Research

Infected historic samples provide an opportunity to simultaneously study both plant and pathogen genomes. The study of the dynamics of past epidemics and the co-evolution of plant-pathogen interactions is of major relevance since the emergence and re-emergence of plant diseases pose a major threat to food security and indirectly to human health. Additionally, the strength of natural and artificial selection experienced by the plant-pathogen system permits observing changes in allele frequencies in short periods of time. My group has centered on the study of 19th and 20th-century outbreaks caused by Phytophthora infestans, the causal agent of potato late blight (Yoshida et al. 2013, eLife; Yoshida et al. 2014, PLoS Pathogens) and by the fungus Magnaporthe oryzae, the causal agent of rice blast (Latorre et al. 2020, BMC Biology; Latorre et al, 2022, biorXiv) and wheat blast (Latorre et al. 2023, PLoS Biology).

DNA can also be retrieved from thousands years old archaeobotanical remains such as seeds, grains, and cobs, it is possible to study key genomic changes that allow crop domestication and its subsequent spread. Using this approach it is feasible to characterize the genetic diversity of crops prior to the advent of modern agriculture, from where modern cultivars have sampled their alleles. Using an approach that combines ancient genomics with quantitative and population genetics, my group started characterizing the spread of maize in North America and its adaptation to a temperate climate. Using techniques borrowed from breeding (genomic selection) we predicted for the first time polygenic quantitative traits on archaeological remains (Swarts et al. 2017, Science). 

The post-domestication dispersal of crops domesticated in the Americas presents a unique opportunity to study the population history and adaptation of plants. This is the case of potato, which arrived to Europe after the Spanish Conquest. Our group reconstructed in great detail the population history of European potatoes combining present-day and historical genomes. Our work showed how allele frequencies can be used under an admixture graph framework to study the relations between temporally and spatially diverse populations (Gutaker et al, 2019, Nature Ecology and  Evolution). 

Herbaria specimens collections started after the European conquest of the Americas, when globalization, and its subsequent movement of people, plant, goods, diseases, etc, started. Those collections are therefore ideal to characterize plant invasions and colonizations that took place in the last hundreds of years. As a proof-of-principle experiment, we explored the colonization of North America by Arabidopsis thaliana using a dense time-series spanning the 19th and 20th centuries to examine the evolutionary forces that have shaped genetic variation in a recently introduced species. We estimated the long-term nuclear mutation rate and showed that although genetic drift predominates, purifying selection also shapes diversity in this colonizing population (Exposito-Alonso et al., 2018. PLoS Genetics). 

Ancient DNA (aDNA) differs from freshly extracted DNA; it is highly fragmented and the nucleotides are often damaged. We found that the amount of plant DNA in herbarium samples is high, but fragmentation occurs six times faster than in animal remains (Weiß et al., 2016, RSOS). Therefore, we have established laboratory and bioinformatics protocols to deal with very short DNA fragments (Gutaker et al., 2017, BioTechniques). 

For non-model species with small or large genomes diversity is traditionally assessed using restriction‐enzyme‐based sequencing. However, age‐related DNA damage and fragmentation preclude the use of this approach for ancient herbarium DNA. We refined a method that combines reduced‐representation sequencing and hybridization‐capture to overcome this challenge and efficiently compare contemporary and historical specimens (Lang et al., 2020, Molecular Ecology Resources). 

We have compiled in a methodological paper best practices for plant aDNA isolation, preparation for sequencing, bioinformatic processing and authentication (Latorre et al., 2020, Current Protocols in Plant Biology).

Because aDNA studies can only be correctly interpreted if aDNA can be distinguished from modern contamination, we have developed a computational method to authenticate aDNA even from low-coverage experiments (Weiß et al., eLife, 2015).

We have developed nQuire, a statistical framework that distinguishes between diploids, triploids and tetraploids using NGS (Weiß et al., BMC Bioinformatics). nQuire is useful in epidemiological studies of pathogens, artificial selection experiments, and for historical or ancient samples where intact nuclei are not preserved (e.g. Gutaker et al., 2019, Nature Ecology and Evolution).