The largest animals of the past and present are suspension feeders, surviving by filtering and eating huge quantities of some of the smallest food in the ocean. This strategy has evolved repeatedly in a wide diversity of animals—from sponges to mammals—and is thought to be the most efficient feeding option for supporting the body mass of ocean giants like whales and the largest sharks. Despite the charisma and protected status of many large-bodied suspension feeders, we know surprisingly little about their ecologies and the tools they use to process water on industrial scales. This is largely due to challenges of studying these animals and their filters in the wild. We integrate a variety of disciplines and scales in the study of suspension feeding in the basking shark, a cosmopolitan species and the second largest fish, reaping >30 kg of plankton per day. We will use animal-borne tag sensors, drones and swarms of underwater robots to capture and dissect basking shark feeding events, to quantify the mechanics, behavior, and hydrodynamics of foraging. These data will be integrated with morphological and anatomical investigations of basking sharks’ fundamental filtering tools—stiff bristles called gill rakers, lining the throat by the tens of thousands—using advanced bioimaging and engineering techniques to quantify their geometries and materials. Behavioral and material data will converge in multi-scale optimization models of flow and filtration, in digital simulations and performance tests of 3D printed biomimics, building from fine-scale biological architectures to explore hypothetical shapes and arrangements, using machine learning to integrate model predictions from micron to meter scales. Seeing a high-performance biological system through the lenses of materials science, animal behavior, robotics and architecture, we gain a unique mechanistic window into the interaction of basking sharks with their environment, and the tools and energetics of a specialized ecology that has existed for millions of years. Exploiting this intersection of disciplines, we will learn how mineralized materials can be modified into high-throughput filters; create new integrated approaches and tools to characterize complex bio-architectures and monitor large aquatic animals in their habitats; and gain bioinspiration for new filter design in a wide range of applications.

The state of brain circuits is changed by neuromodulatory signals such as dopamine, serotonin or acetylcholine, which are released in spatially and temporally precise, phasic or tonic patterns depending on the physiological context. These neuromodulators act on multiple receptor classes, exerting diverse physiological effects through distinct signaling pathways. Obtaining a clearer picture of the function of neuromodulatory-driven signals in neural information processing and plasticity requires methods for remote-control of specific neurotransmitter receptors with high temporal precision in defined neurons and in behaving animals. We combine labs with expertise in synthetic chemistry and photochemistry (Ellis-Davies) and behavioral neuropharmacology (Mourot) in order to develop technologies to gain optical control over DA neuromodulation in the freely-moving mouse, for studies of neural circuits and behaviors associated with same-sex social interactions. Synthetic photochemical tools (caged and photoswitchable compounds) are, in principle, able to mimic the timing, amplitude and spread of naturally occurring neuronal signals. However, their in vivo use has been highly restricted, notably because these probes are stimulated by UV or visible light that penetrates tissue very poorly. To overcome this issue, we propose to use lanthanide-doped upconverting nanoparticles (UCNPs), which will be functionalized with caged/photoswitchable ligands for glutamate and dopamine receptors, enabling non-invasive control of the very receptors of the brain in behaving mice. We will further combine near-infrared photocontrol with chemo-genetic strategies to achieve optical control of receptors in defined neuronal targets. Leveraging UCNPs in this way will provide a platform for the interrogation of dopamine-related neuropsychiatric disorders in groups of animals in naturalistic environments, and will represent a milestone in our understanding of how dopamine impacts a variety of personality traits and same-sex interactions. The strategies developed here will be applicable to most receptors, neural circuits and animal models, thus our project bears enormous potential in changing the landscape of our understanding of neuromodulation, both in health and disease.

Functional enzymes exist in an ensemble of conformations. Protein dynamics, including the transition between different conformational states, have been shown to play an important role in enzymatic functions. Additionally, conformational ensembles are thought to play a key role in enzyme evolution. For example, the evolution of a new catalytic function may depend on a shift in the conformational ensemble. This theoretical model is widely acknowledged, however, there is currently little experimental evidence linking conformational and catalytic ensembles with evolution. Thus, developing dynamic, high-resolution and single-molecule level views of enzymes is a pivotal step to advance our fundamental understanding of enzyme evolution. In this proposal, we aim to capture a detailed resolution picture of conformational and catalytic changes across the evolutionary transitions between divergent enzyme functions. This will be achieved via the integration of diverse cutting-edge approaches, including: directed evolution (DE), single-molecule enzyme kinetics, dynamic structural biology, and computational simulations. We will characterize multiple series of enzyme intermediates that constitute complete adaptive transitions from one function to another. Conformational ensembles and motions will be characterized using serial femtosecond crystallography (SFX) combined with extensive molecular dynamic (MD) simulation. Catalytic fluctuations and heterogeneity will be experimentally measured using single-molecule enzymology techniques, and conformational changes in the reaction will be determined by kinetic-crystallography. Furthermore, we will identify the intramolecular amino acid networks that are associated with changes in conformational motions and ensembles. Finally, all experimental data will be integrated into an integrated computational model in order to describe the process of enzyme evolution as topology change in the conformational energy landscapes. This work will lead to an unprecedented experimental and computational understanding of the interplay between conformational and catalytic ensembles of enzymes and their evolutionary dynamics. These in-depth molecular views of enzyme evolution will not only advance our basic scientific knowledge but also lead to technological advances for the design and generation novel enzymes in the laboratory.

Directed evolution is a technique to investigate evolutionary dynamics and evolve nucleic acids and proteins for research, diagnostic and therapeutic applications. It uses cycles of mutation and screening or selection to mimic Darwinian Evolution. The aim of these experiments is to screen or select large libraries of molecule variants with different sequences (‘genotypes’) and retain those sequences having the required properties (or required ‘phenotypes’). At the moment one essential limitation of Directed Evolution methods is that the properties of each variant in the library are assessed based on bulk samples, i.e. on the average activity of a large numbers of copies of the same molecule. While these copies are identical at the sequence level, they may still exhibit different phenotypes, for example due to the fact that the same sequence can assume multiple three dimensional structures. These ‘heterogeneous’ phenotypes can play important roles in evolutionary dynamics, but at the moment there is no convenient Directed Evolution tool to address them. Here we combine expertise from the fields of directed evolution and single-molecule microscopy to develop a novel approach that allows us to perform directed evolution while looking at the behaviour of single-molecules. The method can be used to perform accurate phenotypic characterization measuring multiple aspects of the phenotype in parallel, something that can be challenging with conventional approaches. For example it can be used to study how a molecule with a specific sequence can sometimes play a role in different chemical reactions, how to achieve ‘specialization’ and what the consequences of specialization are in terms of efficiency. As discussed above, the method can also reveal the existence of phenotypic heterogeneity in molecules with identical sequence. This approach will allow us to address several biological questions that cannot be addressed with conventional methods. These include, the evolution of heterogeneous phenotypes in populations of RNA molecules under selection for binding, the role of heterogeneity in defining the evolutionary potential of antibodies and the correlation between rate and accuracy in DNA polymerases. It will also provide a potent route to develop biomolecules for biomedical and industrial applications, notably in the field of single-molecule technologies.

The extracellular space (ECS) forms an important but understudied frontier in neuroscience. It consists of the narrow gaps that surround all brain cells, which are filled with interstitial fluid and extracellular matrix molecules, occupying around one fifth of the volume of the brain. Even though all extracellular signaling molecules and nutrients must transit through the ECS to reach their targets, we know very little about the shape and dynamics of this brain compartment, or its influence on brain function. Although the ECS has received much less attention than neuronal and glial networks, it plays a fundamental functional role in brain health and disease, serving as a reservoir of ions for electrical activity and providing an essential microenvironment for the well-being of cells and brain homeostasis. Based on pioneering theoretical and biophysical studies, we know the diffusivity and geometry of the ECS are major determinants of how molecules (endogenous substances or medical drugs) can spread around the brain or get cleared from it. However, mapping the biophysical landscape of the ECS with enough spatial resolution in live brain tissue has been impossible to accomplish until now for lack of appropriate tools. Bringing together a team of leading researchers, this multidisciplinary project will study key properties of the ECS, focusing on its dynamic organization, its role in material transport (in brain perivascular spaces and parenchyma) and its impact on brain function at the cellular and systems level. To this end, we will develop several innovative investigative tools that will allow us to image and manipulate key aspects of the ECS, and to study its structure and function in brain slices, in vivo and in silico. Specifically, we will (1) engineer novel chemical tools to label hyaluronan (HA) and to manipulate its biological activity, (2) develop super-resolution microscopy technology to study impact of ECS on synaptic function in brain slices and to enable ECS visualization in the intact brain in vivo, (3) make biophysical measurements to quantify diffusion of molecules through the ECS, (4) investigate ECS control of ‘glymphatic’ function in the sleep-wake cycle, (5) construct morphologically realistic mathematical models to simulate substance transport through the ECS. This combination of methodologies will enable us to better understand how this brain compartment influences brain physiology, focusing on single neuron function and sleep.

Uncontrolled loss of neurons is a hallmark of neurodegenerative diseases (NDs) such as Alzheimer’s disease and amyotrophic lateral sclerosis (ALS). In NDs, an abnormal assembly of proteins and mRNAs known as a biomolecular condensate (BC) is believed to contribute to neurotoxicity and ensuing neuronal death. Despite high mortality and poor quality of patients’ lives, as well as the enormous socio-economic burden of patient care, the exact molecular and cellular mechanisms underlying ND pathogenesis remain largely unknown. This roadblock in ND studies primarily originates from mutual dependency between “molecular assembly” of general ND proteins and “functionality” of individual ND proteins. Molecular assembly is a dynamic process that can take place locally, and changes actively in its physical property by transitioning among soluble phase, liquid-like droplets, hydrogels and insoluble aggregates. To our knowledge, there is currently no technique to disassemble ND-related BCs to distinguish these physical and biochemical characteristics, especially in a physiologically relevant context. By integrating expertise in neurophysiology, chemical biology, and soft-matter biophysics, we propose to develop a conceptually unique methodology termed “dePOLYMER”. Here, light illumination or chemical administration actuates a genetically-encoded molecular probe designed to polymerize actin to generate constrictive force against an intended object such as BCs in living cells. When force density exceeds the surface tension of target BCs, dePOLYMER is expected to trigger their dispersion. We will specifically target stress granules as a model for non-pathogenic BCs, as well as pathogenic ALS-related BCs. Using a model for ALS, we will next aim to demonstrate dispersion of BCs consisting of ALS proteins in a manner specific for constituent proteins and cell types. In summary, the goal of our study is to offer a novel technique to efficiently disperse BCs in a rapidly inducible manner. Implementation of dePOLYMER in ALS model systems may enable future identification of the molecular and cellular foundation of neurotoxicity, thus a potentially new treatment strategy for NDs. Due to the inherently modular design of dePOLYMER probes, the technique should be generalizable to virtually all BCs whose molecular components are known, let alone other ND-related BCs.

Some coral reefs recover from climate change-driven disturbances relatively quickly, while others stay barren because coral populations fail to replenish themselves though larval recruitment. Here, we propose to study neuro-molecular mechanisms of settlement decision-making in coral larvae, to identify intrinsic processes that might be responsible for success or failure of coral reef recovery. Our first aim is to identify and map all types of neurons (connectome) in a coral larva at different stages of competency and commitment to settlement. It will be accomplished by performing single-cell RNA-sequencing to identify neuronal types and mapping their molecular markers onto the neural network using serial section electron microscopy, in situ hybridization and immunohistochemistry. Our second aim is to validate the role of neuron-specific candidate genes by perturbing their function (using CRISPR/Cas9 knock-downs and antibody blocking) and assessing the effect on larval settlement behavior, gene expression, and neuronal morphology. We will use Acropora millepora, which is a representative of the largest and the most ecologically important genus of reef-building corals.

One of the great questions concerns fate vs. chance. Are events destined to occur, or are they the result of specific antecedent conditions, any of which, were they different, would have led to a different outcome? Anolis lizards are recognized as among the best groups to investigate this question, and detailed study of their evolution has suggested a strong role for determinism. These studies, however, have focused on the phenotype; the extent to which genome evolution is deterministic or historically contingent is an open question. Until recently, detailed investigation of genome evolution has been limited to model organisms, for which little data are available on their ecology and behavior in natural settings, precluding study of the factors that have shaped their evolution. Now, advances in genomics and gene editing methods allow evolution to be studied in non-model organisms, permitting holistic studies from the genome to selective pressures in nature. Anolis lizards are the ideal group to combine genomic and organismal studies at multiple levels to address the extent to which evolutionary diversification is deterministic or contingent. This project will proceed as follows: Genomics: We will test for evolutionary determinism by investigating whether convergently evolved phenotypes are the result of the same or different genetic changes. Functional Tests of Candidate Loci: We will take advantage of recent advances in gene editing to directly test the role of these genetic changes in producing convergent phenotypes. To do so, we will use CRISPR-cas9 gene editing to modify genes in the lizard Anolis sagrei. We will examine genetically modified offspring to test the hypothesis that sequence variants produce the putative phenotypes. Behavior and Physiology: We will identify genes and the underlying sequence changes associated with the evolution of these traits, introduce these specific sequence variants into brown anoles, and then test whether the introduced sequence alterations produce the hypothesized behavioral and physiological phenotypes. Natural Selection: Once we have identified genetic changes underlying putatively adaptive phenotypes, we will design field experiments to test the hypothesis that natural selection favors those genotypes/phenotypes under particular environmental circumstances.

All existing life on Earth is the result of natural selection acting upon earlier forms of life, shaping the lineages over time. In order for selective advantages to accumulate over successive generations, the offspring must retain some attributes of the parent. Thus at some point early in life’s origins, a sustainably propagating cell must have emerged, one that could divide and produce offspring with reasonable fidelity. While genetic information propagation is largely understood--nucleic acids or their precursors preserve information by sequence complementarity--it is less obvious how the other properties of primitive cells, such as their membrane composition, could have been preserved across many generations. This remains a key open question in the emergence of life on Earth. We propose to answer this question by taking a bottom-up approach, in which we attempt to build a synthetic cell that can accomplish the essential tasks of stable propagation--membrane growth, feeding, and division--using the simplest components possible. In order to succeed, we will address a number of questions fundamental to biology. For example, to create a synthetic cell that can grow its own membrane, we must discover the mechanisms governing phospholipid synthesis, which could be determined by the rates of molecular motion or chemical reactions, the availability of nutrients, or incompatibilities between the molecules involved. Similarly, to feed our synthetic cell to sustain its growth, we must elucidate the physical rules governing membrane permeability. Finally, to induce division, we must understand the fundamental physics governing membrane bending, curvature, and fusion. We will combine cutting-edge techniques and perspectives from across all three fields of science to succeed in this ambitious endeavor. Kuruma, a biologist, will synthesize membrane material from simple building blocks in vesicles. Wang, a chemist, will analyze the properties of membranes to understand lipid packing, linking together membrane permeability with membrane fusion. And, Rogers, a physicist, will explore the physics of membrane bending and devise new strategies to induce cell fission. By working together, we will paint a comprehensive picture of how stable cell propagation could have emerged, as well as solve one part of the puzzle of how life as we know it could have come to exist.

How organs control their growth to achieve the correct final size is an enduring mystery of biology. In wildtype Drosophila, for example, left and right wing areas rarely differ by more than 1%. We previously found that the hormone Dilp8 controls this developmental precision, as in absence of the hormone, the fluctuating asymmetry (FA) of bilateral organs is strongly increased. In this proposal, we hypothesize that the hormone Dilp8 and cell death cooperate to limit wing size variability. To test this idea and explore its consequences, we will integrate methods from developmental biology with quantitative data analysis and modeling from physics. In particular, we will show how changes in cell death in dilp8 mutants are quantitatively related to changes in final wing size. Our results will provide new insights into how organ size is controlled and refined in biological systems.

T cells are key effectors of adaptive immunity that are constantly moving, can enter most tissues, and operate in large crowds. Millions of densely packed T cells continuously roam through lymphatic tissue in search of foreign antigen; activated T cells divide vigorously, flock to tissues, mount local immune responses to pathogens or cancer cells, and remain there to guard against further intrusions. Crowding is normally expected to deter motion and lead to jamming, an effect confirmed in very diverse systems ranging from pedestrians and vehicular traffic to cellular monolayers and granular material. Yet T cells defy this general principle and are capable of fluid motion even in extremely dense environments such as lymphatic tissue and the epidermis, where there is no apparent room to maneuver. How they achieve this is unclear: although novel microscopy modalities can visualize migrating T cells in living tissue, studies of T cell movement have primarily focused on individual or few cells and have not addressed potential crowding effects. We hypothesize that (1) T cells have evolved to cooperate effectively in large groups and avoid crowding issues, such that (2) impaired T cell crowd operation is largely confined to anomalous tissue environments such as tumors. Here, we aim to unravel the mechanisms that facilitate fluid, “jam-free” motion of densely packed T cells, and determine in which situations T cell traffic might nevertheless be disturbed by emerging detrimental crowding effects. We will study T cell crowds in a wide range of conditions in silico, in vitro and in vivo: we will (1) conduct simulations to predict how T cell crowds operate in challenging conditions and which emerging crowd behavior is expected; (2) design microfluidic devices to expose T cell crowds to hallmark crowding scenarios, identify molecules involved in cellular crosstalk within crowds, and identify transcriptional signatures associated with efficient crowd operation; (3) examine T cell crowds in human and mouse tissues to test whether dynamic or static patterns indicating jamming can be confirmed in physiological or pathological conditions in vivo. Thus, by combining our unique joint expertise in crowd dynamics, immunology, and computational biology, we will shed light on how T cell crowds, a system quite unlike any other many-particle system studied so far, maintain motion with such remarkable efficiency in many conditions and what it would take to disrupt their smooth flow.

Keen observers of nature have often wondered why diverse species seem to behave similarly. For example, different species of Hawaiian spiders spin similar web architectures, diverse anoles lizard species bob their heads with the same styles and speeds, and distinct species of damselflies avoid predators using the same techniques. These and many other striking examples are thought to represent the convergent evolution of behaviors. Does this convergence reflect changes in the same genes or does evolution act through many genetic routes to create the same behaviors? Genetic differences clearly play a role in behavioral variation, but it remains challenging to identify the genes that underlie evolution of behaviors. However, convergence in the genetic basis of developmental or physiological traits has been discovered with many specific examples of genes and mechanisms, so it is possible to use studies of convergence to discover how behaviors evolve. Therefore, we will use a powerful comparative system to discover the genes and molecular mechanisms that underlie convergent evolution of behaviors for the first time across divergent animals.
The Caenorhabditis nematodes offer a unique experimental platform to connect behavioral differences to genetic differences. Starting with the keystone model organism, C. elegans, and existing data, we will characterize and classify genetic differences across wild isolates from three species of Caenorhabditis - C. briggsae, C. elegans, and C. tropicalis. Whole-genome genotype data combined with high-throughput, high-content imaging of behaviors from these same wild isolates will be input into unsupervised machine learning algorithms to create a high-resolution genotype-phenotype map for a range of natural behaviors and examples of convergence. This map will be queried for signatures of shared genetic changes at orthologous genes to identify which variants are most important evolutionarily. The result will provide the first systematic glimpse into the genomic “knobs” that control behaviors at single-variant resolution across species and insights into the repeatability of the evolution of behaviors.

Sea urchins are marine animals genetically close to the vertebrate lineage. Being eye-less and lacking a central nervous system (NS), these animals instead feature dermal photoreceptors dispersed over their spherical body surface and feeding into a decentralized NS. However, sea urchins can visually resolve objects and move towards them, and they can detect looming visual stimuli from any direction and accurately point their spines towards them. Such performance is normally associated with proper eyes feeding information into a brain. Sea urchins thus offer access to a unique visual system of a type that to date has not been studied in terms of its information processing. This alternative solution to vision may also have potential biomimetic applications for robotic miniaturization, smart probes, and intelligent materials where dispersed light detectors control the properties of the material.
The core of the proposed project is to investigate and model the neural mechanisms of information processing, which enables sea urchins to perform spherical vision by deploying an obviously very different mechanism from today's technology, and also very different from visually guided behavior in most other animals. Our study includes molecular and morphological identification of cell types, measurements of behavioral responses and electrophysiological photoreceptor responses, mapping the connectome of sea urchin photoreceptors and NS, and theoretical modelling of the information processing underlying visually guided behavior. We will map the connectomics of the NS and record the activity from key positions in the processing of visual information and generation of locomotory responses. The data will be used for computational modelling of the entire process from visual input to motor control. Special focus will be given to behavioral decisions where small changes in stimuli cause behavioral switches. We will also use genetic approaches to test the agreement between theoretical models and actual behavior.

The Personalized, Regenerative Medicine of the future will rely on being able to make replacement cells and tissues of choice at will and in a robust, predictive manner. However, key challenges have to be overcome before the promise of personalized stem cell therapeutics becomes a reality. This is because stem cell renewal/differentiation are stochastic processes, precluding the differentiation of a stem cell population into a homogeneously differentiated desired cell type, but also leading to spurious differentiation during renewal. This is thought to partly arise from heterogeneous single-cell signaling states among different cells of a population, which are not measurable using classical ‘population-average’ biochemical methods. A mechanistic understanding of how dynamic signaling processes control differentiation/renewal fates at the single-cell level might therefore significantly improve our capacity to robustly and precisely manipulate cell fates for tissue engineering purposes. We propose to use an integrated interdisciplinary strategy to map the dynamic, single-cell signaling programs that control differentiation/renewal using human Pluripotent Stem Cell (hPSC) differentiation into neural stem cells as a differentiation paradigm. Using multiplexed, genetically-encoded biosensors, we will quantitate hPSC single-cell dynamic signaling states by large-scale, multi-color, multi-day timelapse microscopy across millions of cells, to reveal with unparalleled precision how heterogeneous signaling states correlate with renewal/differentiation fates. Using computer vision approaches, we will automatically segment, track and lineage at scale each of the cells that were induced to self-renew or differentiate, and we will extract a panel of signaling, cell-cycle, pluripotency state, and cell morphodynamics features that quantify these dynamic processes. We will then mine these high-dimensional feature sets to build computational models that identify dynamic single-cell signaling patterns associated with robust fate transitions and predict actionable interventions that might cause those transitions. Lastly, using drug perturbations, and/or microfluidic/optogenetic actuators, we will quantitatively test those predictions by evoking synthetic dynamic signaling states that induce robust fate transitions. Our approach will help to significantly clarify the mechanistic basis of signaling-mediated human stem cell fate decisions, providing new avenues to robustly control stem cell fate. This might help establish a larger framework, broadly applicable to other hPSC lines and differentiation routes.

Osteoderms are hard calcified tissues that form within the skin of some animals. They resemble bone, hence the name, but are fundamentally different in several respects. Crocodile and armadillo skin plates, and turtle shells are among the most familiar examples, reportedly forming a protective armour against external predators and aiding locomotion. However, although less visible, osteoderms are also present in many lizards.
In terms of their shape, spatial distribution, and interaction, lizard osteoderms show the highest diversity in the animal kingdom, yet we know little about what drives this extraordinary diversity, how it is controlled, or how it originated. It could be a biproduct of other genetic differences or, more likely, a natural optimization to enhance osteoderm function, protective or otherwise, under conditions specific to each lizard type.
This project brings together a multidisciplinary team of expert engineers, developmental and evolutionary biologists from the UK, Canada and France to investigate the mechanisms underlying the development, patterning, and evolution of osteoderms in lizards. The team will use a range of advanced techniques (e.g. genetic analysis, material testing, imaging, and computer simulations) to investigate lizard osteoderms from the first molecular signalling events and cellular interactions, through to organismal level. Osteoderm mechanical properties will be characterised both as single units and as sheets so as to understand their function during feeding and locomotion.
This is a basic science project focused on a novel biological tissue and its evolutionary implications, but with a systems approach that may shed light on pathological calcifications, as well as aiding the development of biomimetic materials and structures. Most importantly it will train the next generation of scientists, in a multidisciplinary and international setting, providing them with a fundamental knowledge of biological tissues and a diverse skillset with which to address the global challenges of 21st century.

The cell membrane is a double layer of lipid molecules. It plays a critical role in protecting the cell from its environment and in separating the different processes that take place within its interior. Membranes must change their shape in order for the cell to function, especially during cell division, and this depends on membrane curvature. At present, cells are only known to induce curvature by accumulating lipids in one of the layers or using specialised proteins. Our goal is to investigate a new mechanism of inducing membrane curvature by accumulation of hydrocarbons in the middle of the lipid layers that has not been observed before in nature.
These hydrocarbons are like the components of diesel fuel, and are found in photosynthetic cyanobacteria and algae – some of the most abundant and widespread organisms on Earth. Production of hydrocarbons in cyanobacteria or other microbes could substitute for liquid fuels derived from petroleum. As well, cyanobacteria and algae release hydrocarbons into the environment, where they are degraded by other bacteria that clean up oil spills. However despite their environmental and biotechnological importance, the exact cellular role of hydrocarbons has not been determined.
We recently discovered that hydrocarbons are essential for maintaining optimal cell size, growth and division, processes that require cell membranes to curve and bend, and found that cells lacking hydrocarbons have lipid membranes that are less curved or flexible. We showed that hydrocarbons integrate into the cell membranes, and used computer simulations to predict that this induces membrane curvature. To investigate this further, we have assembled a team of scientists from the UK, Sweden and New Zealand. By combining our different skills, we will analyse how hydrocarbons affect the physical properties of algal and cyanobacterial membranes by 1) running computer simulations; 2) studying membranes purified from algae and cyanobacteria; and 3) carrying out experiments on live cells. Together, these simulations and experiments will allow us to explore and quantify how hydrocarbons affect curvature and other membrane properties, and so conclusively establish the role of hydrocarbons in cells. As well as improving our understanding of biology, this information will assist the use of microbes for biofuel production and oil spill cleanup.

Despite effort, it remains incredibly difficult to identify the cellular basis, and/or the causative genetic variants, underlying complex behavior. Understanding how behavior is encoded requires solving a dual problem involving both neurodevelopment and circuit function. Genes build nervous systems; nervous systems are activated to produce behavior. Streelman and Baier will collaborate to develop a unique model system to chart the complex path from genome to brain to behavior, in vertebrates from natural populations. In Lake Malawi, male cichlid fishes construct sand ‘bowers’ to attract females for mating. Bower building is an innate, repeatable natural behavior that we quantify in the lab. Males build two bower types: 1) pits, which are depressions in the sand, and 2) castles, which resemble miniature volcanoes. Species that build these two bower types can interbreed in the lab. Remarkably, first-generation hybrids of pit- and castle- species perform both behaviors in sequence, constructing first a pit and then a castle bower, indicating that a single brain containing two genomes can produce each behavior in succession. Moreover, brain gene expression in these hybrids is biased towards pit- alleles during pit digging, and castle- alleles during castle building. This phenomenon of allele-specific expression matched to behavior is compelling and offers the chance to identify the genome regulatory logic and neural circuitry underlying complex behavior. Streelman’s group will use single-cell RNA-sequencing to pinpoint specific cell populations that mediate context-dependent allele-specific expression in male bower builders. Baier’s team will use genome editing and optogenetic tools to manipulate the neurons that matter in the brains of behaving Malawi bower builders. Our collaborative work will thus identify the neurons responsible for biased allelic gene expression matched to behavior, and then manipulate those neurons to modify behavioral output. Achieving our goals will demonstrate how the genome is activated in particular cell types to produce context-dependent natural social behaviors.

Capturing the dynamic 3D atomic-scale structure of a macromolecular machine while it performs its biological function remains an outstanding goal of biology. Conventional structural tools (e.g. X-ray crystallography, NMR & cryoEM) only provide ‘snapshots’ of stable states along a reaction pathway. Reaction intermediates, and in particular short-lived intermediates, are hard to capture and characterize with such conventional techniques. Here we propose to combine (prior) information from multiple existing static structures of stable states with dynamic datasets of inter-atomic distances obtained by high-throughput non-equilibrium single-molecule FRET (smFRET) measurements in a microfluidic mixer using novel time-resolved multi-pixel single-photon avalanche diode detector. These measurements will be performed on libraries of molecular constructs, sampling multiple inter-atomic distances as function of reaction time. These measured distance distributions will then serve as multiple intra- and inter-domain distance constraints which, together with prior information (available structures), will enable the Rosetta software to achieve large-scale energy optimization-based refinement of time-resolved ‘snap shots’ of complex structures with improved accuracy. These time-resolved Rosetta structures together with intermediary molecular dynamics simulations will allow solving the 3D atomic-level structure of the macromolecule for each sampled reaction time point, eventually producing a 3D structural dynamic movie of the macromolecule in action. To demonstrate the utility of the proposed method, we will solve the dynamic structure of RNA polymerase during transcription initiation (promoter binding, bubble opening, abortive initiation, promoter clearance) and a pair of intrinsically disordered proteins (IDPs) involved in transcription regulation (ACTR and NCBD) that adopt a fully folded structure during a coupled folding and binding reaction. In addition to elucidating outstanding questions in transcription by combining detector developments, high-throughput and time-resolved out-of-equilibrium single-molecule FRET measurements with new experimentally-constrained molecular structure computational approaches, this multidisciplinary project will result in a new generic toolkit applicable to a large array of enzymes, proteins and molecular machines.

To understand how the brain represents the world is a central theme in neuroscience. Neural circuits encode information in terms of rate, timing and synchrony of action potentials arising from the activity of a complex spatial organization of excitatory/inhibitory neurons. The identity of the active neurons at any given time has a profound effect onto the final outcome and regulates fundamental human psychophysical behavior. Attempts to decode the complex behaviors in neurons has led to development of several neuromodulation and sensing tools, by which neural activity can be controlled by microelectrodes or through genetically altering specific neural circuits, ultimately to deliver electrical impulses. Unfortunately, these techniques introduce strong perturbations to the observed system, without reaching the desired spatio-temporal resolution. Experimental approaches that allow non-invasive activation of specific phenotypic groups of neurons could introduce systematic variation of the timing of signals revealing how neural ensembles encode information. Here we propose a novel (nano)tool to alter membrane potential in specific neuronal phenotypes in a spatiotemporally controlled manner.
The nFlare project will develop a novel research paradigm in neuroscience based on a new class of injectable nanodevices delivered to neurons and anchored to their membranes. These nanotools have the ability to depolarize cell membranes or to deliver active biomolecules. Electric or chemical stimulation of single cells will be achieved using deep-penetration near infrared excitation light, to reduce invasiveness.
nFlare core aims are: i) the development of nanodevice components able to generate photoelectrochemical current upon illumination with light; ii) state-of-the-art biochemical functionalization of the nanodevice to target specific neurons and to reduce inflammatory response; iii) applicability of such nanoscale devices as high spatio-temporal neuronal activity modulators in 2D and 3D in vitro neuronal networks followed by in vivo validation.
Selective modulation of neural circuits by artificial stimulation of neuronal membrane or controlled delivery of environmental factors could help answering fundamental neurobiological questions.

In 2014, the Nobel Prize in Physiology was awarded for the discovery of place and grid cells that process spatial cues in the mammalian hippocampal formation;, the key structure for both navigation and episodic memory1. Place and grid cells, and additional cell types form the central building blocks in the current circuit models of navigation in mammals. However, a cohesive picture of how these circuits compute space and enable navigation has not been achieved. In fact, emerging evidence suggest that these navigation circuits are highly diverse in cellular phenotypes and functionality; they do not only map aspects of space but also elements like sound, time, and reward. How neural systems of spatial cognition have evolved outside of the mammalian clade is not clear, and comparative studies are critically needed to gain insights to basic functional constraints and structural requirements underlying these neural circuits.
The international research team of four PIs proposes a broad comparative systems neurobiological approach using teleost fish for integrative studies on higher navigation circuits. These fish are ideal models because they have conquered diverse spatial ecologies and show highly specialized sensory adaptions. Also, their brains exhibit an overall lower complexity to mammals, and are highly accessible to experimental manipulation. To establish systematic research on teleostean spatial cognition, the project combines neuroethological, electrophysiological, neuroimaging, and computational methodologies. Introducing a powerful electrophysiological recording technology in freely-moving fish and generating long-needed anatomical atlas resources, the project analyzes four teleost species with differing spatial ecologies. The team will uncover how different sensory modalities like vision, perception of depth, and active electrolocation are integrated during spatial navigation tasks, thereby investigating how top-down mechanisms modulate sensory integration of spatial learning.
Finally, the team will test specific hypotheses developed in small-scaled laboratory setups in an unconstrained natural environment. Here, the group will measure the activity of neurons in freely moving fish that explore a coral reef habitat. This will be the first ever attempt to analyze brain activity underlying navigation in the wild. Altogether, the project will provide new perspectives on the evolution, function, and mechanism of memory systems in animal navigation.