Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, Georgia, USA.
School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.
J Infect Dis. 2021 Jun 16;223(12 Suppl 2):S246-S256. doi: 10.1093/infdis/jiaa655.
Microbiome sequencing has brought increasing attention to the polymicrobial context of chronic infections. However, clinical microbiology continues to focus on canonical human pathogens, which may overlook informative, but nonpathogenic, biomarkers. We address this disconnect in lung infections in people with cystic fibrosis (CF).
We collected health information (lung function, age, and body mass index [BMI]) and sputum samples from a cohort of 77 children and adults with CF. Samples were collected during a period of clinical stability and 16S rDNA sequenced for airway microbiome compositions. We use ElasticNet regularization to train linear models predicting lung function and extract the most informative features.
Models trained on whole-microbiome quantitation outperformed models trained on pathogen quantitation alone, with or without the inclusion of patient metadata. Our most accurate models retained key pathogens as negative predictors (Pseudomonas, Achromobacter) along with established correlates of CF disease state (age, BMI, CF-related diabetes). In addition, our models selected nonpathogen taxa (Fusobacterium, Rothia) as positive predictors of lung health.
These results support a reconsideration of clinical microbiology pipelines to ensure the provision of informative data to guide clinical practice.
微生物组测序越来越关注慢性感染的多微生物环境。然而，临床微生物学仍然专注于典型的人类病原体，这可能会忽略有信息但非致病性的生物标志物。我们在囊性纤维化 (CF) 患者的肺部感染中解决了这一脱节问题。
我们收集了 77 名儿童和成人 CF 患者的健康信息（肺功能、年龄和体重指数 [BMI]）和痰样本。在临床稳定期采集样本，并对气道微生物组进行 16S rDNA 测序。我们使用弹性网络正则化来训练线性模型，以预测肺功能并提取最具信息量的特征。
基于全微生物组定量的模型优于仅基于病原体定量的模型，无论是否包含患者元数据。我们最准确的模型保留了关键的病原体作为负预测因子（假单胞菌、不动杆菌），以及 CF 疾病状态的既定相关因素（年龄、BMI、CF 相关糖尿病）。此外，我们的模型还选择了非病原体分类群（梭杆菌、罗氏菌）作为肺健康的正预测因子。
这些结果支持重新考虑临床微生物学管道，以确保提供有信息的数据来指导临床实践。
