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Biology subjects

Goddard, M. R.

Publications and source records attributed to Goddard, M. R..

2 recordsLinked to original sources

Dynamic gene gain and loss during the divergence of obligate biotrophic powdery mildew pathogens

Powdery mildew outbreaks can result in devastating crop losses across both horticultural and cereal crops. The obligate biotrophic fungi responsible (Erysiphaceae) depend entirely on living plant hosts for survival, extracting nutrients exclusively from living host tissue. Despite their impact, genomic resources for dicot-infecting powdery mildew fungi remain limited, restricting understanding of differentiation in the effector complements that underpin biotrophy across mildew lineages. To address this, we sequenced and assembled genomes from three independent samples each of Podosphaera leucotricha (apple mildew) and P. aphanis sensu lato (strawberry & raspberry mildew). These novel genomes were analysed alongside 44 publicly-available Erysiphaceae genomes to reveal striking inter-species differences in effectorome complements. P. leucotricha encoded the largest predicted effector complement of any powdery mildew pathogen sequenced to date, with major expansions in RALPH and EKA effector families. By contrast, the closely related P. aphanis carried a comparatively small effector repertoire. Together, these data challenge the prevailing view that effector expansion is restricted to monocot-infecting mildews. We observed that the divergence of powdery mildew genera is associated with the contraction of gene families, consistent with a stepwise loss of genes. This pattern was also reflected in the loss of genes that are typically conserved across ascomycete fungi. Taken together our findings highlight the contrasting impact that biotrophic adaptation has had on pathogen genomes, namely the convergent loss of conserved fungal genes alongside the diversification of pathogen effectoromes in response to host immune landscapes.

plant biology↗

Beyond consensus sequence: a quantitative scheme for inferring transmission using deep sequencing in a bacterial transmission model

Genomic surveillance provides a data source complementary to contact tracing to resolve putative transmission chains. However, the role of within-host diversity in transmission is understudied due to a lack of experimental and clinical datasets that capture within-host diversity in both donors and recipients. Here, we assess the utility of deep-sequenced genomic surveillance within a mouse transmission model where the gastrointestinal pathogen Citrobacter rodentium was controllably spread during co-housing of infected and naive animals. We observed that within-host variants were maintained over multiple transmission steps until fixation or elimination. We present a model for inferring the likelihood that a given pair of samples are linked by transmission, by comparing the allelic frequency at variant genomic loci. Our data affirm that within-host single nucleotide variants (iSNVs) can repeatedly pass from donor to recipient along the transmission chain, and the mere sharing of iSNVs between different transmission pairs offers limited confidence in identifying a transmission pair. Beyond the presence and absence of within-host variants, we show that differences arising in the relative abundance of iSNVs can infer transmission pairs with high precision. An important component of our approach is that the inference is based solely on sequence data, without incorporating epidemiological or demographic data for context. Our model, which substantially reduces the number of comparisons a contact tracer needs to consider, may enhance the accuracy of contact tracing and other epidemiological processes, including early detection of emerging transmission clusters.

genomics↗