Repetto, L. | Search bioRxiv

bioRxiv2023

Characterization of Ralstonia pseudosolanacearum diversity and screening host resistance to manage bacterial wilt in South Asia

In South Asia, bacterial wilt pathogens in the Ralstonia solanacearum species complex (RSSC) impose major constraints on eggplant, tomato, and pepper production. To improve the efficacy of bacterial wilt management, the goals of this study were to (1) conduct a survey of RSSC pathogens in Bangladesh and Nepal, (2) characterize the genetic diversity of these isolates, and (3) screen 37 tomato, eggplant, and pepper accessions for resistance to six representative isolates from South Asia. We isolated 99 isolates from Bangladesh and 20 isolates from Nepal and determined that all are phylotype I isolates of the Ralstonia pseudosolanacearum species. We sequenced and assembled draft genomes for 25 isolates. Phylogenomic analyses suggest that there is a wide diversity of endemic phylotype I isolates in South Asia, and possible introductions of two clonal phylotype I lineages into Bangladesh and Nepal. We contextualize our newly described isolates based on prior reports of RSSC diversity in South Asia and global reports of RSSC pathogens on eggplant and pepper. Greenhouse trials revealed multiple tomato, eggplant, and pepper accessions that exhibit promising levels of resistance to six phylotype I isolates from South Asia.

microbiology↗

bioRxiv2019

Using genetics to disentangle the complex relationship between food choices and health status

Despite food choices being one of the most important factors influencing health, efforts to identify individual food groups and dietary patterns that cause disease have been challenging, with traditional nutritional epidemiological approaches plagued by biases and confounding. After identifying 302 individual genetic determinants of dietary intake in 445,779 individuals in the UK Biobank study, we develop a statistical genetics framework that enables us, to directly assess the impact of food choices on health outcomes. We show that the biases which affect observational studies extend also to GWAS, genetic correlations and causal inference through genetics, which can be corrected by applying our methods. Finally, by applying Mendelian Randomization approaches to the corrected results we identify some of the first robust causal associations between eating patterns and cancer, heart disease, obesity, and several other health related risk factors, distinguishing between the effects of specific foods or dietary patterns.

genetics↗