Computational approaches for discovery of common immunomodulators in fungal infections: towards broad-spectrum immunotherapeutic interventions.
Computational approaches for discovery of common immunomodulators in fungal infections: towards broad-spectrum immunotherapeutic interventions.
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DOI：
10.1186/1471-2180-13-224
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发表时间：
2013-10-07
期刊：
影响因子：
4.2
通讯作者：
Murali TM
中科院分区：
文献类型：
--
作者：
Kidane YH;Lawrence C;Murali TM
真菌是人类病原体的第二大类型。侵袭性真菌病原体是临床环境中威胁生命的感染的主要原因。对宿主的毒性和耐药性是与现有抗真菌剂相关的两个主要有害问题。增加宿主对真菌病原体的耐受性和/或免疫力具有缓解这些问题的潜力。宿主的耐受性可以通过调节免疫系统来改善，使得其在所有方面（从病原体的识别到它们从宿主中的清除）更迅速和更有力地响应。了解在尝试真菌暴露、定殖和/或入侵期间受到干扰的生物学过程和基因将有助于指导内源性免疫调节剂和/或激活宿主免疫应答的小分子（如专用佐剂）的鉴定。在这项研究中，我们提出了计算技术和方法，使用公开的转录数据集，预测免疫调节剂，可能会对多种真菌病原体。我们的研究分析了来自宿主细胞暴露于五种真菌病原体的数据集，即互隔交链孢菌，烟曲霉，白色念珠菌，肺孢子虫jirovecii，和Stachybocysteinchartarum。我们观察到宿主对A.和C.白色念珠菌我们的分析确定了这两种病原体持续干扰的生物过程。这些过程包含免疫应答诱导基因如MALT 1、SERPINE 1、ICAM 1和IL 8，以及免疫应答抑制基因如DUSP 8、DUSP 6和SPRED 2。我们假设这些基因属于一组常见的免疫调节剂，它们可能被激活或抑制（激动或拮抗），以使宿主对A.和C.白色念珠菌我们的计算方法和这里描述的方法现在可以应用到新生成的或扩展的数据集，进一步阐明其他药物靶点。此外，确定的免疫调节剂可用于产生实验可检验的假设，这可能有助于发现广谱免疫干预。我们所有的结果都可以在以下补充网站上获得：http://bioinformatics.cs.vt.edu/~murali/supplements/2013-kidane-bmc
Fungi are the second most abundant type of human pathogens. Invasive fungal pathogens are leading causes of life-threatening infections in clinical settings. Toxicity to the host and drug-resistance are two major deleterious issues associated with existing antifungal agents. Increasing a host’s tolerance and/or immunity to fungal pathogens has potential to alleviate these problems. A host’s tolerance may be improved by modulating the immune system such that it responds more rapidly and robustly in all facets, ranging from the recognition of pathogens to their clearance from the host. An understanding of biological processes and genes that are perturbed during attempted fungal exposure, colonization, and/or invasion will help guide the identification of endogenous immunomodulators and/or small molecules that activate host-immune responses such as specialized adjuvants. In this study, we present computational techniques and approaches using publicly available transcriptional data sets, to predict immunomodulators that may act against multiple fungal pathogens. Our study analyzed data sets derived from host cells exposed to five fungal pathogens, namely, Alternaria alternata, Aspergillus fumigatus, Candida albicans, Pneumocystis jirovecii, and Stachybotrys chartarum. We observed statistically significant associations between host responses to A. fumigatus and C. albicans. Our analysis identified biological processes that were consistently perturbed by these two pathogens. These processes contained both immune response-inducing genes such as MALT1, SERPINE1, ICAM1, and IL8, and immune response-repressing genes such as DUSP8, DUSP6, and SPRED2. We hypothesize that these genes belong to a pool of common immunomodulators that can potentially be activated or suppressed (agonized or antagonized) in order to render the host more tolerant to infections caused by A. fumigatus and C. albicans. Our computational approaches and methodologies described here can now be applied to newly generated or expanded data sets for further elucidation of additional drug targets. Moreover, identified immunomodulators may be used to generate experimentally testable hypotheses that could help in the discovery of broad-spectrum immunotherapeutic interventions. All of our results are available at the following supplementary website: http://bioinformatics.cs.vt.edu/~murali/supplements/2013-kidane-bmc
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