A transcriptional signature accurately identifies Aspergillus Infection across healthy and immunosuppressed states.


Journal Article

Invasive aspergillosis (IA) is a major cause of critical illness in immunocompromised (IC) patients. However, current fungal tests are limited. Disease-specific gene expression patterns in circulating host cells show promise as novel diagnostics, however it is unknown whether such a 'signature' exists for IA and the effect of iatrogenic immunosuppression on any such biomarkers. Male BALB/c mice were separated into 6 experimental groups based on Aspergillus fumigatus inhalational exposure and IC status (no immunosuppression, cyclophosphamide, and corticosteroids). Mice were sacrificed 4 days postinfection. Whole blood was assayed for transcriptomic responses in peripheral white blood cells via microarray. An elastic net regularized logistic regression was employed to develop classifiers of IA based on gene expression. Aspergillus infection triggers a powerful response in non-IC hosts with 2718 genes differentially expressed between IA and controls. We generated a 146-gene classifier able to discriminate between non-IC infected and uninfected mice with an AUC of 1. However, immunosuppressive medications exhibited a confounding effect on this transcriptomic classifier. After controlling for the genomic effects of immunosuppression, we were able to generate a 187-gene classifier with an AUC of 0.92 in the absence of immunosuppression, 1 with cyclophosphamide, and 0.9 with steroids. The host transcriptomic response to IA is robust and conserved. Pharmacologic perturbation of the host immune response has powerful effects on classifier performance and must be considered when developing such novel diagnostics. When appropriately designed, host-derived peripheral blood transcriptomic responses demonstrate the ability to accurately diagnose Aspergillus infection, even in the presence of immunosuppression.

Full Text

Duke Authors

Cited Authors

  • Steinbrink, JM; Zaas, AK; Betancourt, M; Modliszewski, JL; Corcoran, DL; McClain, MT

Published Date

  • May 2020

Published In

Volume / Issue

  • 219 /

Start / End Page

  • 1 - 12

PubMed ID

  • 32165060

Pubmed Central ID

  • 32165060

Electronic International Standard Serial Number (EISSN)

  • 1878-1810

Digital Object Identifier (DOI)

  • 10.1016/j.trsl.2020.02.005


  • eng

Conference Location

  • United States