Ecological rules governing helminth-microparasite coinfection
Ecological rules governing helminth-microparasite coinfection
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DOI：
10.1073/pnas.0707221105
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发表时间：
2008-01-15
影响因子：
11.1
通讯作者：
Graham, Andrea L.
中科院分区：
文献类型：
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作者：
Graham, Andrea L.
多种寄生虫共同感染宿主具有重要的流行病学和临床意义。然而，影响的方向和程度在不同的系统之间有很大的差异，到目前为止，还没有一个通用的框架来解释这种差异。群落生态学在解决生物医学中的这类问题上具有巨大的应用潜力。在这里，对实验室小鼠54个实验数据的荟萃分析表明，基本的生态规则支配着广泛的寄生虫类群的共同感染的结果。具体而言，资源为基础的（“自下而上”）和捕食者为基础的（“自上而下”）控制机制相结合，以确定寄生虫种群大小蠕虫共感染的主机。当一种引起贫血的蠕虫与一种需要宿主红细胞的微寄生虫配对时，共感染实施了自下而上的控制（导致微寄生虫密度降低）。与此同时，合并感染损害了免疫系统对微寄生虫的自上而下的控制：蠕虫诱导的炎症细胞因子干扰素（IFN）-γ的抑制越大，微寄生虫密度的增加就越大。这些结果表明，微寄生虫人口增长将是最爆炸性的基础蠕虫不施加资源限制，但强烈调制IFN-γ的反应。令人惊讶的是，简单的规则和生态框架内，分析生物医学数据，从而出现从这个数据集的分析。通过这种跨学科的透镜，预测合并感染的结果可能变得容易处理。
Coinfection of a host by multiple parasite species has important epidemiological and clinical implications. However, the direction and magnitude of effects vary considerably among systems, and, until now, there has been no general framework within which to explain this variation. Community ecology has great potential for application to such problems in biomedicine. Here, metaanalysis of data from 54 experiments on laboratory mice reveals that basic ecological rules govern the outcome of coinfection across a broad spectrum of parasite taxa. Specifically, resource-based ("bottom-up") and predator-based ("top-down") control mechanisms combined to determine microparasite population size in helminth-coinfected hosts. Coinfection imposed bottom-up control (resulting in decreased microparasite density) when a helminth that causes anemia was paired with a microparasite species that requires host red blood cells. At the same time, coinfection impaired top-down control of microparasites by the immune system: the greater the helminth-induced suppression of the inflammatory cytokine interferon (IFN)-gamma, the greater the increase in microparasite density. These results suggest that microparasite population growth will be most explosive when underlying helminths do not impose resource limitations but do strongly modulate IFN-gamma responses. Surprisingly simple rules and an ecological framework within which to analyze biomedical data thus emerge from analysis of this dataset. Through such an interdisciplinary lens, predicting the outcome of coinfection may become tractable.