This PhD project aims to investigate differences in microbiota composition and diversity along the gut–lung axis in patients with pulmonary tuberculosis (TB). Using paired stool and sputum samples, the study will examine how microbiota profiles change before and after antibiotic treatment and how these changes are associated with alterations in peripheral immune cell subsets and plasma proteomic signatures. The project will further assess how antibiotic regimens used to treat drug-susceptible and drug-resistant TB influence microbial communities. By integrating microbiome data with host immune responses and Mycobacterium tuberculosis strain genotypes, the study aims to identify associations with disease severity and clinical heterogeneity, thereby contributing to a better understanding of TB pathogenesis. The study is based on a longitudinal Ethiopian cohort comprising TB patients (n=52) and healthy controls (n=20), with clinical metadata and biological samples collected at baseline, 8 weeks, and follow-up (6–18 months). A total of 181 paired stool and sputum samples collected before and after treatment have been analyzed using 16S rRNA gene sequencing. Microbiome data will be integrated with immunological and proteomic analyses. Flow cytometry will be used to characterize immune cell populations, including granulocyte subsets such as neutrophils, while plasma proteomics using the Olink Reveal platform will identify biomarkers associated with inflammation and disease progression. This multi-omics approach will enable identification of immune–microbiome interactions linked to TB outcomes. The doctoral student, Mira Akber, will receive training in microbiome and immunological data analysis and contribute to parts of the analytical workflow. Progress currently depends on access to specialized bioinformatics support for integrating high-dimensional datasets. Objectives: Aim 1 To assess changes in microbiota composition, diversity, and abundance in the lung compared to the gut between baseline (V1) and 8 weeks of treatment (V2). Aim 2 To compare microbiota profiles between: MDR-TB patients, Drug-susceptible TB patients, and Healthy controls Aim 3 To characterize longitudinal microbiota changes associated with different antibiotic regimens, focusing on differences between first-line and second-line TB treatments. Aim 4 To investigate whether specific TB strains, such as the hypervirulent Beijing strain, exert a stronger influence on microbiota composition and dynamics. Aim 5 To explore associations between microbiota changes (composition and abundance) and host immune responses. Main supervisor: Assoc. prof. Susanna Brighenti Center for Infectious Medicine (CIM) ANA Futura Karolinska Institutet 14152 Huddinge Sweden +46-739-136976