2014年7月15日 訊 /生物谷BIOON/ --感染性疾病常常讓我們渾身不自在，近日，刊登在國際雜誌PLoS ONE上的一篇研究論文中，來自布萊根婦女醫院的研究人員利用特殊的計算機模型揭示了機體感染影響腸道天然菌群的分子機制，相關研究或可幫助臨床醫生們開發治療胃腸道感染及炎症的新型療法。
研究者Lynn Bry表示，我們腸道中的細菌數量是機體細胞的10倍以上，而面臨感染時，這些複雜菌群的生態系統的表現會大大影響我們機體的健康。文章中研究者開發了一種新型計算機算法，它可以對檸檬酸桿菌引發的感染的不同階段進行分析，檸檬酸桿菌可以引發小鼠患病，類似於人類食物中毒的表現。
研究者將檸檬酸桿菌引入小鼠腸道中，兩個月後就可以在小鼠腸道中的多個位點發現不同水平的檸檬酸桿菌，這種新型計算機算法可以幫助研究人員在和感染、炎症相關的腸道複雜菌群中鑑別出細菌的動力學改變。
當小鼠處於感染期間時，研究者在小鼠腸道的不同位置發現了正常菌群的許多破壞情況，比如，研究人員發現小鼠結腸組織中的細菌標記在小鼠感染細菌後出現症狀之前的水平不斷下降，而其它的標記包括梭菌和乳桿菌家族的水平出現了上升的趨勢；更有意思的是，這些標記中的一部分會在腸道感染病原體的位點中發生，其並不會直接損傷宿主的細胞。
研究者Bry表示，從臨床角度來講，我們鑑別出的這些新型微生物標記可以幫助臨床醫生們檢測機體的早期炎症或者胃腸道疾病患者持久耐藥性疾病，比如炎性腸病；而研究者鑑別出的許多時間依賴性的微生物標記或許可以幫助科學家們開發出治療感染和炎性疾病的新型靶向療法。（生物谷Bioon.com）
Dynamics of the Microbiota in Response to Host Infection
Belzer C, Gerber GK, Roeselers G, Delaney M, DuBois A, et al.
Longitudinal studies of the microbiota are important for discovering changes in microbial communities that affect the host. The complexity of these ecosystems requires rigorous integrated experimental and computational methods to identify temporal signatures that promote physiologic or pathophysiologic responses in vivo. Employing a murine model of infectious colitis with the pathogen Citrobacter rodentium, we generated a 2-month time-series of 16S rDNA gene profiles, and quantitatively cultured commensals, from multiple intestinal sites in infected and uninfected mice. We developed a computational framework to discover time-varying signatures for individual taxa, and to automatically group signatures to identify microbial sub-communities within the larger gut ecosystem that demonstrate common behaviors. Application of this model to the 16S rDNA dataset revealed dynamic alterations in the microbiota at multiple levels of resolution, from effects on systems-level metrics to changes across anatomic sites for individual taxa and species. These analyses revealed unique, time-dependent microbial signatures associated with host responses at different stages of colitis. Signatures included a Mucispirillum OTU associated with early disruption of the colonic surface mucus layer, prior to the onset of symptomatic colitis, and members of the Clostridiales and Lactobacillales that increased with successful resolution of inflammation, after clearance of the pathogen. Quantitative culture data validated findings for predominant species, further refining and strengthening model predictions. These findings provide new insights into the complex behaviors found within host ecosystems, and define several time-dependent microbial signatures that may be leveraged in studies of other infectious or inflammatory conditions.