Metagenomic approaches to microbial source tracking
Thesis DisciplineCellular and Molecular Biology
Degree GrantorUniversity of Canterbury
Degree NameMaster of Science
Water sources are susceptible to faecal contamination from animal and human pollution sources. Pollution of our waterways has significant implications on human health, especially from a pathogen perspective. Microbial source tracking (MST) is a promising field which aims to identify the sources of faecal contamination, and thereby allowing for the development of effective management strategies to minimise pollution and the impact on human health. Many of the currently used methods rely on the identification of host-specific markers within the 16S ribosomal RNA (rRNA) gene of bacteria by use of amplification techniques such as polymerase chain reaction (PCR). However, these methods can be limited by sensitivity, quantification, geographical differences and issues of cost which can limit how many markers are evaluated.
Developments in DNA sequencing technologies over the past decade have led to a number of next generation sequencing (NGS) platforms which have a rapid, high throughput approach, resulting in an exponential decrease in the cost of sequencing. This has enabled the development of sequence-based metagenomics, where entire communities from environmental samples can be analysed based on their genetic material. The ability to barcode allows for analysis of multiple samples at once, reducing the cost of sequencing environmental samples even further. This is a promising technique for MST, which has had little investigation to date.
The primary focus of the studies described in this thesis was to evaluate the use of NGS technology through a metagenomic approach. Roche 454 amplicon sequencing was used to sequence a 16S rRNA gene target, amplified from faecal and water samples from various sources in New Zealand. Barcode strategies were incorporated in the amplification design to allow multiple samples to be sequenced simultaneously. A proof-of-concept study initially utilised a small sequence dataset to evaluate a range of analysis tools available. Taxonomic identification and diversity measures were used to evaluate a selection of currently available tools designed for analysing metagenomic data, with the Quantitative Insights Into Microbial Ecology (QIIME) platform decided upon for further studies. A larger study, including 35 faecal samples from 13 difference sources and 10 water samples, resulted in 522,065 raw sequencing reads. Diversity results suggest three phyla, Bacteroidetes, Firmicutes and Proteobacteria, are strongly represented across all faecal sources analysed. Microbial diversity analysis using clustering techniques provided evidence of host source being the largest influence on bacterial diversity, with samples from each source generally clustering together. This technique could not be used to identify sources of contamination sources in water samples as the water samples all clustered separately from the faecal samples. More successful was the use of taxonomic classifications to determine bacteria genera that were potentially specific to one source. Water samples were screened for these genera, with six out of the ten water samples being indicators of either ruminant or human contamination. Faecal and water samples were also analysed for a selection of published 16S rRNA PCR markers, using a computational motif-based search method. Of the twenty motifs screened for, 14 were found to be relatively source-specific for ruminant, human, dog or pig faecal samples, with some cross-reactivity with chicken and possum samples. Using this method, the contamination source for six of the ten water samples was identified, with the remaining four samples found to not have enough sequences to assess with confidence. Both metagenomic strategies produced comparable results which were consistent with previous MST analysis.
This project demonstrates the potential application of next generation sequencing technologies to microbial source tracking, suggesting the possibility this approach to replace existing microbial source tracking methods.