future studies. While we have not characterized the protein structure of the reported compensatory mutations, further studies on the role of compensatory mutations and restored fitness in resistant strains are warranted.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and setting\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA prospective cross-sectional study was conducted on MTB isolates grown on Mycobacterium Growth Indicator Tube (MGIT) and Lowenstein Jensen (LJ) media cultured from extrapulmonary clinical specimen. The clinical samples were obtained from 542 prospectively enrolled presumptive EPTB study participants between August 2022 and October 2023(unpublished data). A total of 189 study participants were bacteriologically confirmed for EPTB. The study participants were\u003cem\u003e\u0026nbsp;\u003c/em\u003eenrolled from six high EPTB hotspot regions of central and northern part of Ethiopia, within a one-year time frame, which reflects the national representativeness of the sampling and shows a snapshot of the current EPTB status in Ethiopia. Clinical specimens were retrieved from lymph node aspirates 81% (153/189) and other clinical specimens 19% (36/189) such as pleural fluid, ascitic fluid, pericardial fluid, synovial fluid and urine. All MTB culture positive isolates were checked for purity and subjected to pDST and WGS. The study protocol was approved by the institutional ethics review boards (IRB) of the Ethiopian Public Health Institute (# EPHI-IRB-433-2022) and Addis Ababa University, College of Natural and Computational Sciences (# CNS-IRB/06/14/2022). The research was performed in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants and/or their legal guardians.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory investigations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenotypic drug sensitivity test (pDST)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhenotypic DST was performed using BACTEC MGIT 960 at a predefined critical drug concentrations\u003csup\u003e11\u003c/sup\u003e of first-line anti-TB drugs: rifampicin [RIF \u0026nbsp;(1.0 \u0026micro;g/ml)], isoniazid [INH (0.1 \u0026micro;g/ml)], streptomycin [STR (1.0 \u0026micro;g/ml)], ethambutol [EMB (5.0 \u0026micro;g/ml)], and pyrazinamide [PZA (100 \u0026micro;g/ml)]. All isolates identified as rifampicin resistant (RR), multidrug resistant (MDR) or isoniazid resistant, rifampicin susceptible TB (Hr-TB) were further tested for second-line drug resistance at predefined critical concentrations\u003csup\u003e12\u003c/sup\u003e of bedaquiline [BDQ (1 \u0026micro;g/ml)], clofazimine [CFZ (1 \u0026micro;g/ml)], delamanid [DLM (0.06 \u0026micro;l/ml)], linezolid [LZD (1 \u0026micro;g/ml), levofloxacin [(LFX (1.0 \u0026micro;g/ml)], moxifloxacin [MFX (0.25 \u0026micro;g/ml)], and ofloxacin [OFX (2 \u0026micro;g/ml)].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNucleic acid (DNA) extraction and library preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMTBC isolates grown on LJ media with confluent growth of 3-4 weeks were used for sequencing. The genomic DNA extraction using the \u003cem\u003eN\u003c/em\u003e-acetyl-\u003cem\u003eN\u003c/em\u003e,\u003cem\u003e\u0026nbsp;N\u003c/em\u003e,\u003cem\u003e\u0026nbsp;N\u003c/em\u003e-trimethyl ammonium bromide\u0026nbsp;(CTAB) method, precipitation, purification and elution was performed following the standard protocol\u003csup\u003e13\u003c/sup\u003e. \u0026nbsp;Briefly, two to three loop-full of MTB colonies were scrubbed from LJ culture and transferred into a tube containing 400\u0026nbsp;𝜇l of Tris-EDTA (TE) buffer. The cells were heat killed with a pre-warmed heat block at 80\u003csup\u003eo\u003c/sup\u003eC for 1hr and lysed using 50\u0026nbsp;𝜇l of lysozyme at 37\u003csup\u003eo\u003c/sup\u003eC for 1h.\u0026nbsp;The concentration and purity of the extracted genomic DNA was measured using the fluorometric Qubit4\u003csup\u003e14\u003c/sup\u003e and spectrophotometric Nano Drop. Library preparation was done using Illumina DNA prep kit following the standard protocol\u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhole genome sequencing (WGS)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and quality control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLibrary preparation was performed at the Armauer Hansen Research Institute (AHRI), and whole genome sequencing was performed at the Ethiopian Public Health Institute (EPHI) using an Illumina NextSeq550 (Illumina San Diego, CA, USA) instrument.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWGS based drug resistance detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo detect drug resistance associated mutations, the sequence reads were aligned to a reference genome \u003cem\u003eM. tuberculosis\u003c/em\u003e H37Rv ATCC 27294 (NC_000962.3). SNPs calling was made using \u0026nbsp;Sam tools v1.6\u003csup\u003e16\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e at thresholds of minimum mapping quality of 20, minimum base quality at a position of 20, minimum read depth at a position of 8X, and maximum strand bias for a position of 90%. To detect hetero resistance, defined as the occurrence of mixed wild type and mutant sub population in an organism\u003csup\u003e17\u003c/sup\u003e, the variant calling was performed using the minimum mapping quality of 20, minimum base quality at a position of 20, minimum read depth at a position of 2X, and maximum strand bias at a position of 10%. Resistance conferring mutation was predicted using two bioinformatics pipelines, MTBseq pipeline\u003csup\u003e18\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e and TB Profiler\u003csup\u003e19\u003c/sup\u003e, \u003csup\u003e\u0026nbsp;\u003c/sup\u003eby application of the MEM algorithm of the Burrows-Wheeler alignment tool v0.7.17\u003csup\u003e20\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were double entered into EpiData version 4.6.0.6 and exported to SPSS version 29.0 software (SPSS Inc., Chicago, Illinois, USA). Descriptive statistics and binary and multinomial logistic regression models were used to describe variables as appropriate. The probability level of \u0026lt;0.05 was considered statistically significant. Kappa statistics were used to evaluate the strength of agreement between pDST and WGS drug resistance prediction. We interpreted a kappa coefficient value as low agreement if (k=\u0026lt;0.4), moderate agreement if (k=0.41-0.6), substantial agreement if (k=0.61-0.8) and great agreement if (k=0.81-1.0), as previously described\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSTR: streptomycin\u003c/p\u003e\n\u003cp\u003eINH: isoniazid\u003c/p\u003e\n\u003cp\u003eRIF: rifampicin\u003c/p\u003e\n\u003cp\u003eEMB: ethambutol\u003c/p\u003e\n\u003cp\u003ePZA: pyrazinamide\u003c/p\u003e\n\u003cp\u003eAMK: amikacin\u003c/p\u003e\n\u003cp\u003eCAP: capreomycin\u003c/p\u003e\n\u003cp\u003eKAN: kanamycin\u003c/p\u003e\n\u003cp\u003eETO: ethionamide\u003c/p\u003e\n\u003cp\u003eDEL: delamanid\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data associated with the main finding is provided in tables and figures. The raw sequence data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) under BioProject number PRJNA1174701.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported, in part by the NIH Fogarty International Center Global Infectious Diseases grant D43TW009127, the Ethiopian Public Health Institute (EPHI), the core support from the Armauer Hansen Research Institute (AHRI), and Addis Ababa University. The supporting institutes had no role in the study design, data collection and analyses, decision for publication, or manuscript preparation. \u0026nbsp;We thank Ashleigh Nicole Cox from Georgia State University for providing us with an English Language edit to this research paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHM designed the study, analyzed data and wrote the manuscript including comments from all authors. BY and GD conducted phenotypic drug sensitivity tests. DHA, KM and DC conducted molecular characterization. AG, AA and BA performed bioinformatics analysis. SM, JMC, MG, LW, KB and AG reviewed the manuscript. DB, KB and LW supervised the study. All authors read and approved of the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Global Tuberculosis Report;2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarhat, M. et al. Drug-resistant tuberculosis: a persistent global health concern. \u003cem\u003eNat. Rev. Microbiol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (10), 617\u0026ndash;635 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization, Global lists of high-burden countries for TB, multi-drug / rifampicin-resistant TB(MDR/RR-TB), and TB/HIV, 2021\u0026ndash;2025. (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. 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Detection of Mycobacterium tuberculosis Rifampicin Resistance Conferred by Borderline rpoB Mutations: Xpert MTB/RIF is Superior to Phenotypic Drug Susceptibility Testing. \u003cem\u003eInfect. Drug Resist.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1345\u0026ndash;1352 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Extrapulmonary tuberculosis, Whole genome sequencing, Ethiopia ","lastPublishedDoi":"10.21203/rs.3.rs-5302564/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5302564/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Globally, drug-resistant tuberculosis (DR-TB) is responsible for 13% of mortality attributable to antimicrobial resistance. In Ethiopia, extrapulmonary tuberculosis (EPTB) is a significant public health challenge, and drug resistance (DR) in EPTB is often overlooked. In a cross-sectional study conducted between August 2022 and October 2023, we aimed to explore the magnitude of phenotypic drug resistance and identify genetic mutations linked to resistance using 189 Mycobacterium tuberculosis (MTB) isolates cultured from extrapulmonary clinical specimens. Additionally, we assessed the agreement of the phenotypic and whole genome sequencing (WGS) based genotypic drug resistance detection. We performed phenotypic drug sensitivity testing (pDST) using liquid culture BD BACTECTM MGITTM 960 system and WGS using Illumina NextSeq500/550. The genomic data analysis pipelines MTBSeq and TBProfiler were used to predict drug resistance-conferring mutations. The agreement between the pDST and WGS was analyzed using SPSS version 29.0 software. Our result demonstrated phenotypic resistance to at least one anti-TB drug was detected in 16.9% (32/189) of the study participants. Isoniazid-resistant rifampicin-susceptible-TB (Hr-TB) and multi-drug-resistant TB (MDR-TB) phenotypes accounted for 2.6% (5/189) and 4.2% (8/189) respectively. Prevalence of MDR-TB was 2.4% (4/170) among newly diagnosed and 21.1% (4/19) among previously treated cases. WGS identified more (14/160, 8.75%) rifampicin-resistant genotypes (RR-TB) compared to pDST (8/189, 4.2%). We have identified a putative compensatory mutation for rifampicin (rpoBSer450Leu, rpoCAsp747Ala) for the first time from an EPTB clinical specimen in Ethiopia. Overall, there was a 3.75% rifampicin mono-resistant-TB(RMR-TB) genotype, which remains undetected using the conventional pDST and represented 42.9% (6/14) of the identified RR-TB genotypes. Mutations conferring rifampicin resistance-interim (rpoB.Ser450Ala) represented the majority (83.3%) of RMR-TB. Changes in ethA genes associated with ethionamide resistance were the most common resistance (n=7, 87.5%) in MDR-TB cases. There was a strong agreement between the pDST and WGS-TB Profiler pipeline to detect RR-TB (kappa=0.8) compared to the MTBSeq pipeline (k=0.58). In conclusion, MDR-TB, Hr-TB, and interim-RMR-TB are equally important public health challenges in the realm of EPTB in Ethiopia. The role of WGS is tremendous in detecting borderline/interim RMR-TB, which will help for tailored, personalized treatment strategies.","manuscriptTitle":"Phenotypic drug resistance and genetic mutations linked to resistance among extrapulmonary tuberculosis patients in Ethiopia: Insights from Whole Genome Sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 10:07:21","doi":"10.21203/rs.3.rs-5302564/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-09T09:39:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-06T09:58:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192734212753023988479825086199950710037","date":"2025-09-05T04:00:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-03T06:10:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113782483941979765845563588543862718169","date":"2025-09-02T06:21:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T15:04:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73992163400465792511821568811154501118","date":"2025-07-23T14:30:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335188180788645408065479910871588161043","date":"2025-02-11T04:29:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292785358277545388395650135050941869789","date":"2024-11-19T14:49:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332156740547760090707206906782057758052","date":"2024-11-19T14:37:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-17T14:36:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-17T14:31:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-14T11:51:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T08:31:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-21T08:34:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"85a5d397-a782-4c8c-ab06-d3236784dc67","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40474930,"name":"Biological sciences/Microbiology"},{"id":40474931,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2026-02-16T16:00:32+00:00","versionOfRecord":{"articleIdentity":"rs-5302564","link":"https://doi.org/10.1038/s41598-026-40253-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-15 15:57:56","publishedOnDateReadable":"February 15th, 2026"},"versionCreatedAt":"2024-12-17 10:07:21","video":"","vorDoi":"10.1038/s41598-026-40253-8","vorDoiUrl":"https://doi.org/10.1038/s41598-026-40253-8","workflowStages":[]},"version":"v1","identity":"rs-5302564","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5302564","identity":"rs-5302564","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}