Fungal biomarker discovery by integration of classifiers

BMC Genomics

Ralf Claus¹  Markus Blaess¹  Daniela Röll²  Rainer König²  Marcus Oswald²  João Pedro Saraiva²  Antje Biering²  Hortense Slevogt³  Cora Assmann³  Tilman Klassert³  Jürgen Löffler⁴  Kristin Czakai⁴ 

[1] Center for Sepsis Control and Care (CSCC), Jena University Hospital;Network Modelling, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute (HKI);Septomics Research Centre, Jena University Hospital;University Hospital Würzburg;

关键词: Immune response;    Systems biology;    Fungal pathogens;    Microarray;    Feature selection;   

DOI  :  10.1186/s12864-017-4006-x

来源: DOAJ

【 摘 要 】

Abstract Background The human immune system is responsible for protecting the host from infection. However, in immunocompromised individuals the risk of infection increases substantially with possible drastic consequences. In extreme, systemic infection can lead to sepsis which is responsible for innumerous deaths worldwide. Amongst its causes are infections by bacteria and fungi. To increase survival, it is mandatory to identify the type of infection rapidly. Discriminating between fungal and bacterial pathogens is key to determine if antifungals or antibiotics should be administered, respectively. For this, in situ experiments have been performed to determine regulation mechanisms of the human immune system to identify biomarkers. However, these studies led to heterogeneous results either due different laboratory settings, pathogen strains, cell types and tissues, as well as the time of sample extraction, to name a few. Methods To generate a gene signature capable of discriminating between fungal and bacterial infected samples, we employed Mixed Integer Linear Programming (MILP) based classifiers on several datasets comprised of the above mentioned pathogens. Results When combining the classifiers by a joint optimization we could increase the consistency of the biomarker gene list independently of the experimental setup. An increase in pairwise overlap (the number of genes that overlap in each cross-validation) of 43% was obtained by this approach when compared to that of single classifiers. The refined gene list was composed of 19 genes and ranked according to consistency in expression (up- or down-regulated) and most of them were linked either directly or indirectly to the ERK-MAPK signalling pathway, which has been shown to play a key role in the immune response to infection. Testing of the identified 12 genes on an unseen dataset yielded an average accuracy of 83%. Conclusions In conclusion, our method allowed the combination of independent classifiers and increased consistency and reliability of the generated gene signatures.

【 授权许可】

Unknown