Currently, contamination of the indoor environment by fungi is suggested to be a public health problem, although scientific evidence on the causal link is still limited. The monitoring of indoor airborne fungal contamination is a common tool to help understanding the link between fungi in houses and respiratory problems. Classical monitoring methods, based on cultivation and microscopic identification, have some limitations. For example, uncultivable or dead fungi (“unknown” fraction) cannot be identified, although they could have an impact on human health.
In this context, molecular tools seem to be a valuable alternative. In this PhD work, different molecular tools were developed, from simplex to multiplex, to detect and identify indoor airborne fungi. The goal was to improve the detection of fungal contaminants, including the “unknown” fraction, as compared to the currently used classical monitoring methods. The necessary air sampling and DNA extraction protocols, adapted to the downstream molecular monitoring methods have also been developed. Through the application of the developed tools to specific case studies, we aimed to improve the current knowledge on fungal contamination.
At first, we developed a specific ITS-based SYBR®green real-time PCR (qPCR) assay for Aspergillus versicolor, a species frequently observed in indoor air and known to be allergenic. Additionally, an ITS-based qPCR assay was developed for the specific detection of Exophiala jeanselmei, a pathogenic yeast suspected to be a part of the “unknown fraction”. The performance of these qPCR methods was assessed. This comparison demonstrated that SYBR®green qPCR assays can be used as a molecular alternative for monitoring of contaminated samples while eliminating the need for culturing and thereby considerably decreasing the required analysis time.
However, qPCR has some limitations especially concerning the discrimination of genetically close species and multiplexing. The first issue was addressed through the use of post-qPCR high resolution melting (HRM) analysis, providing a proof-of-concept for this approach, using 3 closely related Aspergillus, i.e., A. versicolor, Aspergillus creber and Aspergillus sydowii. This HRM tool will allow a more accurate monitoring of these closely related indoor air contaminants, thereby contributing to an improved insight in the causal link between the specific presence of these species and health issues.
The multiplexing issue was overcome through a Luminex xMAP® assay, developed for the simultaneous detection of the 10 most frequently in indoor air found fungi. All the species identified with the classical method were also detected with the xMAP® assay, however in a shorter time frame, and using less sample material. This assay will improve the communication with the involved medical team and the patient.
To provide scientific evidence for the causal link between indoor airborne fungi and health problems, the full diversity needs however to be identified. This cannot be achieved by using a targeted assay. Therefore, next generation sequencing (NGS) could offer a valuable alternative as an open approach multiplex monitoring method. An NGS-based metagenomics approach was used to investigate the “unknown” agents in air samples of offices in contact with air-conditioning reservoirs and showed the first detection of E. jeanselmei in indoor air. Finally, a metagenomics analysis was performed to investigate the indoor airborne fungal diversity in contaminated residences in Brussels where people with health problems were living. This demonstrated that NGS could contribute to improved data concerning the indoor airborne fungal diversity, as compared to the currently used classical methods.
The methods developed in this PhD work and the insights obtained are a first step for a better understanding of the causal link between indoor airborne fungi and public health.