Key Expoitable Results (KERs)

Microfluidic technology can expedite nucleic acid testing by converting the functions of bulky laboratory instruments and protracted bench methodologies into easy-to-use and inexpensive miniaturised systems without compromising speed and reliability. We developed a lab-on-a-chip (LOC) platform that integrates a dimethyl adipimidate (DMA)-based functionalised silica DNA isolation and pre-concentration method with a rapid and real-time loop-mediated isothermal amplification (LAMP) for detecting domoic acid-producing phytoplankton, Pseudo-nitzschia. An optimised design of a lab on a chip extraction module achieved a maximum DNA capture capacity of 61.73 ± 0.98 ng μL−1. The DMA-based method reduced reagent costs per sample by 97% compared to a commercial nucleic acid isolation kit. A subsequent on-chip LAMP process was capable of sensitively quantifying cytochrome P450 homologous to the dabD gene, coding for a component of the domoic acid toxin production pathway, with a limit-of-detection of 10 cells per mL. LAMP-based detection of the target gene was achieved using dry-preserved reagents with a shelf-life of five months without refrigeration. There was no significant difference in assay performance between the preserved LAMP and freshly prepared LAMP mixtures. The total analysis time at the LOD of 10 cells per mL, from sample to result, was achieved within one hour. Our results demonstrate the long-term stability of assay reagents, rapid turnaround, and cost-effectiveness, offering a simple and economical approach to environmental monitoring and environmental bio-hazard diagnostics.

The library provides a shortlist of enzyme candidates obtained from data mining for later experimental characterisation. Enzymes candidates have potential for industrial applications, such as molecular diagnostics and bioremediation, as well as potentially new bioactive compounds that may support the growth of the biotechnological and pharmaceutical industry. Candidate enzymes may also contribute to research towards a better understanding of biodiversity, with applications in environmental and ecological conservation.

The main computational goals of AtlantECO for Advances in Systems Ecology were to develop cutting edge network analysis methods, to define an all-Atlantic interactome and ecological niches, and to provide indicators of ecosystem stability and sensitivity to environmental stressors and drivers. Here, we reviewed the literature for ecological network reconstruction methods from several disciplines (microbiology, ecology, bioinformatics). We built a computational pipeline for the inference of species ecological networks from heterogeneous data types, combining statistical and ecological metrics, as well as probabilistic and machine learning algorithms. Reference databases of known ecological interactions obtained from the literature can be used to benchmark and validate inferred networks. This review of network inference algorithms is accompanied by a computational workflow: AtlantEcoNet (publicly available at: https://gitlab.univ-nantes.fr/mbudinich/atlanteconet), which builds upon existing software by integrating a selection of complementary methods for ecological network inference, analysis, and validation. This workflow was used by AtlantECO to build a plankton ecological network from omics data compiled for the Atlantic Ocean.

The quantitative description of marine systems is constrained by a major issue of scale separation: phytoplankton production processes occur at sub-centimeter scales, while the contribution to the Earth's biogeochemical cycles is expressed at much larger scales, up to the planetary one. In spite of vastly improved computing power and observational capabilities, the modeling approach has remained anchored to an old view that sees the microscales as unable to substantially affect larger ones. The lack of a widespread theoretical appreciation of the interactions between vastly different scales has led to the proliferation of numerical models with uncertain predictive capabilities. In this paper, we use the phenology of phytoplankton blooms as one example of a macroscopic ecosystem feature affected by microscale interactions. We describe two distinct mechanisms that produce patchiness within a productive water column: turbulent entrainment of less-productive water at the base of the mixed layer, and stirring by slow turbulence of a vertical phytoplankton gradient sustained by depth-dependent light availability. In current eddy-diffusive models, patchiness produced in this way is wiped out very rapidly, because the time scales of irreversible mixing largely overlap those of mechanical stirring. We propose a novel Lagrangian modeling framework that allows for the existence of microscale patchiness, even when that is not fully resolved. We show, with a mixture of theoretical arguments and numerical simulations of increasing realism, how the presence of patchiness, in turn, affects larger-scale properties, demonstrating that the timing of phytoplankton blooms and vertical variability of chlorophyll in the oceanic upper layers is determined by the mutual interplay between the stirring, mixing and growing processes.

The ocean hosts a vast diversity of life, from microscopic plankton to large marine mammals, with their distribution shaped by environmental factors and species-specific traits. While ocean currents largely determine the passive transport of plankton, some species can regulate their buoyancy or actively migrate, such as diel vertical migrators (DVM). Larger, more mobile species, like fish and marine mammals, navigate their environment with stronger swimming abilities. Understanding how marine species move and interact with their environment is crucial for predicting ecosystem shifts, especially as climate change alters ocean conditions. This thesis investigates how species traits and environmental factors influence the transport and connectivity of marine organisms using Lagrangian flow modeling. Three case studies explore the dispersal of different marine species: vertically migrating zooplankton, thermally constrained plankton, and cold-stunned Kemp’s ridley turtles. The first study (Chapter 2) examines DVM zooplankton in the Benguela upwelling system, comparing their movement to that of floating and sinking materials such as phytoplankton, nutrients, and biogenic matter. Results show that DVM zooplankton separate more rapidly from other materials, especially during the upwelling season. However, coherent ocean features like eddies and filaments trap different particle types together, potentially increasing interactions between zooplankton, their prey, and pollutants. Chapter 3 applies network theory to Lagrangian modeling to assess minimum time connectivity pathways in the Atlantic Ocean. Passive plankton can theoretically connect all Atlantic locations within three years, but connectivity time increases with depth. When thermal constraints are introduced, connectivity across the basin is reduced, emphasizing the role of environmental preferences in species dispersal. Chapter 4 investigates Kemp’s ridley turtle strandings in the Netherlands, using Lagrangian modeling to trace their drift pathways. Findings suggest that juvenile turtles become cold-stunned in the southern North Sea and experience temperatures below 12°C for up to a month. These results highlight the need for targeted rehabilitation and conservation strategies for this critically endangered species. Together, these studies demonstrate that both species traits and environmental constraints shape marine organism dispersal, influencing ecological interactions and habitat connectivity. As climate change alters ocean temperatures and circulation, species distributions will shift, affecting marine food webs and conservation planning. Future research should refine species-specific behavioral models and integrate dispersal with ecosystem dynamics to better inform marine conservation strategies.

Marine planktonic communities carry out almost half of the net primary production on our planet, keep atmospheric CO2 levels at roughly half of what they would be otherwise, form the base of food webs and produce more than half of the oxygen we breathe. Considering the wealth and economic importance of ecosystem services and climate regulation provided by our oceans fuelled from the very base of its food web, impacts of climate change on marine plankton are still poorly investigated. Within this project we aim to overcome disciplinary boundaries that contribute to impeding exchanges of scientific results between the research community and policy makers. We aim to build an interactive web tool for policy makers to help visualise future projections of climate change impact on global plankton ecosystems as a function of societal decisions. We continue the effort made by the ETH Environmental Physics (UP) Group (Zürich, Switzerland) that has mapped the biogeography of hundreds of plankton species and use this knowledge to define biomes for the surface ocean and identify hotspots of plankton diversity changes under global warming. We aim to include three fully coupled Earth System Models from the Coupled Model Intercomparison Project Phase CMIP5 using three different representative concentration pathways (RCP 2.6, RCP 4.5, RCP 8.5) covering the period from 2012 until 2100. We will translate projections into quantitative global impact metrics targeted at policy makers and characterize ecosystem impacts as a function of carbon emissions and global warming. International decision-makers are informed on potential future changes in global marine plankton and can start addressing challenges to marine conservation.

Mission Microbiomes AtlantECO (MMA) is an international study of the most fundamental fabric of the ocean — the ocean microbiome — aiming to understand its structure, functioning and connectivity in the Atlantic Ocean. It is an initiative of the European Union’s research and innovation project AtlantECO, which aims at sharing capacity along and across the Atlantic, and providing knowledge-based resources to help design policies for the management and protection of Atlantic Ecosystem Services. The success and legacy of MMA on science and society relies on the fair, open and inclusive collaboration among its Partners, and the exploitation of jointly owned MMA Results. The adoption of the present Data Sharing and Publication Best Practices is therefore essential to achieve the scientific objectives of MMA and maximise their impacts.

Secondary or specialized metabolites show extraordinary structural diversity and potent biological activities relevant for clinical and industrial applications. The biosynthesis of these metabolites usually starts with the assembly of a core ‘scaffold’, which is subsequently modified by tailoring enzymes to define the molecule’s final structure and, in turn, its biological activity profile. Knowledge about reaction and substrate specificity of tailoring enzymes is essential for understanding and computationally predicting metabolite biosynthesis, but this information is usually scattered in the literature. Here, we present MITE, the Minimum Information about a Tailoring Enzyme database. MITE employs a comprehensive set of parameters to annotate tailoring enzymes, defining substrate and reaction specificity by the expressive reaction SMARTS (Simplified Molecular Input Line Entry System Arbitrary Target Specification) chemical pattern language. Both human and machine readable, MITE can be used as a knowledge base, for in silico biosynthesis, or to train machine-learning applications, and tightly integrates with existing resources. Designed as a community-driven and open resource, MITE employs a rolling release model of data curation and expert review. MITE is freely accessible at https://mite.bioinformatics.nl/.

Every few years, juvenile Kemp’s ridley turtles (Lepidochelys kempii) are stranded on the Dutch coasts. The main population distribution of this critically endangered species primarily inhabits the Gulf of Mexico and the east coast of the United States. This study focuses on five reports from the Netherlands between 2007 and 2022, where juvenile turtles were reported to strand alive during the winter, albeit in a hypothermic state. At ambient ocean temperatures between 10°C and 13°C, Kemp’s ridley turtles begin to show an inability to actively swim and remain afloat on the ocean’s surface, a condition termed ‘cold stunning’. Understanding their transport in cold-stunned state can help improve the rehabilitation process of stranded turtles. Cold-stunned turtles are back-tracked as passive, virtual particles from their stranding location using Lagrangian flow modelling. This study investigates when and where these juvenile turtles cross the threshold temperatures between 10° C and 14° C before stranding by tracking the temperature along the trajectories. As expected, the simulations show the transport of the cold-stunned turtles via the English Channel. More surprisingly, the analysis suggests they likely experience cold-stunning in the southern North Sea region and encounter temperatures below 10°C for only a few days to up to three weeks, and below 12°C for up to a month before stranding. The estimate of cold-stunned drift duration of the turtles provides additional knowledge about their health status at the time of stranding. Adherence to rehabilitation protocols for Kemp’s ridley and post-release monitoring are recommended to improve their long-term survival.