Principal Supervisor: Dr. Juliano Sarmento CabralLink opens in a new window
Co-supervisor: Dr. Marco Catoni
PhD project title: Genomic mechanisms and eco-evolutionary dynamics under stressful and changing environments
University of Registration: University of Birmingham
General background: Genomes may vary in DNA content (e.g. amplification of repeated sequences) or in chromosomes (e.g. dysploidy or polyploidy), which can bear adaptive value. In fact, the increase in DNA content and ascending dysploidy may decrease gene linkage, affect gene regulation or repurpose the extra copies. Genomic processes may occur at higher rates under stressful conditions. For example, plant individuals at their niche borders have a higher production rate of unreduced gametes, promoting autopolyploidization. However, it is little understood how different genomic processes interact and how adaptive are the resulting genomic variants to population and species survival. Shedding light to these gaps will unravel how species may respond to impending environmental change, which will likely trigger genomic variants.
Objectives: To assess how variation in DNA content and chromosome number affect eco-evolutionary processes under stressful, changing environmental conditions. Specifically, the project will address 1) whether an increase in DNA content increases individual fitness, population persistence and species survival, caused at random or at specific areas 1.1) of the genome and 1.2) of the species’ range distribution; 2) whether autopolyploidy has similar effects; 3) whether variation of DNA content and chromosome number have synergetic effects when triggered together; 4) identify species more vs. less likely 4.1) to undergo these adaptive processes and 4.2) to respond to ongoing environmental change via genomic variants.
Methods: This project takes a mechanistic eco-evolutionary approach by applying the first genomically-explicit mechanistic model (GeMM - Leidinger et al. 2021) to a study system showing high DNA content and chromosomal variation – the Maxillariinae orchids (Moraes et al. 2022). Maxillariinae orchids are a horticulturally relevant group (e.g. Bifrenaria, Lycaste, Maxillaria). The GeMM simulates in a spatially explicit environmental arena diploid plant individuals competing for space and performing life-history processes (growth, reproduction, dispersal, survival). These processes are controlled by genomically coded parameters. The genome contains coding and non-coding base pair sequences and undergoes mutation and recombination during gametogenesis. The number of genes per ecological traits and level of gene linkage is hitherto species-specific. Individual fitness (the match between environmental preferences and local conditions), population persistence and species survival emerge from simulation experiments. The project will extend GeMM to integrate intraspecific variation in DNA content and chromosome number (first two years). The objectives 1-3 will be tackled via experiments varying i) DNA increase across the genome (2nd year); ii) changes in chromosome number (3rd year); iii) both processes together (4th year). The environmental preferences will be given by distribution models of species for which we have DNA content, chromosome number and phylogenetic relationship (Moraes et al. 2022). Ecological traits will be assembled in collaboration with Prof. Moraes. Simulation experiments will explore current or estimated ancestral traits. Emergent distribution ranges as well as genomic properties will be statistically parameterized to real-world data to estimate the rate of each genomic process (e.g. DEoptim R package). Parameterized species will have their genetic and ecological trait combinations compared and will be simulated under environmental change to address objective 4.
Leidinger L, Vedder D & Cabral JS 2021 Temporal environmental variation may impose differential selection on both genomic and ecological traits. Oikos 130: 1100-1115.
Moraes AP, Engel TBJ, Forni-Martins ER, de Barros F, Felix LP & Cabral JS 2022 Are chromosome number and genome size associated with habit and environmental niche variables? Insights from the Neotropical orchids. Ann Bot 130: 11-25.
BBSRC Strategic Research Priority: Understanding the rules of life - Systems Biology
Techniques that will be undertaken during the project:
Computational programming (e.g. Julia, R)
Computational project management (e.g. RMarkdown, github)
Agent-based modelling and mechanistic models (e.g. GeMM)
Big data analyses and management (e.g. relational data banks)
High performance cluster computing
Phylogenetic ancestral trait reconstruction and macroevolutionary models
Phenomelogical species distribution models (e.g. Maxent, GAMs, ensemble models)
Macroecological analyses (e.g. genetic and ecological traits across species)