Researching from mutations to global change
We like to call our lab "interdisciplinary and non-traditional", as we approach the science with a core question and mission of understanding and preserving nature, and we use different disciplines to address it. This also means that we need to collaborate, so we often conduct large projects together, Below some of our latest lines of work depicted at different scales:
Global level: Biodiversity and community niche modeling
Studying species records from the last centuries, we have learned that many species have already lost the race against extinction, and the majority have shrunk their geographic distribution range. Some of these (species or populations) extinctions are direct influence from us, e.g. habitat destruction or hunting, but others are rather indirect, i.e. species cannot keep up adapting to changing atmospheric and oceanic conditions. However, these models have often been conducted at low spatial resolution. A recent aim of the lab has been to use abundant citizen-science species sighting records (GBIF.org, inaturalist.org, Fig.), and images of the Earth and maps of weather records (NAIP, Google Earth Engine, Fig). Lauren recently showed that we can associate plant species presence in California with certain "habitat looks" using deep learning (Gillespie, Ruffley, Exposito-Alonso, 2022 bioRxiv). This may be a key new approach to map species and communities shifts over time.
Observations of plant species from GBIF.org (left), and high-resolution imagery of ecosystems (right) from NAIP.
(from Gillespie, Ruffley, Exposito-Alonso, 2022 bioRxiv)
Species level: Studying species ranges and the loss of genetic diversity
Community changes like the ones described above have an impact on reducing geographic ranges of species, and this leads to genetic diversity loss. Adaptation will become harder as for those species that shrink geographically and lose genetic diversity, commencing a vicious extinction circle.
In the lab, we have worked with public databases of genetic information (NCBI), amasing ~10,000 georeferred samples of individuals from 20 plants and animal species. With this, we want to understand general principles of how within-species variation is structured in space, and how it may be loss with geographic shifts (Exposito-Alonso et al. 2022 Science).
Records of historic specimens such as herbaria are ideal "genetic snapshots" in time that may one day allow us to validate theories of genetic diversity loss.
In the past, Moi studied a 400-year-old lineage of Arabidopsis thaliana that was isolated in North America. We identified over 5,000 new mutations, some of which generated novel morphological differences likely related to adaptation (Exposito-Alonso et al. 2018 PLOS Genetics). Although we expect that pre-existing genetic diversity will be key to maintain, this work showed that even large organisms such as plants could, even at contemporary time-scales, evolve and adapt from new mutations.
Genetic diversity loss projections using the "mutations-area relationship" theory (left).Simulations of population loss in space (right) (Exposito-Alonso et al., 2022 Science).
Population level: Predicting short-term genetic evolution across climates
The field of evolutionary biology is transforming from a historical science, where one studies past patterns of diversification to learn biological principles, to a predictive science, where we apply those principles to forecast the future. In the past, we have pioneered the development of bioinformatic tools to study how much climate will change locally for different populations of a species, and how many potentially useful genetic variants they harbor to face new climates (Exposito-Alonso et al. 2018 Nature Ecology Evolution, 2019 Nature). The next challenge to realistically project the fate of populations is to account for the stochasticity of demography during adaptation, and for the myriad of complex combinations of adaptive genetic variants that give the wide gradient of plant fitness values observed in nature.
In order to empirically test models of short-term responses of organisms to climate, we created a community of about 100 researchers to conduct large-scale real-time evolution experiments in 45 locations ranging from the Mediterranean to Scandinavia, and California to Israel. This crowd-sourced project, called “Genomics of rapid Evolution to Novel Environments” (GrENE-net.org, Fig. left) follows an evolve and resequence approach for live monitoring of genetic evolution over time. Combining this data with predictive population genetic models will allow me to robustly assess the predictability of rapid plant evolution to climate change.
Organismal level: Dissecting plant ecological strategies coping with water
Ultimately, whether plants adapt or perish to the new climatic conditions, will depend on the ecophysiological strategies they have at their disposal. With Arabidopsis thaliana as a model, we are comprehensively studying traits that may be related to drought adaptation, and disentangling their joint or separate genetic architectures. Some of these traits will aid seasonal adaptation, some general resilience, and extremity or variability of future climate may favor non-compatible phenotype combinations or different strategies over time (Exposito-Alonso et al 2018 Evolution).
Examples of Arabidopsis survivor and extinct genotypes in outdoor field experiments simulating low rainfall (Exposito-Alonso et al. 2019 Nature)
Case study of a genome-wide association study of Arabidopsis thaliana ecotypes containing natural genetic polymorphisms scored for drought resilience (Exposito-Alonso et al 2018 Nature Ecology Evolution)
Gene level: Polygenic engineering of drought adaptive variants
Genetically rescue plants from extinction is challenging. We think the reasons are twofold: Overall plant performance in natural conditions is expected to be determined by hundreds, if not thousands, of genetic players (most of which we don’t know!). In addition, the physiological pathways relevant for climate adaptation known for one plant species might may be different in other species. This realization lead us to use our evolutionary and probabilistic understanding of adaptation to develop molecular tools to speed up adaptation. Because new mutations are the ultimate fuel of adaptation, we are leveraging CRISPR as a mutagenesis technology informed by deep learning models that can tell us what mutations in the genome would be most likely to provide an advantage under future stressful environments. By growing engineered plants in drought conditions in the lab, we can test our ability to predict fitness effects of mutations as well as evaluate whether genetic engineering strategies could be potentially used in the future to prudently aid the conservation of species in critical danger.