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Although some studies have investigated TB and COVID-19 co-infection in terms of immune responses, microbial dysbiosis in such cases remains unexplored. In this study, we understand the interface between TB and COVID-19 by systematically inspecting the microbial composition of sputum samples collected from four groups of individuals: TB only, COVID-19 only, and both TB and COVID-19 (TBCOVID) infected patients, and uninfected group (Controls). Besides metagenomic analysis of the microbiome of these sputum samples, we also performed whole-genome sequencing analysis of a subset of TB-positive samples. Different bioinformatic analyses ensured data quality and revealed significant differences in the microbial composition between Control vs disease groups. To understand the effect of COVID-19 on TB, we compared TBCOVID vs TB samples and observed (i) higher read counts of TB-causing bacteria in the TBCOVID group, and (ii) differential abundance of several taxa, including\n                      <jats:italic toggle=\"yes\">Capnocytophaga gingivalis<\/jats:italic>\n                      . Functional profiling with PICRUSt2 revealed elevated pathways, including the pulmonary surfactant lipid metabolism pathway (with a fold change of 7.46) in the TBCOVID group. Further clustering of these pathways revealed a sub-cluster of individuals with adverse treatment outcomes. Two individuals in this sub-cluster had a respiratory pathogen,\n                      <jats:italic toggle=\"yes\">Stenotrophomonas maltophilia<\/jats:italic>\n                      \u2014knowing such information on key respiratory pathogens in a patient can help personalize the patient's antibiotic regimen. Overall, our study reveals the effect of COVID-19 on the airway microbiome of TB patients and encourages the use of co-microbial\/co-pathogen profiling to personalize TB treatment.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>IMPORTANCE<\/jats:title>\n                    <jats:p>\n                      The community of microbes in an individual\u2019s airway tract can play a complex role in respiratory diseases like tuberculosis (TB) and COVID-19. Although changes in microbial composition in TB and COVID-19 patients have been studied separately, we present a first-of-its-kind investigation of the airway-tract microbiome of individuals simultaneously infected with TB and COVID-19 pathogens. Our results highlight that co-infection with COVID-19 in TB patients alters the abundance of certain bacterial species and their related pathways. For instance,\n                      <jats:italic toggle=\"yes\">Capnocytophaga gingivalis<\/jats:italic>\n                      is abundant in co-infected patients, but not in TB-only patients. This species and other differentially abundant species we identified in the co-morbid condition, if replicated in independent cohorts, can help explain how COVID-19 could exacerbate the severity of lung infection in TB patients. Our study also stimulates future longitudinal studies using expanded data sets to understand the role of concomitant pathogens and assess whether adjusting the antibiotic regimen accordingly can improve TB treatment outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1128\/spectrum.02220-25","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T14:01:11Z","timestamp":1772546471000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Dissecting the effect of single- and co-infection of TB and COVID-19 pathogens on the sputum microbiome"],"prefix":"10.1128","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2300-8171","authenticated-orcid":true,"given":"Brintha","family":"Vijayakumar Padmavathy","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras","place":["Chennai, India"]},{"id":[{"id":"https:\/\/ror.org\/03v0r5n49","id-type":"ROR","asserted-by":"publisher"}],"name":"Center for Integrative Biology and Systems Medicine, IIT Madras","place":["Chennai, India"]},{"id":[{"id":"https:\/\/ror.org\/03v0r5n49","id-type":"ROR","asserted-by":"publisher"}],"name":"Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashok Kumar","family":"Shanmugavel","sequence":"additional","affiliation":[{"name":"ICMR National Institute for Research in Tuberculosis (NIRT)","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9203-2664","authenticated-orcid":false,"given":"Sivakumar","family":"Shanmugam","sequence":"additional","affiliation":[{"name":"ICMR National Institute for Research in Tuberculosis (NIRT)","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8490-4087","authenticated-orcid":true,"given":"Manikandan","family":"Narayanan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras","place":["Chennai, India"]},{"id":[{"id":"https:\/\/ror.org\/03v0r5n49","id-type":"ROR","asserted-by":"publisher"}],"name":"Center for Integrative Biology and Systems Medicine, IIT Madras","place":["Chennai, India"]},{"id":[{"id":"https:\/\/ror.org\/03v0r5n49","id-type":"ROR","asserted-by":"publisher"}],"name":"Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"235","reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2666-5247(22)00359-7"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.909011"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygeno.2024.110816"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e31562"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.2478\/jtim-2020-0010"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.657006"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tube.2020.102020"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijid.2021.02.090"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.1010093"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2023.1240419"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2023.1227581"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41392-022-00974-4"},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2024.1323723"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2022.836987"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1128\/spectrum.01901-21"},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2023.1140548"},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12071742"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnut.2021.626254"},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tube.2022.102182"},{"key":"e_1_3_5_21_2","doi-asserted-by":"publisher","DOI":"10.53854\/liim-3302-7"},{"key":"e_1_3_5_22_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-021-05933-y"},{"key":"e_1_3_5_23_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/70.1.41"},{"key":"e_1_3_5_24_2","first-page":"1354","volume-title":"44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","author":"Andrews S","year":"2010","unstructured":"Andrews S. 2010. 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