Rifampicin and isoniazid drug resistance among patients diagnosed with pulmonary tuberculosis in southwestern Uganda.

Multidrug-resistant tuberculosis (MDR-TB) has become a major threat to the control of tuberculosis globally. Uganda is among the countries with a relatively high prevalence of tuberculosis despite significant control efforts. In this study, the drug resistance of Mycobacterium tuberculosis to rifampicin (RIF) and isoniazid (INH) was investigated among patients diagnosed with pulmonary tuberculosis in Southwestern Uganda. A total of 283 sputum samples (266 from newly diagnosed and 17 from previously treated patients), collected between May 2018 and April 2019 at four different TB diagnostic centres, were assessed for RIF and INH resistance using high-resolution melt curve analysis. The overall prevalence of monoresistance to INH and RIF was 8.5% and 11% respectively, while the prevalence of MDR-TB was 6.7%. Bivariate analysis showed that patients aged 25 to 44 years were at a higher risk of developing MDR-TB (cOR 0.253). Furthermore, among the newly diagnosed patients, the prevalence of monoresistance to INH, RIF and MDR-TB was 8.6%, 10.2% and 6.4% respectively; while among the previously treated cases, these prevalence rates were 5.9%, 23.5% and 11.8%. These rates are higher than those reported previously indicating a rise in MTB drug resistance and may call for measures used to prevent a further rise in drug resistance. There is also a need to conduct frequent drug resistance surveys, to monitor and curtail the development and spread of drug-resistant TB.

Introduction

Tuberculosis (TB) is a severe infectious disease associated with high rates of mortality and morbidity worldwide despite intense efforts. One of the major factors sustaining the TB epidemic is the increasing number of Mycobacterium tuberculosis (MTB) strains that do not respond to TB therapy [1-4]. Currently, the standard TB treatment regimen combines four first-line antibiotics, isoniazid (INH), rifampin (RIF), pyrazinamide (PZA), and ethambutol (EMB), which renders a patient noncontagious when they are properly administered. However, inadequate treatment can result in drug-resistant MTB among these patients (acquired resistance), and those resistant strains can be transmitted to other individuals (primary resistance). MDR-TB, described as resistance to both INH and RIF (the two most effective antibiotics for TB treatment) is of great concern because second-line antimycobacterial drugs require long-term administration, are expensive, and have a wide variety of side effects [5]. MDR-TB emerged in the early 1990s and continues to rise each year [1, 6]. In 2017 the world health organization (WHO) reported that approximately 4.1% of new TB cases and 19% of previously treated cases had developed multidrug resistance [7]. In Uganda, the prevalence of MDR-TB is estimated at 12% and 1% among previously-treated and newly diagnosed patients respectively by 2019 [8]. Continuous screening of antibiotic resistance in TB patients, focusing primarily on INH and RIF, is critical since these two medications are the backbone of TB treatment [9]. Resistance to RIF is related to mutations in the rpoB gene, which encodes the β-subunit of bacterial DNA-dependent RNA polymerase, while INH resistance is a result of two pathways involving large mutations in the katG (encodes a catalase-peroxidase enzyme which converts INH to its active form) and inhA genes [10]. An amino acid substitution on codon 315 of katG due to mutation accounts for 42%–95% of INH resistance [11] while a mutation at the promoter region of the inhA gene encoding the enoyl-ACP- reductase, confers 6%–34% of INH resistance in MTB strains [11]. There are several techniques applied in the detection of drug resistance. The conventional drug susceptibility testing (DST) systems which focus on the culture of MTB is time-consuming as it takes approximately 2 to 4 weeks due to the slow growth of this bacteria [12]. Other approaches such as microscopic observation drug susceptibility (MODS; Hardy Diagnostics) and thin-layer agar (TLA; NanoLogix, Inc.) assays are quicker but are highly operator dependent, expensive and technically challenging in resource-limited areas [12]. The establishment of Molecular techniques that identify mutations associated with drug resistance drastically decreases the diagnostic delay and, in some cases, may prove to be more specific than phenotypic DST [13]. High-resolution melt curve analysis (HRMA) is a molecular technique that can be used to detect subtle genetic mutations conferring drug resistance in MTB and is based on the detection of a variation of the deoxyribonucleic acid (DNA) sequence demonstrated by fluorescence changes in the melting temperature of a double-stranded DNA amplicon after real-time polymerase chain reaction (qPCR) [14]. This overall sensitivity and specificity of HRMA is approximately 94% and 99%, respectively in the detection of MDR-TB [15-17]. It is also simple, quick and easy to perform hence can be used to screen a vast number of samples within a limited time. This study utilized the HRMA technique to screen for RIF and INH among patients diagnosed with pulmonary tuberculosis (PTB) in Southwestern Uganda, a region heavily affected by the TB/HIV epidemic [18, 19].

Materials and methods

Ethics approval and consent to participate

Ethical clearance for this study was obtained from the Mbarara University of Science and Technology Institutional Review Board committee (ref. 13/08-17) and clearance was acquired from the Uganda National Council for Science and Technology Research under (ref. HS2379). Permission was also obtained from the office of the Prime minister to access the refugee camps while the health facility administrators granted permission to access their facilities. Participants consented to enroll in the study upon completing an informed consent form.

Study setting and population

This was a cross-sectional study conducted in the Southwestern region of Uganda between May 2018 and April 2019. The Cochran sampling technique [20] was used to determine the sample size and a total of 283 consenting patients were recruited. Four recruitment centres were utilized; two regional referral hospitals (Kabale and Mbarara regional referral hospitals) and two health centres located within two refugee camps (Oruchinga and Nakivale health centre IV). Patients aged ≥ 18 years, diagnosed with PTB for the first time and those previously treated for TB at any of the four study locations were eligible to participate in this study. Non-sputum samples from patients with extrapulmonary TB were excluded.

Data and sample collection

Sputum samples from eligible patients were collected and confirmed for TB using either smear microscopy or Cepheid GeneXpert. Besides the sample collection, demographic characteristics such as age, gender, ethnicity, place of residence, level of income, HIV status before enrollment were obtained from the eligible patients using a standard clinical form. Information about certain risk factors for exposure to resistant strains including imprisonment, previous history of TB treatment and history of recent migration into the country were also collected. The sputum samples were refrigerated at 4°C at the recruitment centres, for not more than 72 hours, and later transported in a cold box to Mbarara University of Science and Technology Genomics and Translational Laboratory for processing and molecular analysis.

DNA extraction and confirmation of MTB in sputum samples

The genomic DNA from each patients’ sputum sample was processed by standardized protocols [21, 22]. The samples were then screened and confirmed as MTB by detection of a 123-bp fragment of the IS6110 gene which is common among the members of the MTBC.

Real-time PCR and high-resolution melting analysis

Three pairs of primers (S1 Table) targeting specific sites at which mutations associated with RIF and INH drug resistance were utilized. One of each of the primer pairs, amplifying either the rpoB [the 81-bp RIF resistance determining region (RRDR)], katG [the -315 site (INH resistance)] and the promoter region of InhA [the -8 and -15 sites (INH resistance)]. The amplification was carried out in a Bio-Rad CFX96 Touch™ using a Lunar® Universal genotyping master mix. All qPCR assays were performed in a final volume of 20 μl reaction mixture containing the following components per reaction: 1.25 μl (0.5 mM final concentration) of each primer, 12.5 μl of 2x HRM PCR master mix, 2 μl of PCR water and 3 μl (5–50 ng) of genomic DNA. The Bio-Rad CFX96 Touch™ Real-Time PCR Detection System was programmed for PCR amplification and a melting curve stage. The thermal cycling parameters were 10 min at 95°C of pre-PCR stage and an amplification stage of 40 cycles consisting of 95°C for 5 secs and 10 secs at 60°C. The qPCR amplification was followed by melt curve analysis, which was initiated by a holding step at 65°C for 5 sec (to allow reassociation of DNA), followed by a slow temperature increase to 95°C at a rate of 0.1°C/s with continuous fluorescence data acquisition. The wild-type MTB (H37Rv), and nuclease-free water were included in each run as positive control and negative controls respectively. The HRMA curve was analysed using Bio-Rad CFX96 Touch™ manager software. Converting the wild-type melting profile to a horizontal line and normalizing the melting profiles of the analyzed samples against the wild-type profile provided the difference temperature plots (S1 Fig).

Statistical analysis

Data were entered into Microsoft Excel 2010 software and then exported to SPSS version 25 (IBM, Chicago, USA) for analysis. The Chi-square test was computed to determine significance for observed differences. The threshold for statistical significance was set at p ≤ 0.05.

Results

A total of 283 sputum samples were analyzed, out of which 266 were from newly diagnosed patients and 17 previously treated patients. The median age of all patients was 36 years and the majority were male TB patients (73.1%). 5.7% and 6.4% of the samples were collected from prisoners and refugees respectively (Table 1).

Table 1

Demographic characteristics of patients enrolled for the study between May 2018 and April 2019.

Characteristic	Category	N = 283; n (%)
Age	≤ 24	45 (15.9)
	25–44	158 (55.8)
	45–64	57 (20.1)
	≥ 65	23 (8.1)
Gender	Male	207 (73.1)
	Female	76 (26.9)
HIV Status	Positive	76 (26.9)
	Negative	78 (27.6)
	Unknown	129 (45.6)
Level of income	High	23 (8.1)
	Low	260 (91.9)
Prisoner	No	267(94.3)
	Yes	16 (5.7)
Refugee	No	246 (86.9)
	Yes	37 (13.1)
TB in the past	No	266 (94)
	Yes	17 (6)

The overall monoresistance to rifampicin and isoniazid was found in 11% (95% CI: 0.077–0.150; p, 0.087) and 8.5% (95% CI: 0.056–0.123; p, 0.692) of the patients, respectively. Monoresistance to rifampicin and isoniazid was found in 11% (95% CI: 0.077–0.150; p, 0.087) and 8.5% (95% CI: 0.056–0.123; p, 0.692) of all the patients, respectively. Resistance to RIF and INH among newly diagnosed patients was 10.2% and 8.6%, while among previously treated patients, resistance to RIF and INH was 23.5% and 5.9% respectively. Furthermore, 4.9% of the samples from newly diagnosed with INH monoresistance, were found to have mutations in the InhA region while 8.6% had mutations in the katG region, a condition that can lead to phenotypic isoniazid drug resistance [2]. The overall resistance to both drugs (MDR-TB) was 6.7% (95% CI: 0.026–0.075; p, 0.982) among all patients (6.4% among newly diagnosed and 11.8% among previously treated patients). Of the cases with MDR-TB, 6.4% had mutations in both the rpoB and KatG regions, while 3.9% had mutations in both the rpoB and InhA sites (Table 2).

Table 2

Frequency of rpoB, KatG and InhA mutations in Mycobacterium tuberculosis causing PTB in southwestern Uganda; May 2018 and April 2019.

Frequency of mutation (pattern of resistance)	Newly diagnosed	Previously-treated	All cases	p-value*
	N = 266	N = 17	N = 283
Monoresistance to	n (%)	n (%)	n (%); 95% CI
RIF	27 (10.2)	4 (23.5)	31 (11.0); (0.077–0.150)	0.087
INH £	23 (8.6)	1 (5.9)	24 (8.5); (0.056–0.123)	0.692
i. InhA mutations	13 (4.9)	0 (0.0)	13 (4.6); (0.026–0.075)	0.351
ii. KatG mutations	23 (8.6)	1 (5.9)	24 (8.5); (0.056–0.123)	0.692
MDR ¥	17 (6.4)	2 (11.8)	19 (6.7); (0.039–0.097)	0.982
i. rpoB & KatG mutations	17 (6.4)	1 (5.9)	18 (6.4); (0.039–0.097)	0.934
ii. rpoB & InhA mutations	9 (3.4)	2 (11.8)	11 (3.9); (0.016–0.058)	0.441

Mutation patterns detected using HRMA:

£One or more mutations leading to single drug (INH) resistance;

¥More than one mutation leading to resistance of both drugs (multidrug resistance);

*p-value obtained by chi-square statistic.

Bivariate analysis revealed that the previously treated patients were more likely to have MDR than the newly diagnosed patients (cOR: 1.092; 95% CI: 0.137–8.737). This relation, however, was not statistically significant (p >0.05) given the very low sample size of the previously treated patients sample". Furthermore, there was a substantial correlation between age and drug resistance: patients aged 25 to 44 years old (cOR 0.253; 95% CI: 0.070–0.922; p = 0.037) were more likely to have MDR-TB. Among all the patients included in this study, 54.4% of them had their HIV status known at the time of the study, with 26.9% of them being HIV positive. There was no substantial correlation between MDR-TB HIV infection and this category of patients (p = 0.324). No other variables were observed to be associated with MDR-TB in the region (Table 3).

Table 3

Bivariate analysis of factors associated with multidrug resistance among PTB patients in southwestern Uganda.

Risk factor	RIF/INH Susceptible, n (%)	RIF/INH resistant, n (%)	cOR (95% CI)	P-value
Age (years)		0.141
≤ 24	43 (95.6)	2 (4.4)	0.221 (0.037–1.312)	0.097
25–44	150 (94.9)	8 (5.1)	0.253 (0.070–0.922)	0.037*
45–64	53 (93.0)	4 (7.0)	0.358 (0.081–1.577)	0.175
≥ 65	19 (82.6)	4 (17.4)	1.000	-
Gender		0.647
Male	193 (93.2)	14 (6.8)	1.306 (0.416–4.098)	0.648
Female	72 (94.7)	4(5.3)	1.000
Level of income		0.632
low	244 (93.8)	16 (6.2)	0.689 (0.148–3.199)	0.634
High	21 (91.3)	2 (8.7)	1.000
HIV status		0.539
Positive	72 (94.7)	4 (5.3)	0.968 (0.274–3.422)	0.960
Negative	71 (91.0)	7 (9.0)	1.718 (0.579–5.099)	0.329
Unknown	122(94.6)	7 (5.4)	1.000
TB in the past		0.934
No	249 (93.6)	17 (6.4)	1.092 (0.137–8.737)	0.934
Yes	16 (94.1)	2 (11.8)	1.000
Refugee		0.328
No	229 (93.1)	17 (6.9)	2.672 (0.345–20.701)	0.347
Yes	36 (97.3)	1 (2.7)
Prisoner		0.091
No	249 (93.3)	18 (6.7)	-	-
Yes	16 (100.0)	0	-	-

Variables included in the bivariate model: age, gender, HIV status, level of income, imprisonment, refugee status and previous history of TB;

*Statistically significant at 95% level of confidence;

cOR: adjusted odds ratio.

Discussion

This study sought to determine the prevalence of RIF and INH resistance among PTB patients in Southwestern Uganda. DNA obtained from 283 sputum samples (each from an individual patient) were analysed. The overall prevalence of RIF and INH monoresistance was found to be at 11% and 8.5% respectively. This was significantly higher than that of a similar study carried out in part of this region between May 2007 and April 2008 which found RIF and INH monoresistance rates were at 4.8 and 3.2 per cent, respectively [23]. Studies have shown that an increase in drug resistance levels can be attributed to a variety of factors, including delayed diagnosis, non-continuous drug resistance surveillance [24, 25], and TB medication stock-outs in health facilities [19] resulting in treatment interruptions and the spread of drug-resistant strains in the population. Furthermore, studies have also shown that health care workers especially those involved in tuberculosis infection control, diagnosis, and treatment are a risk factor for resistance to any anti-TB medication [25]. However, studies are needed to investigate the role of such factors, and any other ones, in the rise of drug resistance in this region.

Our findings show that RIF monoresistance was higher (23.5%) in those patients with a history of prior TB treatment as compared to 10.2% of those newly diagnosed patients, while the rate of MDR was 6.4% and 11.8% in newly diagnosed and previously treated patients, respectively. Studies have shown that previous TB drug exposure, especially in inadequate or inappropriate doses, can result in an increased risk of developing mono-drug or multidrug-resistant tuberculosis [25-28]. The findings of this study of higher prevalence of MTB drug resistance among previously treated patients is consistent with findings of other Ugandan studies [25, 29, 30] and other neighbouring countries [4, 31, 32]. These high levels of drug resistance among previously treated patients raise concerns about TB treatment compliance and the successful application of drug resistance preventive measures such as directly observed therapy (DOT) [9, 19]. More research, however, is needed to better understand the underlying causes of this greater occurrence of drug resistance among this category of patients in this region. There is also a need to reinforce and ensure strict compliance with the various policies developed by the Ministry of Health and the National Tuberculosis and Leprosy Program (NTLP).

Our study showed no significant association between patient characteristics such as imprisonment, refugee status, or HIV status and the development of MDR-TB, despite some studies showing that such factors are a strong independent risk factor for MDR-TB [4, 27, 33, 34]. For example, TB patients co-infected with HIV [35, 36] and the refugees [37, 38] are more likely to develop MDR-TB. Nevertheless, our results are comparable to those of a national survey of drug-resistant TB in Uganda [25] and a recent study in Arua [24], which found no significant association between HIV-TB co-infection and the development of MDR-TB. Nonetheless, integrated HIV/TB management is important for both diseases’ management. Disaggregated by age, our study showed that the patients aged 25 to 44 years had higher levels of MDR-TB (cOR 0.253; 95% CI: 0.070–0.922), suggesting that this category of patients are at a higher risk of having MDR-TB than the older population.

Study limitations

We did not test for all the possible mutations associated with rifampicin and isoniazid resistance. However, we targeted the most dominant mutations associated with rifampicin and isoniazid resistance, hence likely that we identified a majority of the resistance to these drugs in the region. Another significant limitation of our analysis is the small sample size of the patients who have previously been treated for tuberculosis, which limits the precision with which we can estimate the relationship between prior treatment experience and the likelihood of developing drug resistance. Furthermore, HIV testing was not conducted on patients who did not know their HIV status at the time of the study, which could not enable us to conclude on HIV as a risk factor for drug resistance.

Conclusion

There is a relatively high prevalence of MDR-TB among PTB patients in Southwestern Uganda since settings with an MDR-TB prevalence of more than 3% especially among newly treated patients are considered as having a high MDR-TB burden (WHO 2015). This highlights the value of continuous drug resistance screening for all TB patients, as well as the strengthening of drug resistance control measures.

Primer pairs utilized in the high-resolution melting temperature curve analysis.

(TIF)

Click here for additional data file.

An illustration of the RT-PCR (Bio-Rad CFX96 Touch™) high-resolution melting temperature curve analysis using rpoB primers.

a) melt temperature of the target region b) derived melting curve. Green = sample; Black = negative control and Red = Positive control.

(TIF)

Click here for additional data file.

11 May 2021

PONE-D-21-11660

Rifampicin and Isoniazid drug resistance among patients diagnosed withpulmonary tuberculosis in southwestern Uganda

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Reviewer #1: While the study appears sound, the data presentation is flawed. Specifically according to table 3, 258 of the susceptible patients have a history of past TB, while in the results it is stated the contrary (266 new and 17 relapses). As a result after statistical analysis, the authors erroneously claim in line 160-161,

that "Newly diagnosed patients were more likely to have MDR than patients who had previously received treatment". Also in table 3, the number of MDR cases taken for statistical analysis is 8 for all variables. The study found a total of 19 MDR cases. It is not clear what the number 8 corresponds to; so is unclear the whole statistical analysis based in table 3.

I advise the authors to improve table 2 by putting only numbers of samples with mutations found for each of the mutations, instead of all (mutations found and not found). This will make the data presentation easier to follow. Also, in the table header please put mutation found/not found instead of susceptible/resistance.

The authors use age groups 18-37, 38-57, 58-77, >=78. The WHO standard forms use other age groups (15-24, 25-44, 45-64, >=65. The authors may consider reanalysing their data according to these groups to better check for age-resistance correlations.

Line 30 contains a typo error. Please put 5.9% instead of 9.5%

Reviewer #2: Surveillance for drug resistance TB is very important however the manuscript has many flaws that I have highlighted within the submitted manuscript. I also think the rationale for the work done is not well articulated and moreover the amount of work done is minimal. There will be much benefit if this method has been compared with at least one traditional method for DR determination. This will allow for comparison in terms of simplicity, sensitivity and specificity as well as cost.

Other comments

1. The manuscript needs a through language review

2. Lines 31-32: what is the reference data

3. Lines 44-49: very long sentence, loosing meaning

4. Lines 54-56: A major reason is that these are the backbone of TB treatment

5. Line 67: The authors indicated in the background that they find the methods, they used to assess DR as simple and straight forward. I do not agree that detection of mutations using melting curves is simpler in the Africa context

6. The authors should be mindful that the biomarkers (315 for KatG and -8 and -15 for inhA ) that they only used to analyse for INH detects less than 90% of isoniazid resistance in Uganda. This was not discussed as a limitation of the study.

PLoS One. 2018; 13(5): e0198091.Published online 2018 May 30. doi: 10.1371/journal.pone.0198091

7. The referenced article for the primers as indicated in the methodology section does not correspond to the cited article (Poudel et al. 2012). The cited article analyzed for DR conferring mutations among INH and RIF resistance phenotypes. There was no RT-PCR analysis nor the study designed primers for DR analysis.

8. I find setting P value less than 0.005 as indication of significance of difference in measured variable too high. I suggest the authors need to review the statistical analysis again. I think the burden of DR in TB is very crucial, that one needs to be critical in following leads

9. The authors need to reduce the amount of results repeated within the discussion. Also the discussion has too many speculations

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Reviewer #1: No

Reviewer #2: No

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22 Jun 2021

I have attached a rebuttal letter to answer all the queries raised - please see end of PDF

Submitted filename: Response to reviewers-4.docx

Click here for additional data file.

9 Aug 2021

PONE-D-21-11660R1

Rifampicin and Isoniazid drug resistance among patients diagnosed withpulmonary tuberculosis in southwestern Uganda

PLOS ONE

Dear Dr. Bazira,

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Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Partly

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors may consider the following points:

Line 22-27: Consider rephrasing, like for example:

A total of 283 sputum samples (266 from newly diagnosed and 17 from previously treated patients), collected between May 2018 and April 2019 at four different TB diagnostic centres, were assessed for RIF and INH resistance using high-resolution melt curve analysis. The overall prevalence of monoresistance to INH and RIF was 8.5% and 11% respectively, while prevalence of MDR-TB was 6.7%.

29-31: Consider rephrasing, like for example:

Furthermore, among the newly diagnosed patients, prevalence of resistance to INH, RIF and MDR-TB was 8.6%, 10.2% and 6.4% respectively; while among the previously treated cases, these prevalence rates were 5.9%, 23.5% and 11.8%.

Line 29: “8.6% had resistance to INH” Please specify: Is this monoresistance or any resistance?

Line 134: Remove “suspects”. These patients are confirmed for TB as stated in the methods section, by smear microscopy and/or Xpert.

Lines 138-147: Please consider rephrasing, it is difficult to follow. Use either % or “per cent”. For example:

Monoresistance to rifampicin and isoniazid was found in 11% (95% CI: 0.077-0.150; p, 0.087) and 8.5% (95% CI: 0.056-0.123; p, 0.692) of all the patients, respectively. Resistance to RIF and INH among newly diagnosed patients was 10.2% and 8.6 %, while among previously treated patients, resistance to RIF and INH was 23.5% and 5.9 % respectively. Furthermore, 4.9% of the samples from newly diagnosed with INH monoresistance, were found to have mutations in the InhA region while 8.6% had mutations in the katG region, a condition that can lead to phenotypic isoniazid drug resistance (Palomino 2009). The overall resistance to both drugs (MDR-TB) was 6.7% (95% CI: 0.026-0.075; p, 0.982) among all patients (6.4 % among newly diagnosed and 11.8% among previously treated patients). Of the cases with MDR-TB, 6.4% had mutations in both the rpoB and KatG regions, while 3.9% had mutations in both the rpoB and InhA sites

Lines 216-217: This sentence is not correct: “settings with an MDR-TB prevalence of more than 3% especially among previously treated patients are considered as having a high MDR-TB burden”

Reviewer #2: line 41-43: the definition for acquired and primary resistance is wrong. Primary resistance a newly diagnosed case that was infected with a resistance strain and the reverse is true for acquired.

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Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

9 Sep 2021

A revised version of the manuscript that addresses the points raised during the review process has been uploaded

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

18 Oct 2021

Rifampicin and Isoniazid drug resistance among patients diagnosed withpulmonary tuberculosis in southwestern Uganda

PONE-D-21-11660R2

Dear Dr. Bazira

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Christophe Sola, Pharm.D. Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

lane 154-155 : "This relation, however, was not statistically significant ", please add to this sentence : "given the very low sample size of the previously treated patients sample"

Reviewers' comments:

21 Oct 2021

PONE-D-21-11660R2

Rifampicin and Isoniazid drug resistance among patients diagnosed with pulmonary tuberculosis in southwestern Uganda

Dear Dr. Bazira:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Pr. Christophe Sola

Academic Editor

PLOS ONE