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Blood stream infections can be caused by several pathogens such as viruses, fungi and bacteria and can cause severe clinical complications including sepsis. Delivery of appropriate and quick treatment is mandatory. However, it requires a rapid identification of the invading pathogen. The current gold standard for pathogen identification relies on blood cultures and these methods require a long time to gain the needed diagnosis. The use of  in situ  experiments attempts to identify pathogen specific immune responses but these often lead to heterogeneous biomarkers due to the high variability in methods and materials used. Using gene expression profiles for machine learning is a developing approach to discriminate between types of infection, but also shows a high degree of inconsistency. To produce consistent gene signatures, capable of discriminating fungal from bacterial infection, we have employed Support Vector Machines (SVMs) based on Mixed Integer Linear Programming (MILP). Combining classifiers by joint optimization constraining them to the same set of discriminating features increased the consistency of our biomarker list independently of leukocyte-type or experimental setup. Our gene signature showed an enrichment of genes of the lysosome pathway which was not uncovered by the use of independent classifiers. Moreover, our results suggest that the lysosome genes are specifically induced in monocytes. Real time qPCR of the identified lysosome-related genes confirmed the distinct gene expression increase in monocytes during fungal infections. Concluding, our combined classifier approach presented increased consistency and was able to “unmask” signaling pathways of less-present immune cells in the used datasets.

Combination of Classifiers Identifies Fungal-Specific Activation of Lysosome Genes in Human Monocytes