Development of DNA massive sequencing techniques and Real-Time PCR for the detection, identification and quantitation of Phytophthora spp. in environmental samples.

Abstract

In recent years the increase of global plant trade and human movement has promoted the risk of introduction of invasive plants and exotic pathogens. Biological invasions operate globally and are considered to be the second cause of biodiversity loss after direct habitat alteration and destruction. In this context, Phytophthora is one of the most important and aggressive plant pathogen in agriculture and forestry. Early detection and identification of its pathways are of high importance to minimize the threat that they pose to natural ecosystems. Different molecular-based methods, including real-time PCR and Next Generation Sequencing, have been developed and applied for the detection of plant pathogens in environmental samples. These methods allow fast and accurate pathogen detection and identification even when the inoculum amount is low. Therefore, the main objective of this thesis was the development of a new method for Phytophthora detection in environmental samples starting from extraction of environmental DNA (eDNA) from different sources (soil, roots and water) and different ecosystems. Different studies have applied High Throughput Sequencing (HTS) for the detection of Phytophthora species in soil samples, but not, to date, for water. In the Chapter 3, genus-specific primers were adapted to assess Phytophthora species diversity in natural ecosystems using high-throughput amplicon pyrosequencing of eDNA from soil and water environments, based in the polymorphic and widely accepted barcoding target Internal Transcribed Spacer 1 (ITS1). The assay was validated with a control reaction with DNA of pure cultures. The objectives raised and developed of this study were: a) as main objective, development and application of HTS (High Throughput Sequencing) of Phytophthora-specific PCR amplicons to investigate the presence of Phytophthora in soil samples from different plant communities in natural forests, plantations and aquatic environments in the north of Spain; b) optimization of the conditions for emPCR amplification in order to obtain the best results in the pyrosequencing run; c) development of a bioinformatics pipeline for NGS data, focusing in the optimization of a barcoding threshold value to separate Molecular Operational Taxonomic Units (MOTUs). Different score coverage threshold values were tested for optimal Phytophthora species separation in the bioinformatics analyses. Clustering at 99 % was the best criteria to separate most of the Phytophthora species. Multiple Molecular Operational Taxonomic Units (MOTUs) corresponding to 36 distinct Phytophthora species were amplified in the environmental samples. Pyrosequencing of amplicons from soil samples revealed low Phytophthora diversity (13 species) in comparison with the 35 species detected in water samples. Thirteen of the MOTUs detected in rivers and streams did not show significant matches to sequences in international sequence databases, revealing that eDNA pyrosequencing is a useful strategy to assess Phytophthora species diversity in natural ecosystems. Once the technique was developed and validated, another objective was proposed in Chapter 2, focused on the oak decline. The evergreen holm oak (Quercus ilex) is the most representative tree species in the Iberian Peninsula and the main tree in oak-rangeland ecosystems (dehesas). Oak decline in non-calcareous soils in south-western Spain has been associated with Phytophthora cinnamomi for decades. However, other Phytophthora species such as P. quercina and P. psychrophila have been associated with Quercus decline in the eastern part of Spain where calcareous soils are predominant. With the aim of investigating the involvement of Phytophthora spp. in oak decline in eastern Spain, two forests in different geographical areas (Alcoi and Vallivana) were selected as sampling sites. Soil and root samples were analysed in parallel by amplicon pyrosequencing and real-time PCR. Metabarcoding analyses showed Phytophthora