Next-Generation-Sequencing Genomic and Metagenomic Analysis of Phytopathogenic Prokaryotes

Phytopatology, as a discipline that deals with the complexity of living communities, needs methods to screen what could be considered \u2018useful\u2019 data from \u2018background noise\u2019. Until the Nineties, this was achieved by simplifications that were deemed adequate enough: from Koch\u2019s postulates that require microorganisms to be culturable, to DNA barcoding that assumed genetic markers to be universal and precise enough to distinguish minute differences, to the disease triangle model that mostly downplayed the role of the micro-community context the pathogens find themselves in. With the introduction of whole genome sequencing (WGS) tecnologies in the last thirty years, we started to realise that those simplifications, while not wrong, constituted sufficient but not necessary conditions: some pathogens (e.g. phytoplasmas) are remarkably difficult to cultivate in vitro, DNA structural variations can produce diverse strains without changing markers, and the micro-community can significantly impact on a pathogen\u2019s ability to spread. This work shows, from different perspectives all tied by the use of WGS data and analysis, how a deeper understanding of these complex dynamics can prompt new practical concepts to manage economically impactful plant diseases: The characterisation of Pseudomonas sp. strain Pf4 shows how the most fit strains, both from pathogens and biocontrol agents, derive their qualities from sizable sets of \u2018secondary\u2019 \u2013 but in fact crucial, as we are now aware \u2013 metabolites (SM) gene clusters; The comparison of the biocontrol activity of Pf4 and Pf11 shows that while a wide set of SM clusters is important, the inclusion of such set doesn\u2019t necessarily translate into a \u2018stronger\u2019 control activity, but points to a better adaptability to changing environmental conditions; The use of third-generation WGS, which produces longer (~10,000 nts) reads, was essential to characterise the CRAFRU 12.29 and 14.08 strains \u2013 one producing hypersentive response (HR) on leaves, the other not \u2013, as their difference lies in a transposon-mediated structural variation that would not have been possible to identify with older sequencing methods; Developing the Phytoassembly pipeline contributed to a novel method of obtaining phytoplasma (and other non-culturable organism) genome, which circumvent the laborious in vitro protocols employed so far to obtain similar results; The Phytoassembly pipeline showed its potentiality by not only isolating a Chicory Phyllody (ChP) phytoplasma, but allowing to detect the presence of a companion spiroplasma, later shown to frequently occur together in mixed infections of chicory; Phytoassembly also helped characterising a Cassava Frogskin Disease (CFSD) phytoplasma, which showed some differences from other representative in the group; The spatialisation of the genomic samples from the kiwifruit endophyte populations allows to correlate their spatial and temporal variation to the severity of the symptoms displayed by the plants and the time of Pseudomonas syringae pt. actinidiae (Psa) infection. On the whole, the research projects presented in this work give insights into the greater complexity of microbial genome structure and variation, the dynamics between pathogens and the wider microbial community, the necessity for research methodologies based on more complex data, and the essential role that WGS technologies plays and will play in plant protection research and development

Genomic structural variations affecting virulence during clonal expansion of Pseudomonas syringae pv. actinidiae biovar 3 in Europe

Pseudomonas syringae pv. actinidiae (Psa) biovar 3 caused pandemic bacterial canker of Actinidia chinensis and Actinidia deliciosa since 2008. In Europe, the disease spread rapidly in the kiwifruit cultivation areas from a single introduction. In this study, we investigated the genomic diversity of Psa biovar 3 strains during the primary clonal expansion in Europe using single molecule real-time (SMRT), Illumina and Sanger sequencing technologies. We recorded evidences of frequent mobilization and loss of transposon Tn6212, large chromosome inversions, and ectopic integration of IS sequences (remarkably ISPsy31, ISPsy36, and ISPsy37). While no phenotype change associated with Tn6212 mobilization could be detected, strains CRAFRU 12.29 and CRAFRU 12.50 did not elicit the hypersensitivity response (HR) on tobacco and eggplant leaves and were limited in their growth in kiwifruit leaves due to insertion of ISPsy31 and ISPsy36 in the hrpS and hrpR genes, respectively, interrupting the hrp cluster. Both strains had been isolated from symptomatic plants, suggesting coexistence of variant strains with reduced virulence together with virulent strains in mixed populations. The structural differences caused by rearrangements of self-genetic elements within European and New Zealand strains were comparable in number and type to those occurring among the European strains, in contrast with the significant difference in terms of nucleotide polymorphisms. We hypothesize a relaxation, during clonal expansion, of the selection limiting the accumulation of deleterious mutations associated with genome structural variation due to transposition of mobile elements. This consideration may be relevant when evaluating strategies to be adopted for epidemics management

A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning.

Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10×5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF- β signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors

A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning

Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10x5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF- β signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors