Whole-genome sequencing and machine learning reveal key drivers of delayed sputum conversion in rifampicin-resistant tuberculosis
Rifampicin-resistant tuberculosis (RR-TB) remains a major global health challenge, with delayed sputum culture conversion (SCC) predicting poor treatment outcomes. This study integrated whole-genome sequencing (WGS) and machine learning to identify clinical and genomic determinants of SCC failure in...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Cellular and Infection Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2025.1641385/full |
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| author | Qing Fang Xiangchen Li Xiangchen Li Yewei Lu Junshun Gao Yvette Wu Yi Chen Yang Che |
| author_facet | Qing Fang Xiangchen Li Xiangchen Li Yewei Lu Junshun Gao Yvette Wu Yi Chen Yang Che |
| author_sort | Qing Fang |
| collection | DOAJ |
| description | Rifampicin-resistant tuberculosis (RR-TB) remains a major global health challenge, with delayed sputum culture conversion (SCC) predicting poor treatment outcomes. This study integrated whole-genome sequencing (WGS) and machine learning to identify clinical and genomic determinants of SCC failure in 150 RR-TB patients (2019–2023). Phenotypic and genotypic analysis revealed high rates of isoniazid resistance (74.0%) and rpoB mutations (97.3%, predominantly Ser450Leu), with 90% of strains belonging to Lineage 2 (Beijing family). While 64.7% achieved 2-month SCC, 18.0% remained culture-positive at 6 months. Univariate analysis linked 2-month SCC failure to smear positivity, resistance to isoniazid, amikacin, capreomycin, and levofloxacin, and pre-XDR-TB status, though only smear positivity (aOR=2.41, P=0.008) and levofloxacin resistance (aOR=2.83, P=0.003) persisted as independent predictors in multivariable analysis. A Random Forest model achieved robust prediction of SCC failure (AUC: 0.86 ± 0.06 at 2 months; 0.76 ± 0.10 at 6 months), identifying levofloxacin resistance (feature importance: 6.37), embB_p.Met306Ile (5.94), and smear positivity (5.12) as top 2-month predictors, while katG_p.Ser315Thr (4.85) and gyrA_p.Asp94Gly (3.43) dominated 6-month predictions. These findings underscore smear positivity, levofloxacin resistance, and specific resistance mutations as critical drivers of SCC failure, guiding targeted RR-TB treatment strategies. |
| format | Article |
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| institution | Kabale University |
| issn | 2235-2988 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Cellular and Infection Microbiology |
| spelling | doaj-art-dc2d6c0b4da64c32af8a9b5e60fb79052025-08-20T03:40:47ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882025-08-011510.3389/fcimb.2025.16413851641385Whole-genome sequencing and machine learning reveal key drivers of delayed sputum conversion in rifampicin-resistant tuberculosisQing Fang0Xiangchen Li1Xiangchen Li2Yewei Lu3Junshun Gao4Yvette Wu5Yi Chen6Yang Che7Departments of Pulmonary Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, ChinaJiaxing Key Laboratory of Clinical Laboratory Diagnostics and Translational Research, Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, ChinaCosmos Wisdom Mass Spectrometry Center of Zhejiang University Medical School, Hangzhou, Zhejiang, ChinaCosmos Wisdom Mass Spectrometry Center of Zhejiang University Medical School, Hangzhou, Zhejiang, ChinaCosmos Wisdom Mass Spectrometry Center of Zhejiang University Medical School, Hangzhou, Zhejiang, ChinaFountain Valley School, Colorado Springs, CO, United StatesInstitute of Tuberculosis Prevention and Control, Ningbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, ChinaInstitute of Tuberculosis Prevention and Control, Ningbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, ChinaRifampicin-resistant tuberculosis (RR-TB) remains a major global health challenge, with delayed sputum culture conversion (SCC) predicting poor treatment outcomes. This study integrated whole-genome sequencing (WGS) and machine learning to identify clinical and genomic determinants of SCC failure in 150 RR-TB patients (2019–2023). Phenotypic and genotypic analysis revealed high rates of isoniazid resistance (74.0%) and rpoB mutations (97.3%, predominantly Ser450Leu), with 90% of strains belonging to Lineage 2 (Beijing family). While 64.7% achieved 2-month SCC, 18.0% remained culture-positive at 6 months. Univariate analysis linked 2-month SCC failure to smear positivity, resistance to isoniazid, amikacin, capreomycin, and levofloxacin, and pre-XDR-TB status, though only smear positivity (aOR=2.41, P=0.008) and levofloxacin resistance (aOR=2.83, P=0.003) persisted as independent predictors in multivariable analysis. A Random Forest model achieved robust prediction of SCC failure (AUC: 0.86 ± 0.06 at 2 months; 0.76 ± 0.10 at 6 months), identifying levofloxacin resistance (feature importance: 6.37), embB_p.Met306Ile (5.94), and smear positivity (5.12) as top 2-month predictors, while katG_p.Ser315Thr (4.85) and gyrA_p.Asp94Gly (3.43) dominated 6-month predictions. These findings underscore smear positivity, levofloxacin resistance, and specific resistance mutations as critical drivers of SCC failure, guiding targeted RR-TB treatment strategies.https://www.frontiersin.org/articles/10.3389/fcimb.2025.1641385/fullrifampicin-resistant tuberculosiswhole-genome sequencingsputum culture conversionmachine learningdrug resistance mutations |
| spellingShingle | Qing Fang Xiangchen Li Xiangchen Li Yewei Lu Junshun Gao Yvette Wu Yi Chen Yang Che Whole-genome sequencing and machine learning reveal key drivers of delayed sputum conversion in rifampicin-resistant tuberculosis Frontiers in Cellular and Infection Microbiology rifampicin-resistant tuberculosis whole-genome sequencing sputum culture conversion machine learning drug resistance mutations |
| title | Whole-genome sequencing and machine learning reveal key drivers of delayed sputum conversion in rifampicin-resistant tuberculosis |
| title_full | Whole-genome sequencing and machine learning reveal key drivers of delayed sputum conversion in rifampicin-resistant tuberculosis |
| title_fullStr | Whole-genome sequencing and machine learning reveal key drivers of delayed sputum conversion in rifampicin-resistant tuberculosis |
| title_full_unstemmed | Whole-genome sequencing and machine learning reveal key drivers of delayed sputum conversion in rifampicin-resistant tuberculosis |
| title_short | Whole-genome sequencing and machine learning reveal key drivers of delayed sputum conversion in rifampicin-resistant tuberculosis |
| title_sort | whole genome sequencing and machine learning reveal key drivers of delayed sputum conversion in rifampicin resistant tuberculosis |
| topic | rifampicin-resistant tuberculosis whole-genome sequencing sputum culture conversion machine learning drug resistance mutations |
| url | https://www.frontiersin.org/articles/10.3389/fcimb.2025.1641385/full |
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