Diagnosis of Bacterial Bloodstream Infections: A 16S Metagenomics Approach.
Diagnosis of Bacterial Bloodstream Infections: A 16S Metagenomics Approach.
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DOI：
10.1371/journal.pntd.0004470
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发表时间：
2016-02
影响因子：
3.8
通讯作者：
Deborggraeve S
中科院分区：
文献类型：
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作者：
Decuypere S;Meehan CJ;Van Puyvelde S;De Block T;Maltha J;Palpouguini L;Tahita M;Tinto H;Jacobs J;Deborggraeve S
细菌性血流感染（bBSI）是危重患者死亡的主要原因之一，因此准确的诊断至关重要。我们在这里报告了一种用于诊断和理解 bBSI 的 16S 宏基因组学方法。该概念验证在布基纳法索 75 名患有严重发热性疾病的儿童（中位年龄 15 个月）身上进行。入院时进行了标准血培养和疟疾检测。对所有患者的血液重复进行 16S 宏基因组学测试。从血液中提取总 DNA，通过 PCR 扩增细菌 16S rRNA 基因的 V3-V4 区域，并在 Illumina MiSeq 测序仪上进行深度测序。配对的读数经过策划、分类标记和过滤。血培养诊断出 12 名患者患有 bBSI，但结合血培养和 16S 宏基因组学结果，这一数字增加至 22 名患者。与标准血培养相比，除了具有更高的灵敏度外，16S 宏基因组学还揭示了对 bBSI 本质的重要新见解。与近期未诊断出疟疾的患者相比，患有急性疟疾或从疟疾中恢复的患者出现多种微生物血流感染的风险高出 7 倍（p 值 = 0.046）。已知疟疾会影响上皮肠道功能，因此可能促进细菌从肠腔转移到血液。重要的是，患有这种多种微生物血液感染的患者无法幸存发热性疾病的风险因素高出 9 倍（p 值 = 0.030）。我们的数据表明，16S 宏基因组学是诊断和理解 bBSI 的强大方法。这项概念验证研究还表明，适当的对照样品对于检测环境污染引起的背景信号至关重要。细菌性血流感染（bBSI）是危重患者死亡的最大原因之一，标准诊断仍通过血培养方法进行。 16S 核糖体 RNA 基因的并行深度测序（16S 宏基因组学）是一个快速发展的新研究领域，用于分析细菌群落。我们设计了一种 16S 宏基因组学方法来鉴定 bBSI 患者血液中的细菌，并评估了该方法在布基纳法索 75 名严重发热性疾病儿童中的表现。与标准血培养相比，除了具有更高的灵敏度外，16S 宏基因组学还揭示了对 bBSI 本质的重要新见解。患有急性疟疾或最近从急性疟疾中康复的患者出现多种微生物血流感染的风险增加，这本身就是死亡的重大风险。这项概念验证研究表明，16S 宏基因组学是诊断和理解 bBSI 的强大方法，而且适当的对照样本对于正确的数据解释也至关重要。
Bacterial bloodstream infection (bBSI) is one of the leading causes of death in critically ill patients and accurate diagnosis is therefore crucial. We here report a 16S metagenomics approach for diagnosing and understanding bBSI. The proof-of-concept was delivered in 75 children (median age 15 months) with severe febrile illness in Burkina Faso. Standard blood culture and malaria testing were conducted at the time of hospital admission. 16S metagenomics testing was done retrospectively and in duplicate on the blood of all patients. Total DNA was extracted from the blood and the V3–V4 regions of the bacterial 16S rRNA genes were amplified by PCR and deep sequenced on an Illumina MiSeq sequencer. Paired reads were curated, taxonomically labeled, and filtered. Blood culture diagnosed bBSI in 12 patients, but this number increased to 22 patients when combining blood culture and 16S metagenomics results. In addition to superior sensitivity compared to standard blood culture, 16S metagenomics revealed important novel insights into the nature of bBSI. Patients with acute malaria or recovering from malaria had a 7-fold higher risk of presenting polymicrobial bloodstream infections compared to patients with no recent malaria diagnosis (p-value = 0.046). Malaria is known to affect epithelial gut function and may thus facilitate bacterial translocation from the intestinal lumen to the blood. Importantly, patients with such polymicrobial blood infections showed a 9-fold higher risk factor for not surviving their febrile illness (p-value = 0.030). Our data demonstrate that 16S metagenomics is a powerful approach for the diagnosis and understanding of bBSI. This proof-of-concept study also showed that appropriate control samples are crucial to detect background signals due to environmental contamination. Bacterial bloodstream infection (bBSI) is one of the biggest causes of mortality in critically ill patients and standard diagnosis is still done by blood culture methods. Parallel deep sequencing of the 16S ribosomal RNA genes (16S metagenomics) is a new and rapidly evolving research field for profiling bacterial communities. We designed a 16S metagenomics approach for the identification of bacteria in the blood of patients with a bBSI, and evaluated its performance in 75 children with severe febrile illness in Burkina Faso. In addition to superior sensitivity compared to standard blood culture, 16S metagenomics revealed important novel insights into the nature of bBSI. Patients with acute malaria or recently recovered from acute malaria are at increased risk of presenting polymicrobial bloodstream infection, which was in itself a significant risk for non-survival. This proof-of-concept study shows that 16S metagenomics is a powerful approach to diagnose and understand bBSI but also that appropriate control samples are crucial for correct data interpretation.