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艰难梭菌感染期间黏膜免疫与肠道微生物群相互作用的系统建模

Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection.

作者信息

Leber Andrew, Viladomiu Monica, Hontecillas Raquel, Abedi Vida, Philipson Casandra, Hoops Stefan, Howard Brad, Bassaganya-Riera Josep

机构信息

The Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Nutritional Immunology and Molecular Medicine Laboratory (www.nimml.org), Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America.

Nutritional Immunology and Molecular Medicine Laboratory (www.nimml.org), Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Department of Biological Sciences, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America.

出版信息

PLoS One. 2015 Jul 31;10(7):e0134849. doi: 10.1371/journal.pone.0134849. eCollection 2015.

Abstract

Clostridium difficile infections are associated with the use of broad-spectrum antibiotics and result in an exuberant inflammatory response, leading to nosocomial diarrhea, colitis and even death. To better understand the dynamics of mucosal immunity during C. difficile infection from initiation through expansion to resolution, we built a computational model of the mucosal immune response to the bacterium. The model was calibrated using data from a mouse model of C. difficile infection. The model demonstrates a crucial role of T helper 17 (Th17) effector responses in the colonic lamina propria and luminal commensal bacteria populations in the clearance of C. difficile and colonic pathology, whereas regulatory T (Treg) cells responses are associated with the recovery phase. In addition, the production of anti-microbial peptides by inflamed epithelial cells and activated neutrophils in response to C. difficile infection inhibit the re-growth of beneficial commensal bacterial species. Computational simulations suggest that the removal of neutrophil and epithelial cell derived anti-microbial inhibitions, separately and together, on commensal bacterial regrowth promote recovery and minimize colonic inflammatory pathology. Simulation results predict a decrease in colonic inflammatory markers, such as neutrophilic influx and Th17 cells in the colonic lamina propria, and length of infection with accelerated commensal bacteria re-growth through altered anti-microbial inhibition. Computational modeling provides novel insights on the therapeutic value of repopulating the colonic microbiome and inducing regulatory mucosal immune responses during C. difficile infection. Thus, modeling mucosal immunity-gut microbiota interactions has the potential to guide the development of targeted fecal transplantation therapies in the context of precision medicine interventions.

摘要

艰难梭菌感染与广谱抗生素的使用有关,会引发强烈的炎症反应,导致医院获得性腹泻、结肠炎甚至死亡。为了更好地了解艰难梭菌感染从起始到扩散再到消退过程中黏膜免疫的动态变化,我们构建了一个针对该细菌的黏膜免疫反应计算模型。该模型使用来自艰难梭菌感染小鼠模型的数据进行校准。该模型表明,辅助性T细胞17(Th17)效应反应在结肠固有层和肠腔共生细菌群体清除艰难梭菌及结肠病理过程中起关键作用,而调节性T(Treg)细胞反应与恢复阶段相关。此外,炎症上皮细胞和活化中性粒细胞在艰难梭菌感染时产生的抗菌肽会抑制有益共生细菌的重新生长。计算模拟表明,分别或共同去除中性粒细胞和上皮细胞来源的对共生细菌重新生长的抗菌抑制作用,可促进恢复并将结肠炎症病理降至最低。模拟结果预测,通过改变抗菌抑制作用加速共生细菌重新生长,结肠炎症标志物(如中性粒细胞浸润和结肠固有层中的Th17细胞)会减少,感染时间也会缩短。计算建模为艰难梭菌感染期间重新填充结肠微生物群和诱导调节性黏膜免疫反应的治疗价值提供了新见解。因此,对黏膜免疫-肠道微生物群相互作用进行建模有可能在精准医学干预背景下指导靶向粪便移植疗法的开发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3fe/4521955/0fee11931129/pone.0134849.g001.jpg

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