Pathogen detection and microbial community compositions during fungal infections

Abstract

The kingdom fungi is crucial for life on earth and is highly diverse. Yet fungi are challenging to characterize. They can be difficult to culture and may be morphologically indistinct in culture. They can have complex genomes of over 1 Gb in size but are underrepresented in whole genome sequence databases. Overall their description and analysis lags far behind other microbes such as bacteria. At the same time, classification of species via high throughput sequencing without prior purification is increasingly becoming the norm for pathogen detection, microbiome studies, and environmental monitoring. However, standardized procedures for characterizing unknown fungi from complex sequencing data have not yet been established. The first two papers of this thesis explore the use of nanopore sequencing technologies for fungal pathogen identification (Chapters 2 & 3). In Chapter 2, I conducted a proof-of-concept study assessing shotgun metagenomics using nanopore sequencing technology for the detection of fungal wheat pathogens. I sampled wheat leaves with disease symptoms as well as healthy plants from an experimental field, and performed DNA sequencing of leaf extracts followed by data analysis. The results indicated that this method is robust and capable of diagnosing three major fungal diseases. I then trialled this strategy for detection of the clinical fungal pathogen Pneumocystis jirovecii (Chapter 3). Together with collaborators, I performed shotgun metagenomics on lung fluid samples from three healthy individuals and three patients with confirmed Pneumocystis infections. I applied a similar bioinformatics pipeline for identification and compared the results with other classification pipelines. The pathogen species was always identified in the samples from patients with confirmed P. jirovecii infection but not in the negative control samples. During these studies, I noticed some limitations of the shotgun metagenomics strategy and inconsistency in results arising from different classification strategies. To overcome these problems, I designed a method development project to try to improve fungal classifications by benchmarking different strategies on a mock fungal community dataset (Chapter 4). Several papers have critically assessed many algorithms for species classification on simulated datasets or bacterial community datasets, but comparisons of sequencing strategies for complex fungal communities using real data and different identification pipelines are rare. I identified key factors that influence the accuracy of classifications, both for mock community datasets and public datasets. Optimisation of these methods also lead to more accurate community composition analysis. These results provide guidelines for the design of sequence-based community analysis for fungal species. In Chapter 5, I reviewed the literature around the technological advances that are being applied to plant biosecurity and discussed the potential application of sequencing-based strategies. I overviewed the pros and cons of different sequencing-based strategies for studying fungi, discussed potential future directions and challenges for the method development, and listed the potential fields of fungal biology that could benefit from sequencing technologies. Show more