Moi Lab

Our lab works at the interface of plant ecology, evolutionary genetics, and bioinformatics. We develop new computational methods and analyze genomic, experimental, and climate data to understand the dynamics of adaptation of populations and the mutations driving them. We then use this knowledge to forecast species evolutionary outcomes under global change and figure out how to aid adaptation in the future to avoid species extinctions.

An important current project of the lab, co-coordinated with François Vasseur (CNRS) and Niek Scheepens (U. Tübingen), is a distributed global evolution experiment, GrENE-net.org, which aims to test the evolvability of the plant Arabidopsis thaliana in many climates around the world.

To learn more about the lab visit www.moisesexpositoalonso.org

Lab Tabs

Our Research

Monitoring biodiversity & genetic variation

Studying species records from the last centuries, we have learned that many species have already lost the race against extinction. Some of these 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. Species adapt and evolve by the process of natural selection over genetic variation. However, this is harder as species shrink geographically and lose genetic diversity, commencing a vicious extinction circle. To work against this, we need to have a good understanding on the past and current status of a species genetic diversity and geographic distributions. In the lab, we work with public databases of genetic information (NCBI), species sighting records (GBIF.org, inaturalist.org, Fig. right, top), and images of the Earth and maps of weather records (Landsat missions, Google Earth Engine, Fig. right, bottom). We integrate these data using deep learning approaches to generate geographic visualizations of species composition and diversity and their changes over time, to find genetic coldspots and recent diversity losses.

Records of historic specimens such as herbaria are great "genetic snapshots" in time that allow us to track such changes in time and learn about evolutionary principles. For example, in the past we have studied a 400-year-old lineage of A. 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). This shown that even large organisms such as plants could, even at contemporary time-scales, rapidly evolve and adapt from new mutations.

Predicting genetic evolution to climate change

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 bioRxiv). 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.

Polygenic engineering of adaptive variants

Genetically rescue plants from extinction is challenging. I 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 powerful 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 (including humans!).

News

Tweets

Lab PI

Moises Exposito-Alonso

Staff Associate

Plant Biology

Carnegie Institution for Science

Profile

Affiliation

Carnegie Affiliation:

DPB Employees

DPB Affiliation:

DPB Faculty

Labs:

Moi Lab

Teaching

Calendar

Websites

Lab Members

Nicolas Alexandre (Visiting Scholar)
My research focuses on understanding how evolution shapes ecology using population and quantitative genetics in order to link phenotypic variation (P) with genotypic variation (G) and fitness (F) in wild populations of hummingbirds. I am particularly interested in the evolution of complex, quantitative traits. The bills of hummingbirds represent an excellent model for studying adaptive evolution in quantitative traits given their widespread distribution and feasible collection, and recently available genomic resources. One method I use to characterize the genomic architecture of phenotypic variation between species is to leverage phenotypic variation within species. Then, through fine-mapping of natural genome-wide variation, I am localizing putatively causal loci segregating as polymorphisms (SNPs or structural variants) that underlie phenotypic variation for bill shape. Heritable variation for beak shape has been leveraged to identify loci in various passerines such as Darwin’s finches, great tits, and bullfinches. My primary research focuses on developing the Broad-tailed hummingbird (BTH; Selasphorus platycercus) as a model system as a candidate lineage in which to explore the genetic architecture of bill shape. However, I work with various species in the Americas.
Lisa Couper (Graduate Student)
I am a graduate student in the Biology department interested in infectious disease ecology. My research focuses on how climate change affects vector-borne disease dynamics, and how vectors may adapt to changing temperatures. My prior work has focused on tick microbial ecology, and the drivers of variation in Lyme disease incidence in the US. I completed my undergrad at UNC in Environmental Science where I studied the effects of human land use on coastal phytoplankton communities.
Lucas Czech (Postdoctoral Fellow)
Lucas is a computer scientist and bioinformatician who is interested in analyzing and visualizing biological data. His goal is to develop and apply novel methods that help biologistis and ecologists to make sense of life and nature. Hence, he focusses on algorithm and software development, with a particular interest in machine learning and high performance computing. During his studies, he also gained experience in areas such as speech recognition, cryptography, and physics. During his PhD, he worked on novel methods to analyze metagenomic data in a phylogenetic context. In the Moi lab, his first postdoc position, he wants to develop and implement new ways to integrate biological data with other sources of information such as satellite images. His goal is to help understanding the patterns and interactions that govern ecology and evolution, from mutations in DNA to transformations of landscapes, in the global context of climate change.
Joel Erberich (Graduate Student)
I am a grad student studying genetic and stomatal variation and development in plants at the Bergmann lab. I completed my undergrad at UCSD in human biology, where I studied limb evo-devo in the Cooper lab. Since my childhood, I've been fascinated by the diversity of life.
Lauren Gillespie (Graduate Student)
I am a graduate student in the Computer Science department interested in bio-inspired machine learning. My work aims to apply cutting-edge ML technologies to climate change-related ecological problems in concert with developing better ML methods for working with complex ecological and genomic data. I have previously worked on interesting problems in evolutionary computation, computer vision, and high performance computing and I completed my undergrad at Southwestern University where I studied computer science and chemistry.
Shannon Hateley (Postdoctoral Fellow)
Shannon Hateley is a computational biologist interested in using genomics methods to understand organismal response to climate change. Prior to coming to Carnegie, she was a senior staff scientist at AncestryDNA. Dr. Hateley obtained her Ph.D. in Molecular and Cell Biology from University of California, Berkeley, with a designated emphasis in Computational and Genomics Biology. During her studies, she worked both at lab bench and computationally on gene expression, building tools for RNA transcript characterization, and detecting disease-associated genetic variants. Dr. Hateley also holds a B.A. from St. John’s College where she studied the Great Books. She spends her free time on dance, tinkering, and climate organizing.
Clara Kieschnick (Visitng Scholar)
I am an undergraduate studying biology and comparative literature. At Moi Lab, my research focuses on genetic adaptations to evolving climate conditions. Outside of the lab, I am committed to conservation and sustainability, and I hope to one day incorporate genetics into a career related to restoration ecology or sustainable agriculture.
Laura Leventhal (Graduate Student)
Laura earned her BA in Biology from the College of Wooster and completed an undergraduate research thesis examining pollen movement in different spatial aggregations at different points in time in Brassica rapa. Since graduating, Laura has done research for the Chicago Botanic Gardens, the Bureau of Land Management, and the National Park Service. She has also worked for the Schmitt, Gremer, Strauss, and Maloof labs at UC Davis, where she keyed in on her interest in evolutionary ecology. She is excited about studying plant adaptations to climate change and is particularly fascinated by the complicated pressures faced by a species at their range limit. At Moi Lab, Laura is hoping to learn more about genetic tools and a global approach to evolutionary ecology.
Veronica Pagowski (Graduate Student)
Veronica is a first year PhD student at Hopkins Marine Station of Stanford University who completed her BSc in Oceanography and Biology at the University of British Columbia in 2019. As an undergraduate student, she pursued a thesis on the distribution and phylogeny of a common fish virus in the Pacific Northwest, helped organize oceanographic fieldwork, and worked as a science educator in several settings. In the Moi lab, she hopes to explore how predictive models of species adaptation can be applied to marine environments.
Yunru Peng (Lab Technician)
I graduated from UC Berkeley in May 2019 with a BSc in microbial biology and an emphasis in computational genomics. I am interested in genetics and genomics. During my undergraduate, I used a novel CRISPR/Cas9 multiplex editing technique to modify soybean genome, aiming to enhance the plant's immunity. I also worked in projects related to microbial and zebrafish genetics. I think of genetics as a unifying lens to examine the rich biodiversity and evolutionary history on Earth. At Moi Lab, I will examine the genomes of evolving populations and develop CRISPR tools for polygenic editing. Outside of research, I like books, games, and the outdoors.
Alanna Pyke (Graduate Student)
Alanna Pyke is a first year Genetics PhD student at Stanford. She graduated in 2019 with a B.S. in Molecular, Cellular, and Developmental Biology from Yale University. She worked in the Caccone Lab for 2.5 years at Yale and was involved in multiple projects that centered around conservation and population genetics. She spent a year after graduating as a post-baccalaureate student at the Dana-Farber Cancer Institute in the Polyak Lab, where she investigated intratumoral cellular heterogeneity in metastases. She is broadly interested in questions of evolution and environmental influences on genetic adaptation.
Megan Ruffley (Postdoctoral Fellow)
I am interested in using machine learning algorithms to understand selection across plant genomes in response to stress. I received my PhD in Bioinformatics and Computational Biology at the University of Idaho where I focused on performing simulation-based model inference using machine learning algorithms in areas ranging from demographic inference and phylogenetics to community-wide assembly mechanisms. This research was concentrated on disjunct plants of the Pacific Northwest temperate rainforest, but also focused on community-wide plant ecosystems, such as island plant communities. I am currently interested in continuing to apply machine learning algorithms to novel problems in evolutionary biology that can aid in solving our world’s most challenging problems. In the Moi and Rhee labs, I continue to investigate these algorithms as I study the relationship between genetic adaptation and response to stress in economically and agriculturally important crop plants. Investigating such adaptations to stress aid in our struggle to understand the future impacts of climate change.
Eva Sehr (Visitor)
A large part of Eva's scientific activities has been devoted to the molecular basis of drought stress responses in diverse crop and forestry species. She was involved in projects where RADseq, classical SNP genotyping, and comparative transcriptomics were used for finding key genes and pathways driving stress tolerance. The fact that high amount of data is beeing generated and its proper analysis is a challanging task, she decided to enroll in a Master's program studying Bioinformatics. In her Master Thesis supervised by Moi, she will use deep learning framework to flexibly design and test multiple Neural Network models that can better infer genetic vulnerabilities of populations to a climate impact.
Sebastian Toro Arana (Graduate Student)
Sebastian is interested in using computational tools and engineering principles to study the underlying mechanisms of plant stress resistance, immunity and plant-microbe-environment interactions. His goal is to use new technologies to help tackle the challenges of climate change and food security. As an undergraduate and research technician at MIT, he worked on exploring brain decision making systems using computational analysis tools and genetic engineering but recently he has decided to work on more pressing global problems. On a more personal note, he loves enjoying nature and music.

2013-2018, Biological Engineering and Computer Science, MIT

2018-2020, Research Technician, McGovern Institute for Brain Research

2020-on, Graduate Student in Biology, Stanford University
Willian Viana (Graduate Student)
Willian got his bachelor’s in Agronomy at the Federal University of Santa Catarina in Brazil. Five years ago, he did a summer internship at Carnegie in the Dinneny Lab, where he studied the development of Setaria viridis root system under drought conditions. Now he is graduate student in the Biology Program at Stanford. He is broadly interested in understanding the mechanisms by which plants respond and adapt to abiotic changes to their environment.

Publications

Google Scholar Profile

ORCID Profile: 0000-0001-5711-0700

2020

Seasonal timing adaptation across the geographic range of Arabidopsis thaliana. Exposito-Alonso M. (2020). PNAS, 117 (18) 9665-9667

Evolutionary genomics improves prediction of species responses to climate change. Waldvogel AM, Feldmeyer B, Rolshausen G, Exposito-Alonso M, Rellstab C, Kofler R, Mock T, Schmid K, Schmitt I, Bataillon T, Savolainen O, Bergland A, Flatt T, Guillaume F, Pfenninger M. (2020) Evolution Letters, https://doi.org/10.1002/evl3.154

2019

The Earth BioGenome project: Opportunities and Challenges for Plant Genomics and Conservation Exposito-Alonso, M., Drost, HG., Burbano, H.A., Weigel, D. (2019) The Plant Journal, https://doi.org/10.1111/tpj.14631

Natural selection on the Arabidopsis thaliana genome in present and future climates. Exposito-Alonso, M., 500 Genomes Field Experiment Team, Burbano, H. A., Bossdorf, O., Nielsen, R., Weigel, D. (2019) Nature, https://doi.org/10.1038/s41586-019-1520-9

Highlights: Surridge C. Genomic futurology. Nat Plants. 2019;5: 1027. doi:10.1038/s41477-019-0532-7

A rainfall-manipulation experiment with 517 Arabidopsis thaliana accessions. Exposito-Alonso, M., Rodríguez, R.G., Barragán, C., Capovilla, G., Chae, E., Devos, J., Dogan, E.S., Friedemann, C., Gross, C.^**, Lang, P., Lundberg, D., Middendorf, V.^**, Kageyama, J., Karasov, T., Kersten, S., Petersen, S., Rabbani, L., Regalado, J., Reinelt, L.^**, Rowan, B., Seymour, D.K., Symeonidi, E., Schwab, R., Tran, D.T.N., Venkataramani, K., Van de Weyer, A.-L., Vasseur, F., Wang, G., Wedegärtner, R.^**, Weiss, F., Wu, R., Xi, W., Zaidem, M., Zhu, W., García-Arenal, F., Burbano, H.A., Bossdorf, O., Weigel, D., (2019) bioRxiv (final version), https://doi.org/10.1101/186767.

2018

Spatio-temporal variation in fitness responses to contrasting environments in Arabidopsis thaliana. Exposito-Alonso, M., Brennan, A., Alonso-Blanco, C., Picó, F.X., (2018) Evolution, 71:550. https://doi.org/10.1111/evo.13508.

Adaptive diversification of growth allometry in the plant Arabidopsis thaliana. Vasseur, F., Exposito-Alonso, M., Ayala-Garay, O., Wang, G., Enquist, B.J., Violle, C., Ville, D., Weigel, D., (2018) PNAS, https://doi.org/10.1073/pnas.1709141115

The rate and effect of new mutations in a colonizing plant lineage. Exposito-Alonso, M.*, Becker, C*., Schuenemann, V.J., Reitter, E., Setzer, C., Slovak, R., Brachi, B., Hagmann, J., Grimm, D.G., Jiahui, C., Busch, W., Bergelson, J., Ness, R.W., Krause, J., Burbano, H.A., Weigel, D., (2018) PLOS Genetics, 14, e1007155. https://doi.org/10.1371/journal.pgen.1007155

Cover highlight > http://blogs.plos.org/biologue/files/2018/02/Feb_2018.pdf

Max Planck Highlight: Evolutionwatch for plants > https://www.mpg.de/11956930/evolutionwatch-plants

Genomic basis and evolutionary potential for extreme drought adaptation in Arabidopsis thaliana. Exposito-Alonso, M., Vasseur, F., Ding, W., Wang, G., Burbano, H.A., Weigel, D., (2018). Nature Ecology & Evolution 2, 352–358. https://doi.org/10.1038/s41559-017-0423-0

Max Planck Highlight: Life on the edge > https://www.mpg.de/11861946/arabidopsis-climate-change

Behind the paper > https://natureecoevocommunity.nature.com/users/77335-moises-exposito-alo...

2017

On the post-glacial spread of human commensal Arabidopsis thaliana. Lee, C.-R., Svardal, H., Farlow, A., Exposito-Alonso, M., Ding, W., Novikova, P., Alonso-Blanco, C., Weigel, D., Nordborg, M., (2017). Nature Communications. 8, 14458. https://doi.org/10.1038/ncomms14458.

2016

Epigenomic diversity in a global collection of Arabidopsis thaliana accessions. Kawakatsu, T., Huang, S. C., Jupe, F., Sasaki, E., Schmitz, R. J., Urich, M. A., Castanon, R., Nery, J. R., Barragan, C., He, Y., Chen, H., Dubin, M., Lee, C. R., Wang, C., Bemm, F., Becker, C., O'Neil, R., O'Malley, R. C., Quarless, D. X., 1001 Genomes Consortium, Schork, N. J., Weigel, D., Nordborg, M., Ecker, J. R. (2016). Cell 166, 492-505, doi.org/10.1016/j.cell.2016.06.044.

Effector-triggered immune response in Arabidopsis thaliana is a quantitative trait. Iakovidis, M., Teixeira, P.J.P.L., Exposito-Alonso, M., Cowper, M.G., Law, T.F., Liu, Q., Vu, M.C., Dang, T.M., Corwin, J.A., Weigel, D., Dangl, J.L., Grant, S.R., (2016). Genetics 204, 337–353. https://doi.org/10.1534/genetics.116.190678.

1,135 Genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. 1001 Genomes Consortium, (2016). Cell 166, 481–491. https://doi.org/10.1016/j.cell.2016.05.063.

Websites

My Website

Lab website

Join Us

Check opportunities here: https://moisesexpositoalonso.org/team

Search form