Transcriptional profiles predict treatment outcome in patients with tuberculosis and diabetes at diagnosis and at two weeks after initiation of anti-tuberculosis treatment.
Transcriptional profiles predict treatment outcome in patients with tuberculosis and diabetes at diagnosis and at two weeks after initiation of anti-tuberculosis treatment.
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DOI：
10.1016/j.ebiom.2022.104173
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发表时间：
2022-08
期刊：
影响因子：
11.1
通讯作者：
Cliff, Jacqueline M.
中科院分区：
文献类型：
--
作者：
van Doorn, Cassandra L. R.;Eckold, Clare;Ronacher, Katharina;Ruslami, Rovina;van Veen, Suzanne;Lee, Ji-Sook;Kumar, Vinod;Kerry-Barnard, Sarah;Malherbe, Stephanus T.;Kleynhans, Leanie;Stanley, Kim;Hill, Philip C.;Joosten, Simone A.;van Crevel, Reinout;Wijmenga, Cisca;Critchley, Julia A.;Walzl, Gerhard;Alisjahbana, Bachti;Haks, Marielle C.;Dockrell, Hazel M.;Ottenhoff, Tom H. M.;Vianello, Eleonora;Cliff, Jacqueline M.
在全球范围内，结核病(TB)治疗成功率约为85%，治疗失败、复发和死亡发生在相当大比例的肺结核患者中。糖尿病(DM)患者的治疗成功率较低。在确诊后早期预测治疗结果，特别是在结核病-糖尿病患者中，将使个人能够及早适应治疗，并可能改善全球结核病控制。样本是在一项纵向队列研究中收集的，对象是来自南非(n=94)和印度尼西亚(n=81)的成年结核病患者，他们患有糖尿病(n=59)、中度高血糖(n=79)或正常血糖/无糖尿病(n=37)。监测治疗结果，并在治疗期间和12个月的随访中将患者分为良好(治愈)或较差(失败、复发、死亡)。使用无偏倚的RNA-Seq和靶向基因dcRT-MLPA对结核病治疗前、治疗中和治疗结束时的全血转录图谱进行表征。我们报告了在治疗开始前和整个治疗过程中观察到的全血转录组图谱在结核病治疗结果好与差的患者之间的差异。8基因和22基因的血液转录特征分别在结核病治疗开始时(AUC=0·815)和开始治疗后两周(AUC=0·834)区分治疗效果好的患者和治疗效果差的患者。通过在外部队列中交叉验证该签名获得了高精度(AUC=0.749)。这些发现表明，转录图谱可以作为治疗失败和成功的预后生物标记物，即使在伴随的糖尿病患者中也是如此。作为TANDEM联盟的一部分，导致这些成果的研究得到了欧洲共同体第七框架方案(305279号赠款协议)和荷兰科学研究组织(NWO-TOP 91214038号赠款协议)的资助。导致在印度验证队列中公布结果的研究得到了挪威全球卫生和疫苗研究研究理事会项目的支持：挪威卑尔根大学179342、192534和248042号研究报告。
Globally, the tuberculosis (TB) treatment success rate is approximately 85%, with treatment failure, relapse and death occurring in a significant proportion of pulmonary TB patients. Treatment success is lower among people with diabetes mellitus (DM). Predicting treatment outcome early after diagnosis, especially in TB-DM patients, would allow early treatment adaptation for individuals and may improve global TB control. Samples were collected in a longitudinal cohort study of adult TB patients from South Africa (n = 94) and Indonesia (n = 81), who had concomitant DM (n = 59), intermediate hyperglycaemia (n = 79) or normal glycaemia/no DM (n = 37). Treatment outcome was monitored, and patients were categorized as having a good (cured) or poor (failed, recurrence, died) outcome during treatment and 12 months follow-up. Whole blood transcriptional profiles before, during and at the end of TB treatment were characterized using unbiased RNA-Seq and targeted gene dcRT-MLPA. We report differences in whole blood transcriptome profiles, which were observed before initiation of treatment and throughout treatment, between patients with a good versus poor TB treatment outcome. An eight-gene and a 22-gene blood transcriptional signature distinguished patients with a good TB treatment outcome from patients with a poor TB treatment outcome at diagnosis (AUC = 0·815) or two weeks (AUC = 0·834) after initiation of TB treatment, respectively. High accuracy was obtained by cross-validating this signature in an external cohort (AUC = 0·749). These findings suggest that transcriptional profiles can be used as a prognostic biomarker for treatment failure and success, even in patients with concomitant DM. The research leading to these results, as part of the TANDEM Consortium, received funding from the European Community's Seventh Framework Programme (FP7/2007-2013 Grant Agreement No. 305279) and the Netherlands Organization for Scientific Research (NWO-TOP Grant Agreement No. 91214038). The research leading to the results presented in the Indian validation cohort was supported by Research Council of Norway Global Health and Vaccination Research (GLOBVAC) projects: RCN 179342, 192534, and 248042, the University of Bergen (Norway).
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
DOI：
10.1016/j.jctube.2021.100279
发表时间：
2021-12
影响因子：
2
作者：
Alayu Alemu M;Yesuf A;Girma F;Adugna F;Melak K;Biru M;Seyoum M;Abiye T
通讯作者：
Abiye T
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
2.2
作者：
Gregorutti, Baptiste;Michel, Bertrand;Saint-Pierre, Philippe
通讯作者：
Saint-Pierre, Philippe
影响因子：
24.3
作者：
Heyckendorf, Jan;Marwitz, Sebastian;Lange, Christoph
通讯作者：
Lange, Christoph