Ecological networking of cystic fibrosis lung infections.
Ecological networking of cystic fibrosis lung infections.
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DOI：
10.1038/s41522-016-0002-1
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发表时间：
2016
影响因子：
9.2
通讯作者：
Widder S
中科院分区：
文献类型：
--
作者：
Quinn RA;Whiteson K;Lim YW;Zhao J;Conrad D;LiPuma JJ;Rohwer F;Widder S
在多种微生物感染的情况下，治疗特定病原体带来了挑战，因为对社区其他成员的未知后果。微生物之间的生态相互作用的存在可以改变它们的生理和对治疗的反应。例如，在囊性纤维化肺多微生物感染中，对临床分离株的抗菌药物敏感性测试通常不能预测抗生素的疗效。需要新的方法来确定微生物群落内的相互关系，以更好地预测治疗结果。在这里，我们对囊性纤维化肺微生物组使用了一种生态网络方法，其特征在于使用16S rRNA基因测序和宏基因组学。该分析表明，该社区分为三个相互作用组：革兰氏阳性厌氧菌，铜绿假单胞菌和金黄色葡萄球菌。铜绿假单胞菌和S.金黄色葡萄球菌组均与厌氧组反相关，表明功能性拮抗作用。当患者临床稳定时，这些主要分组也是稳定的，然而，在急性加重期间，这些群体分裂。从宏基因组学数据注释的功能模块的共现网络支持底层分类结构是由组的核心代谢的差异驱动的。拓扑分析的功能网络确定的类异戊二烯生物合成的非甲羟戊酸途径作为一个基石的微生物群落，它可以与抗生素磷霉素的目标。这项研究利用生态理论来确定针对多种微生物疾病的新型治疗方法，并具有更可预测的结果。研究肺部复杂微生物感染的不同生态学可能有助于指导囊性纤维化的治疗选择。该疾病是复杂的，由于感染了不同的病原微生物群落。由于混合微生物生态系统内相互作用的影响，基于识别和靶向特定微生物的治疗的成功可能是不可预测的。维也纳大学的Stefanie Widder与美国的Robert Quinn及其同事一起，使用10年来收集的肺部感染样本，研究了6例囊性纤维化特定病例中的这些相互作用。他们确定了涉及的三种主要细菌，并提出了几种关于可能影响抗生素治疗成功的相互关系的假设。研究人员认为，他们的新假设和整体“生态理论”将导致对特定治疗方案可能益处的更可靠预测。
In the context of a polymicrobial infection, treating a specific pathogen poses challenges because of unknown consequences on other members of the community. The presence of ecological interactions between microbes can change their physiology and response to treatment. For example, in the cystic fibrosis lung polymicrobial infection, antimicrobial susceptibility testing on clinical isolates is often not predictive of antibiotic efficacy. Novel approaches are needed to identify the interrelationships within the microbial community to better predict treatment outcomes. Here we used an ecological networking approach on the cystic fibrosis lung microbiome characterized using 16S rRNA gene sequencing and metagenomics. This analysis showed that the community is separated into three interaction groups: Gram-positive anaerobes, Pseudomonas aeruginosa, and Staphylococcus aureus. The P. aeruginosa and S. aureus groups both anti-correlate with the anaerobic group, indicating a functional antagonism. When patients are clinically stable, these major groupings were also stable, however, during exacerbation, these communities fragment. Co-occurrence networking of functional modules annotated from metagenomics data supports that the underlying taxonomic structure is driven by differences in the core metabolism of the groups. Topological analysis of the functional network identified the non-mevalonate pathway of isoprenoid biosynthesis as a keystone for the microbial community, which can be targeted with the antibiotic fosmidomycin. This study uses ecological theory to identify novel treatment approaches against a polymicrobial disease with more predictable outcomes. Studying the varying ecology of complex microbial infections in the lungs may help guide treatment options in cystic fibrosis. The disease is complicated due to infection with a diverse community of pathogenic microorganisms. The success of treatments based on identifying and targeting specific microbes can be unpredictable due to the effects of interactions within the mixed microbial ecosystem. Stefanie Widder at the University of Vienna together with Robert Quinn and co-workers in the USA, studied these interactions in six specific cases of cystic fibrosis using lung infection samples gathered over 10 years. They identified three main groups of bacteria involved and developed several hypotheses about inter-relationships that can affect the success of antibiotic treatments. The researchers argue that their new hypotheses and overall ‘ecological theory’ will lead to more-reliable predictions of the likely benefits of specific treatment regimes.