Nature:人類微生物組計劃揭示機體微生物群落之間的關聯及動力學特性

2014年4月18日 訊 /生物谷BIOON/ --近日，刊登在國際著名雜誌Nature上的一篇研究論文中，來自美國密西根大學的研究人員通過研究闡明，在健康人機體中不同類別的細菌也會發生多樣性的變異；研究者Patrick D. Schloss表示，理解微生物群體的多樣性以及單一類型細菌「變型」的機制將幫助我們更好的利用這些微生物群體來估測人類疾病發生的風險以及開發新型個體化療法。

研究者表示，基於個體口腔的細菌群落數據就可以預測該個體胃腸道中的細菌群落類型，而且在機體兩個不同位置的細菌類型也完全不同。科學家們開展人類微生物組計劃就是理解人類機體微生物群落的改變和健康改變之間的關聯。

該計劃中超過200名科學家花費了5年時間對來自300個健康成年個體的樣本進行分析，這些樣本來自於個體機體中的28個不同位置，包括口腔、鼻子、腸道、耳朵以及肘部等。研究者對這些樣本進行研究來揭示是否以母乳餵養的個體和腸道微生物的不同類型相關等。

研究者通過研究表明，由於每一個個體的微生物組都不同，但這並不能促使個體不健康，因此研究引發個體微生物組發生改變的因子也顯得至關重要。

理解人類機體中微生物群落髮生改變的分子機制對於開發新型靶向療法，利用有益菌種、糞便移植或者新型抗生素來治療人類疾病非常重要，相關研究由美國國立衛生研究院提供資助。（生物谷Bioon.com）

Dynamics and associations of microbial community types across the human body

Tao Ding & Patrick D. Schloss

A primary goal of the Human Microbiome Project (HMP) was to provide a reference collection of 16S ribosomal RNA gene sequences collected from sites across the human body that would allow microbiologists to better associate changes in the microbiome with changes in health1. The HMP Consortium has reported the structure and function of the human microbiome in 300 healthy adults at 18 body sites from a single time point2, 3. Using additional data collected over the course of 12–18 months, we used Dirichlet multinomial mixture models4 to partition the data into community types for each body site and made three important observations. First, there were strong associations between whether individuals had been breastfed as an infant, their gender, and their level of education with their community types at several body sites. Second, although the specific taxonomic compositions of the oral and gut microbiomes were different, the community types observed at these sites were predictive of each other. Finally, over the course of the sampling period, the community types from sites within the oral cavity were the least stable, whereas those in the vagina and gut were the most stable. Our results demonstrate that even with the considerable intra- and interpersonal variation in the human microbiome, this variation can be partitioned into community types that are predictive of each other and are probably the result of life-history characteristics. Understanding the diversity of community types and the mechanisms that result in an individual having a particular type or changing types, will allow us to use their community types to assess disease risk and to personalize therapies.