The INCREASE project: Intelligent Collections of food‐legume genetic resources for European agrofood systems

Food legumes are crucial for all agriculture-related societal challenges, including climate change mitigation, agrobiodiversity conservation, sustainable agriculture, food security and human health. The transition to plant-based diets, largely based on food legumes, could present major opportunities for adaptation and mitigation, generating significant co-benefits for human health. The characterization, maintenance and exploitation of food-legume genetic resources, to date largely unexploited, form the core development of both sustainable agriculture and a healthy food system. INCREASE will implement, on chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), lentil (Lens culinaris) and lupin (Lupinus albus and L. mutabilis), a new approach to conserve, manage and characterize genetic resources. Intelligent Collections, consisting of nested core collections composed of single-seed descent-purified accessions (i.e., inbred lines), will be developed, exploiting germplasm available both from genebanks and on-farm and subjected to different levels of genotypic and phenotypic characterization. Phenotyping and gene discovery activities will meet, via a participatory approach, the needs of various actors, including breeders, scientists, farmers and agri-food and non-food industries, exploiting also the power of massive metabolomics and transcriptomics and of artificial intelligence and smart tools. Moreover, INCREASE will test, with a citizen science experiment, an innovative system of conservation and use of genetic resources based on a decentralized approach for data management and dynamic conservation. By promoting the use of food legumes, improving their quality, adaptation and yield and boosting the competitiveness of the agriculture and food sector, the INCREASE strategy will have a major impact on economy and society and represents a case study of integrative and participatory approaches towards conservation and exploitation of crop genetic resources

Plant Science's Next Top Models

Model organisms are at the core of life science research. Notable examples include the mouse as a model for humans, baker's yeast for eukaryotic unicellular life and simple genetics, or the enterobacteria phage λ in virology. Plant research was an exception to this rule, with researchers relying on a variety of non-model plants until the eventual adoption of Arabidopsis thaliana as primary plant model in the 1980s. This proved to be an unprecedented success, and several secondary plant models have since been established. Currently, we are experiencing another wave of expansion in the set of plant models

Special issue on climate-smart agriculture (CSA)

CSA strategies, policies, partnerships and investments; ‘CSA-Plan’: strategies to put CSA into practice; The mitigation pillar of CSA; Agricultural diversification as an adaptation strategy; Climate services and insurance: scaling; CSA Closing the gender gap in agriculture under climate change; How can the Data Revolution contribute to climate action?; Climate change and CSA in the current political climat

Application of multivariate statistics and machine learning to phenotypic imaging and chemical high-content data

Image-based high-content screens (HCS) hold tremendous promise for cell-based phenotypic screens. Challenges related to HCS include not only storage and management of data, but critical analysis of the complex image-based data. I implemented a data storage and screen management framework and developed approaches for data analysis of a number high-content microscopy screen formats. I visualized and analysed pilot screens to develop a robust multi-parametric assay for the identification of genes involved in DNA damage repair in HeLa cells. Further, I developed and implemented new approaches for image processing and screen data normalization. My analyses revealed that the ubiquitin ligase RNF8 plays a central role in DNA-damage response and that a related ubiquitin ligase RNF168 causes the cellular and developmental phenotypes characteristic for the RIDDLE syndrome. My approaches also uncovered a role for the MMS22LTONSL complex in DSB repair and its role in the recombination-dependent repair of stalled or collapsed replication forks. The discovery of novel bioactive molecules is a challenge because the fraction of active candidate molecules is usually small and confounded by noise in experimental readouts. Cheminformatics can improve robustness of chemical high-throughput screens and functional genomics data sets by taking structure-activity relationships into account. I applied statistics, machine learning and cheminformatics to different data sets to discern novel bioactive compounds. I showed that phenothiazines and apomorphines are regulators for cell differentiation in murine embryonic stem cells. Further, I pioneered computational methods for the identification of structural features that influence the degradation and retention of compounds in the nematode C. elegans. I used chemoinformatics to assemble a comprehensive screening library of previously approved drugs for redeployment in new bioassays. A combination of chemical genetic interactions, cheminformatics and machine learning allowed me to predict novel synergistic antifungal small molecule combinations from sensitized screens with the drug library. In another study on the biological effects of commonly prescribed psychoactive compounds, I discovered a strong link between lipophilicity and bioactivity of compounds in yeast and unexpected off-target effects that could account for unwanted side effects in humans. I also investigated structure-activity relationships and assessed the chemical diversity of a compound collection that was used to probe chemical-genetic interactions in yeast. Finally, I have made these methods and tools available to the scientific community, including an open source software package called MolClass that allows researchers to make predictions about bioactivity of small molecules based on their chemical structure

Impact of mobilising collective intelligence in clinical research planning

New methods of conducting research have been emerging outside clinical research. For example, worldwide game players helped to construct protein molecular which scientists had been struggling with for 15 years. In these examples, researchers leveraged collective intelligence of people who were not usually involved in research. My research aims to investigate whether and how mobilising collective intelligence could be used in the planning of a randomised controlled trial. To achieve this aim, I first conducted a scoping review to describe the methods of mobilising collective intelligence across different research fields. From this scoping review, I proposed a framework for implementing a research project using these new methods. Second, I conducted a qualitative study involving online survey and semi-structured interviews to investigators, researchers or coordinators of research projects mobilising collective intelligence. Drawing on their experience, I provided good practice advice for the governance, planning, and conducting of research involving collective intelligence. Finally, I developed a proof-of-concept study using case vignettes to leverage patients’ collective intelligence to improve trial organisation. Patients proposed several suggestions to improve the logistical organisation of trials. They also highlighted the importance of changing one-size-fits-all approach of trial organisation. In conclusion, the work in this thesis provides the first comprehensive accounts of methods used to mobilise collective intelligence across different research disciplines. The proof-of-concept study provided an example of leveraging patients’ collective intelligence to explore ideas and perspectives to improve clinical trial planning