Inflammation and B cell activation define a plasma proteome signature predicting tuberculosis in people with HIV.

IF 4.7 1区 生物学 Q1 MICROBIOLOGY
mBio Pub Date : 2025-10-08 Epub Date: 2025-08-28 DOI:10.1128/mbio.01585-25
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
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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.

炎症和B细胞活化定义了预测HIV感染者结核病的血浆蛋白质组特征。
迫切需要改进的生物标志物来预测活动性结核病(TB)的进展,特别是在高风险的艾滋病毒感染者中。我们使用高通量血浆蛋白质组学和机器学习来识别与该人群中结核病进展相关的特征。从瑞士HIV队列研究中,我们分析了91名后来发展为结核病的参与者在结核病诊断前至少6个月收集的血浆样本。我们选择了293名符合人口统计学和临床参数的对照组,他们仍然没有结核病,以达到针对活动性结核病的风险评分。总共分析了583个样品,每个样品定量了613-1,283个蛋白质。随机森林分类器预测病例结核病进展的中位数概率(33%)显著高于对照组(16%;P < 0.001)。在这个匹配的人群中,得分达到了接受者工作特征曲线下的面积为0.77,精确度召回曲线(AUPRC)下的面积为0.60(与预期的AUPRC为0.29相比),以及使用最佳阈值0.311的特异性为87.3%和灵敏度为58.6%。进展为活动性结核病的个体的血浆蛋白质组显示出向全身性炎症、B细胞活化和免疫球蛋白产生的明显转变。与进展为活动性结核病无关,蛋白质组评分与免疫抑制的更广泛指标相关,包括较低的CD4计数和未抑制的HIV RNA。这表明整合蛋白质组学和临床数据可以提高评分的整体预测能力。我们仍然缺乏可靠的工具来预测艾滋病毒感染者中谁会患上结核病。此外,推动疾病进展的潜在生物学事件仍然知之甚少。我们的研究揭示了早期免疫变化,包括B细胞活化和抗体反应的意外改变。这些发现表明体液免疫可能在结核病发病机制中发挥比以前认识到的更重要的作用,并为生物标志物的发现和靶向预防提供了有希望的新方向。
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来源期刊
mBio
mBio MICROBIOLOGY-
CiteScore
10.50
自引率
3.10%
发文量
762
审稿时长
1 months
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.
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