Katharina Kusejko, Mohammad Arefian, Diane Duroux, Marius Zeeb, Cédric Dollé, Matthias Hoffmann, Niklaus Labhardt, Gilles Wandeler, Matthias Cavassini, Sabine Haller, Enos Bernasconi, Doris Russenberger, Roger D Kouyos, Huldrych F Günthard, Ben C Collins, Johannes Nemeth
{"title":"Inflammation and B cell activation define a plasma proteome signature predicting tuberculosis in people with HIV.","authors":"Katharina Kusejko, Mohammad Arefian, Diane Duroux, Marius Zeeb, Cédric Dollé, Matthias Hoffmann, Niklaus Labhardt, Gilles Wandeler, Matthias Cavassini, Sabine Haller, Enos Bernasconi, Doris Russenberger, Roger D Kouyos, Huldrych F Günthard, Ben C Collins, Johannes Nemeth","doi":"10.1128/mbio.01585-25","DOIUrl":null,"url":null,"abstract":"<p><p>Improved biomarkers for predicting progression to active tuberculosis (TB) are urgently needed, especially in people with HIV, who are at elevated risk. We used high-throughput plasma proteomics and machine learning to identify signatures associated with TB progression in this population. From the Swiss HIV Cohort Study, we analyzed plasma samples collected at least 6 months before TB diagnosis from 91 participants who later developed TB. We selected 293 controls matched for demographic and clinical parameters who remained TB-free to achieve a risk score specific to active TB. In total, 583 samples were analyzed, with 613-1,283 proteins quantified per sample. A random forest classifier predicted a significantly higher median probability of TB progression for cases (33%) than for controls (16%; <i>P</i> < 0.001). In this matched population, the score achieved an area under the receiver-operating characteristic curve of 0.77, an area under the precision-recall curve (AUPRC) of 0.60 (as compared to an expected AUPRC of 0.29), as well as a specificity of 87.3% and a sensitivity of 58.6% using the optimal threshold of 0.311. The plasma proteome of individuals who progressed to active TB showed a distinct shift toward systemic inflammation, B cell activation, and immunoglobulin production. Independent of progression to active TB, the proteome score correlated with broader indicators of immune suppression, including lower CD4 counts and unsuppressed HIV RNA. This suggests that integrating proteomic and clinical data could enhance the overall predictive power of the score.IMPORTANCEWe still lack reliable tools to predict who will develop tuberculosis (TB) among people with HIV. Moreover, the underlying biological events driving progression remain poorly understood. Our study reveals early immune changes that include unexpected alterations in B cell activation and antibody responses. These findings suggest that humoral immunity may play a more important role in TB pathogenesis than previously recognized and offer promising new directions for biomarker discovery and targeted prevention.</p>","PeriodicalId":18315,"journal":{"name":"mBio","volume":" ","pages":"e0158525"},"PeriodicalIF":4.7000,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12505960/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"mBio","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1128/mbio.01585-25","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/8/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Improved biomarkers for predicting progression to active tuberculosis (TB) are urgently needed, especially in people with HIV, who are at elevated risk. We used high-throughput plasma proteomics and machine learning to identify signatures associated with TB progression in this population. From the Swiss HIV Cohort Study, we analyzed plasma samples collected at least 6 months before TB diagnosis from 91 participants who later developed TB. We selected 293 controls matched for demographic and clinical parameters who remained TB-free to achieve a risk score specific to active TB. In total, 583 samples were analyzed, with 613-1,283 proteins quantified per sample. A random forest classifier predicted a significantly higher median probability of TB progression for cases (33%) than for controls (16%; P < 0.001). In this matched population, the score achieved an area under the receiver-operating characteristic curve of 0.77, an area under the precision-recall curve (AUPRC) of 0.60 (as compared to an expected AUPRC of 0.29), as well as a specificity of 87.3% and a sensitivity of 58.6% using the optimal threshold of 0.311. The plasma proteome of individuals who progressed to active TB showed a distinct shift toward systemic inflammation, B cell activation, and immunoglobulin production. Independent of progression to active TB, the proteome score correlated with broader indicators of immune suppression, including lower CD4 counts and unsuppressed HIV RNA. This suggests that integrating proteomic and clinical data could enhance the overall predictive power of the score.IMPORTANCEWe still lack reliable tools to predict who will develop tuberculosis (TB) among people with HIV. Moreover, the underlying biological events driving progression remain poorly understood. Our study reveals early immune changes that include unexpected alterations in B cell activation and antibody responses. These findings suggest that humoral immunity may play a more important role in TB pathogenesis than previously recognized and offer promising new directions for biomarker discovery and targeted prevention.
期刊介绍:
mBio® is ASM''s first broad-scope, online-only, open access journal. mBio offers streamlined review and publication of the best research in microbiology and allied fields.