Gut microbiota and tuberculosis

Abstract

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a significant global health challenge. Recent advancements in gut microbiota (GM) research have shed light on the intricate relationship between GM and TB, suggesting that GM alterations may influence host susceptibility, disease progression, and response to antituberculosis drugs. This review systematically synthesizes and analyzes the current research progress on the relationship between GM and TB, focusing on six key aspects: (1) bidirectional effects between GM dynamics and TB progression; (2) the interaction between GM and anti-TB drugs; (3) GM and TB immune response; (4) GM as a potential target for diagnosis and treatment of TB; (5) multi-omics and artificial intelligence (AI) technologies in GM-TB research; (6) current challenges and future directions in GM-TB research. We highlight the bidirectional nature of the GM–TB interaction, where MTB infection can lead to GM dysbiosis, and changes can affect the host's immune response, contributing to TB onset and progression. Advanced molecular techniques, such as next-generation sequencing and metagenomics, along with AI, play pivotal roles in elucidating these complex interactions. Future research directions include investigating the relationship between GM and TB vaccine efficacy, exploring GM's potential in TB prevention, developing microbiome-based diagnostic and prognostic tools, and examining the role of GM in TB recurrence. By addressing these areas, we aim to provide a comprehensive perspective on the latest advancements in GM and TB research and offer insights for future studies and clinical applications. Ultimately, the development of novel microbiome-based strategies may offer new tools and insights for the effective control and management of TB, a disease that continues to pose a significant threat to public health.

Keywords

artificial intelligence / gut microbiota / microbiome-based diagnostics / Mycobacterium tuberculosis / omics technologies / tuberculosis

References

[1] Zumla, Alimuddin, Mario Raviglione, Richard Hafner, and C. Fordham von Reyn. 2013. “Tuberculosis.” New England Journal of Medicine 368: 745-755. https://doi.org/10.1056/NEJMra1200894

[2] Natarajan, Arvind, P. M. Beena, Anushka V. Devnikar, and Sagar Mali. 2020. “A Systemic Review on Tuberculosis.” Indian Journal of Tuberculosis 67: 295-311. https://doi.org/10.1016/j.ijtb.2020.02.005

[3] World Health Organization. 2024. “Global Tuberculosis Report 2024.” Geneva: World Health Organization: 1−68. https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2024

[4] Lin, Zixun, Liqin Sun, Cheng Wang, Fuxiang Wang, Jun Wang, Qian Li, and Hongzhou Lu. 2023. “Bottlenecks and Recent Advancements in Detecting Mycobacterium Tuberculosis in Patients With HIV.” iLABMED 1: 44-57. https://doi.org/10.1002/ila2.11

[5] Chen, Zhi, Tao Wang, Jingli Du, Lin Sun, Guirong Wang, Ruizi Ni, Yajing An, et al. 2025. “Decoding the WHO Global Tuberculosis Report 2024: A Critical Analysis of Global and Chinese Key Data.” Zoonoses 5(1): 1. https://doi.org/10.15212/zoonoses-2024-0061

[6] Wang, Juanjuan, Ningning Zhu, Xiaomin Su, Yunhuan Gao, and Rongcun Yang. 2023. “Gut-Microbiota-Derived Metabolites Maintain Gut and Systemic Immune Homeostasis.” Cells 12: 793. https://doi.org/10.3390/cells12050793

[7] Ma, Pei-Jun, Meng-Meng Wang, and Yun Wang. 2022. “Gut Microbiota: A New Insight Into Lung Diseases.” Biomedicine & Pharmacotherapy 155: 113810. https://doi.org/10.1016/j.biopha.2022.113810

[8] Zhao, Min'an, Jiayi Chu, Shiyao Feng, Chuanhao Guo, Baigong Xue, Kan He, and Lisha Li. 2023. “Immunological Mechanisms of Inflammatory Diseases Caused by Gut Microbiota Dysbiosis: A Review.” Biomedicine & Pharmacotherapy 164: 114985. https://doi.org/10.1016/j.biopha.2023.114985

[9] Sittipo, Panida, Stefani Lobionda, Yun Kyung Lee, and Craig L. Maynard. 2018. “Intestinal Microbiota and the Immune System in Metabolic Diseases.” Journal of Microbiology 56: 154-162. https://doi.org/10.1007/s12275-018-7548-y

[10] Xue, Kaiyang, Jiawei Li, and Ruijie Huang. 2024. “The Immunoregulatory Role of Gut Microbiota in the Incidence, Progression, and Therapy of Breast Cancer.” Frontiers in Cellular and Infection Microbiology 14: 1411249. https://doi.org/10.3389/fcimb.2024.1411249

[11] Enaud, Raphaël, Renaud Prevel, Eleonora Ciarlo, Fabien Beaufils, Gregoire Wieërs, Benoit Guery, and Laurence Delhaes. 2020. “The Gut-Lung Axis in Health and Respiratory Diseases: A Place for Inter-Organ and Inter-Kingdom Crosstalks.” Frontiers in Cellular and Infection Microbiology 10: 9. https://doi.org/10.3389/fcimb.2020.00009

[12] Marsland, Benjamin J., Aurélien Trompette, and Eva S. Gollwitzer. 2015. “The Gut-Lung Axis in Respiratory Disease.” Annals of the American Thoracic Society 12(Suppl 2): S150-S156. https://doi.org/10.1513/AnnalsATS.201503-133AW

[13] Harris, Nicola L., and Benjamin J. Marsland. 2025. “The Gut-Lung Axis: Protozoa Join the Party.” Cell 188: 275-277. https://doi.org/10.1016/j.cell.2024.12.027

[14] Park, Jin-Sun, Eun-Jung Lee, Jae-Chul Lee, Won-Ki Kim, and Hee-Sun Kim. 2007. “Anti-Inflammatory Effects of Short Chain Fatty Acids in IFN-γ-Stimulated RAW 264.7 Murine Macrophage Cells: Involvement of NF-κB and ERK Signaling Pathways.” International Immunopharmacology 7: 70-77. https://doi.org/10.1016/j.intimp.2006.08.015

[15] Chen, Lingming, Guoliang Zhang, Guobao Li, Wei Wang, Zhenhuang Ge, Yi Yang, Xing He, et al. 2022. “Ifnar Gene Variants Influence Gut Microbial Production of Palmitoleic Acid and Host Immune Responses to Tuberculosis.” Nature Metabolism 4: 359-373. https://doi.org/10.1038/s42255-022-00547-3

[16] Yunusbaeva, Milyausha, Liliya Borodina, Darya Terentyeva, Anna Bogdanova, Aigul Zakirova, Shamil Bulatov, Radick Altinbaev, Fanil Bilalov, and Bayazit Yunusbayev. 2024. “Excess Fermentation and Lactic Acidosis as Detrimental Functions of the Gut Microbes in Treatment-Naive TB Patients.” Frontiers in Cellular and Infection Microbiology 14: 1331521. https://doi.org/10.3389/fcimb.2024.1331521

[17] Enjeti, Aditya, Harindra Darshana Sathkumara, and Andreas Kupz. 2023. “Impact of the Gut-Lung Axis on Tuberculosis Susceptibility and Progression.” Frontiers in Microbiology 14: 1209932. https://doi.org/10.3389/fmicb.2023.1209932

[18] Cao, Demin, Weihua Liu, Na Lyu, Boxing Li, Weibo Song, Yanxiao Yang, Jianliang Zhu, Zhiguo Zhang, and Baoli Zhu. 2021. “Gut Mycobiota Dysbiosis in Pulmonary Tuberculosis Patients Undergoing Anti-Tuberculosis Treatment.” Microbiology Spectrum 9: e0061521. https://doi.org/10.1128/spectrum.00615-21

[19] Huang, Shiang-Fen, Ying-Ying Yang, Kun-Ta Chou, Chang-Phone Fung, Fu-Der Wang, and Wei-Juin Su. 2019. “Systemic Proinflammation After Mycobacterium Tuberculosis Infection Was Correlated to the Gut Microbiome in HIV-Uninfected Humans.” European Journal of Clinical Investigation 49: e13068. https://doi.org/10.1111/eci.13068

[20] Zhuo, Qiqi, Xianyi Zhang, Kehong Zhang, Chan Chen, Zhen Huang, and Yuzhong Xu. 2023. “The Gut and Lung Microbiota in Pulmonary Tuberculosis: Susceptibility, Function, and New Insights Into Treatment.” Expert Review of Anti-infective Therapy 21: 1355-1364. https://doi.org/10.1080/14787210.2023.2283036

[21] Namasivayam, Sivaranjani, Keith D. Kauffman, John A. McCulloch, Wuxing Yuan, Vishal Thovarai, Lara R. Mittereder, Giorgio Trinchieri, Daniel L. Barber, and Alan Sher. 2019. “Correlation Between Disease Severity and the Intestinal Microbiome in Mycobacterium Tuberculosis-Infected Rhesus Macaques.” mBio 10: e01018-e01019. https://doi.org/10.1128/mBio.01018-19

[22] Naidoo, Charissa C., Georgina R. Nyawo, Benjamin G. Wu, Gerhard Walzl, Robin M. Warren, Leopoldo N. Segal, and Grant Theron. 2019. “The Microbiome and Tuberculosis: State of the Art, Potential Applications, and Defining the Clinical Research Agenda.” The Lancet Respiratory Medicine 7: 892-906. https://doi.org/10.1016/S2213-2600(18)30501-0

[23] Yuan, Zongxiang, Yiwen Kang, Chuye Mo, Shihui Huang, Fang Qin, Junhan Zhang, Fengyi Wang, et al. 2024. “Causal Relationship Between Gut Microbiota and Tuberculosis: A Bidirectional Two-Sample Mendelian Randomization Analysis.” Respiratory Research 25: 16. https://doi.org/10.1186/s12931-023-02652-7

[24] Han, MeiQing, Xia Wang, JiaMin Zhang, Lin Su, Hafiz Muhammad Ishaq, Duan Li, JunWei Cui, HuaJie Zhao, and Fan Yang. 2024. “Gut Bacterial and Fungal Dysbiosis in Tuberculosis Patients.” BMC Microbiology 24: 141. https://doi.org/10.1186/s12866-024-03275-8

[25] Wang, Shuting, Liya Yang, Haiyang Hu, Longxian Lv, Zhongkang Ji, Yanming Zhao, Hua Zhang, et al. 2022. “Characteristic Gut Microbiota and Metabolic Changes in Patients With Pulmonary Tuberculosis.” Microbial Biotechnology 15: 262-275. https://doi.org/10.1111/1751-7915.13761

[26] Wang, Yue, Yali Deng, Nianqiang Liu, Yanggui Chen, Yuandong Jiang, Zihao Teng, Zhi Ma, Yuxue Chang, and Yang Xiang. 2022. “Alterations in the Gut Microbiome of Individuals With Tuberculosis of Different Disease States.” Frontiers in Cellular and Infection Microbiology 12: 836987. https://doi.org/10.3389/fcimb.2022.836987

[27] Hu, Yongfei, Yuqing Feng, Jiannan Wu, Fei Liu, Zhiguo Zhang, Yanan Hao, Shihao Liang, et al. 2019. “The Gut Microbiome Signatures Discriminate Healthy From Pulmonary Tuberculosis Patients.” Frontiers in Cellular and Infection Microbiology 9: 90. https://doi.org/10.3389/fcimb.2019.00090

[28] Maji, Abhijit, Richa Misra, Darshan B. Dhakan, Vipin Gupta, Nitish K. Mahato, Rituja Saxena, Parul Mittal, et al. 2018. “Gut Microbiome Contributes to Impairment of Immunity in Pulmonary Tuberculosis Patients by Alteration of Butyrate and Propionate Producers.” Environmental Microbiology 20: 402-419. https://doi.org/10.1111/1462-2920.14015

[29] Namasivayam, Sivaranjani, Mamoudou Maiga, Wuxing Yuan, Vishal Thovarai, Diego L. Costa, Lara R. Mittereder, Matthew F. Wipperman, et al. 2017. “Longitudinal Profiling Reveals a Persistent Intestinal Dysbiosis Triggered by Conventional Anti-Tuberculosis Therapy.” Microbiome 5: 71. https://doi.org/10.1186/s40168-017-0286-2

[30] Luo, Mei, Yong Liu, Pengfei Wu, Dong-Xia Luo, Qun Sun, Han Zheng, Richard Hu, et al. 2017. “Alternation of Gut Microbiota in Patients With Pulmonary Tuberculosis.” Frontiers in Physiology 8: 822. https://doi.org/10.3389/fphys.2017.00822

[31] Winglee, Kathryn, Emiley Eloe-Fadrosh, Shashank Gupta, Haidan Guo, Claire Fraser, and William Bishai. 2014. “Aerosol Mycobacterium Tuberculosis Infection Causes Rapid Loss of Diversity in Gut Microbiota.” PLOS One 9: e97048. https://doi.org/10.1371/journal.pone.0097048

[32] Alvarado-Peña, Néstor, David Galeana-Cadena, Itzel Alejandra Gómez-García, Xavier Soberón Mainero, and Eugenia Silva-Herzog. 2023. “The Microbiome and the Gut-Lung Axis in Tuberculosis: Interplay in the Course of Disease and Treatment.” Frontiers in Microbiology 14: 1237998. https://doi.org/10.3389/fmicb.2023.1237998

[33] Diallo, Dramane, Shan Sun, Anou Moise Somboro, Bocar Baya, Amadou Kone, Bassirou Diarra, Mohamed Nantoume, et al. 2023. “Metabolic and Immune Consequences of Antibiotic Related Microbiome Alterations During First-Line Tuberculosis Treatment in Bamako, Mali.” Research square Preprint: 3232670. https://doi.org/10.21203/rs.3.rs-3232670/v1

[34] Namasivayam, Sivaranjani, Bassirou Diarra, Seydou Diabate, Yeya dit Sadio Sarro, Amadou Kone, Bourahima Kone, Mohamed Tolofoudie, et al. 2020. “Patients Infected With Mycobacterium Africanum Versus Mycobacterium Tuberculosis Possess Distinct Intestinal Microbiota.” PLoS Neglected Tropical Diseases 14: e0008230. https://doi.org/10.1371/journal.pntd.0008230

[35] Majlessi, L., F. Sayes, J. F. Bureau, A. Pawlik, V. Michel, G. Jouvion, M. Huerre, et al. 2017. “Colonization With Helicobacter Is Concomitant With Modified Gut Microbiota and Drastic Failure of the Immune Control of Mycobacterium Tuberculosis.” Mucosal Immunology 10: 1178-1189. https://doi.org/10.1038/mi.2016.140

[36] Arias, Lilibeth, Galo Adrián Goig, Paula Cardona, Manuela Torres-Puente, Jorge Díaz, Yaiza Rosales, Eric Garcia, et al. 2019. “Influence of Gut Microbiota on Progression to Tuberculosis Generated by High Fat Diet-Induced Obesity in C3HeB/FeJ Mice.” Frontiers in Immunology 10: 2464. https://doi.org/10.3389/fimmu.2019.02464

[37] Pott, Johanna, and Mathias Hornef. 2012. “Innate Immune Signalling at the Intestinal Epithelium in Homeostasis and Disease.” EMBO Reports 13: 684-698. https://doi.org/10.1038/embor.2012.96

[38] Rivière, Audrey, Marija Selak, David Lantin, Frédéric Leroy, and Luc De Vuyst. 2016. “Bifidobacteria and Butyrate-Producing Colon Bacteria: Importance and Strategies for Their Stimulation in the Human Gut.” Frontiers in Microbiology 7: 979. https://doi.org/10.3389/fmicb.2016.00979

[39] Mori, Giorgia, Mark Morrison, and Antje Blumenthal. 2021. “Microbiome-Immune Interactions in Tuberculosis.” PLOS Pathogens 17: e1009377. https://doi.org/10.1371/journal.ppat.1009377

[40] Comberiati, Pasquale, Maria Di Cicco, Francesco Paravati, Umberto Pelosi, Alessandro Di Gangi, Stefania Arasi, Simona Barni, et al. 2021. “The Role of Gut and Lung Microbiota in Susceptibility to Tuberculosis.” International Journal of Environmental Research and Public Health 18: 12220. https://doi.org/10.3390/ijerph182212220

[41] Usuda, Haruki, Takayuki Okamoto, and Koichiro Wada. 2021. “Leaky Gut: Effect of Dietary Fiber and Fats on Microbiome and Intestinal Barrier.” International Journal of Molecular Sciences 22: 7613. https://doi.org/10.3390/ijms22147613

[42] Wood, Madeleine R., Elaine A. Yu, and Saurabh Mehta. 2017. “The Human Microbiome in the Fight Against Tuberculosis.” American Journal of Tropical Medicine and Hygiene 96: 1274-1284. https://doi.org/10.4269/ajtmh.16-0581

[43] Osei Sekyere, John, Nontuthuko E. Maningi, and Petrus B. Fourie. 2020. “Mycobacterium Tuberculosis, Antimicrobials, Immunity, and Lung-Gut Microbiota Crosstalk: Current Updates and Emerging Advances.” Annals of the New York Academy of Sciences 1467: 21-47. https://doi.org/10.1111/nyas.14300

[44] Duarte, S. M. B., J. T. Stefano, L. Miele, F. R. Ponziani, M. Souza-Basqueira, Lsrr Okada, F. G. de Barros Costa, et al. 2018. “Gut Microbiome Composition in Lean Patients With Nash Is Associated With Liver Damage Independent of Caloric Intake: A Prospective Pilot Study.” Nutrition, Metabolism & Cardiovascular Diseases 28: 369-384. https://doi.org/10.1016/j.numecd.2017.10.014

[45] Li, Yue, Liangjie Zhao, Meiling Hou, Tianlin Gao, Jin Sun, Hao Luo, Fengdan Wang, et al. 2022. “Lactobacillus Casei Improve Anti-Tuberculosis Drugs-Induced Intestinal Adverse Reactions in Rat by Modulating Gut Microbiota and Short-Chain Fatty Acids.” Nutrients 14: 1668. https://doi.org/10.3390/nu14081668

[46] Segal, Leopoldo N., Jose C. Clemente, Yonghua Li, Chunhai Ruan, Jane Cao, Mauricio Danckers, Alison Morris, et al. 2017. “Anaerobic Bacterial Fermentation Products Increase Tuberculosis Risk in Antiretroviral-Drug-Treated HIV Patients.” Cell Host & Microbe 21: 530-537.e534. https://doi.org/10.1016/j.chom.2017.03.003

[47] Lachmandas, Ekta, Corina N. A. M. van den Heuvel, Michelle S. M. A. Damen, Maartje C. P. Cleophas, Mihai G. Netea, and Reinout van Crevel. 2016. “Diabetes Mellitus and Increased Tuberculosis Susceptibility: The Role of Short-Chain Fatty Acids.” Journal of Diabetes Research 2016: 6014631. https://doi.org/10.1155/2016/6014631

[48] Yu, Ziqi, Xiang Shen, Aiyao Wang, Chong Hu, and Jianyong Chen. 2023. “The Gut Microbiome: A Line of Defense Against Tuberculosis Development.” Frontiers in Cellular and Infection Microbiology 13: 1149679. https://doi.org/10.3389/fcimb.2023.1149679

[49] Khan, Nargis, Laura Mendonca, Achal Dhariwal, Ghislaine Fontes, Dick Menzies, Jianguo Xia, Maziar Divangahi, and Irah L. King. 2019. “Intestinal Dysbiosis Compromises Alveolar Macrophage Immunity to Mycobacterium Tuberculosis.” Mucosal Immunology 12: 772-783. https://doi.org/10.1038/s41385-019-0147-3

[50] Mayer-Barber, Katrin D., and Daniel L. Barber. 2015. “Innate and Adaptive Cellular Immune Responses to Mycobacterium Tuberculosis Infection.” Cold Spring Harbor Perspectives in Medicine 5(12): a018424. https://doi.org/10.1101/cshperspect.a018424

[51] Yang, Fang, Yi Yang, Lingming Chen, Zhiyi Zhang, Linna Liu, Chunmin Zhang, Qiongdan Mai, et al. 2022. “The Gut Microbiota Mediates Protective Immunity Against Tuberculosis via Modulation of lncRNA.” Gut Microbes 14: 2029997. https://doi.org/10.1080/19490976.2022.2029997

[52] Roy, Supriya, and Suneela Dhaneshwar. 2023. “Role of Prebiotics, Probiotics, and Synbiotics in Management of Inflammatory Bowel Disease: Current Perspectives.” World Journal of Gastroenterology 29: 2078-2100. https://doi.org/10.3748/wjg.v29.i14.2078

[53] Negi, Shikha, Susanta Pahari, Hilal Bashir, and Javed N. Agrewala. 2019. “Gut Microbiota Regulates Mincle Mediated Activation of Lung Dendritic Cells to Protect Against Mycobacterium Tuberculosis.” Frontiers in Immunology 10: 1142. https://doi.org/10.3389/fimmu.2019.01142

[54] Liu, Yue, Jiaqi Wang, and Changxin Wu. 2021. “Microbiota and Tuberculosis: A Potential Role of Probiotics, and Postbiotics.” Frontiers in Nutrition 8: 626254. https://doi.org/10.3389/fnut.2021.626254

[55] Azad, Md. Abul Kalam, Manobendro Sarker, Tiejun Li, and Jie Yin. 2018. “Probiotic Species in the Modulation of Gut Microbiota: An Overview.” BioMed Research International 2018: 9478630. https://doi.org/10.1155/2018/9478630

[56] Huey, Samantha L., Elaine A. Yu, Julia L. Finkelstein, Marshall J. Glesby, Wesley Bonam, David G. Russell, and Saurabh Mehta. 2021. “Nutrition, Inflammation, and the Gut Microbiota Among Outpatients With Active Tuberculosis Disease in India.” American Journal of Tropical Medicine and Hygiene 105: 1645-1656. https://doi.org/10.4269/ajtmh.21-0310

[57] Cheung, Man Kit, Wai Yip Lam, Wendy Yin Wan Fung, Patrick Tik Wan Law, Chun Hang Au, Wenyan Nong, Kai Man Kam, Hoi Shan Kwan, and Stephen Kwok Wing Tsui. 2013. “Sputum Microbiota in Tuberculosis as Revealed by 16S rRNA Pyrosequencing.” PLoS One 8: e54574. https://doi.org/10.1371/journal.pone.0054574

[58] Caradonna, L., L. Amati, T. Magrone, N. M. Pellegrino, E. Jirillo, D. Caccavo, L. Caradonna, et al. 2000. “Invited Review: Enteric Bacteria, Lipopolysaccharides and Related Cytokines in Inflammatory Bowel Disease: Biological and Clinical Significance.” Journal of Endotoxin Research 6: 205-214. https://doi.org/10.1177/09680519000060030101. https://www.ncbi.nlm.nih.gov/pubmed/11052175

[59] Kamdar, Karishma, Samira Khakpour, Jingyu Chen, Vanessa Leone, Jennifer Brulc, Thomas Mangatu, Dionysios A. Antonopoulos, et al. 2016. “Genetic and Metabolic Signals during Acute Enteric Bacterial Infection Alter the Microbiota and Drive Progression to Chronic Inflammatory Disease.” Cell Host & Microbe 19: 21-31. https://doi.org/10.1016/j.chom.2015.12.006

[60] Dumas, Alexia, Dan Corral, André Colom, Florence Levillain, Antonio Peixoto, Denis Hudrisier, Yannick Poquet, and Olivier Neyrolles. 2018. “The Host Microbiota Contributes to Early Protection Against Lung Colonization by Mycobacterium Tuberculosis.” Frontiers in Immunology 9: 2656. https://doi.org/10.3389/fimmu.2018.02656

[61] Hauptmann, M., B. Kalsdorf, J. E. Akoh-Arrey, C. Lange, and U. E. Schaible. 2024. “Microbiota Alterations in Patients Treated for Susceptible or Drug-Resistant TB.” International Journal of Tuberculosis and Lung Disease Open 1: 355-361. https://doi.org/10.5588/ijtldopen.24.0325

[62] Liu, Na, Jinfeng Liu, Binjie Zheng, Xiangchang Zeng, Zixin Ye, Xinyi Huang, Wenhui Liu, et al. 2023. “Gut Microbiota Affects Sensitivity to Immune-Mediated Isoniazid-Induced Liver Injury.” Biomedicine & Pharmacotherapy 160: 114400. https://doi.org/10.1016/j.biopha.2023.114400

[63] Namasivayam, Sivaranjani, Matthew Zimmerman, Sandra Oland, Han Wang, Lara R. Mittereder, Véronique Dartois, and Alan Sher. 2023. “The Dysbiosis Triggered by First-Line Tuberculosis Antibiotics Fails to Reduce Their Bioavailability.” mBio 14: e0035323. https://doi.org/10.1128/mbio.00353-23

[64] Negi, Shikha, Susanta Pahari, Hilal Bashir, and Javed N. Agrewala. 2020. “Intestinal Microbiota Disruption Limits the Isoniazid Mediated Clearance of Mycobacterium Tuberculosis in Mice.” European Journal of Immunology 50: 1976-1987. https://doi.org/10.1002/eji.202048556

[65] Hu, Yongfeng, Qianting Yang, Bo Liu, Jie Dong, Lilian Sun, Yafang Zhu, Haoxiang Su, et al. 2019. “Gut Microbiota Associated With Pulmonary Tuberculosis and Dysbiosis Caused by Anti-Tuberculosis Drugs.” Journal of Infection 78: 317-322. https://doi.org/10.1016/j.jinf.2018.08.006

[66] Wipperman, Matthew F., Daniel W. Fitzgerald, Marc Antoine Jean Juste, Ying Taur, Sivaranjani Namasivayam, Alan Sher, James M. Bean, Vanni Bucci, and Michael S. Glickman. 2017. “Antibiotic Treatment for Tuberculosis Induces a Profound Dysbiosis of the Microbiome That Persists Long After Therapy Is Completed.” Scientific Reports 7: 10767. https://doi.org/10.1038/s41598-017-10346-6

[67] Khan, Nargis, Aurobind Vidyarthi, Sajid Nadeem, Shikha Negi, Girish Nair, and Javed N. Agrewala. 2016. “Alteration in the Gut Microbiota Provokes Susceptibility to Tuberculosis.” Frontiers in Immunology 7: 529. https://doi.org/10.3389/fimmu.2016.00529

[68] Zhang, Chi, Qi Ouyang, Xianyuan Zhou, Yingfeng Huang, Yu Zeng, Li Deng, Dachuan Lin, and Weidong Zheng. 2023. “In Vitro Activity of Tetracycline Analogs Against Multidrug-Resistant and Extensive Drug Resistance Clinical Isolates of Mycobacterium Tuberculosis.” Tuberculosis 140: 102336. https://doi.org/10.1016/j.tube.2023.102336

[69] Rothstein, David M. 2016. “Rifamycins, Alone and in Combination.” Cold Spring Harbor Perspectives in Medicine 6: a027011. https://doi.org/10.1101/cshperspect.a027011

[70] Wipperman, Matthew F., Shakti K. Bhattarai, Charles Kyriakos Vorkas, Venkata Suhas Maringati, Ying Taur, Laurent Mathurin, Katherine McAulay, et al. 2021. “Gastrointestinal Microbiota Composition Predicts Peripheral Inflammatory State During Treatment of Human Tuberculosis.” Nature Communications 12: 1141. https://doi.org/10.1038/s41467-021-21475-y

[71] Weersma, Rinse K., Alexandra Zhernakova, and Jingyuan Fu. 2020. “Interaction Between Drugs and the Gut Microbiome.” Gut 69: 1510-1519. https://doi.org/10.1136/gutjnl-2019-320204

[72] Hill, D. A., C. Hoffmann, M. C. Abt, Y. Du, D. Kobuley, T. J. Kirn, F. D. Bushman, and D. Artis. 2010. “Metagenomic Analyses Reveal Antibiotic-Induced Temporal and Spatial Changes in Intestinal Microbiota With Associated Alterations in Immune Cell Homeostasis.” Mucosal Immunology 3: 148-158. https://doi.org/10.1038/mi.2009.132

[73] Keeney, Kristie M., Sophie Yurist-Doutsch, Marie-Claire Arrieta, and B. Brett Finlay. 2014. “Effects of Antibiotics on Human Microbiota and Subsequent Disease.” Annual Review of Microbiology 68: 217-235. https://doi.org/10.1146/annurev-micro-091313-103456

[74] Ramappa, Vidyasagar, and Guruprasad P. Aithal. 2013. “Hepatotoxicity Related to Anti-Tuberculosis Drugs: Mechanisms and Management.” Journal of Clinical and Experimental Hepatology 3: 37-49. https://doi.org/10.1016/j.jceh.2012.12.001

[75] Jia, Baolei, Xiao Han, Kyung Hyun Kim, and Che Ok Jeon. 2022. “Discovery and Mining of Enzymes From the Human Gut Microbiome.” Trends in Biotechnology 40: 240-254. https://doi.org/10.1016/j.tibtech.2021.06.008

[76] Doestzada, Marwah, Arnau Vich Vila, Alexandra Zhernakova, Debby P. Y. Koonen, Rinse K. Weersma, Daan J. Touw, Folkert Kuipers, Cisca Wijmenga, and Jingyuan Fu. 2018. “Pharmacomicrobiomics: A Novel Route Towards Personalized Medicine?” Protein & Cell 9: 432-445. https://doi.org/10.1007/s13238-018-0547-2

[77] Wu, Chunli, Hengzhong Yi, Yanmei Hu, Danlin Luo, Zhigang Tang, Xinmin Wen, Yong Zhang, et al. 2023. “Effects of Second-Line Anti-Tuberculosis Drugs on the Intestinal Microbiota of Patients With Rifampicin-Resistant Tuberculosis.” Frontiers in Cellular and Infection Microbiology 13: 1127916. https://doi.org/10.3389/fcimb.2023.1127916

[78] Pei, Shengfei, Li Yang, Huixia Gao, Yuzhen Liu, Jianhua Lu, Er Hei Dai, Chunyan Meng, Fumin Feng, and Yuling Wang. 2025. “The Association Between the Gut Microbiome and Antituberculosis Drug-Induced Liver Injury.” Frontiers in Pharmacology 16: 1512815. https://doi.org/10.3389/fphar.2025.1512815

[79] Gong, Jin-Yu, Huan Ren, Hui-Qing Chen, Kai Xing, Chen-Lin Xiao, and Jian-Quan Luo. 2022. “Magnesium Isoglycyrrhizinate Attenuates Anti-Tuberculosis Drug-Induced Liver Injury by Enhancing Intestinal Barrier Function and Inhibiting the LPS/TLRs/NF-κB Signaling Pathway in Mice.” Pharmaceuticals 15: 1130. https://doi.org/10.3390/ph15091130

[80] Zhuang, Li, Ling Yang, Linsheng Li, Zhaoyang Ye, and Wenping Gong. 2024. “Mycobacterium Tuberculosis: Immune Response, Biomarkers, and Therapeutic Intervention.” MedComm 5(5): e419. https://doi.org/10.1002/mco2.419

[81] Ye, Zhaoyang, Linsheng Li, Ling Yang, Li Zhuang, Ashok Aspatwar, Liang Wang, and Wenping Gong. 2024. “Impact of Diabetes Mellitus on Tuberculosis Prevention, Diagnosis, and Treatment From an Immunologic Perspective.” Exploration 4: 20230138. https://doi.org/10.1002/EXP.20230138

[82] Wang, Wei, Jingxin Li, Yuejin Liang, and Wenping Gong. 2024. “Editorial: Immunological Aspects of Emerging and Re-Emerging Zoonoses.” Frontiers in Immunology 15: 1392382. https://doi.org/10.3389/fimmu.2024.1392382

[83] Zhuang, Li, Zhaoyang Ye, Linsheng Li, Ling Yang, and Wenping Gong. 2023. “Next-Generation TB Vaccines: Progress, Challenges, and Prospects.” Vaccines 11: 1304. https://doi.org/10.3390/vaccines11081304

[84] Frati, Franco, Cristina Salvatori, Cristoforo Incorvaia, Alessandro Bellucci, Giuseppe Di Cara, Francesco Marcucci, and Susanna Esposito. 2018. “The Role of the Microbiome in Asthma: The Gut-Lung Axis.” International Journal of Molecular Sciences 20: 123. https://doi.org/10.3390/ijms20010123

[85] Thibeault, Charlotte, Norbert Suttorp, and Bastian Opitz. 2021. “The Microbiota in Pneumonia: From Protection to Predisposition.” Science Translational Medicine 13: eaba0501. https://doi.org/10.1126/scitranslmed.aba0501

[86] Silva, Fabiola, Raphaël Enaud, Elizabeth Creissen, Marcela Henao-Tamayo, Laurence Delhaes, and Angelo Izzo. 2022. “Mouse Subcutaneous BCG Vaccination and Mycobacterium Tuberculosis Infection Alter the Lung and Gut Microbiota.” Microbiology Spectrum 10: e0169321. https://doi.org/10.1128/spectrum.01693-21

[87] Chu, Hiutung, and Sarkis K. Mazmanian. 2013. “Innate Immune Recognition of the Microbiota Promotes Host-Microbial Symbiosis.” Nature Immunology 14: 668-675. https://doi.org/10.1038/ni.2635

[88] Rakoff-Nahoum, Seth, Justin Paglino, Fatima Eslami-Varzaneh, Stephen Edberg, and Ruslan Medzhitov. 2004. “Recognition of Commensal Microflora by Toll-Like Receptors Is Required for Intestinal Homeostasis.” Cell 118: 229-241. https://doi.org/10.1016/j.cell.2004.07.002

[89] Danne, Camille, Grigory Ryzhakov, Maria Martínez-López, Nicholas Edward Ilott, Fanny Franchini, Fiona Cuskin, Elisabeth C. Lowe, et al. 2017. “A Large Polysaccharide Produced by Helicobacter Hepaticus Induces an Anti-Inflammatory Gene Signature in Macrophages.” Cell Host & Microbe 22: 733-745.e735. https://doi.org/10.1016/j.chom.2017.11.002

[90] Wolf, Andrea J., and David M. Underhill. 2018. “Peptidoglycan Recognition by the Innate Immune System.” Nature Reviews Immunology 18: 243-254. https://doi.org/10.1038/nri.2017.136

[91] Brown, Gordon D. 2006. “Dectin-1: A Signalling Non-TLR Pattern-Recognition Receptor.” Nature Reviews Immunology 6: 33-43. https://doi.org/10.1038/nri1745

[92] Föh, Bandik, Jana Sophia Buhre, Hanna B. Lunding, Maria E. Moreno-Fernandez, Peter König, Christian Sina, Senad Divanovic, and Marc Ehlers. 2022. “Microbial Metabolite Butyrate Promotes Induction of IL-10+IgM+ Plasma Cells.” PLoS One 17: e0266071. https://doi.org/10.1371/journal.pone.0266071

[93] Peng, Yi-Lei, Si-Han Wang, Yu-Long Zhang, Man-Yun Chen, Kang He, Qing Li, Wei-Hua Huang, and Wei Zhang. 2024. “Effects of Bile Acids on the Growth, Composition and Metabolism of Gut Bacteria.” npj Biofilms Microbiomes 10: 112. https://doi.org/10.1038/s41522-024-00566-w

[94] Takiishi, Tatiana, Camila Ideli Morales Fenero, and Niels Olsen Saraiva Câmara. 2017. “Intestinal Barrier and Gut Microbiota: Shaping Our Immune Responses Throughout Life.” Tissue Barriers 5: e1373208. https://doi.org/10.1080/21688370.2017.1373208

[95] Jakobsson, Hedvig E., Ana M. Rodríguez-Piñeiro, André Schütte, Anna Ermund, Preben Boysen, Mats Bemark, Felix Sommer, et al. 2015. “The Composition of the Gut Microbiota Shapes the Colon Mucus Barrier.” EMBO Reports 16: 164-177. https://doi.org/10.15252/embr.201439263

[96] Quigley, Eamonn M. M. 2013. “Gut Bacteria in Health and Disease.” Gastroenterology & Hepatology 9: 560-569. https://www.ncbi.nlm.nih.gov/pubmed/24729765

[97] Lin, Jie, Dongli Chen, Yongen Yan, Jiang Pi, Junfa Xu, Lingming Chen, and Biying Zheng. 2024. “Gut Microbiota: A Crucial Player in the Combat Against Tuberculosis.” Frontiers in Immunology 15: 1442095. https://doi.org/10.3389/fimmu.2024.1442095

[98] Cho, Ilseung, and Martin J. Blaser. 2012. “The Human Microbiome: At the Interface of Health and Disease.” Nature Reviews Genetics 13: 260-270. https://doi.org/10.1038/nrg3182

[99] Zheng, Danping, Timur Liwinski, and Eran Elinav. 2020. “Interaction Between Microbiota and Immunity in Health and Disease.” Cell Research 30: 492-506. https://doi.org/10.1038/s41422-020-0332-7

[100] Atarashi, Koji, Takeshi Tanoue, Tatsuichiro Shima, Akemi Imaoka, Tomomi Kuwahara, Yoshika Momose, Genhong Cheng, et al. 2011. “Induction of Colonic Regulatory T Cells by Indigenous Clostridium Species.” Science 331: 337-341. https://doi.org/10.1126/science.1198469

[101] Fedele, Giorgio, Paola Stefanelli, Fabiana Spensieri, Cecilia Fazio, Paola Mastrantonio, and Clara M. Ausiello. 2005. “Bordetella Pertussis-Infected Human Monocyte-Derived Dendritic Cells Undergo Maturation and Induce Th1 Polarization and Interleukin-23 Expression.” Infection and Immunity 73: 1590-1597. https://doi.org/10.1128/IAI.73.3.1590-1597.2005

[102] Atarashi, Koji, Takeshi Tanoue, Minoru Ando, Nobuhiko Kamada, Yuji Nagano, Seiko Narushima, Wataru Suda, et al. 2015. “Th17 Cell Induction by Adhesion of Microbes to Intestinal Epithelial Cells.” Cell 163: 367-380. https://doi.org/10.1016/j.cell.2015.08.058

[103] Loftfield, Erikka, Roni T. Falk, Joshua N. Sampson, Wen-Yi Huang, Autumn Hullings, Gwen Murphy, Stephanie J. Weinstein, et al. 2022. “Prospective Associations of Circulating Bile Acids and Short-Chain Fatty Acids With Incident Colorectal Cancer.” JNCI Cancer Spectrum 6: pkac027. https://doi.org/10.1093/jncics/pkac027

[104] Bachem, Annabell, Christina Makhlouf, Katrina J. Binger, David P. de Souza, Deidra Tull, Katharina Hochheiser, Paul G. Whitney, et al. 2019. “Microbiota-Derived Short-Chain Fatty Acids Promote the Memory Potential of Antigen-Activated CD8(+) T Cells.” Immunity 51: 285-297.e285. https://doi.org/10.1016/j.immuni.2019.06.002

[105] Schulthess, Julie, Sumeet Pandey, Melania Capitani, Kevin C. Rue-Albrecht, Isabelle Arnold, Fanny Franchini, Agnieszka Chomka, et al. 2019. “The Short Chain Fatty Acid Butyrate Imprints an Antimicrobial Program in Macrophages.” Immunity 50: 432-445.e437. https://doi.org/10.1016/j.immuni.2018.12.018

[106] Vinolo, Marco A. R., Hosana G. Rodrigues, Renato T. Nachbar, and Rui Curi. 2011. “Regulation of Inflammation by Short Chain Fatty Acids.” Nutrients 3: 858-876. https://doi.org/10.3390/nu3100858

[107] Arpaia, Nicholas, Clarissa Campbell, Xiying Fan, Stanislav Dikiy, Joris van der Veeken, Paul deRoos, Hui Liu, et al. 2013. “Metabolites Produced by Commensal Bacteria Promote Peripheral Regulatory T-Cell Generation.” Nature 504: 451-455. https://doi.org/10.1038/nature12726

[108] Furusawa, Yukihiro, Yuuki Obata, Shinji Fukuda, Takaho A. Endo, Gaku Nakato, Daisuke Takahashi, Yumiko Nakanishi, et al. 2013. “Commensal Microbe-Derived Butyrate Induces the Differentiation of Colonic Regulatory T Cells.” Nature 504: 446-450. https://doi.org/10.1038/nature12721

[109] Smith, Patrick M., Michael R. Howitt, Nicolai Panikov, Monia Michaud, Carey Ann Gallini, Mohammad Bohlooly-Y, Jonathan N. Glickman, and Wendy S. Garrett. 2013. “The Microbial Metabolites, Short-Chain Fatty Acids, Regulate Colonic Treg Cell Homeostasis.” Science 341: 569-573. https://doi.org/10.1126/science.1241165

[110] Peterson, Daniel A., Nathan P. McNulty, Janaki L. Guruge, and Jeffrey I. Gordon. 2007. “IgA Response to Symbiotic Bacteria as a Mediator of Gut Homeostasis.” Cell Host & Microbe 2: 328-339. https://doi.org/10.1016/j.chom.2007.09.013

[111] Mazmanian, Sarkis K., Cui Hua Liu, Arthur O. Tzianabos, and Dennis L. Kasper. 2005. “An Immunomodulatory Molecule of Symbiotic Bacteria Directs Maturation of the Host Immune System.” Cell 122: 107-118. https://doi.org/10.1016/j.cell.2005.05.007

[112] Wang, Yulian, Lisi Huang, Tian Huang, Suxia Geng, Xiaomei Chen, Xin Huang, Peilong Lai, Xin Du, and Jianyu Weng. 2022. “The Gut Bacteria Dysbiosis Contributes to Chronic Graft-Versus-Host Disease Associated With a Treg/Th1 Ratio Imbalance.” Frontiers in Microbiology 13: 813576. https://doi.org/10.3389/fmicb.2022.813576

[113] Yang, Yicheng, Hanwen Zhang, Yaoyao Wang, Jing Xu, Songren Shu, Peizhi Wang, Shusi Ding, et al. 2024. “Promising Dawn in the Management of Pulmonary Hypertension: The Mystery Veil of Gut Microbiota.” iMeta 3: e159. https://doi.org/10.1002/imt2.159

[114] Ashique, Sumel, Gabriele De Rubis, Ekta Sirohi, Neeraj Mishra, Mohd Rihan, Ashish Garg, Ruby-Jean Reyes, et al. 2022. “Short Chain Fatty Acids: Fundamental Mediators of the Gut-Lung Axis and Their Involvement in Pulmonary Diseases.” Chemico-Biological Interactions 368: 110231. https://doi.org/10.1016/j.cbi.2022.110231

[115] Vorkas, Charles Kyriakos, Matthew F. Wipperman, Kelin Li, James Bean, Shakti K. Bhattarai, Matthew Adamow, Phillip Wong, et al. 2018. “Mucosal-Associated Invariant and γδ T Cell Subsets Respond to Initial Mycobacterium Tuberculosis Infection.” JCI Insight 3: e121899. https://doi.org/10.1172/jci.insight.121899

[116] Constantinides, Michael G. 2018. “Interactions Between the Microbiota and Innate and Innate-Like Lymphocytes.” Journal of Leukocyte Biology 103: 409-419. https://doi.org/10.1002/JLB.3RI0917-378R

[117] Lee, Naeun, and Wan-Uk Kim. 2017. “Microbiota in T-Cell Homeostasis and Inflammatory Diseases.” Experimental & Molecular Medicine 49: e340. https://doi.org/10.1038/emm.2017.36

[118] Cadena, Anthony M., Yixuan Ma, Tao Ding, MacKenzie Bryant, Pauline Maiello, Adam Geber, Philana Ling Lin, JoAnne L. Flynn, and Elodie Ghedin. 2018. “Profiling the Airway in the Macaque Model of Tuberculosis Reveals Variable Microbial Dysbiosis and Alteration of Community Structure.” Microbiome 6: 180. https://doi.org/10.1186/s40168-018-0560-y

[119] Han, MeiQing, Xia Wang, Lin Su, Shiqi Pan, Ningning Liu, Duan Li, Liang Liu, et al. 2024. “Intestinal Microbiome Dysbiosis Increases Mycobacteria Pulmonary Colonization in Mice by Regulating the Nos2-Associated Pathways.” eLife 13: RP99282. https://doi.org/10.7554/eLife.99282.3

[120] Yinghong, Lu, Shi Wenpei, Hu Yi, Xia Fan, Ning Zhu, Wu Meiying, Chen Cheng, O. Hu Yue, and O. Xu Biao. 2021. “A Comparative Study on the Difference of Gut Microbiota and Its Biomarkers Between Patients With Pulmonary Tuberculosis and Healthy Controls.” Chinese Journal of Tuberculosis and Respiratory Diseases 44: 939-946. https://doi.org/10.3760/cma.j.cn112147-20210315-00170

[121] Luo, Dan, Bo-Yi Yang, Kai Qin, Chong-Yu Shi, Nian-Sa Wei, Hai Li, Yi-Xiang Qin, et al. 2023. “Untargeted Metabolomics of Feces Reveals Diagnostic and Prognostic Biomarkers for Active Tuberculosis and Latent Tuberculosis Infection: Potential Application for Precise and Non-Invasive Identification.” Infection and Drug Resistance 16: 6121-6138. https://doi.org/10.2147/IDR.S422363

[122] Sahu, Sukanya, Sandeep R. Kaushik, Shweta Chaudhary, Amit kumar Mahapatra, Rukuwe Kappa, Wetesho Kapfo, Sourav Saha, et al. 2023. “Gut Microbiota Dysbiosis Observed in Tuberculosis Patients Resolves Partially With Anti-Tuberculosis Therapy.” medRxiv Preprint: 23291387. https://doi.org/10.1101/2023.06.14.23291387

[123] Luies, Laneke, Mari van Reenen, Katharina Ronacher, Gerhard Walzl, and Du Toit Loots. 2017. “Predicting Tuberculosis Treatment Outcome Using Metabolomics.” Biomarkers in Medicine 11: 1057-1067. https://doi.org/10.2217/bmm-2017-0133

[124] Nielsen, Stine S. F., Line K. Vibholm, Ida Monrad, Rikke Olesen, Giacomo S. Frattari, Marie H. Pahus, Jesper F. Højen, et al. 2021. “SARS-CoV-2 Elicits Robust Adaptive Immune Responses Regardless of Disease Severity.” eBioMedicine 68: 103410. https://doi.org/10.1016/j.ebiom.2021.103410

[125] Zhou, Zheng, Bao Sun, Dongsheng Yu, and Chunsheng Zhu. 2022. “Gut Microbiota: An Important Player in Type 2 Diabetes Mellitus.” Frontiers in Cellular and Infection Microbiology 12: 834485. https://doi.org/10.3389/fcimb.2022.834485

[126] Zhang, Ting, Jin-ke Cheng, and Yao-min Hu. 2022. “Gut Microbiota as a Promising Therapeutic Target for Age-Related Sarcopenia.” Ageing Research Reviews 81: 101739. https://doi.org/10.1016/j.arr.2022.101739

[127] Diallo, Dramane, Anou M. Somboro, Seydou Diabate, Bacar Baya, Amadou Kone, Yeya S. Sarro, Bourahima Kone, et al. 2021. “Antituberculosis Therapy and Gut Microbiota: Review of Potential Host Microbiota Directed-Therapies.” Frontiers in Cellular and Infection Microbiology 11: 673100. https://doi.org/10.3389/fcimb.2021.673100

[128] Gavrilova, N. N., I. A. Ratnikova, A. K. Sadanov, K. Bayakisheva, Z. J. Tourlibaeva, and O. A. Belikova. 2014. “Application of Probiotics in Complex Treatment of Tuberculosis.” International Journal of Engineering Research and Applications 4: 13-18. https://www.ijera.com/papers/Vol4_issue11/Part%20-%204/B0411041318.pdf

[129] Trivedi, Riddhi, and Kalyani Barve. 2020. “Gut Microbiome a Promising Target for Management of Respiratory Diseases.” Biochemical Journal 477: 2679-2696. https://doi.org/10.1042/BCJ20200426

[130] Eribo, Osagie A., Nelita du Plessis, Mumin Ozturk, Reto Guler, Gerhard Walzl, and Novel N. Chegou. 2020. “The Gut Microbiome in Tuberculosis Susceptibility and Treatment Response: Guilty or Not Guilty?” Cellular and Molecular Life Sciences 77: 1497-1509. https://doi.org/10.1007/s00018-019-03370-4

[131] Li, Yue, Liangjie Zhao, Changyu Sun, Jingyi Yang, Xinyue Zhang, Sheng Dou, Qinglian Hua, Aiguo Ma, and Jing Cai. 2023. “Regulation of Gut Microflora by Lactobacillus Casei Zhang Attenuates Liver Injury in Mice Caused by Anti-Tuberculosis Drugs.” International Journal of Molecular Sciences 24: 9444. https://doi.org/10.3390/ijms24119444

[132] Ahmad Khan, Faiz, Irina Y. Gelmanova, Molly F. Franke, Sidney Atwood, Nataliya A. Zemlyanaya, Irina A. Unakova, Yevgeniy G. Andreev, et al. 2016. “Aggressive Regimens Reduce Risk of Recurrence After Successful Treatment of MDR-TB.” Clinical Infectious Diseases 63: 214-220. https://doi.org/10.1093/cid/ciw276

[133] Motta, Ilaria, Martin Boeree, Dumitru Chesov, Keertan Dheda, Gunar Günther,, Charles Robert Horsburgh, Yousra Kherabi, et al. 2024. “Recent Advances in the Treatment of Tuberculosis.” Clinical Microbiology and Infection 30: 1107-1114. https://doi.org/10.1016/j.cmi.2023.07.013

[134] Martineau, Adrian R., David A. Jolliffe, Richard L. Hooper, Lauren Greenberg, John F. Aloia, Peter Bergman, Gal Dubnov-Raz, et al. 2017. “Vitamin D Supplementation to Prevent Acute Respiratory Tract Infections: Systematic Review and Meta-Analysis of Individual Participant Data.” BMJ 356: i6583. https://doi.org/10.1136/bmj.i6583

[135] Satam, Heena, Kandarp Joshi, Upasana Mangrolia, Sanober Waghoo, Gulnaz Zaidi, Shravani Rawool, Ritesh P. Thakare, et al. 2023. “Next-Generation Sequencing Technology: Current Trends and Advancements.” Biology 12: 997. https://doi.org/10.3390/biology12070997

[136] Kinde, Isaac, Jian Wu, Nick Papadopoulos, Kenneth W. Kinzler, and Bert Vogelstein. 2011. “Detection and Quantification of Rare Mutations With Massively Parallel Sequencing.” Proceedings of the National Academy of Sciences 108: 9530-9535. https://doi.org/10.1073/pnas.1105422108

[137] Margulies, Marcel, Michael Egholm, William E. Altman, Said Attiya, Joel S. Bader, Lisa A. Bemben, Jan Berka, et al. 2005. “Genome Sequencing in Microfabricated High-Density Picolitre Reactors.” Nature 437: 376-380. https://doi.org/10.1038/nature03959

[138] Frey, Kenneth G., and Kimberly A. Bishop-Lilly. 2015. “Next-Generation Sequencing for Pathogen Detection and Identification.” Methods in Microbiology 42: 525-554. https://doi.org/10.1016/bs.mim.2015.06.004

[139] Eisenstein, Michael. 2023. “Illumina Faces Short-Read Rivals.” Nature Biotechnology 41: 3-5. https://doi.org/10.1038/s41587-022-01632-4

[140] Szoboszlay, Márton, Laetitia Schramm, David Pinzauti, Jeanesse Scerri, Anna Sandionigi, and Manuele Biazzo. 2023. “Nanopore Is Preferable Over Illumina for 16S Amplicon Sequencing of the Gut Microbiota When Species-Level Taxonomic Classification, Accurate Estimation of Richness, or Focus on Rare Taxa Is Required.” Microorganisms 11: 804. https://doi.org/10.3390/microorganisms11030804

[141] Jeon, Min-Seung, Da Min Jeong, Huijeong Doh, Hyun Ah Kang, Hyungtaek Jung, and Seong-il Eyun. 2023. “A Practical Comparison of the Next-Generation Sequencing Platform and Assemblers Using Yeast Genome.” Life Science Alliance 6: e202201744. https://doi.org/10.26508/lsa.202201744

[142] Carbo, Ellen C., Kees Mourik, Stefan A. Boers, Bas Oude Munnink, David Nieuwenhuijse, Marcel Jonges, Matthijs R. A. Welkers, et al. 2023. “A Comparison of Five Illumina, Ion Torrent, and Nanopore Sequencing Technology-Based Approaches for Whole Genome Sequencing of SARS-CoV-2.” European Journal of Clinical Microbiology & Infectious Diseases 42: 701-713. https://doi.org/10.1007/s10096-023-04590-0

[143] Ormandjieva, A., and M. Ivanova. 2023. “Evaluation of Gendx Protocol for HLA NGS Genotyping Using the Ion Torrent Sequencing Platform.” Acta Medica Bulgarica 50: 11-17. https://doi.org/10.2478/amb-2023-0024

[144] Sharma, Anupma, Guobin Luo, Na Li, Giorgio Pea, and Fiona Hyland. 2024. “Abstract 331: Ion Torrent^Tm NGS Sequencing of the TERT Promoter Hotspots With Ion AmpliSeq^TM HD Technology.” Cancer Research 84: 331. https://doi.org/10.1158/1538-7445.Am2024-331

[145] Ling, Xiaoting, Chenghan Wang, Linlin Li, Liqiu Pan, Chaoyu Huang, Caixia Zhang, Yunhua Huang, et al. 2023. “Third-Generation Sequencing for Genetic Disease.” Clinica Chimica Acta 551: 117624. https://doi.org/10.1016/j.cca.2023.117624

[146] Ermini, Luca, and Patrick Driguez. 2024. “The Application of Long-Read Sequencing to Cancer.” Cancers 16: 1275. https://doi.org/10.3390/cancers16071275

[147] Wang, Yusha, Ruikai Jia, Hua Ye, and Xiaoshu Ma. 2024. “A New Method and Application of Pacbio Sequencing for Low Copy and Difficulty Preparation Plasmids.” Journal of Bioinformatics and Systems Biology 07: 129-138. https://doi.org/10.26502/jbsb.5107085

[148] Howorka, Stefan, Stephen Cheley, and Hagan Bayley. 2001. “Sequence-Specific Detection of Individual DNA Strands Using Engineered Nanopores.” Nature Biotechnology 19: 636-639. https://doi.org/10.1038/90236

[149] Wei, Jiangtao, Hao Hong, Xing Wang, Xin Lei, Minjie Ye, and Zewen Liu. 2024. “Nanopore-Based Sensors for DNA Sequencing: A Review.” Nanoscale 16: 18732-18766. https://doi.org/10.1039/d4nr01325e

[150] Pei, Yang, Melanie Tanguy, Adam Giess, Abhijit Dixit, Louise C. Wilson, Richard J. Gibbons, Stephen R. F. Twigg, Greg Elgar, and Andrew O. M. Wilkie. 2024. “A Comparison of Structural Variant Calling From Short-Read and Nanopore-Based Whole-Genome Sequencing Using Optical Genome Mapping as a Benchmark.” Genes 15: 925. https://doi.org/10.3390/genes15070925

[151] Ross, Michael G., Carsten Russ, Maura Costello, Andrew Hollinger, Niall J. Lennon, Ryan Hegarty, Chad Nusbaum, and David B. Jaffe. 2013. “Characterizing and Measuring Bias in Sequence Data.” Genome Biology 14: R51. https://doi.org/10.1186/gb-2013-14-5-r51

[152] Kumawat, Rameshwar L., Milan Kumar Jena, Sneha Mittal, and Biswarup Pathak. 2024. “Advancement of Next-Generation DNA Sequencing through Ionic Blockade and Transverse Tunneling Current Methods.” Small 20: e2401112. https://doi.org/10.1002/smll.202401112

[153] Dahui, Qin. 2019. “Next-Generation Sequencing and Its Clinical Application.” Cancer Biology & Medicine 16: 4-10. https://doi.org/10.20892/j.issn.2095-3941.2018.0055

[154] Suwinski, Pawel, ChuangKee Ong, Maurice H. T. Ling, Yang Ming Poh, Asif M. Khan, and Hui San Ong. 2019. “Advancing Personalized Medicine Through the Application of Whole Exome Sequencing and Big Data Analytics.” Frontiers in Genetics 10: 49. https://doi.org/10.3389/fgene.2019.00049

[155] Peymani, Fatemeh, Aiman Farzeen, and Holger Prokisch. 2022. “RNA Sequencing Role and Application in Clinical Diagnostic.” Pediatric Investigation 6: 29-35. https://doi.org/10.1002/ped4.12314

[156] Zhang, Hong, Lin He, and Lei Cai. 2018. “Transcriptome Sequencing: RNA-Seq.” Methods in Molecular Biology 1754: 15-27. https://doi.org/10.1007/978-1-4939-7717-8_2

[157] Meskini, Maryam, Mohammad Saber Zamani, Amir Amanzadeh, Saeid Bouzari, Morteza Karimipoor, Andrea Fuso, Abolfazl Fateh, and Seyed Davar Siadat. 2024. “Epigenetic Modulation of Cytokine Expression in Mycobacterium Tuberculosis-Infected Monocyte Derived-Dendritic Cells: Implications for Tuberculosis Diagnosis.” Cytokine 181: 156693. https://doi.org/10.1016/j.cyto.2024.156693

[158] Barros-Silva, Daniela, C. Joana Marques, Rui Henrique, and Carmen Jerónimo. 2018. “Profiling DNA Methylation Based on Next-Generation Sequencing Approaches: New Insights and Clinical Applications.” Genes 9: 429. https://doi.org/10.3390/genes9090429

[159] Tiwari, Neha, Megha Bansal, and Jai Gopal Sharma. 2021. “Metagenomics: A Powerful Lens Viewing the Microbial World.” In Wastewater Treatment Reactors, 309-339. Amsterdam: Elsevier. https://doi.org/10.1016/B978-0-12-823991-9.00015-0

[160] Chiu, Charles Y., and Steven A. Miller. 2019. “Clinical Metagenomics.” Nature Reviews Genetics 20: 341-355. https://doi.org/10.1038/s41576-019-0113-7

[161] Hussen, Bashdar Mahmud, Sara Tharwat Abdullah, Abbas Salihi, Dana Khdr Sabir, Karzan R. Sidiq, Mohammed Fatih Rasul, Hazha Jamal Hidayat, et al. 2022. “The Emerging Roles of NGS in Clinical Oncology and Personalized Medicine.” Pathology - Research and Practice 230: 153760. https://doi.org/10.1016/j.prp.2022.153760

[162] Qahwaji, Rowaid, Ibraheem Ashankyty, Naif S. Sannan, Mohannad S. Hazzazi, Ammar A. Basabrain, and Mohammad Mobashir. 2024. “Pharmacogenomics: A Genetic Approach to Drug Development and Therapy.” Pharmaceuticals 17: 940. https://doi.org/10.3390/ph17070940

[163] Browne, Hilary P., Samuel C. Forster, Blessing O. Anonye, Nitin Kumar, B. Anne Neville, Mark D. Stares, David Goulding, and Trevor D. Lawley. 2016. “Culturing of ‘Unculturable’ Human Microbiota Reveals Novel Taxa and Extensive Sporulation.” Nature 533: 543-546. https://doi.org/10.1038/nature17645

[164] Dubey, Anamika, Muneer Ahmad Malla, and Ashwani Kumar. 2022. “Role of Next-Generation Sequencing (NGS) in Understanding the Microbial Diversity.” In Molecular Genetics and Genomics Tools in Biodiversity Conservation, 307-328. Singapore: Springer. https://doi.org/10.1007/978-981-16-6005-4_16

[165] Kumar, Kishore R., Mark J. Cowley, and Ryan L. Davis. 2024. “Next-Generation Sequencing and Emerging Technologies.” Seminars in Thrombosis and Hemostasis 50: 1026-1038. https://doi.org/10.1055/s-0044-1786397

[166] Johnson, Jethro S., Daniel J. Spakowicz, Bo-Young Hong, Lauren M. Petersen, Patrick Demkowicz, Lei Chen, Shana R. Leopold, et al. 2019. “Evaluation of 16S rRNA Gene Sequencing for Species and Strain-Level Microbiome Analysis.” Nature Communications 10: 5029. https://doi.org/10.1038/s41467-019-13036-1

[167] Hong, Bo-Young, Nancy Paula Maulén, Alexander J. Adami, Hector Granados, María Elvira Balcells, and Jorge Cervantes. 2016. “Microbiome Changes During Tuberculosis and Antituberculous Therapy.” Clinical Microbiology Reviews 29: 915-926. https://doi.org/10.1128/CMR.00096-15

[168] Mann, Elizabeth R., Ying Ka Lam, and Holm H. Uhlig. 2024. “Short-Chain Fatty Acids: Linking Diet, the Microbiome and Immunity.” Nature Reviews Immunology 24: 577-595. https://doi.org/10.1038/s41577-024-01014-8

[169] Yang, Wenjing, and Yingzi Cong. 2021. “Gut Microbiota-Derived Metabolites in the Regulation of Host Immune Responses and Immune-Related Inflammatory Diseases.” Cellular & Molecular Immunology 18: 866-877. https://doi.org/10.1038/s41423-021-00661-4

[170] Truong, Duy Tin, Eric A. Franzosa, Timothy L. Tickle, Matthias Scholz, George Weingart, Edoardo Pasolli, Adrian Tett, Curtis Huttenhower, and Nicola Segata. 2015. “MetaPhlAn2 for Enhanced Metagenomic Taxonomic Profiling.” Nature Methods 12: 902-903. https://doi.org/10.1038/nmeth.3589

[171] Luo, Ying, Ying Xue, Wei Liu, Huijuan Song, Yi Huang, Guoxing Tang, Feng Wang, et al. 2022. “Development of Diagnostic Algorithm Using Machine Learning for Distinguishing Between Active Tuberculosis and Latent Tuberculosis Infection.” BMC Infectious Diseases 22: 965. https://doi.org/10.1186/s12879-022-07954-7

[172] Ozdemir, Tanel, Alex J. H. Fedorec, Tal Danino, and Chris P. Barnes. 2018. “Synthetic Biology and Engineered Live Biotherapeutics: Toward Increasing System Complexity.” Cell Systems 7: 5-16. https://doi.org/10.1016/j.cels.2018.06.008

[173] Borody, Thomas J., Sudarshan Paramsothy, and Gaurav Agrawal. 2013. “Fecal Microbiota Transplantation: Indications, Methods, Evidence, and Future Directions.” Current Gastroenterology Reports 15: 337. https://doi.org/10.1007/s11894-013-0337-1

[174] Nyirenda, John L. Z., Annabelle Bockey, Dirk Wagner, and Berit Lange. 2023. “Effect of Tuberculosis (TB) and Diabetes Mellitus (DM) Integrated Healthcare on Bidirectional Screening and Treatment Outcomes Among TB Patients and People Living With DM in Developing Countries: A Systematic Review.” Pathogens and Global Health 117: 36-51. https://doi.org/10.1080/20477724.2022.2046967

[175] Wu, Hao, Sofia Forslund, Zeneng Wang, and Guoping Zhao. 2025. “Human Gut Microbiome Researches Over the Last Decade: Current Challenges and Future Directions.” Phenomics 5: 1-7. https://doi.org/10.1007/s43657-023-00131-z

[176] Cameron, Ellen S., Philip J. Schmidt, Benjamin J.-M. Tremblay, Monica B. Emelko, and Kirsten M. Müller. 2021. “Enhancing Diversity Analysis by Repeatedly Rarefying Next Generation Sequencing Data Describing Microbial Communities.” Scientific Reports 11: 22302. https://doi.org/10.1038/s41598-021-01636-1

[177] Mousavi-Sagharchi, Seyyed Mohammad Amin, Elina Afrazeh, Seyyedeh Fatemeh Seyyedian-Nikjeh, Maryam Meskini, Delaram Doroud, and Seyed Davar Siadat. 2024. “New Insight in Molecular Detection of Mycobacterium Tuberculosis.” AMB Express 14: 74. https://doi.org/10.1186/s13568-024-01730-3

[178] Meskini, Maryam, Nahid Madadi, Kamal Ahmadi, Farzam Vaziri, Abolfazl Fateh, and Seyed Davar Siadat. 2023. “Tuberculosis Prevention, Diagnosis, and Treatment Financial Profile During 2006--2021: PART A.” Cost Effectiveness and Resource Allocation 21: 68. https://doi.org/10.1186/s12962-023-00479-z

[179] Pavelescu, Luciana Alexandra, Monica Profir, Robert Mihai Enache, Oana Alexandra Roşu, Sanda Maria Creţoiu, and Bogdan Severus Gaspar. 2024. “A Proteogenomic Approach to Unveiling the Complex Biology of the Microbiome.” International Journal of Molecular Sciences 25: 10467. https://doi.org/10.3390/ijms251910467

[180] Pan, Sheng, and Ru Chen. 2020. “Metaproteomic Analysis of Human Gut Microbiome in Digestive and Metabolic Diseases.” Advances in Clinical Chemistry 97: 1-12. https://doi.org/10.1016/bs.acc.2019.12.002

[181] Saraswat, Istuti, and Anjana Goel. 2025. “Therapeutic Modulation of the Microbiome in Oncology: Current Trends and Future Directions.” Current Pharmaceutical Biotechnology 26: 680-699. https://doi.org/10.2174/0113892010353600241109132441

[182] Gilbert, Jack A., Martin J. Blaser, J Gregory Caporaso, Janet K. Jansson, Susan V. Lynch, and Rob Knight. 2018. “Current Understanding of the Human Microbiome.” Nature Medicine 24: 392-400. https://doi.org/10.1038/nm.4517

[183] Lai, Lisa A., Zachary Tong, Ru Chen, and Sheng Pan. 2019. “Metaproteomics Study of the Gut Microbiome.” Functional Proteomics 1871: 123-132. https://doi.org/10.1007/978-1-4939-8814-3_8

[184] Allué-Guardia, Anna, Juan I. García, and Jordi B. Torrelles. 2021. “Evolution of Drug-Resistant Mycobacterium Tuberculosis Strains and Their Adaptation to the Human Lung Environment.” Frontiers in Microbiology 12: 612675. https://doi.org/10.3389/fmicb.2021.612675

[185] Ullah, Nadeem, Ling Hao, Jo-Lewis Banga Ndzouboukou, Shiyun Chen, Yaqi Wu, Longmeng Li, Eman Borham Mohamed, Yangbo Hu, and Xionglin Fan. 2021. “Label-Free Comparative Proteomics of Differentially Expressed Mycobacterium Tuberculosis Protein in Rifampicin-Related Drug-Resistant Strains.” Pathogens 10: 607. https://doi.org/10.3390/pathogens10050607

[186] Wu, Zhuhua, Wenjing Wei, Ying Zhou, Huixin Guo, Jiao Zhao, Qinghua Liao, Liang Chen, Xiaoli Zhang, and Lin Zhou. 2020. “Integrated Quantitative Proteomics and Metabolome Profiling Reveal Msmeg_6171 Overexpression Perturbing Lipid Metabolism of Mycobacterium Smegmatis Leading to Increased Vancomycin Resistance.” Frontiers in Microbiology 11: 1572. https://doi.org/10.3389/fmicb.2020.01572

[187] Bendre, Ameya D., Peter J. Peters, and Janesh Kumar. 2021. “Recent Insights Into the Structure and Function of Mycobacterial Membrane Proteins Facilitated by Cryo-EM.” The Journal of Membrane Biology 254: 321-341. https://doi.org/10.1007/s00232-021-00179-w

[188] An, Yajing, Ruizi Ni, Li Zhuang, Ling Yang, Zhaoyang Ye, Linsheng Li, Seppo Parkkila, Ashok Aspatwar, and Wenping Gong. 2025. “Tuberculosis Vaccines and Therapeutic Drug: Challenges and Future Directions.” Molecular Biomedicine 6: 4. https://doi.org/10.1186/s43556-024-00243-6

[189] Arora, Gunjan, Ankur Bothra, Gareth Prosser, Kriti Arora, and Andaleeb Sajid. 2021. “Role of Post-Translational Modifications in the Acquisition of Drug Resistance in Mycobacterium Tuberculosis.” The FEBS Journal 288: 3375-3393. https://doi.org/10.1111/febs.15582

[190] Kontsevaya, Irina, Christoph Lange, Patricia Comella-Del-Barrio, Cristian Coarfa, Andrew R. DiNardo, Stephen H. Gillespie, Matthias Hauptmann, et al. 2021. “Perspectives for Systems Biology in the Management of Tuberculosis.” European Respiratory Review 30: 200377. https://doi.org/10.1183/16000617.0377-2020

[191] Pan, Xian, Shan Jiang, Xinzhuang Zhang, Zhenzhong Wang, Xin Wang, Liang Cao, and Wei Xiao. 2024. “Recent Strategies in Target Identification of Natural Products: Exploring Applications in Chronic Inflammation and Beyond.” British Journal of Pharmacology. In press. https://doi.org/10.1111/bph.17356

[192] Ehrt, Sabine, Dirk Schnappinger, and Kyu Y. Rhee. 2018. “Metabolic Principles of Persistence and Pathogenicity in Mycobacterium Tuberculosis.” Nature Reviews Microbiology 16: 496-507. https://doi.org/10.1038/s41579-018-0013-4

[193] Saiboonjan, Bhanubong, Sittiruk Roytrakul, Arunnee Sangka, Viraphong Lulitanond, Kiatichai Faksri, and Wises Namwat. 2021. “Proteomic Analysis of Drug-Susceptible and Multidrug-Resistant Nonreplicating Beijing Strains of Mycobacterium tuberculosis Cultured In Vitro.” Biochemistry and Biophysics Reports 26: 100960. https://doi.org/10.1016/j.bbrep.2021.100960

[194] Whiston, Emily, and John W. Taylor. 2016. “Comparative Phylogenomics of Pathogenic and Nonpathogenic Species.” G3-genes Genomes Genetics 6: 235-244. https://doi.org/10.1534/g3.115.022806

[195] Melnik, Andre, Valentina Cappelletti, Federico Vaggi, Ilaria Piazza, Marco Tognetti, Carmen Schwarz, Gea Cereghetti, et al. 2020. “Comparative Analysis of the Intracellular Responses to Disease-Related Aggregation-Prone Proteins.” Journal of Proteomics 225: 103862. https://doi.org/10.1016/j.jprot.2020.103862

[196] Sharma, Smriti, Rahul Bhat, Rohit Singh, Sumit Sharma, Priya Wazir, Parvinder Pal Singh, Ram A. Vishwakarma, and Inshad Ali Khan. 2020. “High-Throughput Screening of Compounds Library to Identify Novel Inhibitors Against Latent Mycobacterium tuberculosis Using Streptomycin-Dependent Mycobacterium Tuberculosis 18b Strain as a Model.” Tuberculosis 124: 101958. https://doi.org/10.1016/j.tube.2020.101958

[197] Robison, Heather M., Cole A. Chapman, Haowen Zhou, Courtney L. Erskine, Elitza Theel, Tobias Peikert, Cecilia S. Lindestam Arlehamn, et al. 2021. “Risk Assessment of Latent Tuberculosis Infection through a Multiplexed Cytokine Biosensor Assay and Machine Learning Feature Selection.” Scientific Reports 11: 20544. https://doi.org/10.1038/s41598-021-99754-3

[198] Jayaraman, Manikandan, Vijayakumar Gosu, Rajalakshmi Kumar, and Jeyakanthan Jeyaraman. 2024. “Computational Insights Into Potential Marine Natural Products as Selective Inhibitors of Mycobacterium Tuberculosis InhA: A Structure-Based Virtual Screening Study.” Computational Biology and Chemistry 108: 107991. https://doi.org/10.1016/j.compbiolchem.2023.107991

[199] Khan, Muhammad Fayaz, Amjad Ali, Hafiz Muzzammel Rehman, Sadiq Noor Khan, Hafiz Muhammad Hammad, Maaz Waseem, Yurong Wu, Taane G. Clark, and Abdul Jabbar. 2024. “Exploring Optimal Drug Targets through Subtractive Proteomics Analysis and Pangenomic Insights for Tailored Drug Design in Tuberculosis.” Scientific Reports 14: 10904. https://doi.org/10.1038/s41598-024-61752-6

[200] Wang, Menglong, Wei Pan, Yao Xu, Jishou Zhang, Jun Wan, and Hong Jiang. 2022. “Microglia-Mediated Neuroinflammation: A Potential Target for the Treatment of Cardiovascular Diseases.” Journal of Inflammation Research 15: 3083-3094. https://doi.org/10.2147/JIR.S350109

[201] Nikitushkin, Vadim, Margarita Shleeva, Dmitry Loginov, Filip Dyčka F., Jan Sterba, and Arseny Kaprelyants. 2022. “Shotgun Proteomic Profiling of Dormant, ‘Non-Culturable’ Mycobacterium Tuberculosis.” PLoS One 17: e0269847. https://doi.org/10.1371/journal.pone.0269847

[202] Tucci, Paula, Madelón Portela, Carlos Rivas Chetto, Gualberto González-Sapienza, and Mónica Marín. 2020. “Integrative Proteomic and Glycoproteomic Profiling of Mycobacterium Tuberculosis Culture Filtrate.” PLoS One 15: e0221837. https://doi.org/10.1371/journal.pone.0221837

[203] Mateos, Jesús, Olivia Estévez, África González-Fernández, Luis Anibarro, Ángeles Pallarés, Rajko Reljic, José M Gallardo, Isabel Medina, and Mónica Carrera. 2019. “High-Resolution Quantitative Proteomics Applied to the Study of the Specific Protein Signature in the Sputum and Saliva of Active Tuberculosis Patients and Their Infected and Uninfected Contacts.” Journal of Proteomics 195: 41-52. https://doi.org/10.1016/j.jprot.2019.01.010

[204] Villela, Anne Drumond, Paula Eichler, Antonio Frederico Michel Pinto, Valnês Rodrigues-Junior, John R. Yates III, Cristiano Valim Bizarro, Luiz Augusto Basso, and Diógenes Santiago Santos. 2015. “Gene Replacement and Quantitative Mass Spectrometry Approaches Validate Guanosine Monophosphate Synthetase as Essential for Mycobacterium Tuberculosis Growth.” Biochemistry and Biophysics Reports 4: 277-282. https://doi.org/10.1016/j.bbrep.2015.10.005

[205] Abdullaeva, Yulduzkhon, Binoy Ambika Manirajan, Bernd Honermeier, Sylvia Schnell, and Massimiliano Cardinale. 2021. “Domestication Affects the Composition, Diversity, and Co-Occurrence of the Cereal Seed Microbiota.” Journal of Advanced Research 31: 75-86. https://doi.org/10.1016/j.jare.2020.12.008

[206] Li, Zheng, Jianqing Ma, Xingye Li, Matthew T. V. Chan, William K. K. Wu, Zhanyong Wu, and Jianxiong Shen. 2019. “Aberrantly Expressed Long Non-Coding RNAs in Air Pollution-Induced Congenital Defects.” Journal of Cellular and Molecular Medicine 23: 7717-7725. https://doi.org/10.1111/jcmm.14645

[207] Mogasale, Vittal, Vijayalaxmi V. Mogasale, and Amber Hsiao. 2020. “Economic Burden of Cholera in Asia.” Vaccine 38(Suppl 1): A160-A166. https://doi.org/10.1016/j.vaccine.2019.09.099

[208] Gardner, Alycia, Álvaro de Mingo Pulido, and Brian Ruffell. 2020. “Dendritic Cells and Their Role in Immunotherapy.” Frontiers in Immunology 11: 924. https://doi.org/10.3389/fimmu.2020.00924

[209] Chopra, Hitesh, Yugal Kishore Mohanta, Pradipta Ranjan Rauta, Ramzan Ahmed, Saurov Mahanta, Piyush Kumar Mishra, Paramjot Panda, et al. 2023. “An Insight Into Advances in Developing Nanotechnology Based Therapeutics, Drug Delivery, Diagnostics and Vaccines: Multidimensional Applications in Tuberculosis Disease Management.” Pharmaceuticals 16: 581. https://doi.org/10.3390/ph16040581

[210] Yao, Fusheng, Ruiqi Zhang, Qiao Lin, Hui Xu, Wei Li, Min Ou, Yiting Huang, et al. 2024. “Plasma Immune Profiling Combined With Machine Learning Contributes to Diagnosis and Prognosis of Active Pulmonary Tuberculosis.” Emerging Microbes & Infections 13: 2370399. https://doi.org/10.1080/22221751.2024.2370399

[211] Robak, Aleksandra, Michał Kistowski, Grzegorz Wojtas, Anna Perzanowska, Tomasz Targowski, Agata Michalak, Grzegorz Krasowski, Michał Dadlez, and Dominik Domański. 2022. “Diagnosing Pleural Effusions Using Mass Spectrometry-Based Multiplexed Targeted Proteomics Quantitating Mid- to High-Abundance Markers of Cancer, Infection/Inflammation and Tuberculosis.” Scientific Reports 12: 3054. https://doi.org/10.1038/s41598-022-06924-y

[212] Chen, Jing, Yu-Shuai Han, Wen-Jing Yi, Huai Huang, Zhi-Bin Li, Li-Ying Shi, Li-Liang Wei, et al. 2020. “Serum sCD14, PGLYRP2 and FGA as Potential Biomarkers for Multidrug-Resistant Tuberculosis Based on Data-Independent Acquisition and Targeted Proteomics.” Journal of Cellular and Molecular Medicine 24: 12537-12549. https://doi.org/10.1111/jcmm.15796

[213] Fulcher, James M., Lye Meng Markillie, Hugh D. Mitchell, Sarah M. Williams, Kristin M. Engbrecht, David J. Degnan, Lisa M. Bramer, et al. 2024. “Parallel Measurement of Transcriptomes and Proteomes From Same Single Cells Using Nanodroplet Splitting.” Nature Communications 15: 10614. https://doi.org/10.1038/s41467-024-54099-z

[214] Ahmad, Rushdy, and Bogdan Budnik. 2023. “A Review of the Current State of Single-Cell Proteomics and Future Perspective.” Analytical and Bioanalytical Chemistry 415: 6889-6899. https://doi.org/10.1007/s00216-023-04759-8

[215] Albrethsen, Jakob, Jeppe Agner, Sander R. Piersma, Peter Højrup, Thang V. Pham, Karin Weldingh, Connie R. Jimenez, Peter Andersen, and Ida Rosenkrands. 2013. “Proteomic Profiling of Mycobacterium Tuberculosis Identifies Nutrient-Starvation-Responsive Toxin-Antitoxin Systems.” Molecular & Cellular Proteomics 12: 1180-1191. https://doi.org/10.1074/mcp.M112.018846

[216] Hua, Yongfeng, Min Cheng, Bo Liu, Jie Dong, Lilian Sun, Jian Yang, Fan Yang, Xinchun Chen, and Qi Jin. 2020. “Metagenomic Analysis of the Lung Microbiome in Pulmonary Tuberculosis−A Pilot Study.” Emerging Microbes & Infections 9: 1444-1452. https://doi.org/10.1080/22221751.2020.1783188

[217] Kruh-Garcia, Nicole A., Lisa M. Wolfe, Lelia H. Chaisson, William O. Worodria, Payam Nahid, Jeff S. Schorey, J. Lucian Davis, and Karen M. Dobos. 2014. “Detection of Mycobacterium tuberculosis Peptides in the Exosomes of Patients With Active and Latent M. Tuberculosis Infection Using MRM-MS.” PLoS One 9: e103811. https://doi.org/10.1371/journal.pone.0103811

[218] Saleh, Sara, An Staes, Stijn Deborggraeve, and Kris Gevaert. 2019. “Targeted Proteomics for Studying Pathogenic Bacteria.” Proteomics 19: e1800435. https://doi.org/10.1002/pmic.201800435

[219] Zheng, Weihao, Michael Borja, Leah C. Dorman, Jonathan Liu, Andy Zhou, Amanda Seng, Ritwicq Arjyal, et al. 2025. “Single-Cell Analysis Reveals Mycobacterium Tuberculosis Esx-1-Mediated Accumulation of Permissive Macrophages in Infected Mouse Lungs.” Science Advances 11: eadq8158. https://doi.org/10.1126/sciadv.adq8158

[220] Geraldes, Inês, Mónica Fernandes, Alexandra G. Fraga, and Nuno S. Osório. 2022. “The Impact of Single-Cell Genomics on the Field of Mycobacterial Infection.” Frontiers in Microbiology 13: 989464. https://doi.org/10.3389/fmicb.2022.989464

[221] Bespyatykh, Ju. A., E. A. Shitikov, and E. N. Ilina. 2017. “Proteomics for the Investigation of Mycobacteria.” Acta Naturae 9: 15-25. https://doi.org/10.32607/20758251-2017-9-1-15-25. https://www.ncbi.nlm.nih.gov/pubmed/28461970

[222] Chung, Eun Seon, Prathitha Kar, Maliwan Kamkaew, Ariel Amir, and Bree B. Aldridge. 2024. “Single-Cell Imaging of the Mycobacterium Tuberculosis Cell Cycle Reveals Linear and Heterogenous Growth.” Nature Microbiology 9: 3332-3344. https://doi.org/10.1038/s41564-024-01846-z

[223] Kaur, Simran, Nupur Angrish, Madavan Vasudevan, and Garima Khare. 2024. “Global Proteomics Reveals Pathways of Mesenchymal Stem Cells Altered by Mycobacterium Tuberculosis.” Scientific Reports 14: 30677. https://doi.org/10.1038/s41598-024-75722-5

[224] Devasundaram, Santhi, Akilandeswari Gopalan, Sulochana D. Das, and Alamelu Raja. 2016. “Proteomics Analysis of Three Different Strains of Mycobacterium Tuberculosis Under In Vitro Hypoxia and Evaluation of Hypoxia Associated Antigen's Specific Memory T Cells in Healthy Household Contacts.” Frontiers in Microbiology 7: 1275. https://doi.org/10.3389/fmicb.2016.01275

[225] Forrellad, Marina A., Laura I Klepp, Andrea Gioffré, Julia Sabio y García, Hector R. Morbidoni, María de la Paz Santangelo, Angel A. Cataldi, and Fabiana Bigi. 2013. “Virulence Factors of the Mycobacterium Tuberculosis Complex.” Virulence 4: 3-66. https://doi.org/10.4161/viru.22329

[226] Goldberg, Michael F., Neeraj K. Saini, and Steven A. Porcelli. 2014. “Evasion of Innate and Adaptive Immunity by Mycobacterium Tuberculosis.” Microbiology Spectrum 2: 1-24. https://doi.org/10.1128/microbiolspec.MGM2-0005-2013

[227] Soto-Heredero, Gonzalo, Manuel M. Gómez de las Heras, Enrique Gabandé-Rodríguez, Jorge Oller, and María Mittelbrunn. 2020. “Glycolysis−A Key Player in the Inflammatory Response.” The FEBS Journal 287: 3350-3369. https://doi.org/10.1111/febs.15327

[228] Dar, Hamza Arshad, Tahreem Zaheer, Nimat Ullah, Syeda Marriam Bakhtiar, Tianyu Zhang, Muhammad Yasir, Esam I. Azhar, and Amjad Ali. 2020. “Pangenome Analysis of Mycobacterium Tuberculosis Reveals Core-Drug Targets and Screening of Promising Lead Compounds for Drug Discovery.” Antibiotics 9: 819. https://doi.org/10.3390/antibiotics9110819

[229] Bagcchi, Sanjeet. 2023. “WHO's Global Tuberculosis Report 2022.” The Lancet Microbe 4: e20. https://doi.org/10.1016/S2666-5247(22)00359-7

[230] Curreli, Sabrina, Francesca Benedetti, Weirong Yuan, Arshi Munawwar, Fiorenza Cocchi, Robert C. Gallo, Nicholas E. Sherman, and Davide Zella. 2022. “Characterization of the Interactome Profiling of Mycoplasma Fermentans DnaK in Cancer Cells Reveals Interference With Key Cellular Pathways.” Frontiers in Microbiology 13: 1022704. https://doi.org/10.3389/fmicb.2022.1022704

[231] McKinney, John D., Kerstin Höner zu Bentrup, Ernesto J. Muñoz-Elías, Andras Miczak, Bing Chen, Wai-Tsing Chan, Dana Swenson, et al. 2000. “Persistence of Mycobacterium Tuberculosis in Macrophages and Mice Requires the Glyoxylate Shunt Enzyme Isocitrate Lyase.” Nature 406: 735-738. https://doi.org/10.1038/35021074

[232] Ma, Y. W., Y. H. Lu, J. B. Yi, Y. P. Feng, T. S. Herng, X. Liu, D. Q. Gao, et al. 2012. “Room Temperature Ferromagnetism in Teflon Due to Carbon Dangling Bonds.” Nature Communications 3: 727. https://doi.org/10.1038/ncomms1689

[233] Miyoshi-Akiyama, Tohru, Jizi Zhao, Hidehito Kato, Ken Kikuchi, Kyoichi Totsuka, Yasushi Kataoka, Masanori Katsumi, and Takehiko Uchiyama. 2003. “Streptococcus Dysgalactiae-Derived Mitogen (SDM), a Novel Bacterial Superantigen: Characterization of Its Biological Activity and Predicted Tertiary Structure.” Molecular Microbiology 47: 1589-1599. https://doi.org/10.1046/j.1365-2958.2003.03411.x

[234] Paroha, Ruchi, Jia Wang, and Sunhee Lee. 2024. “PDCD4 as a Marker of mTOR Pathway Activation and Therapeutic Target in Mycobacterial Infections.” Microbiology Spectrum 12: e0006224. https://doi.org/10.1128/spectrum.00062-24

[235] Cole, S. T., R. Brosch, J. Parkhill, T. Garnier, C. Churcher, D. Harris, S. V. Gordon, et al. 1998. “Deciphering the Biology of Mycobacterium Tuberculosis From the Complete Genome Sequence.” Nature 393: 537-544. https://doi.org/10.1038/31159

[236] Lu, Qian, Wei Zhang, Jun Fang, Jianjian Zheng, Chunsheng Dong, and Sidong Xiong. 2020. “Mycobacterium Tuberculosis Rv1096, Facilitates Mycobacterial Survival by Modulating the NF-κB/MAPK Pathway as Peptidoglycan N-Deacetylase.” Molecular Immunology 127: 47-55. https://doi.org/10.1016/j.molimm.2020.08.005

[237] Gehring, Adam J., Karen M. Dobos, John T. Belisle, Clifford V. Harding, and W Henry Boom. 2004. “Mycobacterium tuberculosis LprG (Rv1411c): A Novel TLR-2 Ligand That Inhibits Human Macrophage Class II MHC Antigen Processing.” Journal of Immunology 173: 2660-2668. https://doi.org/10.4049/jimmunol.173.4.2660.

[238] Walburger, Anne, Anil Koul, Giorgio Ferrari, Liem Nguyen, Cristina Prescianotto-Baschong, Kris Huygen, Bert Klebl, et al. 2004. “Protein Kinase G From Pathogenic Mycobacteria Promotes Survival Within Macrophages.” Science 304: 1800-1804. https://doi.org/10.1126/science.1099384

[239] Voskuil, Martin I., Dirk Schnappinger, Kevin C. Visconti, Maria I. Harrell, Gregory M. Dolganov, David R. Sherman, and Gary K. Schoolnik. 2003. “Inhibition of Respiration by Nitric Oxide Induces a Mycobacterium Tuberculosis Dormancy Program.” Journal of Experimental Medicine 198: 705-713. https://doi.org/10.1084/jem.20030205

[240] Zahrt, Thomas C., and Vojo Deretic. 2001. “Mycobacterium Tuberculosis Signal Transduction System Required for Persistent Infections.” Proceedings of the National Academy of Sciences 98: 12706-12711. https://doi.org/10.1073/pnas.221272198

[241] Akhter, Yusuf, Matthias T. Ehebauer, Sangita Mukhopadhyay, and Seyed E. Hasnain. 2012. “The PE/PPE Multigene Family Codes for Virulence Factors and Is a Possible Source of Mycobacterial Antigenic Variation: Perhaps More?” Biochimie 94: 110-116. https://doi.org/10.1016/j.biochi.2011.09.026

[242] Campbell, Elizabeth A., Nataliya Korzheva, Arkady Mustaev, Katsuhiko Murakami, Satish Nair, Alex Goldfarb, and Seth A. Darst. 2001. “Structural Mechanism for Rifampicin Inhibition of Bacterial RNA Polymerase.” Cell 104: 901-912. https://doi.org/10.1016/s0092-8674(01)00286-0

[243] Domenech, Pilar, Michael B. Reed, and Clifton E. Barry, 2005. “Contribution of the Mycobacterium Tuberculosis MmpL Protein Family to Virulence and Drug Resistance.” Infection and Immunity 73: 3492-3501. https://doi.org/10.1128/IAI.73.6.3492-3501.2005

[244] Banerjee, Asesh, Eugenie Dubnau, Annaik Quemard, V. Balasubramanian, Kyung Sun Um, Theresa Wilson, Des Collins, Geoffrey De Lisle, and William R. Jacobs. 1994. “inhA, a Gene Encoding a Target for Isoniazid and Ethionamide in Mycobacterium Tuberculosis.” Science 263: 227-230. https://doi.org/10.1126/science.8284673

[245] Takayama, Kuni, Cindy Wang, and Gurdyal S. Besra. 2005. “Pathway to Synthesis and Processing of Mycolic Acids in Mycobacterium Tuberculosis.” Clinical Microbiology Reviews 18: 81-101. https://doi.org/10.1128/CMR.18.1.81-101.2005

[246] Beier, Sara, and Stefan Bertilsson. 2013. “Bacterial Chitin Degradation-Mechanisms and Ecophysiological Strategies.” Frontiers in Microbiology 4: 149. https://doi.org/10.3389/fmicb.2013.00149

[247] Sachdeva, Preeti, Richa Misra, Anil K. Tyagi, and Yogendra Singh. 2010. “The Sigma Factors of Mycobacterium Tuberculosis: Regulation of the Regulators.” The FEBS Journal 277: 605-626. https://doi.org/10.1111/j.1742-4658.2009.07479.x

[248] Rodrigue, Sébastien, Roberta Provvedi, Pierre-Étienne Jacques, Luc Gaudreau, and Riccardo Manganelli. 2006. “The σ Factors Ofmycobacterium Tuberculosis.” FEMS Microbiology Reviews 30: 926-941. https://doi.org/10.1111/j.1574-6976.2006.00040.x

[249] Sala, Claudia, Nina T. Odermatt, Paloma Soler-Arnedo, Muhammet F. Gülen, Sofia von Schultz, Andrej Benjak, and Stewart T. Cole. 2018. “EspL Is Essential for Virulence and Stabilizes EspE, EspF and EspH Levels in Mycobacterium Tuberculosis.” PLOS Pathogens 14: e1007491. https://doi.org/10.1371/journal.ppat.1007491

[250] Su, Haibo, Shenglin Zhu, Lin Zhu, Wei Huang, Honghai Wang, Zhi Zhang, and Ying Xu. 2016. “Recombinant Lipoprotein Rv1016c Derived From Mycobacterium tuberculosis Is a TLR-2 Ligand That Induces Macrophages Apoptosis and Inhibits MHC II Antigen Processing.” Frontiers in cellular and infection microbiology 6: 147. https://doi.org/10.3389/fcimb.2016.00147

[251] Aagaard, Claus, Truc Hoang, Jes Dietrich, Pere-Joan Cardona, Angelo Izzo, Gregory Dolganov, Gary K. Schoolnik, et al. 2011. “A Multistage Tuberculosis Vaccine That Confers Efficient Protection Before and After Exposure.” Nature Medicine 17: 189-194. https://doi.org/10.1038/nm.2285

[252] Champion, Matthew M., Emily A. Williams, Richard S. Pinapati, and Patricia A. DiGiuseppe Champion. 2014. “Correlation of Phenotypic Profiles Using Targeted Proteomics Identifies Mycobacterial Esx-1 Substrates.” Journal of Proteome Research 13: 5151-5164. https://doi.org/10.1021/pr500484w

[253] Mukamolova, Galina V., Obolbek A. Turapov, Danielle I. Young, Arseny S. Kaprelyants, Douglas B. Kell, and Michael Young. 2002. “A Family of Autocrine Growth Factors in Mycobacterium Tuberculosis.” Molecular Microbiology 46: 623-635. https://doi.org/10.1046/j.1365-2958.2002.03184.x

[254] Rivas-Santiago, Bruno, Cesar E. Rivas Santiago, Julio E. Castañeda-Delgado, Juan C. León-Contreras, Robert E. W. Hancock, and Rogelio Hernandez-Pando. 2013. “Activity of LL-37, CRAMP and Antimicrobial Peptide-Derived Compounds E2, E6 and CP26 Against Mycobacterium Tuberculosis.” International Journal of Antimicrobial Agents 41: 143-148. https://doi.org/10.1016/j.ijantimicag.2012.09.015

[255] Troy, Amber, Sandra C. Esparza-Gonzalez, Alicia Bartek, Elizabeth Creissen, Linda Izzo, and Angelo A. Izzo. 2020. “Pulmonary Mucosal Immunity Mediated through CpG Provides Adequate Protection Against Pulmonary Mycobacterium Tuberculosis Infection in the Mouse Model. A Role for Type I Interferon.” Tuberculosis 123: 101949. https://doi.org/10.1016/j.tube.2020.101949

[256] Matsumoto, Takehisa, Masayuki Hashimoto, Ching-Hao Teng, Po-Chuen Hsu, Yusuke Ota, Masaru Takamizawa, Ryosuke Kato, and Tatsuya Negishi. 2020. “Molecular Characterization of a Carbon Dioxide-Dependent Escherichia Coli Small-Colony Variant Isolated From Blood Cultures.” International Journal of Medical Microbiology 310: 151431. https://doi.org/10.1016/j.ijmm.2020.151431

[257] Herrera Ramírez, J. C., A Ch De la Mora, A. De la Mora Valle, G. Lopez-Valencia, R. M. B. Hurtado, T. B. Rentería Evangelista, J. L. Rodríguez Castillo, et al. 2017. “Immunopathological Evaluation of Recombinant Mycobacterial Antigen Hsp65 Expressed in Lactococcus Lactis as a Novel Vaccine Candidate.” Iranian Journal of Veterinary Research 18: 197-202. https://pubmed.ncbi.nlm.nih.gov/29163649/

[258] Kubinak, Jason L., and June L. Round. 2016. “Do Antibodies Select a Healthy Microbiota?” Nature Reviews Immunology 16: 767-774. https://doi.org/10.1038/nri.2016.114

[259] Ratledge, Colin, and Lynn G. Dover. 2000. “Iron Metabolism in Pathogenic Bacteria.” Annual Review of Microbiology 54: 881-941. https://doi.org/10.1146/annurev.micro.54.1.881

[260] Hoang, Truc, Claus Aagaard, Jes Dietrich, Joseph P. Cassidy, Gregory Dolganov, Gary K. Schoolnik, Carina Vingsbo Lundberg, Else Marie Agger, and Peter Andersen. 2013. “ESAT-6 (EsxA) and TB10.4 (EsxH) Based Vaccines for Pre- and Post-Exposure Tuberculosis Vaccination.” PLOS One 8: e80579. https://doi.org/10.1371/journal.pone.0080579

[261] Fraga-Corral, M., M. Carpena, P. Garcia-Oliveira, A. G. Pereira, M. A. Prieto, and J. Simal-Gandara. 2022. “Analytical Metabolomics and Applications in Health, Environmental and Food Science.” Critical Reviews in Analytical Chemistry 52: 712-734. https://doi.org/10.1080/10408347.2020.1823811

[262] Krautkramer, Kimberly A., Jing Fan, and Fredrik Bäckhed. 2021. “Gut Microbial Metabolites as Multi-Kingdom Intermediates.” Nature Reviews Microbiology 19: 77-94. https://doi.org/10.1038/s41579-020-0438-4

[263] Folz, Jacob, Rebecca Neal Culver, Juan Montes Morales, Jessica Grembi, George Triadafilopoulos, David A. Relman, Kerwyn Casey Huang, Dari Shalon, and Oliver Fiehn. 2023. “Human Metabolome Variation Along the Upper Intestinal Tract.” Nature Metabolism 5: 777-788. https://doi.org/10.1038/s42255-023-00777-z.

[264] Daliri, Eric Banan-Mwine, Shuai Wei, Deog H. Oh, and Byong H. Lee. 2017. “The Human Microbiome and Metabolomics: Current Concepts and Applications.” Critical Reviews in Food Science and Nutrition 57: 3565-3576. https://doi.org/10.1080/10408398.2016.1220913

[265] Hu, Yangyang, Guangyu Jiang, Yalun Wen, Yuchen Shao, Ge Yang, and Feng Qu. 2025. “Selection of Aptamers Targeting Small Molecules by Capillary Electrophoresis: Advances, Challenges, and Prospects.” Biotechnology Advances 78: 108491. https://doi.org/10.1016/j.biotechadv.2024.108491

[266] Fernández-García, Miguel, Fernanda Rey-Stolle, Julien Boccard, Vineel P. Reddy, Antonia García, Bridgette M. Cumming, Adrie J. C. Steyn, Serge Rudaz, and Coral Barbas. 2020. “Comprehensive Examination of the Mouse Lung Metabolome Following Mycobacterium Tuberculosis Infection Using a Multiplatform Mass Spectrometry Approach.” Journal of Proteome Research 19: 2053-2070. https://doi.org/10.1021/acs.jproteome.9b00868

[267] Bartels, Benjamin, and Aleš Svatoš. 2015. “Spatially Resolved In Vivo Plant Metabolomics by Laser Ablation-Based Mass Spectrometry Imaging (MSI) Techniques: LDI-MSI and LAESI.” Frontiers in Plant Science 6: 471. https://doi.org/10.3389/fpls.2015.00471

[268] Takáts, Zoltán, Justin M. Wiseman, Bogdan Gologan, and R Graham Cooks. 2004. “Mass Spectrometry Sampling Under Ambient Conditions With Desorption Electrospray Ionization.” Science 306: 471-473. https://doi.org/10.1126/science.1104404

[269] Prideaux, Brendan, Laura E. Via, Matthew D. Zimmerman, Seokyong Eum, Jansy Sarathy, Paul O'Brien, Chao Chen, et al. 2015. “The Association Between Sterilizing Activity and Drug Distribution Into Tuberculosis Lesions.” Nature Medicine 21: 1223-1227. https://doi.org/10.1038/nm.3937

[270] Blanc, Landry, Anne Lenaerts, Véronique Dartois, and Brendan Prideaux. 2018. “Visualization of Mycobacterial Biomarkers and Tuberculosis Drugs in Infected Tissue by MALDI-MS Imaging.” Analytical Chemistry 90: 6275-6282. https://doi.org/10.1021/acs.analchem.8b00985

[271] Andreas, Nicholas J., Robindra Basu Roy, Maria Gomez-Romero, Verena Horneffer-van der Sluis, Matthew R. Lewis, Stephane S. M. Camuzeaux, Beatriz Jiménez, et al. 2020. “Performance of Metabonomic Serum Analysis for Diagnostics in Paediatric Tuberculosis.” Scientific Reports 10: 7302. https://doi.org/10.1038/s41598-020-64413-6

[272] Olivier, Cara, and Laneke Luies. 2024. “Metabolic Insights Into HIV/TB Co-Infection: An Untargeted Urinary Metabolomics Approach.” Metabolomics 20: 78. https://doi.org/10.1007/s11306-024-02148-5

[273] Mason, Shayne, A. Marceline Tutu Van Furth, Regan Solomons, Ron A. Wevers, Mari Van Reenen, and Carolus J. Reinecke. 2016. “A Putative Urinary Biosignature for Diagnosis and Follow-Up of Tuberculous Meningitis in Children: Outcome of a Metabolomics Study Disclosing Host-Pathogen Responses.” Metabolomics 12: 110. https://doi.org/10.1007/s11306-016-1053-2

[274] Isaiah, Simon, Du Toit Loots, A. Marceline Tutu van Furth, Elmarie Davoren, Sabine van Elsland, Regan Solomons, Martijn van der Kuip, and Shayne Mason. 2024. “Urinary Markers of Mycobacterium Tuberculosis and Dysbiosis in Paediatric Tuberculous Meningitis Cases Undergoing Treatment.” Gut Pathogens 16: 14. https://doi.org/10.1186/s13099-024-00609-9

[275] Ren, Sheng, Anna A. Hinzman, Emily L. Kang, Rhonda D. Szczesniak, and Long Jason Lu. 2015. “Computational and Statistical Analysis of Metabolomics Data.” Metabolomics 11: 1492-1513. https://doi.org/10.1007/s11306-015-0823-6

[276] Anwardeen, Najeha R., Ilhame Diboun, Younes Mokrab, Asma A. Althani, and Mohamed A. Elrayess. 2023. “Statistical Methods and Resources for Biomarker Discovery Using Metabolomics.” BMC Bioinformatics 24: 250. https://doi.org/10.1186/s12859-023-05383-0

[277] Anh, Nguyen Ky, Nguyen Thi Hai Yen, Nguyen Tran Nam Tien, Nguyen Ky Phat, Young Jin Park, Ho-Sook Kim, Dinh Hoa Vu, et al. 2024. “Metabolic Phenotyping and Global Functional Analysis Facilitate Metabolic Signature Discovery for Tuberculosis Treatment Monitoring.” Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease 1870: 167064. https://doi.org/10.1016/j.bbadis.2024.167064

[278] Yi, Wen-Jing, Yu-Shuai Han, Li-Liang Wei, Li-Ying Shi, Huai Huang, Ting-Ting Jiang, Zhi-Bin Li, et al. 2019. “L-Histidine, Arachidonic Acid, Biliverdin, and L-Cysteine-Glutathione Disulfide as Potential Biomarkers for Cured Pulmonary Tuberculosis.” Biomedicine & Pharmacotherapy 116: 108980. https://doi.org/10.1016/j.biopha.2019.108980

[279] Ding, Yi, Robert-Jan Raterink, Rubén Marín-Juez, Wouter J. Veneman, Koen Egbers, Susan van den Eeden, Mariëlle C. Haks, et al. 2020. “Tuberculosis Causes Highly Conserved Metabolic Changes in Human Patients, Mycobacteria-Infected Mice and Zebrafish Larvae.” Scientific Reports 10: 11635. https://doi.org/10.1038/s41598-020-68443-y

[280] Albors-Vaquer, A., A. Rizvi, M. Matzapetakis, P. Lamosa, A. V. Coelho, A. B. Patel, S. C. Mande, et al. 2020. “Active and Prospective Latent Tuberculosis Are Associated With Different Metabolomic Profiles: Clinical Potential for the Identification of Rapid and Non-Invasive Biomarkers.” Emerging Microbes & Infections 9: 1131-1139. https://doi.org/10.1080/22221751.2020.1760734

[281] Yang, Boyi, Xiaojing Guo, Chongyu Shi, Gang Liu, Xiaoling Qin, Shiyi Chen, Li Gan, et al. 2024. “Alterations in Purine and Pyrimidine Metabolism Associated With Latent Tuberculosis Infection: Insights From Gut Microbiome and Metabolomics Analyses.” mSystems 9: e0081224. https://doi.org/10.1128/msystems.00812-24

[282] Chen, Siyu, Chunyan Li, Zhonghua Qin, Lili Song, Shiyuan Zhang, Chongxiang Sun, Pengwei Zhuang, et al. 2023. “Serum Metabolomic Profiles for Distinguishing Lung Cancer From Pulmonary Tuberculosis: Identification of Rapid and Noninvasive Biomarker.” Journal of Infectious Diseases 228: 1154-1165. https://doi.org/10.1093/infdis/jiad175

[283] Anh, Nguyen Ky, Nguyen Ky Phat, Nguyen Quang Thu, Nguyen Tran Nam Tien, Cho Eunsu, Ho-Sook Kim, Duc Ninh Nguyen, et al. 2024. “Discovery of Urinary Biosignatures for Tuberculosis and Nontuberculous Mycobacteria Classification Using Metabolomics and Machine Learning.” Scientific Reports 14: 15312. https://doi.org/10.1038/s41598-024-66113-x

[284] Weiner 3rd January, Jeroen, Maertzdorf, Jayne S. Sutherland, Fergal J. Duffy, Ethan Thompson, Sara Suliman, Gayle McEwen, Bonnie Thiel, et al. 2018. “Metabolite Changes in Blood Predict the Onset of Tuberculosis.” Nature Communications 9: 5208. https://doi.org/10.1038/s41467-018-07635-7

[285] Jeyanathan, Mangalakumari, Maryam Vaseghi-Shanjani, Sam Afkhami, Jensine A. Grondin, Alisha Kang, Michael R. D'Agostino, Yushi Yao, et al. 2022. “Parenteral BCG Vaccine Induces Lung-Resident Memory Macrophages and Trained Immunity via the Gut-Lung Axis.” Nature Immunology 23: 1687-1702. https://doi.org/10.1038/s41590-022-01354-4

[286] Anh, Nguyen Ky, Nguyen Ky Phat, Nguyen Thi Hai Yen, Rannissa Puspita Jayanti, Vo Thuy Anh Thu, Young Jin Park, and Yong-Soon Cho, et al. 2023. “Comprehensive Lipid Profiles Investigation Reveals Host Metabolic and Immune Alterations During Anti-Tuberculosis Treatment: Implications for Therapeutic Monitoring.” Biomedicine & Pharmacotherapy 158: 114187. https://doi.org/10.1016/j.biopha.2022.114187

[287] Opperman, Monique, Du Toit Loots, Mari van Reenen, Katharina Ronacher, Gerhard Walzl, and Ilse du Preez. 2021. “Chronological Metabolic Response to Intensive Phase TB Therapy in Patients With Cured and Failed Treatment Outcomes.” ACS Infectious Diseases 7: 1859-1869. https://doi.org/10.1021/acsinfecdis.1c00162

[288] Suster, Carl J. E., David Pham, Jen Kok, and Vitali Sintchenko. 2024. “Emerging Applications of Artificial Intelligence in Pathogen Genomics.” Frontiers in Bacteriology 3: 1326958. https://doi.org/10.3389/fbrio.2024.1326958

[289] D'Urso, Fabiana, and Francesco Broccolo. 2024. “Applications of Artificial Intelligence in Microbiome Analysis and Probiotic Interventions—An Overview and Perspective Based on the Current State of the Art.” Applied Sciences 14: 8627. https://doi.org/10.3390/app14198627

[290] Bai, Defeng, Tong Chen, Jiani Xun, Chuang Ma, Hao Luo, Haifei Yang, Chen Cao, et al. 2025. “EasyMetagenome: A User-Friendly and Flexible Pipeline for Shotgun Metagenomic Analysis in Microbiome Research.” iMeta 4: e70001. https://doi.org/10.1002/imt2.70001

[291] Turnbaugh, Peter J., Ruth E. Ley, Micah Hamady, Claire M. Fraser-Liggett, Rob Knight, and Jeffrey I. Gordon. 2007. “The Human Microbiome Project.” Nature 449: 804-810. https://doi.org/10.1038/nature06244

[292] King, Charles H., Hiral Desai, Allison C. Sylvetsky, Jonathan LoTempio, Shant Ayanyan, Jill Carrie, Keith A. Crandall, et al. 2019. “Baseline Human Gut Microbiota Profile in Healthy People and Standard Reporting Template.” PLOS One 14: e0206484. https://doi.org/10.1371/journal.pone.0206484

[293] Markowitz, V. M., I.-M. A. Chen, K. Palaniappan, K. Chu, E. Szeto, Y. Grechkin, A. Ratner, et al. 2012. “IMG: The Integrated Microbial Genomes Database and Comparative Analysis System.” Nucleic Acids Research 40: D115-D122. https://doi.org/10.1093/nar/gkr1044

[294] Wishart, David S., AnChi Guo, Eponine Oler, Fei Wang, Afia Anjum, Harrison Peters, Raynard Dizon, et al. 2022. “HMDB 5.0: The Human Metabolome Database for 2022.” Nucleic Acids Research 50: D622-D631. https://doi.org/10.1093/nar/gkab1062

[295] Guijas, Carlos, J Rafael Montenegro-Burke, Xavier Domingo-Almenara, Amelia Palermo, Benedikt Warth, Gerrit Hermann, Gunda Koellensperger, et al. 2018. “METLIN: A Technology Platform for Identifying Knowns and Unknowns.” Analytical Chemistry 90: 3156-3164. https://doi.org/10.1021/acs.analchem.7b04424

[296] Kanehisa, M. 2000. “KEGG: Kyoto Encyclopedia of Genes and Genomes.” Nucleic Acids Research 28: 27-30. https://doi.org/10.1093/nar/28.1.27

[297] Karp, Peter D., Richard Billington, Ron Caspi, Carol A. Fulcher, Mario Latendresse, Anamika Kothari, Ingrid M. Keseler, et al. 2019. “The BioCyc Collection of Microbial Genomes and Metabolic Pathways.” Briefings in Bioinformatics 20: 1085-1093. https://doi.org/10.1093/bib/bbx085

[298] Szklarczyk, Damian, Rebecca Kirsch, Mikaela Koutrouli, Katerina Nastou, Farrokh Mehryary, Radja Hachilif, Annika L. Gable, et al. 2023. “The STRING Database in 2023: Protein-Protein Association Networks and Functional Enrichment Analyses for Any Sequenced Genome of Interest.” Nucleic Acids Research 51: D638-D646. https://doi.org/10.1093/nar/gkac1000

[299] Hunter, Sarah, Matthew Corbett, Hubert Denise, Matthew Fraser, Alejandra Gonzalez-Beltran, Christopher Hunter, Philip Jones, et al. 2014. “EBI Metagenomics—A New Resource for the Analysis and Archiving of Metagenomic Data.” Nucleic Acids Research 42: D600-D606. https://doi.org/10.1093/nar/gkt961

[300] Dai, Die, Jiaying Zhu, Chuqing Sun, Min Li, Jinxin Liu, Sicheng Wu, Kang Ning, et al. 2022. “GMrepo V2: A Curated Human Gut Microbiome Database With Special Focus on Disease Markers and Cross-Dataset Comparison.” Nucleic Acids Research 50: D777-D784. https://doi.org/10.1093/nar/gkab1019

[301] DeSantis, T. Z., P. Hugenholtz, N. Larsen, M. Rojas, E. L. Brodie, K. Keller, T. Huber, et al. 2006. “Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible With ARB.” Applied and Environmental Microbiology 72: 5069-5072. https://doi.org/10.1128/AEM.03006-05

[302] Quast, Christian, Elmar Pruesse, Pelin Yilmaz, Jan Gerken, Timmy Schweer, Pablo Yarza, Jörg Peplies, and Frank Oliver Glöckner. 2012. “The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools.” Nucleic Acids Research 41: D590-D596. https://doi.org/10.1093/nar/gks1219

[303] Cole, James R., Qiong Wang, Jordan A. Fish, Benli Chai, Donna M. McGarrell, Yanni Sun, C. Titus Brown, et al. 2014. “Ribosomal Database Project: Data and Tools for High Throughput rRNA Analysis.” Nucleic Acids Research 42: D633-D642. https://doi.org/10.1093/nar/gkt1244

[304] Qi, Changlu, Yiting Cai, Kai Qian, Xuefeng Li, Jialiang Ren, Ping Wang, Tongze Fu, et al. 2023. “gutMDisorder V2.0: A Comprehensive Database for Dysbiosis of Gut Microbiota in Phenotypes and Interventions.” Nucleic Acids Research 51: D717-D722. https://doi.org/10.1093/nar/gkac871

[305] Koren, Omry, Dan Knights, Antonio Gonzalez, Levi Waldron, Nicola Segata, Rob Knight, Curtis Huttenhower, and Ruth E. Ley. 2013. “A Guide to Enterotypes Across the Human Body: Meta-Analysis of Microbial Community Structures in Human Microbiome Datasets.” PLOS Computational Biology 9: e1002863. https://doi.org/10.1371/journal.pcbi.1002863

[306] Novielli, Pierfrancesco, Donato Romano, Michele Magarelli, Pierpaolo Di Bitonto, Domenico Diacono, Annalisa Chiatante, Giuseppe Lopalco, et al. 2024. “Explainable Artificial Intelligence for Microbiome Data Analysis in Colorectal Cancer Biomarker Identification.” Frontiers in Microbiology 15: 1348974. https://doi.org/10.3389/fmicb.2024.1348974

[307] Wang, Ming-Gui, Shou-Quan Wu, Meng-Meng Zhang, and Jian-Qing He. 2022. “Urine Metabolomics and Microbiome Analyses Reveal the Mechanism of Anti-Tuberculosis Drug-Induced Liver Injury, as Assessed for Causality Using the Updated RUCAM: A Prospective Study.” Frontiers in Immunology 13: 1002126. https://doi.org/10.3389/fimmu.2022.1002126

[308] Lv, Xinna, Ye Li, Botao Cai, Wei He, Ren Wang, Minghui Chen, Junhua Pan, and Dailun Hou. 2023. “Utility of Machine Learning and Radiomics Based on Cavity for Predicting the Therapeutic Response of MDR-TB.” Infection and Drug Resistance 16: 6893-6904. https://doi.org/10.2147/IDR.S435984

[309] Chen, Jingfang, Youli Jiang, Zhihuan Li, Mingshu Zhang, Linlin Liu, Ao Li, and Hongzhou Lu. 2024. “Predictive Machine Learning Models for Anticipating Loss to Follow-Up in Tuberculosis Patients Throughout Anti-TB Treatment Journey.” Scientific Reports 14: 24685. https://doi.org/10.1038/s41598-024-74942-z

[310] Peetluk, Lauren S., Peter F. Rebeiro, Felipe M. Ridolfi, Bruno B. Andrade, Marcelo Cordeiro-Santos, Afranio Kritski, Betina Durovni, et al. 2022. “A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes.” Clinical Infectious Diseases 74: 973-982. https://doi.org/10.1093/cid/ciab598

[311] Sambarey, Awanti, Kirk Smith, Carolina Chung, Harkirat Singh Arora, Zhenhua Yang, Prachi P. Agarwal, and Sriram Chandrasekaran. 2024. “Integrative Analysis of Multimodal Patient Data Identifies Personalized Predictors of Tuberculosis Treatment Prognosis.” iScience 27: 109025. https://doi.org/10.1016/j.isci.2024.109025

[312] Liao, Kuang-Ming, Chung-Feng Liu, Chia-Jung Chen, Jia-Yih Feng, Chin-Chung Shu, and Yu-Shan Ma. 2023. “Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis.” Diagnostics 13: 1075. https://doi.org/10.3390/diagnostics13061075

[313] Gupta, Ankit, Darshan B. Dhakan, Abhijit Maji, Rituja Saxena, Vishnu Prasoodanan PK, Shruti Mahajan, Shruti Mahajan, Joby Pulikkan, et al. 2019. “Association of Flavonifractor Plautii, a Flavonoid-Degrading Bacterium, With the Gut Microbiome of Colorectal Cancer Patients in India.” mSystems 4: e00438-19. https://doi.org/10.1128/mSystems.00438-19

[314] Shi, Yushu, Liangliang Zhang, Christine B. Peterson, Kim-Anh Do, and Robert R. Jenq. 2022. “Performance Determinants of Unsupervised Clustering Methods for Microbiome Data.” Microbiome 10: 25. https://doi.org/10.1186/s40168-021-01199-3

[315] Lipton, Zachary C. 2018. “The Mythos of Model Interpretability: In Machine Learning, the Concept of Interpretability Is Both Important and Slippery.” Queue 16: 31-57. https://doi.org/10.1145/3236386.3241340

[316] Lee, Sang-Mok, Hong-Tae Park, Seojoung Park, Jun Ho Lee, Danil Kim, Han Sang Yoo, and Donghyuk Kim. 2023. “A Machine Learning Approach Reveals a Microbiota Signature for Infection With Mycobacterium Avium Subsp. Paratuberculosis in Cattle.” Microbiology Spectrum 11: e0313422. https://doi.org/10.1128/spectrum.03134-22

[317] Hosu, Mojisola Clara, Lindiwe Modest Faye, and Teke Apalata. 2024. “Predicting Treatment Outcomes in Patients With Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm.” Pathogens 13: 923. https://doi.org/10.3390/pathogens13110923

[318] Ahamed Fayaz, Shaik, Lakshmanan Babu, Loganathan Paridayal, Mahalingam Vasantha, Palaniyandi Paramasivam, Karuppasamy Sundarakumar, and Chinnaiyan Ponnuraja. 2024. “Machine Learning Algorithms to Predict Treatment Success for Patients With Pulmonary Tuberculosis.” PLoS One 19: e0309151. https://doi.org/10.1371/journal.pone.0309151

[319] Larkins-Ford, Jonah, Yonatan N. Degefu, Nhi Van, Artem Sokolov, and Bree B. Aldridge. 2022. “Design Principles to Assemble Drug Combinations for Effective Tuberculosis Therapy Using Interpretable Pairwise Drug Response Measurements.” Cell Reports Medicine 3: 100737. https://doi.org/10.1016/j.xcrm.2022.100737

[320] Xiong, Yan, Xiaojun Ba, Ao Hou, Kaiwen Zhang, Longsen Chen, and Ting Li. 2018. “Automatic Detection of Mycobacterium Tuberculosis Using Artificial Intelligence.” Journal of Thoracic Disease 10: 1936-1940. https://doi.org/10.21037/jtd.2018.01.91

[321] Sharma, Anshu, Anurag Sharma, Rahul Malhotra, Parulpreet Singh, Ripon K. Chakrabortty, Shubham Mahajan, and Amit Kant Pandit. 2021. “An Accurate Artificial Intelligence System for the Detection of Pulmonary and Extra Pulmonary Tuberculosis.” Tuberculosis 131: 102143. https://doi.org/10.1016/j.tube.2021.102143

[322] Baur, Sebastien, Zaid Nabulsi, Wei-Hung Weng, Jake Garrison, Louis Blankemeier, Sam Fishman, Christina Chen, et al. 2024. “HeAR--Health Acoustic Representations.” arXiv Preprint: 02522. https://doi.org/10.48550/arXiv.2403.02522

[323] Callahan, Benjamin J., Paul J. McMurdie, Michael J. Rosen, Andrew W. Han, Amy Jo A. Johnson, and Susan P. Holmes. 2016. “DADA2: High-Resolution Sample Inference From Illumina Amplicon Data.” Nature Methods 13: 581-583. https://doi.org/10.1038/nmeth.3869

[324] Yellapu, Gayatri Devi, Gowrisree Rudraraju, Narayana Rao Sripada, Baswaraj Mamidgi, Charan Jalukuru, Priyanka Firmal, Venkat Yechuri, et al. 2023. “Development and Clinical Validation of Swaasa AI Platform for Screening and Prioritization of Pulmonary TB.” Scientific Reports 13: 4740. https://doi.org/10.1038/s41598-023-31772-9

[325] Gholami, Amir, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W. Mahoney, and Kurt Keutzer. 2022. “A Survey of Quantization Methods for Efficient Neural Network Inference.” arXiv Preprint 13630. [preprint] https://doi.org/10.48550/arXiv.2103.13630

[326] Liang, Shufan, Jiechao Ma, Gang Wang, Jun Shao, Jingwei Li, Hui Deng, Chengdi Wang, and Weimin Li. 2022. “The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis.” Frontiers in Medicine 9: 935080. https://doi.org/10.3389/fmed.2022.935080

[327] Du, Jingli, Yue Su, Juan Qiao, Shang Gao, Enjun Dong, Ruilan Wang, Yanhui Nie, et al. 2024. “Application of Artificial Intelligence in Diagnosis of Pulmonary Tuberculosis.” Chinese Medical Journal 137: 559-561. https://doi.org/10.1097/CM9.0000000000003018

[328] Jamal, Salma, Mohd. Khubaib, Rishabh Gangwar, Sonam Grover, Abhinav Grover, and Seyed E. Hasnain. 2020. “Artificial Intelligence and Machine Learning Based Prediction of Resistant and Susceptible Mutations in Mycobacterium Tuberculosis.” Scientific Reports 10: 5487. https://doi.org/10.1038/s41598-020-62368-2

[329] Shevtsova, Daria, Anam Ahmed, Iris W. A. Boot, Carmen Sanges, Michael Hudecek, John J. L. Jacobs, Simon Hort, and Hubertus J. M. Vrijhoef. 2024. “Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study.” JMIR Human Factors 11: e47031. https://doi.org/10.2196/47031

[330] Leikas, Jaana, Aditya Johri, Marko Latvanen, Nina Wessberg, and Antti Hahto. 2022. “Governing Ethical AI Transformation: A Case Study of AuroraAI.” Frontiers in Artificial Intelligence 5: 836557. https://doi.org/10.3389/frai.2022.836557

[331] Kuziemsky, Craig E., Dillon Chrimes, Simon Minshall, Michael Mannerow, and Francis Lau. 2024. “AI Quality Standards in Health Care: Rapid Umbrella Review.” Journal of Medical Internet Research 26: e54705. https://doi.org/10.2196/54705

[332] Mardis, Elaine R. 2008. “Next-Generation DNA Sequencing Methods.” Annual Review of Genomics and Human Genetics 9: 387-402. https://doi.org/10.1146/annurev.genom.9.081307.164359

[333] Ansorge, Wilhelm J. 2009. “Next-Generation DNA Sequencing Techniques.” New Biotechnology 25: 195-203. https://doi.org/10.1016/j.nbt.2008.12.009

[334] Jovel, Juan, Jordan Patterson, Weiwei Wang, Naomi Hotte, Sandra O'Keefe, Troy Mitchel, Troy Perry, et al. 2016. “Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics.” Frontiers in Microbiology 7: 459. https://doi.org/10.3389/fmicb.2016.00459

[335] Namasivayam, Sivaranjani, Alan Sher, Michael S. Glickman, and Matthew F. Wipperman. 2018. “The Microbiome and Tuberculosis: Early Evidence for Cross Talk.” mBio 9: e01420-18. https://doi.org/10.1128/mBio.01420-18

[336] Wu, Xuangao, Ting Zhang, TianShun Zhang, and Sunmin Park. 2024. “The Impact of Gut Microbiome Enterotypes on Ulcerative Colitis: Identifying Key Bacterial Species and Revealing Species Co-Occurrence Networks Using Machine Learning.” Gut Microbes 16: 2292254. https://doi.org/10.1080/19490976.2023.2292254

[337] He, Ruiqiao, Pan Li, Jinfeng Wang, Bota Cui, Faming Zhang, and Fangqing Zhao. 2022. “The Interplay of Gut Microbiota Between Donors and Recipients Determines the Efficacy of Fecal Microbiota Transplantation.” Gut Microbes 14: 2100197. https://doi.org/10.1080/19490976.2022.2100197

[338] Huang, Zhiqiang, Kun Liu, Wenwen Ma, Dezhi Li, Tianlu Mo, and Qing Liu. 2022. “The Gut Microbiome in Human Health and Disease−Where Are We and Where Are We Going? A Bibliometric Analysis.” Frontiers in Microbiology 13: 1018594. https://doi.org/10.3389/fmicb.2022.1018594

[339] Janda, J. Michael, and Sharon L. Abbott. 2007. “16S rRNA Gene Sequencing for Bacterial Identification in the Diagnostic Laboratory: Pluses, Perils, and Pitfalls.” Journal of Clinical Microbiology 45: 2761-2764. https://doi.org/10.1128/JCM.01228-07

[340] Leek, Jeffrey T., Robert B. Scharpf, Héctor Corrada Bravo, David Simcha, Benjamin Langmead, W. Evan Johnson, Donald Geman, Keith Baggerly, and Rafael A. Irizarry. 2010. “Tackling the Widespread and Critical Impact of Batch Effects in High-Throughput Data.” Nature Reviews Genetics 11: 733-739. https://doi.org/10.1038/nrg2825

[341] Johnson, W. Evan, Cheng Li, and Ariel Rabinovic. 2007. “Adjusting Batch Effects in Microarray Expression Data Using Empirical Bayes Methods.” Biostatistics 8: 118-127. https://doi.org/10.1093/biostatistics/kxj037

[342] Smyth, G. K. 2005. “Limma: Linear Models for Microarray Data.” Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer New York, 397-420. https://doi.org/10.1007/0-387-29362-0_23

[343] Valdes, Camilo, Vitalii Stebliankin, and Giri Narasimhan. 2019. “Large Scale Microbiome Profiling in the Cloud.” Bioinformatics 35: i13-i22. https://doi.org/10.1093/bioinformatics/btz356

[344] Manconia, Andrea, Matteo Gnocchia, Luciano Milanesia, Osvaldo Marullob, and Giuliano Armano. 2023. “Framing Apache Spark in Life Sciences.” Heliyon 9: e13368. https://doi.org/10.1016/j.heliyon.2023.e13368

[345] Brown, Joseph, Meg Pirrung, and Lee Ann McCue. 2017. “FQC Dashboard: Integrates FastQC Results Into a Web-Based, Interactive, and Extensible FASTQ Quality Control Tool.” Bioinformatics 33: 3137-3139. https://doi.org/10.1093/bioinformatics/btx373

[346] Sewe, Steven O., Gonçalo Silva, Paulo Sicat, Susan E. Seal, and Paul Visendi. 2022. “Trimming and Validation of Illumina Short Reads Using Trimmomatic, Trinity Assembly, and Assessment of RNA-Seq Data.” Methods in Molecular Biology 2443: 211-232. https://doi.org/10.1007/978-1-0716-2067-0_11

[347] Liu, Yong-Xin, Lei Chen, Tengfei Ma, Xiaofang Li, Maosheng Zheng, Xin Zhou, Liang Chen, et al. 2023. “EasyAmplicon: An Easy-to-Use, Open-Source, Reproducible, and Community-Based Pipeline for Amplicon Data Analysis in Microbiome Research.” iMeta 2: e83. https://doi.org/10.1002/imt2.83

[348] Hall, Michael, and Robert G. Beiko. 2018. “16S rRNA Gene Analysis With QIIME2.” Methods in Molecular Biology 1849: 113-129. https://doi.org/10.1007/978-1-4939-8728-3_8

[349] Blanco-Míguez, Aitor, Francesco Beghini, Fabio Cumbo, Lauren J. McIver, Kelsey N. Thompson, Moreno Zolfo, Paolo Manghi, et al. 2023. “Extending and Improving Metagenomic Taxonomic Profiling With Uncharacterized Species Using MetaPhlAn 4.” Nature Biotechnology 41: 1633-1644. https://doi.org/10.1038/s41587-023-01688-w

[350] Lu, Jennifer, Natalia Rincon, Derrick E. Wood, Florian P. Breitwieser, Christopher Pockrandt, Ben Langmead, Steven L. Salzberg, and Martin Steinegger. 2022. “Metagenome Analysis Using the Kraken Software Suite.” Nature Protocols 17: 2815-2839. https://doi.org/10.1038/s41596-022-00738-y

[351] Hua, Xinwei, Jessica McGoldrick, Nour Nakrour, Kyle Staller, Daniel Chulyong Chung, Ramnik Joseph Xavier, and Hamed Khalili. 2024. “Gut Microbiome Structure and Function in Asymptomatic Diverticulosis.” Genome Medicine 16: 105. https://doi.org/10.1186/s13073-024-01374-9

[352] Douglas, Gavin M., Vincent J. Maffei, Jesse R. Zaneveld, Svetlana N. Yurgel, James R. Brown, Christopher M. Taylor, Curtis Huttenhower, and Morgan G. I. Langille. 2020. “PICRUSt2 for Prediction of Metagenome Functions.” Nature Biotechnology 38: 685-688. https://doi.org/10.1038/s41587-020-0548-6

[353] McMurdie, Paul J., and Susan Holmes. 2013. “Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data.” PLoS One 8: e61217. https://doi.org/10.1371/journal.pone.0061217

[354] Zemke, Anna C., Yasmin Hilliam, Amanda L. Stapleton, Adam J. Kimple, Jennifer L. Goralski, Amber D. Shaffer, Joseph M. Pilewski, et al. 2024. “Elexacaftor-Tezacaftor-Ivacaftor Decreases Pseudomonas Abundance in the Sinonasal Microbiome in Cystic Fibrosis.” International Forum of Allergy & Rhinology 14: 928-938. https://doi.org/10.1002/alr.23288

[355] Dhariwal, Achal, Jasmine Chong, Salam Habib, Irah L. King, Luis B. Agellon, and Jianguo Xia. 2017. “MicrobiomeAnalyst: A Web-Based Tool for Comprehensive Statistical, Visual and Meta-Analysis of Microbiome Data.” Nucleic Acids Research 45: W180-W188. https://doi.org/10.1093/nar/gkx295

[356] Uritskiy, Gherman V., Jocelyne DiRuggiero, and James Taylor. 2018. “MetaWRAP−A Flexible Pipeline for Genome-Resolved Metagenomic Data Analysis.” Microbiome 6: 158. https://doi.org/10.1186/s40168-018-0541-1

[357] Edgar, Robert C. 2013. “UPARSE: Highly Accurate OTU Sequences From Microbial Amplicon Reads.” Nature Methods 10: 996-998. https://doi.org/10.1038/nmeth.2604

[358] Sanschagrin, Sylvie, and Etienne Yergeau. 2014. “Next-Generation Sequencing of 16S Ribosomal RNA Gene Amplicons.” Journal of Visualized Experiments 29: 51709. https://doi.org/10.3791/51709

[359] Jing, Gongchao, Zheng Sun, Honglei Wang, Yanhai Gong, Shi Huang, Kang Ning, Jian Xu, and Xiaoquan Su. 2017. “Parallel-META 3: Comprehensive Taxonomical and Functional Analysis Platform for Efficient Comparison of Microbial Communities.” Scientific Reports 7: 40371. https://doi.org/10.1038/srep40371

[360] Kohl, Thomas Andreas, Christian Utpatel, Viola Schleusener, Maria Rosaria De Filippo, Patrick Beckert, Daniela Maria Cirillo, and Stefan Niemann. 2018. “MTBseq: A Comprehensive Pipeline for Whole Genome Sequence Analysis of Mycobacterium Tuberculosis Complex Isolates.” PeerJ 6: e5895. https://doi.org/10.7717/peerj.5895

[361] Rognes, Torbjørn, Tomáš Flouri, Ben Nichols, Christopher Quince, and Frédéric Mahé. 2016. “VSEARCH: A Versatile Open Source Tool for Metagenomics.” PeerJ 4: e2584. https://doi.org/10.7717/peerj.2584

[362] Zhou, Yuanping, Yong-Xin Liu, and Xuemeng Li. 2024. “USEARCH 12: Open-Source Software for Sequencing Analysis in Bioinformatics and Microbiome.” iMeta 3: e236. https://doi.org/10.1002/imt2.236

[363] Zhou, Ruwen, Siu Kin Ng, Joseph Jao Yiu Sung, Wilson Wen Bin Goh, and Sunny Hei Wong. 2023. “Data Pre-Processing for Analyzing Microbiome Data−A Mini Review.” Computational and Structural Biotechnology Journal 21: 4804-4815. https://doi.org/10.1016/j.csbj.2023.10.001

[364] Bolger, Anthony M., Marc Lohse, and Bjoern Usadel. 2014. “Trimmomatic: A Flexible Trimmer for Illumina Sequence Data.” Bioinformatics 30: 2114-2120. https://doi.org/10.1093/bioinformatics/btu170

[365] Bolyen, Evan, Jai Ram Rideout, Matthew R. Dillon, Nicholas A. Bokulich, Christian C. Abnet, Gabriel A. Al-Ghalith, Harriet Alexander, et al. 2019. “Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME 2.” Nature Biotechnology 37: 852-857. https://doi.org/10.1038/s41587-019-0209-9

[366] Wood, Derrick E., Jennifer Lu, Ben Langmead. 2019. “Improved Metagenomic Analysis With Kraken 2.” Genome Biology 20: 257. https://doi.org/10.1186/s13059-019-1891-0

[367] Flandrois, Jean-Pierre, Gérard Lina, and Oana Dumitrescu. 2014. “MUBII-TB-DB: A Database of Mutations Associated With Antibiotic Resistance in Mycobacterium Tuberculosis.” BMC Bioinformatics 15: 107. https://doi.org/10.1186/1471-2105-15-107

[368] Beghini, Francesco, Lauren J. McIver, Aitor Blanco-Míguez, Leonard Dubois, Francesco Asnicar, Sagun Maharjan, Ana Mailyan, et al. 2021. “Integrating Taxonomic, Functional, and Strain-Level Profiling of Diverse Microbial Communities With bioBakery 3.” eLife 10: e65088. https://doi.org/10.7554/eLife.65088

[369] Langille, Morgan G. I., Jesse Zaneveld, J Gregory Caporaso, Daniel McDonald, Dan Knights, Joshua A. Reyes, Jose C. Clemente, et al. 2013. “Predictive Functional Profiling of Microbial Communities Using 16S rRNA Marker Gene Sequences.” Nature Biotechnology 31: 814-821. https://doi.org/10.1038/nbt.2676

[370] Lu, Yao, Guangyan Zhou, Jessica Ewald, Zhiqiang Pang, Tanisha Shiri, and Jianguo Xia. 2023. “MicrobiomeAnalyst 2.0: Comprehensive Statistical, Functional and Integrative Analysis of Microbiome Data.” Nucleic Acids Research 51: W310-W318. https://doi.org/10.1093/nar/gkad407

[371] Ewels, Philip, Måns Magnusson, Sverker Lundin, and Max Käller. 2016. “MultiQC: Summarize Analysis Results for Multiple Tools and Samples in a Single Report.” Bioinformatics 32: 3047-3048. https://doi.org/10.1093/bioinformatics/btw354

[372] Chong, Jasmine, David S. Wishart, and Jianguo Xia. 2019. “Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis.” Current Protocols 68: e86. https://doi.org/10.1002/cpbi.86

[373] Tian, Suyan, and Chi Wang. 2021. “An Ensemble of the Icluster Method to Analyze Longitudinal lncRNA Expression Data for Psoriasis Patients.” Human Genomics 15: 23. https://doi.org/10.1186/s40246-021-00323-6

[374] Rohart, Florian, Benoît Gautier, Amrit Singh, and Kim-Anh Lê Cao. 2017. “Mixomics: An R Package for ‘Omics Feature Selection and Multiple Data Integration.” PLoS Computational Biology 13: e1005752. https://doi.org/10.1371/journal.pcbi.1005752

[375] Argelaguet, Ricard, Britta Velten, Damien Arnol, Sascha Dietrich, Thorsten Zenz, John C. Marioni, Florian Buettner, Wolfgang Huber, and Oliver Stegle. 2018. “Multi-Omics Factor Analysis-A Framework for Unsupervised Integration of Multi-Omics Data Sets.” Molecular Systems Biology 14: e8124. https://doi.org/10.15252/msb.20178124

[376] Zhou, Guangyan, Zhiqiang Pang, Yao Lu, Jessica Ewald, and Jianguo Xia. 2022. “OmicsNet 2.0: A Web-Based Platform for Multi-Omics Integration and Network Visual Analytics.” Nucleic Acids Research 50: W527-W533. https://doi.org/10.1093/nar/gkac376

[377] Shannon, Paul, Andrew Markiel, Owen Ozier, Nitin S. Baliga, Jonathan T. Wang, Daniel Ramage, Nada Amin, Benno Schwikowski, and Trey Ideker. 2003. “Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks.” Genome Research 13: 2498-2504. https://doi.org/10.1101/gr.1239303

[378] Abueg, Linelle Ann L., Enis Afgan, Olivier Allart, Ahmed H. Awan, Wendi A. Bacon, Dannon Baker, Madeline Bassetti, et al. 2024. “The Galaxy Platform for Accessible, Reproducible, and Collaborative Data Analyses: 2024 Update.” Nucleic Acids Research 52: W83-W94. https://doi.org/10.1093/nar/gkae410

[379] Gonzalez, Antonio, Jose A. Navas-Molina, Tomasz Kosciolek, Daniel McDonald, Yoshiki Vázquez-Baeza, Gail Ackermann, Jeff DeReus, et al. 2018. “Qiita: Rapid, Web-Enabled Microbiome Meta-Analysis.” Nature Methods 15: 796-798. https://doi.org/10.1038/s41592-018-0141-9

[380] Wilke, Andreas, Jared Bischof, Wolfgang Gerlach, Elizabeth Glass, Travis Harrison, Kevin P. Keegan, Tobias Paczian, et al. 2016. “The MG-RAST Metagenomics Database and Portal in 2015.” Nucleic Acids Research 44: D590-D594. https://doi.org/10.1093/nar/gkv1322

[381] Manghi, Paolo, Aitor Blanco-Míguez, Serena Manara, Amir NabiNejad, Fabio Cumbo, Francesco Beghini, Federica Armanini, et al. 2023. “MetaPhlAn 4 Profiling of Unknown Species-Level Genome Bins Improves the Characterization of Diet-Associated Microbiome Changes in Mice.” Cell Reports 42: 112464. https://doi.org/10.1016/j.celrep.2023.112464

[382] Xiao, Liwen, Fengyi Zhang, and Fangqing Zhao. 2022. “Large-Scale Microbiome Data Integration Enables Robust Biomarker Identification.” Nature Computational Science 2: 307-316. https://doi.org/10.1038/s43588-022-00247-8

[383] Langfelder, Peter, and Steve Horvath. 2008. “WGCNA: An R Package for Weighted Correlation Network Analysis.” BMC Bioinformatics 9: 559. https://doi.org/10.1186/1471-2105-9-559

[384] Saenz, Carmen, Eleonora Nigro, Vithiagaran Gunalan, and Manimozhiyan Arumugam. 2022. “MIntO: A Modular and Scalable Pipeline for Microbiome Metagenomic and Metatranscriptomic Data Integration.” Frontiers in Bioinformatics 2: 846922. https://doi.org/10.3389/fbinf.2022.846922

[385] Kuleshov, Maxim V., Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, et al. 2016. “Enrichr: A Comprehensive Gene Set Enrichment Analysis Web Server 2016 Update.” Nucleic Acids Research 44: W90-W97. https://doi.org/10.1093/nar/gkw377

[386] Tuncbag, Nurcan, Sara J. C. Gosline, Amanda Kedaigle, Anthony R. Soltis, Anthony Gitter, and Ernest Fraenkel. 2016. “Network-Based Interpretation of Diverse High-Throughput Datasets Through the Omics Integrator Software Package.” PLoS Computational Biology 12: e1004879. https://doi.org/10.1371/journal.pcbi.1004879

[387] Baysoy, Alev, Zhiliang Bai, Rahul Satija, and Rong Fan. 2023. “The Technological Landscape and Applications of Single-Cell Multi-Omics.” Nature Reviews Molecular Cell Biology 24: 695-713. https://doi.org/10.1038/s41580-023-00615-w

[388] Bock, Christoph, Matthias Farlik, and Nathan C. Sheffield. 2016. “Multi-Omics of Single Cells: Strategies and Applications.” Trends in Biotechnology 34: 605-608. https://doi.org/10.1016/j.tibtech.2016.04.004

[389] Stuart, Tim, Andrew Butler, Paul Hoffman, Christoph Hafemeister, Efthymia Papalexi, William M. Mauck, Yuhan Hao, et al. 2019. “Comprehensive Integration of Single-Cell Data.” Cell 177: 1888-1902.e1821. https://doi.org/10.1016/j.cell.2019.05.031

[390] Greenacre, Michael, Patrick J. F. Groenen, Trevor Hastie, Alfonso Iodice D'Enza, Angelos Markos, and Elena Tuzhilina. 2022. “Principal Component Analysis.” Nature Reviews Methods Primers 2: 100. https://doi.org/10.1038/s43586-022-00184-w

[391] Cieslak, Matthew C., Ann M. Castelfranco, Vittoria Roncalli, Petra H. Lenz, and Daniel K. Hartline. 2020. “t-Distributed Stochastic Neighbor Embedding (t-SNE): A Tool for Eco-Physiological Transcriptomic Analysis.” Marine Genomics 51: 100723. https://doi.org/10.1016/j.margen.2019.100723

[392] Xu, Yunpei, Hong-Dong Li, Cui-Xiang Lin, Ruiqing Zheng, Yaohang Li, Jinhui Xu, and Jianxin Wang. 2023. “CellBRF: A Feature Selection Method for Single-Cell Clustering Using Cell Balance and Random Forest.” Bioinformatics 39: i368-i376. https://doi.org/10.1093/bioinformatics/btad216

[393] Missarova, Alsu, Jaison Jain, Andrew Butler, Shila Ghazanfar, Tim Stuart, Maigan Brusko, Clive Wasserfall, et al. 2021. “geneBasis: An Iterative Approach for Unsupervised Selection of Targeted Gene Panels From scRNA-Seq.” Genome Biology 22: 333. https://doi.org/10.1186/s13059-021-02548-z

[394] Senghor, Bruno, Cheikh Sokhna, Raymond Ruimy, and Jean-Christophe Lagier. 2018. “Gut Microbiota Diversity According to Dietary Habits and Geographical Provenance.” Human Microbiome Journal 7-8: 1-9. https://doi.org/10.1016/j.humic.2018.01.001

[395] Pant, Archana, Bhabatosh Das, and Gopalakrishnan Aneeshkumar Arimbasseri. 2023. “Host Microbiome in Tuberculosis: Disease, Treatment, and Immunity Perspectives.” Frontiers in Microbiology 14: 1236348. https://doi.org/10.3389/fmicb.2023.1236348

[396] Arya, Rakesh, Hemlata Shakya, Reetika Chaurasia, Surendra Kumar, Joseph M. Vinetz, and Jong Joo Kim. 2024. “Computational Reassessment of RNA-Seq Data Reveals Key Genes in Active Tuberculosis.” PLoS One 19: e0305582. https://doi.org/10.1371/journal.pone.0305582

[397] Verma, Anjali, Tannu Bhagchandani, Ankita Rai, I. Nikita, Urvinder Kaur Sardarni, Neel Sarovar Bhavesh, Sameer Gulati, Rupali Malik, and Ravi Tandon. 2024. “Short-Chain Fatty Acid (SCFA) as a Connecting Link Between Microbiota and Gut-Lung Axis−A Potential Therapeutic Intervention to Improve Lung Health.” ACS Omega 9: 14648-14671. https://doi.org/10.1021/acsomega.3c05846

[398] Zheng, Yuanting, Yaqing Liu, Jingcheng Yang, Lianhua Dong, Rui Zhang, Sha Tian, Ying Yu, et al. 2024. “Multi-Omics Data Integration Using Ratio-Based Quantitative Profiling With Quartet Reference Materials.” Nature Biotechnology 42: 1133-1149. https://doi.org/10.1038/s41587-023-01934-1

[399] Guha, Priyanka, Siddhartha Dutta, Krishna Murti, Jay Karan Charan, Krishna Pandey, V. Ravichandiran, and Sameer Dhingra. 2024. “The Integration of Omics: A Promising Approach to Personalized Tuberculosis Treatment.” Medicine in Omics 12: 100033. https://doi.org/10.1016/j.meomic.2024.100033

[400] Liu, Pinyi, Yanbing Wang, Ge Yang, Qihe Zhang, Lingbin Meng, Ying Xin, and Xin Jiang. 2021. “The Role of Short-Chain Fatty Acids in Intestinal Barrier Function, Inflammation, Oxidative Stress, and Colonic Carcinogenesis.” Pharmacological Research 165: 105420. https://doi.org/10.1016/j.phrs.2021.105420

[401] Sun, Mingming, Wei Wu, Liang Chen, Wenjing Yang, Xiangsheng Huang, Caiyun Ma, Feidi Chen, et al. 2018. “Microbiota-Derived Short-Chain Fatty Acids Promote Th1 Cell IL-10 Production to Maintain Intestinal Homeostasis.” Nature Communications 9: 3555. https://doi.org/10.1038/s41467-018-05901-2

[402] Dikeocha, Ifeoma Julieth, Abdelkodose Mohammed Al-Kabsi, Hsien-Tai Chiu, and Mohammed Abdullah Alshawsh. 2022. “Faecalibacterium Prausnitzii Ameliorates Colorectal Tumorigenesis and Suppresses Proliferation of HCT116 Colorectal Cancer Cells.” Biomedicines 10: 1128. https://doi.org/10.3390/biomedicines10051128

[403] Dong, Chen. 2021. “Cytokine Regulation and Function in T Cells.” Annual Review of Immunology 39: 51-76. https://doi.org/10.1146/annurev-immunol-061020-053702

[404] Noecker, Cecilia, Alexander Eng, Sujatha Srinivasan, Casey M. Theriot, Vincent B. Young, Janet K. Jansson, David N. Fredricks, and Elhanan Borenstein. 2016. “Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links Between Ecological and Metabolic Variation.” mSystems 1: e00013-e00015. https://doi.org/10.1128/mSystems.00013-15

[405] Singh, Vineet, GyuDae Lee, HyunWoo Son, Hong Koh, Eun Soo Kim, Tatsuya Unno, and Jae-Ho Shin. 2022. “Butyrate Producers, “The Sentinel of Gut”: Their Intestinal Significance With and Beyond Butyrate, and Prospective Use as Microbial Therapeutics.” Frontiers in Microbiology 13: 1103836. https://doi.org/10.3389/fmicb.2022.1103836

[406] Duncan, Sylvia H., Adela Barcenilla, Colin S. Stewart, Susan E. Pryde, and Harry J. Flint. 2002. “Acetate Utilization and Butyryl Coenzyme A (CoA):Acetate-CoA Transferase in Butyrate-Producing Bacteria From the Human Large Intestine.” Applied and Environmental Microbiology 68: 5186-5190. https://doi.org/10.1128/AEM.68.10.5186-5190.2002

[407] Joshi, Lavanya, Meenakshi Ponnana, Ramya Sivangala, Lakshmi Kiran Chelluri, Prathiba Nallari, Sitaramaraju Penmetsa, Vijayalakshmi Valluri, and Sumanlatha Gaddam. 2015. “Evaluation of TNF-ɑ, IL-10 and IL-6 Cytokine Production and Their Correlation With Genotype Variants Amongst Tuberculosis Patients and Their Household Contacts.” PLOS One 10: e0137727. https://doi.org/10.1371/journal.pone.0137727

[408] Subramanian, Indhupriya, Srikant Verma, Shiva Kumar, Abhay Jere, and Krishanpal Anamika. 2020. “Multi-Omics Data Integration, Interpretation, and Its Application.” Bioinformatics and Biology Insights 14: 1177932219899051. https://doi.org/10.1177/1177932219899051

[409] Liu, Xi, Yuanli Chen, Hui Ouyang, Jian Liu, Xiaoqing Luo, Yayi Huang, Yan Chen, et al. 2021. “Tuberculosis Diagnosis by Metagenomic Next-Generation Sequencing on Bronchoalveolar Lavage Fluid: A Cross-Sectional Analysis.” International Journal of Infectious Diseases 104: 50-57. https://doi.org/10.1016/j.ijid.2020.12.063

[410] Wu, Jiayu, Kai Wang, Xuemei Wang, Yanli Pang, and Changtao Jiang. 2021. “The Role of the Gut Microbiome and Its Metabolites in Metabolic Diseases.” Protein & Cell 12: 360-373. https://doi.org/10.1007/s13238-020-00814-7

[411] Ney, Lisa-Marie, Maximilian Wipplinger, Martha Grossmann, Nicole Engert, Valentin D. Wegner, and Alexander S. Mosig. 2023. “Short Chain Fatty Acids: Key Regulators of the Local and Systemic Immune Response in Inflammatory Diseases and Infections.” Open Biology 13: 230014. https://doi.org/10.1098/rsob.230014

[412] Creasy, Heather Huot, Victor Felix, Jain Aluvathingal, Jonathan Crabtree, Olukemi Ifeonu, James Matsumura, Carrie McCracken, et al. 2021. “HMPDACC: A Human Microbiome Project Multi-Omic Data Resource.” Nucleic Acids Research 49: D734-D742. https://doi.org/10.1093/nar/gkaa996

[413] Chen, I-Min A, Ken Chu, Krishna Palaniappan, Manoj Pillay, Anna Ratner, Jinghua Huang, Marcel Huntemann, et al. 2019. “IMG/M V.5.0: An Integrated Data Management and Comparative Analysis System for Microbial Genomes and Microbiomes.” Nucleic Acids Research 47: D666-D677. https://doi.org/10.1093/nar/gky901

[414] Galagan, James E., Peter Sisk, Christian Stolte, Brian Weiner, Michael Koehrsen, Farrell Wymore, T. B. K. Reddy, et al. 2010. “TB Database 2010: Overview and Update.” Tuberculosis 90: 225-235. https://doi.org/10.1016/j.tube.2010.03.010

[415] Kapopoulou, Adamandia, Jocelyne M. Lew, and Stewart T. Cole. 2011. “The MycoBrowser Portal: A Comprehensive and Manually Annotated Resource for Mycobacterial Genomes.” Tuberculosis 91: 8-13. https://doi.org/10.1016/j.tube.2010.09.006

[416] Perez-Riverol, Yasset, Andrey Zorin, Gaurhari Dass, Manh-Tu Vu, Pan Xu, Mihai Glont, Juan Antonio Vizcaíno, et al. 2019. “Quantifying the Impact of Public Omics Data.” Nature Communications 10: 3512. https://doi.org/10.1038/s41467-019-11461-w

[417] Pang, Zhiqiang, Jasmine Chong, Guangyan Zhou, David Anderson de Lima Morais, Le Chang, Michel Barrette, Carol Gauthier, et al. 2021. “MetaboAnalyst 5.0: Narrowing the Gap Between Raw Spectra and Functional Insights.” Nucleic Acids Research 49: W388-W396. https://doi.org/10.1093/nar/gkab382

[418] Overbeek, Ross, Robert Olson, Gordon D. Pusch, Gary J. Olsen, James J. Davis, Terry Disz, Robert A. Edwards, et al. 2014. “The SEED and the Rapid Annotation of Microbial Genomes Using Subsystems Technology (RAST).” Nucleic Acids Research 42: D206-D214. https://doi.org/10.1093/nar/gkt1226

[419] Heusden, Peter, Ziphozakhe van, Thoba Mashologu, Robin Lose, Alan Warren, and Christoffels. 2022. “The COMBAT-TB Workbench: Making Powerful Mycobacterium Tuberculosis Bioinformatics Accessible.” mSphere 7: e0099121. https://doi.org/10.1128/msphere.00991-21

[420] Carithers, Latarsha J., and Helen M. Moore. 2015. “The Genotype-Tissue Expression (GTEx) Project.” Biopreservation and Biobanking 13: 307-308. https://doi.org/10.1089/bio.2015.29031.hmm

[421] Lamb, Justin. 2007. “The Connectivity Map: A New Tool for Biomedical Research.” Nature Reviews Cancer 7: 54-60. https://doi.org/10.1038/nrc2044

[422] Reddy, T. B. K., R. Riley, F. Wymore, P. Montgomery, D. DeCaprio, R. Engels, M. Gellesch, et al. 2009. “TB Database: An Integrated Platform for Tuberculosis Research.” Nucleic Acids Research 37: D499-D508. https://doi.org/10.1093/nar/gkn652

[423] Metri, Rahul, Sridhar Hariharaputran, Gayatri Ramakrishnan, Praveen Anand, Upadhyayula S. Raghavender, Bernardo Ochoa-Montaño, Alicia P. Higueruelo, et al. 2015. “SInCRe-Structural Interactome Computational Resource for Mycobacterium Tuberculosis.” Database 2015: bav060. https://doi.org/10.1093/database/bav060

[424] Perez-Riverol, Yasset, Mingze Bai, Felipe da Veiga Leprevost, Silvano Squizzato, Young Mi Park, Kenneth Haug, Adam J. Carroll, et al. 2017. “Discovering and Linking Public Omics Data Sets Using the Omics Discovery Index.” Nature Biotechnology 35: 406-409. https://doi.org/10.1038/nbt.3790

[425] Xia, Jianguo, and David S. Wishart. 2016. “Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis.” Current Protocols 55: 14.10.11-14.10.91. https://doi.org/10.1002/cpbi.11

[426] Sequeira, João C., Vítor Pereira, M. Madalena Alves, M. Alcina Pereira, Miguel Rocha, and Andreia F. Salvador. 2024. “MOSCA 2.0: A Bioinformatics Framework for Metagenomics, Metatranscriptomics and Metaproteomics Data Analysis and Visualization.” Molecular Ecology Resources 24: e13996. https://doi.org/10.1111/1755-0998.13996

[427] Muñoz-Benavent, Maria, Felix Hartkopf, Tim Van Den Bossche, Vitor C. Piro, Carlos García-Ferris, Amparo Latorre, Bernhard Y. Renard, and Thilo Muth. 2020. “gNOMO: A Multi-Omics Pipeline for Integrated Host and Microbiome Analysis of Non-Model Organisms.” NAR Genomics and Bioinformatics 2: lqaa058. https://doi.org/10.1093/nargab/lqaa083

[428] Davis, James J., Alice R. Wattam, Ramy K. Aziz, Thomas Brettin, Ralph Butler, Rory M. Butler, and Philippe Chlenski, et al. 2020. “The Patric Bioinformatics Resource Center: Expanding Data and Analysis Capabilities.” Nucleic Acids Research 48: D606-D612. https://doi.org/10.1093/nar/gkz943

[429] Sinkov, Viacheslav, Oleg Ogarkov, Igor Mokrousov, Yuri Bukin, Svetlana Zhdanova, and Scott K. Heysell. 2018. “New Epidemic Cluster of Pre-Extensively Drug Resistant Isolates of Mycobacterium Tuberculosis Ural Family Emerging in Eastern Europe.” BMC Genomics 19: 762. https://doi.org/10.1186/s12864-018-5162-3

[430] Zoppi, Johanna, Jean-François Guillaume, Michel Neunlist, and Samuel Chaffron. 2021. “MiBiOmics: An Interactive Web Application for Multi-Omics Data Exploration and Integration.” BMC Bioinformatics 22: 6. https://doi.org/10.1186/s12859-020-03921-8

[431] Schuster, Viktoria, Emma Dann, Anders Krogh, and Sarah A. Teichmann. 2024. “MultiDGD: A Versatile Deep Generative Model for Multi-Omics Data.” Nature Communications 15: 10031. https://doi.org/10.1038/s41467-024-53340-z

[432] Dhanda, Sandeep Kumar, Pooja Vir, Deepak Singla, Sudheer Gupta, Shailesh Kumar, and Gajendra P. S. Raghava. 2016. “A Web-Based Platform for Designing Vaccines Against Existing and Emerging Strains of Mycobacterium Tuberculosis.” PLOS One 11: e0153771. https://doi.org/10.1371/journal.pone.0153771

[433] Sandgren, Andreas, Michael Strong, Preetika Muthukrishnan, Brian K. Weiner, George M. Church, and Megan B. Murray. 2009. “Tuberculosis Drug Resistance Mutation Database.” PLOS Medicine 6: e2. https://doi.org/10.1371/journal.pmed.1000002

[434] Cansdale, Annabel, and James P. J. Chong. 2024. “MAGqual: A Stand-Alone Pipeline to Assess the Quality of Metagenome-Assembled Genomes.” Microbiome 12: 226. https://doi.org/10.1186/s40168-024-01949-z

[435] Mulder, Nicola, Russell Schwartz, Michelle D. Brazas, Cath Brooksbank, Bruno Gaeta, Sarah L. Morgan, Mark A. Pauley, et al. 2018. “The Development and Application of Bioinformatics Core Competencies to Improve Bioinformatics Training and Education.” PLoS Computational Biology 14: e1005772. https://doi.org/10.1371/journal.pcbi.1005772

[436] Attwood, Teresa K., Sarah Blackford, Michelle D. Brazas, Angela Davies, and Maria Victoria Schneider. 2019. “A Global Perspective on Evolving Bioinformatics and Data Science Training Needs.” Briefings in Bioinformatics 20: 398-404. https://doi.org/10.1093/bib/bbx100

[437] Searls, David B. 2012. “An Online Bioinformatics Curriculum.” PLoS Computational Biology 8: e1002632. https://doi.org/10.1371/journal.pcbi.1002632

[438] Cantelli, Gaia, Guy Cochrane, Cath Brooksbank, Ellen McDonagh, Paul Flicek, Johanna McEntyre, Ewan Birney, and Rolf Apweiler. 2021. “The European Bioinformatics Institute: Empowering Cooperation in Response to a Global Health Crisis.” Nucleic Acids Research 49: D29-D37. https://doi.org/10.1093/nar/gkaa1077

[439] Stajich, J. E. 2006. “Open Source Tools and Toolkits for Bioinformatics: Significance, and Where Are We?” Briefings in Bioinformatics 7: 287-296. https://doi.org/10.1093/bib/bbl026

[440] Dubilier, Nicole, Margaret McFall-Ngai, and Liping Zhao. 2015. “Microbiology: Create a Global Microbiome Effort.” Nature 526: 631-634. https://doi.org/10.1038/526631a

[441] Li, Jing-Woei, Robert Schmieder, R. Matthew Ward, Joann Delenick, Eric C. Olivares, and David Mittelman. 2012. “SEQanswers: An Open Access Community for Collaboratively Decoding Genomes.” Bioinformatics 28: 1272-1273. https://doi.org/10.1093/bioinformatics/bts128

[442] Arkin, Adam P., Robert W. Cottingham, Christopher S. Henry, Nomi L. Harris, Rick L. Stevens, Sergei Maslov, Paramvir Dehal, et al. 2018. “KBase: The United States Department of Energy Systems Biology Knowledgebase.” Nature Biotechnology 36: 566-569. https://doi.org/10.1038/nbt.4163

[443] Ye, Shiqing, Liang Wang, Shengkai Li, Qingyong Ding, Yu Wang, Xinxin Wan, Xiaoyun Ji, Yongliang Lou, and Xiang Li. 2022. “The Correlation Between Dysfunctional Intestinal Flora and Pathology Feature of Patients With Pulmonary Tuberculosis.” Frontiers in Cellular and Infection Microbiology 12: 1090889. https://doi.org/10.3389/fcimb.2022.1090889

[444] Zhang, Meng, Li Shen, Xia Zhou, and Huidong Chen. 2022. “The Microbiota of Human Lung of Pulmonary Tuberculosis and the Alteration Caused by Anti-Tuberculosis Drugs.” Current Microbiology 79: 321. https://doi.org/10.1007/s00284-022-03019-9

[445] Fontana, Andrea, Concetta Panebianco, Andrea Picchianti-Diamanti, Bruno Laganà, Duccio Cavalieri, Adele Potenza, Riccardo Pracella, et al. 2019. “Gut Microbiota Profiles Differ Among Individuals Depending on Their Region of Origin: an Italian Pilot Study.” International Journal of Environmental Research and Public Health 16: 4065. https://doi.org/10.3390/ijerph16214065

[446] Lawal, Samuel Adefisoye, Athalia Voisin, Hana Olof, Michael Bording-Jorgensen, and Heather Armstrong. 2023. “Diversity of the Microbiota Communities Found in the Various Regions of the Intestinal Tract in Healthy Individuals and Inflammatory Bowel Diseases.” Frontiers in Immunology 14: 1242242. https://doi.org/10.3389/fimmu.2023.1242242

[447] Moon, Ji-Hoi, Dae-Hyun Roh, Kyu Hwan Kwack, and Jae-Hyung Lee. 2023. “Bacterial Single-Cell Transcriptomics: Recent Technical Advances and Future Applications in Dentistry.” Japanese Dental Science Review 59: 253-262. https://doi.org/10.1016/j.jdsr.2023.08.001

[448] McShane, Helen, and Ann Williams. 2014. “A Review of Preclinical Animal Models Utilised for TB Vaccine Evaluation in the Context of Recent Human Efficacy Data.” Tuberculosis 94: 105-110. https://doi.org/10.1016/j.tube.2013.11.003

[449] Ivanov, Ivaylo I., Timur Tuganbaev, Ashwin N. Skelly, and Kenya Honda. 2022. “T Cell Responses to the Microbiota.” Annual Review of Immunology 40: 559-587. https://doi.org/10.1146/annurev-immunol-101320-011829

[450] Yu, You, Weihong Wang, and Faming Zhang. 2023. “The Next Generation Fecal Microbiota Transplantation: To Transplant Bacteria or Virome.” Advanced Science 10: e2301097. https://doi.org/10.1002/advs.202301097

[451] Monette, Anne, Adriana Aguilar-Mahecha, Emre Altinmakas, Mathew G. Angelos, Nima Assad, Gerald Batist, Praveen K. Bommareddy, et al. 2025. “The Society for Immunotherapy of Cancer Perspective on Tissue-Based Technologies for Immuno-Oncology Biomarker Discovery and Application.” Clinical Cancer Research 31: 439-456. https://doi.org/10.1158/1078-0432.CCR-24-2469

RIGHTS & PERMISSIONS

2025 The Author(s). iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.