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Open Access Highly Accessed Methodology article

FungiQuant: A broad-coverage fungal quantitative real-time PCR assay

Cindy M Liu126*, Sergey Kachur1267, Michael G Dwan12, Alison G Abraham3, Maliha Aziz1, Po-Ren Hsueh4, Yu-Tsung Huang5, Joseph D Busch2, Louis J Lamit6, Catherine A Gehring6, Paul Keim126 and Lance B Price1

Author Affiliations

1 Division of Pathogen Genomics, Translational Genomics Research Institute, Flagstaff, AZ, 86011, USA

2 Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, 86011, USA

3 Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21201, USA

4 Departments of Laboratory Medicine and Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan

5 Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan

6 Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA

7 Current address: Ross University School of Medicine, Roseau, Dominica

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BMC Microbiology 2012, 12:255  doi:10.1186/1471-2180-12-255

Published: 8 November 2012

Abstract

Background

Fungal load quantification is a critical component of fungal community analyses. Limitation of current approaches for quantifying the fungal component in the human microbiome suggests the need for new broad-coverage techniques.

Methods

We analyzed 2,085 18S rRNA gene sequences from the SILVA database for assay design. We generated and quantified plasmid standards using a qPCR-based approach. We evaluated assay coverage against 4,968 sequences and performed assay validation following the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines.

Results

We designed FungiQuant, a TaqMan® qPCR assay targeting a 351 bp region in the fungal 18S rRNA gene. Our in silico analysis showed that FungiQuant is a perfect sequence match to 90.0% of the 2,617 fungal species analyzed. We showed that FungiQuant’s is 100% sensitive and its amplification efficiencies ranged from 76.3% to 114.5%, with r2-values of >0.99 against the 69 fungal species tested. Additionally, FungiQuant inter- and intra-run coefficients of variance ranged from <10% and <20%, respectively. We further showed that FungiQuant has a limit of quantification 25 copies and a limit of detection at 5 copies. Lastly, by comparing results from human-only background DNA with low-level fungal DNA, we showed that amplification in two or three of a FungiQuant performed in triplicate is statistically significant for true positive fungal detection.

Conclusions

FungiQuant has comprehensive coverage against diverse fungi and is a robust quantification and detection tool for delineating between true fungal detection and non-target human DNA.