Biology subjects

Yoshida, K.

Publications and source records attributed to Yoshida, K..

57 records · Page 4Linked to original sources

bioRxiv2017

The ash dieback invasion of Europe was founded by two individuals from a native population with huge adaptive potential

Accelerating international trade and climate change make pathogen spread an increasing concern. Hymenoscyphus fraxineus, the causal agent of ash dieback is one such pathogen, moving across continents and hosts from Asian to European ash. Most European common ash (Fraxinus excelsior) trees are highly susceptible to H. fraxineus although a small minority (~5%) evidently have partial resistance to dieback. We have assembled and annotated a draft of the H. fraxineus genome which approaches chromosome scale. Pathogen genetic diversity across Europe, and in Japan, reveals a tight bottleneck into Europe, though a signal of adaptive diversity remains in key host interaction genes (effectors). We find that the European population was founded by two divergent haploid individuals. Divergence between these haplotypes represents the 'shadow' of a large source population and subsequent introduction would greatly increase adaptive potential and the pathogen's threat. Thus, EU wide biological security measures remain an important part of the strategy to manage this disease.

genomics↗

bioRxiv2017

Gene expression polymorphism underpins evasion of host immunity in an asexual lineage of the Irish potato famine pathogen

Outbreaks caused by asexual lineages of fungal and oomycete pathogens are an expanding threat to crops, wild animals and natural ecosystems (Fisher et al. 2012,Kupferschmidt 2012). However, the mechanisms underlying genome evolution and phenotypic plasticity in asexual eukaryotic microbes remain poorly understood (Seidl and Thomma 2014). Ever since the 19th century Irish famine, the oomycete Phytophthora infestans has caused recurrent outbreaks on potato and tomato crops that have been primarily caused by the successive rise and migration of pandemic asexual lineages (Cooke et al. 2012, Yoshida et al. 2013,Yoshida et al. 2014). Here, we reveal patterns of genomic and gene expression variation within a P. infestans asexual lineage by compared sibling strains belonging to the South American EC-1 clone that has dominated Andean populations since the 1990s (Forbes et al. 1997, Oyarzun et al. 1998, Delgado et al. 2013, Yoshida et al. 2013, Yoshida et al. 2014). We detected numerous examples of structural variation, nucleotide polymorphisms and gene conversion within the EC-1 clone. Remarkably, 17 genes are not expressed in one of the two EC-1 isolates despite apparent absence of sequence polymorphisms. Among these, silencing of an effector gene was associated with evasion of disease resistance conferred by a potato immune receptor. These results highlight the exceptional genetic and phenotypic plasticity that underpins host adaptation in a pandemic clonal lineage of a eukaryotic plant pathogen.

plant biology↗

bioRxiv2016

Liberation from equations: An equation-free method reveals the ecological interaction networks within complex microbial ecosystems

O_LIMapping the network of ecological interactions is key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics.\nC_LIO_LIWe developed the sparse S-map method (SSM), which generates a sparse interaction network from a multivariate ecological time-series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non-equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes.\nC_LIO_LIWe showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM whereas, the accuracy of a comparative equation-based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation-based method when datasets were large and the magnitude of observational errors were small. The results were robust to the magnitude of process noise and the functional forms of inter-specific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4-40 week-old) and middle to old age (36-72 week-old) mice.\nC_LIO_LIThe complexity of microbial relationships impedes detailed equation-based modeling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.\nC_LI

ecology↗