Interaction between climatic variation and pathogen diversity shape endemic disease dynamics in the agricultural settings

Abstract

Recurring outbreaks caused by endemic pathogens continue to pose problems in managing plant, wildlife, livestock and human health. Understanding how these outbreaks unfold and what drives the variability in disease epidemics across space and time is less understood, especially in the agricultural settings. In this study, we investigated the contribution of pathogen genetic diversity, climatic variation and their interaction towards disease dynamics, with an integrative approach grounded on multitype, high resolution sequencing data and analysis techniques. This investigation was carried out for bacterial spot disease epidemic by surveying tomato fields for bacterial pathogen (Xanthomonas perforans) across southeastern US over a span of three years. The strength of epidemic severity varied across space and time in the agricultural fields. Disease severity was positively associated with strain diversity, and was linked to environmental fluctuations, specifically, large variation and extreme changes in certain climatic factors. Strain-resolved metagenomics approach revealed that co-existence of multiple pathogen lineages was common in the individual fields, although accompanied by differential lineage dynamics. The co-occurring lineages displayed environmentally dependent fitness contributions. By tracing allelic frequencies in pathogen populations across temporal scales, we find evidence for asynchronous allele cycling across seasons, hinting at the presence of adaptive single-nucleotide polymorphisms (SNPs) being polymorphic in space in response to seasonality. Despite this pathogen heterogeneity, we identified positively selected loci under parallel evolution, which may explain the nature of selection pressures experienced by the pathogen. While single pathogen lineage is assumed to dominate the host in the agricultural settings, our findings challenge this notion by demonstrating genetic diversity in the pathogen population observed within a single field and linking it to the disease dynamics. Our results explain the role of pathogen genetic diversity, climate-dependent compositional dynamics, and differential fitness contributions in dictating the variability of disease epidemics in the agricultural settings. Such findings will be invaluable for building predictive models in disease epidemiology. Our high-resolution combinatorial approach exploiting high- resolution sequence data, metadata types and analysis tools, is general enough to finely investigate disease epidemics at large scales in diverse case-studies concerning plant, animal and human health.