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The Journal of Infectious Diseases logoLink to The Journal of Infectious Diseases
. 2025 Dec 23;233(4):e973–e980. doi: 10.1093/infdis/jiaf638

Accuracy of the Phenotypic 1G Test to Detect Mycobacterium tuberculosis and Drug Resistance From Sputa in the US-Mexico Border

Mia Aguirre 1, Doris Ayala 2, Juan Ignacio Garcia 3,4, Yoscelina E Martinez-Lopez 5, Amberlee D Hicks 6,7, Nadine Chacon 8, Ashley Gay-Cobb 9,10, Alyssa Schami 11,12, Selena Zavala-Perez 13, Ilse A Dominguez-Trejo 14, America M Cruz-Gonzalez 15, Raul Loera-Salazar 16, Javier E Rodríguez-Herrera 17, Esperanza M Garcia-Oropesa 18, Miryoung Lee 19, Adrian Rendón 20, Shu-Hua Wang 21,22, Marcel Yotebieng 23,24, Carlton A Evans 25,26,27, Jordi B Torrelles 28,29,✉,2, Blanca I Restrepo 30,31,32,✉,2
PMCID: PMC12853293  NIHMSID: NIHMS2133382  PMID: 41432525

Abstract

Background

With >10 million new tuberculosis (TB) cases per year, a limitation to TB control is the lack of simple and accurate tests for diagnosis and drug susceptibility testing (DST) in endemic regions. We evaluated the accuracy of the first-generation, low-complexity phenotypic TB test (1G test), designed for simultaneous Mycobacterium tuberculosis (Mtb) detection and resistance to isoniazid, rifampicin, and moxifloxacin, suitable for resource-limited settings.

Methods

A cross-sectional study was conducted with sputa from 426 possible pulmonary TB cases from 2 small Mexican cities bordering Texas. The 1G test was compared against phenotypic TB detection tests in the region, specifically acid-fast bacilli smear microscopy and Mycobacteria Growth Indicator Tube (MGIT) culture, as well as MGIT-DST for resistance to isoniazid, rifampicin, and moxifloxacin.

Results

The 1G test demonstrated ≥98% sensitivity for Mtb detection; 100% sensitivity and 91% (rifampicin), 94% (isoniazid), and 97% (moxifloxacin) specificity for DST; and less contamination than the MGIT (3.5% vs 8.1%, P < .05). The 1G test time to detection of Mtb and simultaneous DST was 17 days, while the MGIT-DST required 2 steps: 7 days for Mtb detection plus 14 more days for DST (21 days total). Our study site drug-resistant TB prevalence was 14% when testing all consecutively enrolled participants vs 6% by passive reporting.

Conclusions

The 1G test is a low-complexity phenotypic TB diagnostic method that is a practical replacement to current culture-based tests. Future studies are warranted to evaluate implementation of the 1G test in decentralized clinics that lack molecular tools, resources, and expertise.

Keywords: culture, diagnosis, DST, phenotypic, tuberculosis


The 1G test, a low-complexity phenotypic assay, detected Mycobacterium tuberculosis and drug resistance with high specificity, low contamination, and short turnaround (<21 days), offering an affordable option for decentralized tuberculosis diagnosis and drug susceptibility testing.


Tuberculosis (TB) remains one of the most prevalent infectious diseases worldwide, with an estimated 10.8 million new cases and 1.25 million deaths in 2023 [1]. TB is a major global health challenge despite advances in diagnostics, antimycobacterial treatment regimens, and public health interventions [2]. This is most notable in low- and middle-income countries, where limited health care access and socioeconomic disparities hinder effective disease control [2].

A obstacle to TB prevention and care is the increasing burden of drug-resistant TB (DR-TB) [2, 3]. Multidrug-resistant TB (MDR-TB) is resistant to rifampicin (RIF) and isoniazid (INH); pre-extensively DR-TB has additional resistance to fluoroquinolone; and extensively DR-TB has additional resistance to fluroquinolone plus either bedaquiline or linezolid [3]. Factors contributing to DR-TB include poor treatment adherence and unregulated use of anti-TB drugs [4]. Furthermore, traditional diagnostic methods such as acid-fast bacilli (AFB) smear microscopy lack sensitivity [5]. Culture-based techniques such as Löwenstein-Jensen (LJ) and BACTEC Mycobacteria Growth Indicator Tube (MGIT; Becton Dickinson) are standards for TB detection and drug susceptibility testing (DST) but are time-consuming and resource intensive [6]. These diagnostic gaps contribute to treatment delays and amplify transmission of DR-TB. Molecular diagnostics have significantly reduced times to TB diagnosis and enabled detection of resistance mechanisms [7]. However, high costs, infrastructure requirements, and specialized personnel needs have restricted their adoption in medium-burden and low-resource settings [7]. Therefore, there is an urgent need for simple, rapid, and affordable diagnostic tools that improve detection of Mycobacterium tuberculosis (Mtb) and its DST.

These challenges to TB control are highly relevant in medium or small cities or rural areas where most patients are treated empirically for drug-susceptible TB, with DST reserved to high-risk groups. In Mexico, decentralized outpatient TB clinics typically establish a TB diagnosis based on clinical presentation, positive sputum smear microscopy for AFB, and when available, chest radiographs. When DR-TB is suspected, sputum samples are referred to Mexico City for DST, resulting in delays that extend for months. Xpert MTB/RIF (Cepheid) is available for selected cases. These limitations hinder timely treatment, promote community transmission, and increase mortality. For example, in the northern sanitary jurisdictions of Tamaulipas, Mexico, the incidence of TB is at least 3-fold higher than in adjacent Texan counties in the United States (35 vs 11 cases/100 000 in 2022, respectively) [8, 9].

The first-generation (1G) phenotypic test (also known as Colour Test, CX-test, TB-CX) is a low-cost, quadrant-based, thin-layer agar plate culture assay for Mtb detection and DST to 3 drugs of choice [10]. The culture media favors accelerated Mtb growth detected by red colonies. Equipment requirement is minimal, making it suitable for use in TB endemic regions, including rural areas. In pilot field studies in Ethiopia, Malawi, and Mozambique, we and others have shown that the 1G test detected DR-Mtb in sputa in a median 14 days, with >97% agreement with LJ culture, LJ-DST for INH and RIF, or Xpert-MTB/RIF (Cepheid) [10–12]. However, the performance of the 1G test from sputum has not been compared with the MGIT and MGIT-DST, which are the most sensitive and fastest phenotypic methods, despite being restricted by high contamination, equipment requirements, and a complex 2-step protocol for DST [13]. Here, we evaluated whether the 1G test could be an accurate and practical alternative to the MGIT with DST, using sputa of patients with possible TB from small Mexican cities bordering Texas.

METHODS

Study Design, Participant Enrollment, and Characterization

In this cross-sectional diagnostic and DST accuracy study, we enrolled adults with possible pulmonary TB based on clinical findings (productive cough >2 weeks, weight loss, fever/chills, abnormal chest x-ray findings) and within 7 days of TB treatment. Sociodemographics and medical information was recorded [14]. Diabetes was defined as fasting glucose ≥126 mg/dL, random ≥200 mg/dL, or hemoglobin A1c ≥6.5%. HIV was determined by positive serology. The study received ethical approval from the institutional review boards in Mexico (003/2022/CEI, 004/2023/CEI) and UTHealth Houston (HSC-SPH-23-0154, HSC-SPH-12-0037).

Sputum Collection, Processing, and Storage

For each patient, the same sputum was evaluated by the 1G and conventional methods. Samples were refrigerated at 4 °C in the TB clinics and transported weekly to the UTHealth laboratory in Texas. Sputa were immediately stored at −20 °C and thawed in a biosafety level 3 laboratory for aliquoting (if >3 mL) and batch processing within 10 days (1× freezing). Sputa underwent standard digestion and decontamination (NALC-NaOH; Hardy Diagnostics) [15]. For a nested subanalysis, some specimens were processed with an alternative salt-mix decontamination (SMD) method [12]. Namely, sputum was mixed with the SMD preparation (1:2 v/v), vortexed briefly, and incubated at room temperature for 10 minutes to 1 hour. Leftover raw or processed sputa were stored at −80 °C. Some frozen aliquots were thawed (2× freezing) for 1G testing (Figure 1, Supplement 1).

Figure 1.

For image description, please refer to the figure legend and surrounding text.

Mtb detection from sputum per the 1G test and conventional sputum AFB smear and MGIT culture protocols. Sputa from 426 participants with possible TB were stored at –20 °C prior to batch processing within 10 days of collection (1× freeze). The number of sputa is indicated for each step, including processing for AFB smear microscopy and phenotypic tests for Mtb complex detection as described in the methods. Gray-shaded boxes = sputa undergoing 2 freezing cycles prior to thawing for the 1G test evaluation: first at −20 °C upon arrival from the field, then at −80 °C with or without prior NALC-NaOH decontamination. Blue boxes = 426 sputa analyzed by the 1G test. 1G test, first-generation test. Abbreviations: AFB, acid-fast bacilli; INH, isoniazid; MFX, moxifloxacin; MGIT manual, Mycobacteria Growth Indicator Tube manually performed; MGIT-960, automated MGIT-960 (Becton Dickinson) instrument; Mtb, Mycobacterium tuberculosis; NALC-NaOH, N-acetyl-L-cysteine–sodium hydroxide sputum digestion and decontamination method; RIF, rifampicin; SMD, salt-mix decontamination; TTD, time to detection of Mtb growth.

1G Test for Mycobacterial Detection and DST

The 1G test consists of a 4-quadrant Petri dish containing an enriched and highly selective 7H11 medium that favors Mtb growth while deterring contamination: 1 quadrant for Mtb detection and the other 3 supplemented with INH (0.2 µg/mL), RIF (1.0 µg/mL), and moxifloxacin (MFX; 0.25 µg/mL) for DST [8, 10–12]. Quadrants were inoculated with 100 µL of decontaminated sputum and incubated at 37 °C with 5% CO₂. Mtb growth was inspected for up to 42 days. Growth was evaluated by readers blinded to conventional methods’ results. Colonies displaying cording and cauliflower-like morphology under a magnifying glass and positive for MPT-64 were classified as Mtb positive.

Phenotypic Conventional Methods for Mtb Detection and DST

Concentrated sputa were used for AFB smear microscopy (Acid-Fast Stain Kit; Hardy Diagnostics). For conventional mycobacterial cultures, NALC-NAOH sputum concentrates were inoculated into MGIT media (Becton Dickinson). A “MGIT-manual” protocol was conducted between June 2020 and September 2023, with sputa cultured into 4-mL MGIT cultures and Mtb detected by AFB smears or colonies after subculture into LJ slants (Supplement 2). An automated MGIT-960 system was available as of October 2023 with interpretation as follows: “contaminated” if growth detected within 2 days, “Mtb positive” if detected between 3 and 42 days and confirmed with AFB smear microscopy, and “negative” if no growth was observed [9].

Sputa with positive growth by any culture method were evaluated for the presence of AFB by smear microscopy and, if positive, evaluated for MPT-64 Ag detection (SD Bioline TB Ag MPT64) for Mtb complex confirmation [16]. Mycobacteria isolated from culture that were positive for AFB smear microscopy but negative for MPT-64 Ag were presumed to be nontuberculous mycobacteria and excluded from analysis given their low frequency (13/439, 2.9%) and to focus on Mtb detection and DST (Supplementary Figure 1).

MGIT-960 DST was the gold standard to evaluate the drug resistance of Mtb isolates obtained from sputum cultures positive for MGIT-manual or MGIT-960. If cultures were negative or contaminated, the isolate from the control well in the 1G test was used instead. MGIT-960 DST was performed with a commercial kit (SIRE; BD Bactec) for INH and RIF DR testing and an in-house method for MFX at a critical concentration of 0.25 µg/mL (AC457960010; Thermo-Scientific Chemicals) [17]. Protocols were validated by reference Mtb clinical isolates (BEI Resources, National Institute of Allergy and Infectious Diseases, National Institutes of Health; Supplementary Table 1).

Statistical Analysis

Statistical analyses were performed in SAS software version 9.4 (SAS Institute Inc). Descriptive statistics are provided. Agreement between the 1G test and conventional DST was assessed by Cohen κ coefficient, with values from 0.61 to 0.80 indicating substantial agreement and 0.81 to 1.00 almost perfect agreement [18]. Median differences were established by the Wilcoxon rank sum test. Categorical variables were compared with the χ2 test or the Fisher exact test when cell counts were <5. The identification of patient characteristics independently associated with DR-TB was conducted in a logistic regression model after adjusting for age and sex and considering any variable with P < .2 by univariable analysis. P < .05 was considered statistically significant. The sample size for evaluating the Mtb detection sensitivity of the 1G test was estimated by the McNemar test [19]. Per World Health Organization (WHO) guidelines for low-complexity tests, we expected the 1G test sensitivity to be >90% or optimally >95% [20]. Hence, for a sensitivity within 1% to 4% of the MGIT-960, we estimated requiring 204 to 616 specimens, respectively, to achieve 80% power at a 2-sided 95% CI.

Role of the Funding Source

The funding source did not play a role in the design, collection, analysis, interpretation, writing, or decision to publish.

RESULTS

Participant Characteristics

We evaluated the sputum from 426 participants with possible TB (Supplementary Table 2). All self-identified as White Hispanics with a median age of 43 years (IQR, 26) and two-thirds were male (n = 294, 69%). Comorbidities included type 2 diabetes (n = 188, 44%), self-reported macrovascular disease (n = 92, 22%), and HIV seropositivity (n = 25, 6%). Social risk factors for TB included excessive alcohol use (n = 73, 17%) and frequent recreational drug use (n = 87, 21%). Seventy (16%) reported a previous TB episode.

Sputum Decontamination Protocols

In a subanalysis aimed at gaining insights into how the SMD sputum treatment performs when compared with the conventional NALC-NaOH method, we initially compared both sputum decontamination methods, followed by Mtb growth detection in the 1G test (Figure 1, Supplement 1). Among 35 sputa processed in parallel with NALC-NaOH or SMD, 34 were positive for Mtb growth (100% concordance). The median time to detection (TTD) for Mtb growth was similar between the sputum decontamination protocols (Table 1 with 1× frozen/defrost cycle specimens): 14.5 days for NALC-NaOH vs 14 days for SMD. Given the comparable performance between the sputum processing protocols, comparison of Mtb growth detection for all 426 specimens in the 1G test vs conventional methods was analyzed jointly, regardless of the processing method.

Table 1.

TTD of Mtb Growth per the 1G Test by Sputum Processing and Freezing Events

Sputum Processing: Freezing Events No. Positivea TTD, d (Median ± IQR) Range, d P Value
NALC-NaOH <.0001
 Frozen 1xb 86 14 ± 6 4–42
 Frozen 2x 265 18 ± 5 4–42
SMD 0.02
 Frozen 1xb 34 14 ± 5 4–42
 Frozen 2x 26 16 ± 7 7–42

Abbreviations: 1×, sputum frozen once at −20 °C prior to weekly processing and culture; 2×, sputum frozen twice (first at −20 °C and leftover refrozen at −80 °C prior to seeding in the 1G test plate); Mtb, Mycobacterium tuberculosis; SMD, salt-mix decontamination; TTD, time to detection of Mtb growth.

aOnly data from the 377 sputum specimens that were positive for Mtb growth on the 1G test are shown.

bThe results of 34 sputum specimens that were frozen once are shown twice given the parallel processing by NALC-NaOH and SMD.

Sensitivity of the 1G Test for Mtb Detection vs Conventional Methods

Out of the 426 sputa analyzed, the 1G test was positive for Mtb detection in 377 (88.5%; Figure 1, Supplementary Figure 1). The 1G test sensitivity was compared with the detection of Mtb with AFB sputum smear microscopy (tested in n = 422), the MGIT-manual (tested in n = 332), and the automated MGIT-960 (tested in n = 87). Results are shown in Table 2. The sensitivity of the 1G test vs positive AFB smear microscopy was 99.7% (345/346 AFB+); against positive MGIT-manual cultures, 99.6% (276/277 MGIT-manual+); against positive automated MGIT-960 cultures, 98.6% (70/71 automated MIGT-960+); and against both MGIT methods combined, 99.4% (346/348 MIGT-manual+ plus automated MGIT-960+). Altogether, the 1G test detected Mtb in 29 negative specimens on AFB smear microscopy, 18 negative cultures, and 6 contaminated MGIT cultures (3 MGIT manual and 3 MGIT-960). When the 1G test was compared against a composite of conventional tests positive by either sputum smear microscopy or manual/automated MGIT cultures, the sensitivity was maintained at 98.9%. Eight sputum specimens were positive by the 1G test but negative by all other methods. The contamination rate of the 1G test was similar to the MGIT-manual (1.2% for both tests although in different sputa) but lower than the MGIT-960 (3.5% vs 8.1%, P = .016).

Table 2.

Performance of the 1G Test and Phenotypic Reference Standard Methods for Mtb Detection From Sputum

Reference Method 1G Testa Other Performance Statistics
Mtb+ Mtb Cont Sensitivity (95% CI), % Test Cont, No. (%) TTD (d), Median ± IQRb
1G test vs sputum AFB (n = 422)
 Mtb+ 345 29 99.7 (98–100) 1G 7 (1.7) 17 ± 7
 Mtb 1 40 AFB 2
 Cont 2 5
1G test vs MGIT-manual (n = 332)
 Mtb+ 276 18 3 99.6 (98–100) 1G 4 (1.2) 17 ± 7
 Mtb 1 29 1 MGIT-M 4 (1.2) ≥14
 Cont 1 3 0
1G test vs MGIT-960 (n = 87)
Mtb+ 70 0 3 98.6 (93–100) 1G 3 (3.5)c 17 ± 10
Mtb 1 8 2 MGIT-960 7 (8.1) 7 ± 3
 Cont 1 0 2
1G test vs all MGIT (n = 419)
Mtb+ 346 18 6 99.4 (98–100) 1G 7 (1.7) 17 ± 7
Mtb 2 37 3 MGIT 11 (2.6)
 Cont 2 3 2
1G test vs composite (n = 426)
Mtb+ 369 8 98.9 (97–100) 1G 7 (1.6) 17 ± 7
Mtb 4 38 Composite
 Cont 2 5

Table includes total number of sputum specimens tested by the 1G test and the listed methods.

Abbreviations: Cont, contamination; MGIT, Mycobacteria Growth Indicator Tube; Mtb, Mycobacterium tuberculosis; TTD, time to detection of Mtb growth in days (d). F for the 1G, this TTD is also the time for DST.

aCalculations exclude the contaminated results.

bThe median (IQR) TTD for the 1G test with specimens frozen 1× is 14 (6), which is comparable to the reference methods, but data showing 17 days are due to inclusion of specimens frozen 2× as shown in Table 3.

c P = .016.

Accuracy of the 1G Test for Mtb DST

The 1G test yielded simultaneous DST for RIF and INH for 376 of the 377 Mtb-positive sputa, with 1 contaminated in the drug-containing well (Supplementary Figure 1). MFX DST was tested in 310 sputa. Of the 376 sputa, 52 had Mtb resistant to at least 1 drug: 41 to INH, 16 to RIF, and 12 to MFX (Table 3). To evaluate the accuracy of the 1G DST when compared with the MGIT-DST as reference, 51 available DR-Mtb isolates were subcultured into MGIT media until automated growth detection. Three cultures were contaminated, leaving 49 Mtb isolates for analysis. Given the time- and resource-intensive nature of the MGIT-DST protocol, a subset of 19 drug-susceptible Mtb isolates by the 1G test were also evaluated by the MGIT-DST as controls with group matching by enrollment year and field site to the DR isolates. The performance of the 1G test relative to the MGIT-DST is shown in Table 3. The 19 drug-susceptible Mtb isolates from the 1G test were confirmed to be susceptible by MGIT-DST. The 1G test suggested a higher number of DR-Mtb isolates vs the reference MGIT-DST: 38 for INH (vs 36), 15 for RIF (vs 10), and 11 for MFX (vs 10). Hence, the 1G test had a sensitivity of 100% for DR-Mtb detection to the 3 antibiotics, and specificity was 94% for INH, 91% for RIF, and 97% for MFX as compared with MGIT-DST. The concordance was substantial for resistance to any drug (κ = 0.86) or RIF (κ = 0.76) and almost perfect for INH and MFX (κ = 0.94 and 0.97, respectively).

Table 3.

Performance of the 1G Test for Detection of DR-Mtb Against MGIT-DST

MGIT Results, No. 1G Test vs MGIT, % DST Concordance
1G Test Results DR DS Conta Sensitivity (95% CI) Specificity (95% CI) κ (95% CI) Interpretation
Any DR 100 (92–100) 83 (61–94) .86 (.73–.99) Substantial agreement
 DR 45 4 3
 DS 0 19 0
INH 100 (90–100) 94 (78–99) .94 (.87–1.00) Almost perfect agreement
 DR 36 2 3
 DS 0 30 0
RIF 100 (69–100) 91 (80–98) .76 (.56–.96) Substantial agreement
 DR 10 5 1
 DS 0 53 2
MFX 100 (70–100) 97 (84–100) .97 (.83–1.00) Almost perfect agreement
 DR 10 1 1
 DS 0 30 2

DST conducted in all the isolates with DR-Mtb (n = 52) plus 19 group-matched DS-Mtb per the 1G test.

Abbreviations: Cont, contaminated; DR, drug resistant; DS, drug susceptible; DST, drug susceptibility testing; MGIT, Mycobacteria Growth Indicator Tube; Mtb, Mycobacterium tuberculosis.

aContaminated results were excluded from the sensitivity, specificity, or concordance analysis.

TTD for Mtb and DST per the 1G Test vs Conventional Methods

For the 1G test, the median time for simultaneous detection of Mtb growth and DST was 14 days for specimens kept at −20 °C prior to culture (1× freezing) and 16 to 18 days for sputa with an additional freeze-thawing cycle (2× freezing; Table 1). The median (IQR) TTD for all the 1G tests was 17 (7) days. All the conventional methods were done with sputum frozen once at −20 °C, with TTD shown in Table 2. Namely, the direct AFB sputum smear microscopy took 2 days. The MGIT-manual took 14 days for initial assessment of mycobacterial growth by AFB smear microscopy, plus an additional culture into LJ slants to confirm Mtb growth, resulting in a total turnaround time of approximately 42 days for detection, without DST results. The automated MGIT-960 required 7 ± 3 days for Mtb detection, and the additional DST required dilutions and subcultures into separate anti-TB drug–containing tubes, followed by incubation for 12 to 14 days for automated MGIT-960 system. Altogether this 2-step MGIT-DST protocol took 19 to 21 days from the time of initial sputum culture.

Characteristics of Participants With DR-TB in Mexican Cities Across the Texas Border

The DST results were used to characterize the epidemiology patterns of DR-TB in our study population. We used the DST data from the 1G test given (1) the high concordance between the 1G test and the automated MGIT-960 (Table 3) and (2) the availability of DST data for all Mtb-positive 1G test results vs only a subset of drug-susceptible Mtb (n = 19) assessed by MGIT-DST. The DR-TB profiles are shown in Table 4. The prevalence of any DR-TB was 14% (52/376), with INH resistance at 11% and RIF or MFX at 4%. Monoresistance was 7% for INH, 0.3% for RIF, and 3% for MFX. MDR-TB was 3% and pre-extensively DR-TB (MDR plus MFX resistance) was 0.6%. Host characteristics were not associated with DR-TB (Supplementary Table 3).

Table 4.

Prevalence of DR-TB in Patients With Pulmonary TB From Mexican Border Communities

Drug Resistance, %a
Type No. Among all Mtb, % Among resistant Mtb, %
Any resistance
 INH-R 41 11 79
 RIF-R 16 4 31
 MFX-R 12 4 23
Monoresistance
 INH-R 27 7 52
 RIF-R 1 0.3 2
 MFX-R 9 3 17
Multidrug resistance
 INH/RIF 12 3 23
 INH/RIF/MFXb 2 0.6 4
 RIF/MFX 1 0.3 2
Total with any resistance 52 14 100

Prevalence based on DR-Mtb detected by the 1G test.

Abbreviations: DR, drug resistant; INH, isoniazid; MFX, moxifloxacin; Mtb, Mycobacterium tuberculosis; R, resistant; RIF, rifampicin; TB, tuberculosis.

aDenominator is 376 for RIF-R and INH-R and 310 for analysis containing MFX-R.

bPre-extensively DR-TB.

DISCUSSION

We evaluated the 1G test in TB clinics from small cities in the Mexican border with Texas, where DR-TB testing is not locally available and referrals to central clinics are limited to cases with high risk of DR-TB. We found that the 1G test was more sensitive for Mtb detection than the MGIT-manual (18 positive specimens by 1G but negative by MGIT-manual) but comparable to automated MGIT cultures and less prone to contamination. Moreover, the 1G test had good concordance with the MGIT-DST for INH, RIF, and MFX, with the added advantage of less contamination, faster results, and a simpler protocol. It also had similar TTD for Mtb but shorter for DST vs the automated MGIT-DST, and the test comprised a simple 1-step process as compared with additional supplies, personnel, and equipment for the MGIT-DST. Altogether, the 1G test is a low-complexity phenotypic TB diagnostics test that offers a practical alternative to phenotypic tests for Mtb detection and DST (eg, LJ or MGIT with DST). Its simplicity makes it suitable for use in decentralized laboratories in mid- and high-burden TB regions, where smear microscopy is routine, biosafety cabinets are available, and molecular testing is not feasible due to high cost and limited resources (eg, trained personnel, expensive instrumentation). Additionally, while molecular testing targets mutations conferring resistance, there remain DR-TB cases that can be detected only with phenotypic methods.

Our study setting shares the limitations for TB diagnosis as many other regions worldwide. Namely, Mexican TB referral clinics on the border with Texas do not offer routine testing for DR-TB, unless the individual is younger than 5 years, has treatment failure, is immunocompromised, or poses a high risk for DR-TB [21]. In some countries with high TB burden, Xpert (Cepheid) is replacing AFB smear microscopy testing, but in Mexico this technology is not subsidized, is costly (>$50/cartridge), and is not readily available [7]. Instead, DST is centralized and results can take up to 6 months [7]. Our results provide support for the value of the 1G test as a technically simple, reliable phenotypic method for diagnosis of TB and DR-TB in these types of settings and without need for additional infrastructure or biosafety considerations.

The 1G test had comparable performance when sputa were processed by the NALC-NaOH or SMD method. The use of SMD for digestion and decontamination plus the 1G test for Mtb isolation and DST testing has the advantage of requiring less sputum (150 μL) and can be performed without equipment. Namely, the SMD vortexing step can be replaced by handshaking or mixing via a disposable transfer pipet to avoid aerosol or froth generation [8, 12]. Furthermore, the 37 °C incubation can be done without CO2 or even at room temperature in countries where the ambient temperature is closer to 37 °C, although with a longer TTD (Torrelles, unpublished findings). However, further evidence is needed to determine the biosafety implications of these modifications, including whether personnel safety still requires the use of a biosafety cabinet.

The 1G test had a shorter turnaround time at 2 weeks and a simpler and economical 1-step protocol vs the 2-step MGIT and MGIT-DST protocols that require equipment and additional steps. The TTD of the 1G test was also shorter when compared to the standard 28 to 42 days required for Mtb detection alone with LJ cultures.

Despite good agreement between the 1G test and MGIT for DST, the nature of their discordant results deserves further evaluation. RIF showed the highest discrepancy. This may reflect RIF instability during the 42-day 1G incubation or the inability of the MGIT-DST to detect low-level RIF resistance in some strains, considering the recent WHO recommendation to lower the MGIT-DST RIF critical concentration from 1.0 to 0.5 mg/L [22–26]. This lower concentration is already used in the 1G test. Understanding the molecular basis for discrepancies is clinically relevant given associations between low-level RIF resistance and poor treatment outcomes [26]. Genotyping is in progress to clarify if discordant findings represent false-positive results.

DR-TB was estimated at 6% in northern Tamaulipas in 2020 by passive reporting, while our results between 2020 and 2024 suggested that DR-TB is more than twice as high (14%). The passive underestimation is likely due to the lack of routine DST testing, although sampling bias in our study cannot be excluded. The MDR-TB rate of 3% in our study population is comparable to 3.2% globally [1]. The 4% prevalence of RIF resistance with the 1G test was below the global estimate of 6.9% and was even lower when determined by the MGIT-DST [27]. Only 2 of 14 (14%) MDR-TB cases had additional resistance to MFX (pre-extensively DR-TB), which is lower than the reported WHO rate of 20% [1].

Detection of MFX-resistant isolates was unexpected, as this drug has not been introduced for TB treatment locally. A possible explanation is cross-resistance due to the unprescribed use of fluoroquinolones in our study population [28]. The WHO recommends MFX for treating drug-susceptible TB, INH-monoresistant TB, and MDR-TB, but our finding points the need for MFX DST prior to its use in patients with TB at the Mexican border [29]. Unlike previous studies, we found no association between DR-TB and host factors [30], which may reflect the small sample number of DR-TB cases in our study.

A study limitation is the lack of simultaneous testing with the 1G test and conventional methods, which required an additional freezing step for two-thirds of the samples tested with the 1G test. Despite this disadvantage, the 1G test demonstrated a robust performance except for a median 3-day delay in TTD. Even though there was perfect concordance between the 1G test and MGIT-DST for a subset of pan-sensitive Mtb isolates identified by the 1G test, we cannot rule out missing DR-Mtb isolates unique to the MGIT-DST. The prevalence of RIF resistance in our community based on the 1G test should be interpreted with caution given its higher prevalence vs MGIT-DST. Most study participants had a positive AFB sputum smear microscopy, and our conclusions should take into consideration this potential bias.

In conclusion, our findings build on previous work to provide support for the 1G test as an accurate, simple, and affordable alternative to current phenotypic methods for TB detection in resource-limited high-burden settings. Used in parallel with sputum smear microscopy at centralized or decentralized clinics, it can enhance mycobacterial detection sensitivity and enable DST, especially where molecular tools such as GeneXpert are not subsidized. Future studies are warranted to evaluate the implementation of the 1G test in decentralized clinics lacking molecular diagnostic capacity, with simple modifications as needed, such as the use of SMD, transfer pipets for mixing with minimal aerosol generation, and incubations at room temperature.

Supplementary Material

jiaf638_Supplementary_Data

Notes

Acknowledgments. We thank the health professionals at the TB clinics from the Secretaría de Salud de Tamaulipas in Reynosa and Matamoros and the US Customs and Border Protection agriculture specialists at the Hidalgo and Cameron international bridges for coordination of study logistics.

Author contributions . Conceptualization: B. I. R., J. B. T., J. I. G., A. R., S.-H. W., M. Y. Formal analysis: B. I. R., M. A., M. L., Y. E. M.-L. Investigation: M. A., D. A., S. Z.-P., J. I. G., A. D. H., N. C., A. G.-C., A. S., I. A. D.-T. Writing–original draft preparation: B. I. R., M. A. Writing–review and editing: all authors. Project administration: B. I. R., J. E. R.-H., R. L.-S., A. C.-G., E. M. G.-O., J. B. T., J. I. G. Funding acquisition: B. I. R., J. B. T., M. Y. All authors have read and agreed to the published version of the manuscript.

Data sharing . Data collected for this study include individual participant data and a data dictionary. The datasets used during the current study are available from the corresponding author on reasonable request.

Disclaimer. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Ethics approval and consent to participate . This study received ethical approval from the institutional review boards in Mexico (003/2022/CEI, 004/2023/CEI) and Texas (HSC-SPH-23-0154 and HSC-SPH-12-0037).

Financial support . This work was supported by the National Institutes of Health, including the National Institute of Allergy and Infectious Diseases (NIAID R01 AI-176309 to J. B. T., B. I. R., and M. Y.; IN-TRAC P30-AI-168439 to B. I. R. and J. B. T.); and the National Institute on Aging (NIA R01-AG082341 to B. I. R.).

Contributor Information

Mia Aguirre, Department of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Brownsville.

Doris Ayala, Department of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Brownsville.

Juan Ignacio Garcia, Population Health and Host Pathogens Interactions Programs; International Center for the Advancement of Research and Education, Texas Biomedical Research Institute, San Antonio.

Yoscelina E Martinez-Lopez, Department of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Brownsville.

Amberlee D Hicks, Population Health and Host Pathogens Interactions Programs; International Center for the Advancement of Research and Education, Texas Biomedical Research Institute, San Antonio.

Nadine Chacon, Population Health and Host Pathogens Interactions Programs.

Ashley Gay-Cobb, Population Health and Host Pathogens Interactions Programs; International Center for the Advancement of Research and Education, Texas Biomedical Research Institute, San Antonio.

Alyssa Schami, Population Health and Host Pathogens Interactions Programs; International Center for the Advancement of Research and Education, Texas Biomedical Research Institute, San Antonio.

Selena Zavala-Perez, Department of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Brownsville.

Ilse A Dominguez-Trejo, Department of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Brownsville.

America M Cruz-Gonzalez, Departamento Estatal de Micobacteriosis, Secretaría de Salud de Tamaulipas.

Raul Loera-Salazar, Departamento Estatal de Micobacteriosis, Secretaría de Salud de Tamaulipas.

Javier E Rodríguez-Herrera, Departamento Estatal de Micobacteriosis, Secretaría de Salud de Tamaulipas.

Esperanza M Garcia-Oropesa, Unidad Académica Multidisciplinaria Reynosa-Aztlán, Universidad Autónoma de Tamaulipas, Reynosa.

Miryoung Lee, Department of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Brownsville.

Adrian Rendón, Centro de Investigación, Prevención y Tratamiento de Infecciones Respiratorias and Hospital Universitario “Dr Jose Eleuterio Gonzalez,”  Nuevo Leon, México.

Shu-Hua Wang, International Center for the Advancement of Research and Education, Texas Biomedical Research Institute, San Antonio; Division of Infectious Disease, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus.

Marcel Yotebieng, International Center for the Advancement of Research and Education, Texas Biomedical Research Institute, San Antonio; Division of General Internal Medicine, Department of Medicine Albert Einstein College of Medicine, The Bronx, New York.

Carlton A Evans, Innovation for Health and Development, Section of Adult Infectious Disease, Department of Infectious Disease, Imperial College London, South Kensington Campus, United Kingdom; Inovacion Por la Salud Y el Desarrollo, Asociacion Benefica PRISMA; Innovation for Health and Development, Laboratory of Research and Development, Faculty of Sciences and Engineering, Universidad Peruana Cayetano Heredia, Lima, Peru.

Jordi B Torrelles, Population Health and Host Pathogens Interactions Programs; International Center for the Advancement of Research and Education, Texas Biomedical Research Institute, San Antonio.

Blanca I Restrepo, Department of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Brownsville; International Center for the Advancement of Research and Education, Texas Biomedical Research Institute, San Antonio; School of Medicine, South Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley, Edinburg.

Supplementary Data

Supplementary materials are available at The Journal of Infectious Diseases online (http://jid.oxfordjournals.org/). Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.

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