Biomarker-based classification of bacterial and fungal whole-blood infections in a genome-wide expression study

Sepsis is a clinical syndrome that can be caused by bacteria or fungi. Early knowledge on the nature of the causative agent is a prerequisite for targeted anti-microbial therapy. Besides currently used detection methods like blood culture and PCR-based assays, the analysis of the transcriptional response of the host to infecting organisms holds great promise. In this study, we aim to examine the transcriptional footprint of infections caused by the bacterial pathogens Staphylococcus aureus and Escherichia coli and the fungal pathogens Candida albicans and Aspergillus fumigatus in a human whole-blood model. Moreover, we use the expression information to build a random forest classifier to determine if the pathogen is bacterial, fungal or neither of the two. After normalizing the transcription intensities using stably expressed reference genes, we filtered the gene set for biomarkers of bacterial or fungal blood infections. This selection is based on differential expression and an additional gene relevance measure. In this way, we identified 38 biomarker genes, including IL6, SOCS3, and IRG1 which were already associated to sepsis by other studies. Using these genes, we trained the classifier and assessed its performance. It yielded a 96% accuracy (sensitivities >93%, specificities >97%) for a 10-fold stratified cross-validation and a 92% accuracy (sensitivities and specificities >83%) for an additional dataset comprising Cryptococcus neoformans infections. Furthermore, the noise-robustness of the classifier suggests high rates of correct class predictions on datasets of new species. In conclusion, this genome-wide approach demonstrates an effective feature selection process in combination with the construction of a well-performing classification model. Further analyses of genes with pathogen-dependent expression patterns can provide insights into the systemic host responses, which may lead to new anti-microbial therapeutic advances. Analysis of innate immune activation on the basis of gene expression of whole blood cells during ex vivo whole blood infection with bacterial (Staphylococcus aureus, Escherichia coli) and fungal pathogens (Candida albicans, Aspergillus fumigatus) in comparison to mock-treated blood.

脓毒症（Sepsis）是一种可由细菌或真菌引发的临床综合征。明确致病原本质的早期信息是开展靶向抗微生物治疗的先决条件。除目前常用的血培养（blood culture）及基于聚合酶链式反应（PCR）的检测方法外，针对宿主对感染病原体的转录应答进行分析亦颇具应用前景。本研究旨在探究细菌病原体金黄色葡萄球菌（Staphylococcus aureus）、大肠埃希菌（Escherichia coli）与真菌病原体白色念珠菌（Candida albicans）、烟曲霉（Aspergillus fumigatus）感染人全血模型后的转录特征。
本研究利用基因表达谱信息构建随机森林分类器（random forest classifier），以判定病原体为细菌、真菌或非感染性。通过稳定表达的参考基因对转录强度进行标准化后，我们基于差异表达分析及额外的基因相关性评分，筛选可用于细菌或真菌血流感染的生物标志物基因集。最终筛选得到38个生物标志物基因，包括此前其他研究已证实与脓毒症相关的IL6、SOCS3及IRG1。
利用上述基因训练分类器并评估其性能：在10折分层交叉验证中，分类器准确率达96%（灵敏度>93%，特异度>97%）；针对包含新型隐球菌（Cryptococcus neoformans）感染的额外数据集，其准确率达92%（灵敏度与特异度均>83%）。此外，该分类器的抗噪性表明其对新病原体数据集亦具备较高的正确分类率。
综上，本全基因组范围的分析方法既实现了高效的特征筛选流程，又构建了性能优异的分类模型。对病原体依赖性表达基因的进一步分析可加深对宿主系统性应答机制的理解，有望推动新型抗微生物治疗手段的研发。本研究通过体外全血感染模型，以模拟处理血液为对照，分析细菌（金黄色葡萄球菌、大肠埃希菌）与真菌病原体（白色念珠菌、烟曲霉）感染后全血细胞的基因表达特征，以此探究先天免疫激活状态。