ERC FUNDED PROJECTS

Arboviruses infect millions of people each year, however, mechanisms that drive viral emergence and maintenance remain largely unknown. A combination of host factors (e.g., human mobility), mosquito factors (e.g., abundance) and viral factors (e.g., transmissibility) interconnect to drive spread. Further, for endemic arboviruses, complex patterns of population immunity, built up over many years, appear key to the emergence of particular lineages. To disentangle the contribution of these different drivers, we need detailed data from the same pathogen system over a long time period from the same location. In addition, we need new methods, which can integrate these different data sources and allow appropriate mechanistic inferences. In this project, I will use the most globally prevalent arbovirus, dengue virus, as a case study. I will focus on Thailand where all four dengue serotypes have circulated endemically for decades and excellent long-term data and isolates exist, to address two fundamental questions: i) How do population-level patterns of immunity evolve over time and what is their impact on strain dynamics? I will use mechanistic models applied to historic serotype-specific case data to reconstruct the evolving immune profile of the population and explore the impact of immunity on viral diversity using sequences from archived isolates from each year over a 50-year period. ii) How do human behaviors, vector densities interact with immunity to dictate spread? I will work with geolocated full genome sequences from across Thailand and use detailed data on how people move, their contact patterns, their immunity profiles and mosquito distributions to study competing hypotheses of how arboviruses spread. I will compare the key drivers of dengue spread with that found for outbreaks of Zika and chikungunya. This proposal addresses fundamental questions about the mechanisms that drive arboviral emergence and spread that will be relevant across disease systems.

1 499 896 €

Start date: 2019-01-01, End date: 2023-12-31

Nonlinear optics is present in our daily life with applications, e.g. light sources for microsurgery or green laser pointer. All of them use bulk materials such as glass fibers or crystals. Generating nonlinear effects from materials at the nanoscale would expand the applications to biology as imaging markers or optoelectronic integrated devices. However, nonlinear signals scale with the volume of a material. Therefore finding materials with high nonlinearities to avoid using high power and large interaction length is challenging. Many studies focus on third order nonlinearities (described by a χ(3) tensor) present in every material (silicon, graphene…) or on metals for enhancing nonlinearities with plasmonics. My approach is to explore second-order χ(2) nanomaterials, since they show higher nonlinearities than χ(3) ones, additional properties such as birefringence, wide band gap for transparency, high refractive index (n>2), and no ohmic losses. Typical χ(2) materials are oxides (BaTiO3, LiNbO3…) with a non-centrosymmetric crystal used for wavelength conversion like in second-harmonic generation (SHG). The key idea is to demonstrate original strategies to enhance SHG of χ(2) nano-oxides with the material itself and without involving any hybrid effects from other materials such as plasmonic resonances of metals. First, I propose to use multiple Mie resonances from BaTiO3 nanoparticles to boost SHG in the UV to NIR range. Up to now, Mie effects at the nanoscale have been measured in materials with no χ(2) nonlinearities (silicon spheres). Second, since χ(2) oxides are difficult to etch, I will overcome this fabrication issue by demonstrating solution processed imprint lithography to form high-quality photonic crystal cavities from nanoparticles. Third, I will use facet processing of single LiNbO3 nanowire to obtain directionality effects for spectroscopy on-a-chip. This work fosters applications and commercial devices offering a sustainable future to this field.

1 500 000 €

Start date: 2017-02-01, End date: 2022-01-31

Diatoms are major contributors of primary production in the ocean and participate in carbon sequestration over geologically relevant timescales. As key components of the Earth’s carbon cycle and marine food webs we need to understand the eco-evolutionary underpinnings of their ecological success to forecast their fate in a future ocean impacted by anthropogenic change. Genomes and epigenomes from model diatoms, as well as hundreds of transcriptomes from multiple species, have revealed genetic and epigenetic processes regulating gene expression in response to changing environments. The Tara Oceans survey has in parallel generated resources to explore diatom abundance, diversity and gene expression in the world’s ocean in widely contrasting conditions. DIATOMIC will build on these resources to understand how evolutionary and ecological processes combine to influence diatom adaptations to their environment at unprecedented spatiotemporal scales. To examine these processes over timescales relevant to current climate change, DIATOMIC includes the pioneering exploration of ancient diatom DNA from the sub-seafloor to reveal the genetic and epigenetic bases of speciation and adaptation that have impacted their ecological success during the last 100,000 years, when Earth experienced major climatological events and an increase in anthropogenic impacts. As a model for exploring eco-evolutionary processes in the past and contemporary ocean we will focus primarily on Chaetoceros because this diatom genus is ancient, ubiquitous, abundant and contributes significantly to carbon export. Key findings will be additionally supported by lab-based studies using the diatom Phaeodactylum for which exemplar molecular tools exist. Specifically, the project will address: 1. What molecular features characterize genome evolution in diatoms? 2. Which processes determine diatom metapopulation structure? 3. What can ancient DNA tell us about diatom adaptations to environmental change in the past?

2 495 753 €

Start date: 2019-11-01, End date: 2024-10-31

A major goal in plant biology is to understand how naturally occurring genetic variation leads to quantitative differences in economically important traits. Efforts to navigate the genotype-to-phenotype map are often focused on linear genetic interactions. As a result, crop breeding is mainly driven by loci with predictable additive effects. However, it has become clear that quantitative trait variation often results from perturbations of complex genetic networks. Thus, understanding epistasis, or interactions between genes, is key for our ability to predictably improve crops. To meet this challenge, this project will reveal and dissect epistatic interactions in gene regulatory networks that guide stem cell differentiation in the model crop tomato. In the first aim, I will utilize exhaustive allelic series for epistatic MADS-box genes that quantitatively regulate flower and fruit production as an experimental model system to study fundamental principles of epistasis that can be applied to other genetic networks. Genome-wide transcript profiling will be used to reveal molecular signatures of epistasis and potential targets for predictable crop improvement by advanced CRISPR/Cas9 gene editing technology. Further, my preliminary data suggests that epistasis is widespread and important across major productivity traits in tomato. Thus, in a second aim, I will access this untapped resource of cryptic genetic variation by sensitizing a tomato diversity panel for weak epistatic effects from unknown natural modifier loci of stem cell differentiation using trans-acting CRISPR/Cas9 editing cassettes. This screen represents a new approach to mutagenesis in plants with potential to reveal cryptic variation in other system. The outcomes of this project will advance our knowledge in a fundamental area of plant genome biology, help uncover and understand the functional architecture of epistasis, and have potential to bring significant improvements to agriculture.

1 499 903 €

Start date: 2019-08-01, End date: 2024-07-31