Gut community structure as a risk factor for infection in Klebsiella -colonized patients.
Gut community structure as a risk factor for infection in Klebsiella -colonized patients.
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肠道群落结构是克雷伯菌定植患者感染的危险因素。
DOI：
10.1101/2023.04.18.23288742
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Bachman,MichaelA
中科院分区：
文献类型：
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作者：
Vornhagen,Jay;Rao,Krishna;Bachman,MichaelA
肺炎克雷伯氏菌复合群成员感染的主要危险因素是先前的肠道定植，并且感染通常由定植菌株引起。尽管肠道作为感染性克雷伯氏菌的储存库很重要，但对肠道微生物组与感染之间的关系知之甚少。为了探索这种关系，我们进行了一项病例对照研究，比较了克雷伯菌定植重症监护患者和血液学/肿瘤学患者的肠道群落结构。例为克雷伯氏菌定植患者，其定植菌株感染（N = 83）。对照组为克雷伯氏菌定植但无症状的患者（N = 149）。首先，我们描述了克雷伯氏菌定植患者的肠道群落结构，与病例状态无关。接下来，我们确定了肠道群落数据对于使用机器学习模型对病例和对照进行分类是有用的，并且肠道群落结构在病例和对照之间是不同的。克雷伯氏菌相对丰度，一个已知的感染风险因素，具有最大的特征重要性，但其他肠道微生物也提供了信息。最后，我们表明，肠道群落结构与细菌基因型或临床变量数据的整合增强了机器学习模型区分病例和对照的能力。这项研究表明，包括患者和克雷伯氏菌衍生的生物标志物的肠道社区数据提高了我们预测克雷伯氏菌定植患者感染的能力。这一步骤提供了一个独特的干预窗口，因为给定的潜在病原体尚未对其宿主造成损害。此外，在定殖阶段的干预可能有助于减轻治疗失败的负担，因为抗菌素耐药性上升。然而，为了了解针对定植的干预措施的治疗潜力，我们必须首先了解定植的生物学，以及定植阶段的生物标志物是否可用于对感染风险进行分层。克雷伯氏菌属包括许多具有不同程度致病潜力的菌种。The K的成员pneumoniae种复合体的致病潜力最高。这些细菌在肠道中定植的患者随后感染定植菌株的风险更高。然而，我们不知道肠道微生物群的其他成员是否可以用作预测感染风险的生物标志物。在这项研究中，我们表明肠道微生物群在发生感染的定植患者与未发生感染的定植患者之间存在差异。此外，我们还表明，将肠道微生物群数据与患者和细菌因素相结合，可以提高预测感染的能力。当我们继续探索定植作为预防潜在病原体定植个体感染的干预点时，我们必须开发有效的方法来预测和分层感染风险。
The primary risk factor for infection with members ofthe Klebsiella pneumoniaespecies complex is prior gut colonization, and infection is often caused by the colonizing strain. Despite the importance of the gut as a reservoir for infectiousKlebsiella, little is known about the association between the gut microbiome and infection. To explore this relationship, we undertook a case-control study comparing the gut community structure ofKlebsiella-colonized intensive care and hematology/oncology patients. Cases wereKlebsiella-colonized patients infected by their colonizing strain (N = 83). Controls wereKlebsiella-colonized patients that remained asymptomatic (N = 149). First, we characterized the gut community structure ofKlebsiella-colonized patients agnostic to case status. Next, we determined that gut community data is useful for classifying cases and controls using machine learning models and that the gut community structure differed between cases and controls.Klebsiellarelative abundance, a known risk factor for infection, had the greatest feature importance but other gut microbes were also informative. Finally, we show that integration of gut community structure with bacterial genotype or clinical variable data enhanced the ability of machine learning models to discriminate cases and controls. This study demonstrates that including gut community data with patient- andKlebsiella-derived biomarkers improves our ability to predict infection inKlebsiella-colonized patients.ImportanceColonization is generally the first step in pathogenesis for bacteria with pathogenic potential. This step provides a unique window for intervention since a given potential pathogen has yet to cause damage to its host. Moreover, intervention during the colonization stage may help alleviate the burden of therapy failure as antimicrobial resistance rises. Yet, to understand the therapeutic potential of interventions that target colonization, we must first understand the biology of colonization and if biomarkers at the colonization stage can be used to stratify infection risk. The bacterial genusKlebsiellaincludes many species with varying degrees of pathogenic potential. Members of theK. pneumoniaespecies complex have the highest pathogenic potential. Patients colonized in their gut by these bacteria are at higher risk of subsequent infection with their colonizing strain. However, we do not understand if other members of the gut microbiota can be used as a biomarker to predict infection risk. In this study, we show that the gut microbiota differs between colonized patients that develop an infection versus those that do not. Additionally, we show that integrating gut microbiota data with patient and bacterial factors improves the ability to predict infections. As we continue to explore colonization as an intervention point to prevent infections in individuals colonized by potential pathogens, we must develop effective means for predicting and stratifying infection risk.