Data_Sheet_1_Microbiotyping the Sinonasal Microbiome.docx

This study offers a novel description of the sinonasal microbiome, through an unsupervised machine learning approach combining dimensionality reduction and clustering. We apply our method to the International Sinonasal Microbiome Study (ISMS) dataset of 410 sinus swab samples. We propose three main sinonasal “microbiotypes” or “states”: the first is Corynebacterium-dominated, the second is Staphylococcus-dominated, and the third dominated by the other core genera of the sinonasal microbiome (Streptococcus, Haemophilus, Moraxella, and Pseudomonas). The prevalence of the three microbiotypes studied did not differ between healthy and diseased sinuses, but differences in their distribution were evident based on geography. We also describe a potential reciprocal relationship between Corynebacterium species and Staphylococcus aureus, suggesting that a certain microbial equilibrium between various players is reached in the sinuses. We validate our approach by applying it to a separate 16S rRNA gene sequence dataset of 97 sinus swabs from a different patient cohort. Sinonasal microbiotyping may prove useful in reducing the complexity of describing sinonasal microbiota. It may drive future studies aimed at modeling microbial interactions in the sinuses and in doing so may facilitate the development of a tailored patient-specific approach to the treatment of sinus disease in the future.

本研究借助融合降维与聚类的无监督机器学习方法，对鼻窦微生物组（sinonasal microbiome）开展了创新性阐释。我们将所提方法应用于包含410份鼻窦拭子样本的国际鼻窦微生物组研究（International Sinonasal Microbiome Study, ISMS）数据集，提出了三种主要的鼻窦“微生物群落型”或“状态”：第一种以棒状杆菌属（Corynebacterium）为主导，第二种以葡萄球菌属（Staphylococcus）为主导，第三种则由鼻窦微生物组的其他核心菌属——链球菌属（Streptococcus）、嗜血杆菌属（Haemophilus）、莫拉克斯氏菌属（Moraxella）以及假单胞菌属（Pseudomonas）——主导。所研究的三种微生物群落型的患病率在健康鼻窦与病变鼻窦之间无显著差异，但基于地域的分布差异却十分显著。本研究同时揭示了棒状杆菌属物种与金黄色葡萄球菌（Staphylococcus aureus）之间潜在的互惠关系，提示鼻窦内各类微生物可达成特定的微生物平衡状态。我们通过将该方法应用于另一项来自不同患者队列、包含97份鼻窦拭子样本的16S rRNA基因序列（16S rRNA gene sequence）数据集，完成了对所提方法的验证。鼻窦微生物分型或可有效简化鼻窦微生物群的描述复杂度，推动未来针对鼻窦内微生物相互作用的建模研究，并借此助力未来个体化精准治疗鼻窦疾病方案的开发。