Moving pictures of the human microbiome.
Moving pictures of the human microbiome.
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DOI：
10.1186/gb-2011-12-5-r50
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发表时间：
2011
期刊：
影响因子：
12.3
通讯作者：
Knight R
中科院分区：
文献类型：
--
作者：
Caporaso JG;Lauber CL;Costello EK;Berg-Lyons D;Gonzalez A;Stombaugh J;Knights D;Gajer P;Ravel J;Fierer N;Gordon JI;Knight R
了解人类微生物组的正常时间变化对于开发假定的微生物组相关疾病（如肥胖，克罗恩病，炎症性肠病和营养不良）的治疗至关重要。然而，测序和计算技术一直是对人类微生物组进行密集时间序列分析的限制因素。在这里，我们提出了迄今为止最大的人体微生物群时间序列分析，涵盖了396个时间点的四个身体部位的两个个体。我们发现，尽管身体部位和个体之间存在稳定的差异，但个体的微生物群在几个月，几周甚至几天内都存在明显的变化。此外，在单个身体部位内发现的总分类群中只有一小部分似乎在所有时间点都存在，这表明没有高丰度的核心时间微生物组存在（尽管可能存在一些微生物，但低于检测阈值）。更多的分类群似乎是持久的，但非永久性的社区成员。这里描述的DNA测序和计算的进步提供了超越我们人类相关的微生物生态的罕见快照的能力，以高分辨率的时间变化评估在长期内，身体栖息地和个人之间。这种能力将使我们能够定义正常变异和病理状态，并评估对治疗干预的反应。
Understanding the normal temporal variation in the human microbiome is critical to developing treatments for putative microbiome-related afflictions such as obesity, Crohn's disease, inflammatory bowel disease and malnutrition. Sequencing and computational technologies, however, have been a limiting factor in performing dense time series analysis of the human microbiome. Here, we present the largest human microbiota time series analysis to date, covering two individuals at four body sites over 396 timepoints. We find that despite stable differences between body sites and individuals, there is pronounced variability in an individual's microbiota across months, weeks and even days. Additionally, only a small fraction of the total taxa found within a single body site appear to be present across all time points, suggesting that no core temporal microbiome exists at high abundance (although some microbes may be present but drop below the detection threshold). Many more taxa appear to be persistent but non-permanent community members. DNA sequencing and computational advances described here provide the ability to go beyond infrequent snapshots of our human-associated microbial ecology to high-resolution assessments of temporal variations over protracted periods, within and between body habitats and individuals. This capacity will allow us to define normal variation and pathologic states, and assess responses to therapeutic interventions.
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影响因子：
64.8
作者：
通讯作者：
--
影响因子：
17.1
作者：
Turnbaugh PJ;Ridaura VK;Faith JJ;Rey FE;Knight R;Gordon JI
通讯作者：
Gordon JI
DOI：
10.1073/pnas.0807920105
发表时间：
2008-11-18
影响因子：
11.1
作者：
Fierer, Noah;Hamady, Micah;Knight, Rob
通讯作者：
Knight, Rob
DOI：
10.1073/pnas.1000080107
发表时间：
2011-03-15
影响因子：
11.1
作者：
Caporaso, J. Gregory;Lauber, Christian L.;Knight, Rob
通讯作者：
Knight, Rob
影响因子：
7
作者：
Peterson, Jane;Garges, Susan;Guyer, Mark
通讯作者：
Guyer, Mark