The recent discovery of 7-deazapurine bases in bacterial and bacteriophage DNA revealed an unexpected interplay between RNA and DNA modification pathways. In phages, these hypermodified bases could play major roles in pathogen-host interactions, DNA replication, DNA-packaging into capsids, gene expression and bacteriophage evolution. Using a combination of genetic, biochemical and sequencing approaches, our objectives are to discover i) how widespread is this phenomenon, ii) the exact pathway(s) leading to the incorporation of these alternative bases into DNA, iii) the “raison d’être” for these modified bases, and iv) if this phenomenon arose as an offensive or as a defensive tool.
To characterize the distribution of 7-deazapurine bases in bacteriophages DNA, we will use several approaches including comparative genomics, mass-spectrometry analysis of modified bases, high-throughput sequencing of restriction endonuclease resistant environmental virome samples, and genome sequencing of phages infecting a wide range of bacterial hosts. This will generate a collection of diverse, sequenced and partially characterized phages that will be made publicly available via deposition in the Félix d’Hérelle Reference Center for Bacterial Viruses. Biochemical, molecular biology and genetic studies will be performed on phages harboring a variety 7-deazapurine variants to elucidate these novel DNA modification pathways. We will also study the role(s) played by the DNA modifications in phage biology, with a particular emphasis on understanding their potential role in overcoming host defences or in facilitating phage-mediated destruction of unmodified host DNA. In addition to classical biochemical, genetic and omics methods, third generation sequencing (PacBio and Nanopore) technologies will be exploited. These can already detect simple DNA modifications, like methylation, but we will combine machine learning with the sequencing of phage DNA molecules containing specific 7-deazapurines to expand the range of detectable DNA modifications. Although the scope of this project is limited to deciphering the role of 7-deazapurines in bacteriophage DNA, our results could spark developments of new genome editing tools and increased knowledge on gene regulation and epigenetics that may some day change paradigms in human health.

Enzymes catalyze molecular transformations in living systems while being pushed and squeezed by other biomacromolecules. This molecular crowding generates excluded volume effects that result in large quantitative consequences on the rates and the equilibria of enzymatic activity by molecular mechanisms that are not yet fully elucidated. To recreate these features, we propose packing tens to hundreds of enzymes within bacteriophage capsids to create nanoreactors (NRs). Our nanoreactors allow control over packing density, protein environment, and boundaries at the nanoscale. Regulation of these conditions is key for understanding in vivo enzyme behaviour, and facilitating metabolic engineering efforts. We will focus on the mevalonate (MVA) isoprenoid pathway, which, despite demanding enzymatic cascade reactions, is widely used for production of industrially useful biochemicals. Limitations may be related to the lack of proximity between partner enzymes, unfavourable interactions under molecular crowding conditions, and/or unfavourable molecular ratios between cascade enzymes. Here we pursue an integrated study to produce materials where MVA enzyme packing and stoichiometry are controlled to mimic the complexity of the intracellular environment. The kinetics of enzymes within NRs will be measured using both ensemble and single-molecule techniques. The enzymatic activity of single nanoreactors will be monitored using a combination of atomic force and fluorescence microscopies, which will allow the in situ study and manipulation of single NRs in real time to provide individualized details not accessible with averaging ensemble approaches. These studies will be supported by multiscale modelling of the crowded enzyme environment. Our experiments will reveal the optimum biophysical conditions for maximizing metabolic flux in the MVA pathway. Further, designed NRs will be assembled and assessed within living cells. Our project has both fundamental and applied aims. First, to understand how crowding affects enzymes at the nanoscale, including metabolite diffusion and protein conformational changes. Second, to improve the biosynthesis of isoprenoids in biological and industrial systems.

Epithelial collective cell migration (CCM) is important in biological processes such as organ development, tissue maintenance and tissue repair. CCM usually occurs on curved surfaces such as that found in blood vessel walls and intestinal villi. However, current CCM research are still conducted on flat surfaces. This is due to lack of techniques to fabricate tubular and spherical microstructures resembling that of organs as well as difficulty in observing and measuring the collective dynamic behaviour of cells on these surfaces. Nevertheless, recent studies are beginning to show that cells do behave differently on curved surfaces. However, these studies were mainly focused on single cells, as such, there is limited insight into CCM which involves complex interactions among multiple cells.
Here, we claim that curved surfaces can significantly influence CCM. As such, we propose to systematically study CCM on such surfaces to better understand their underlying mechanisms. Our project will combine distinct but complementary scientific expertises coming from biology, biophysics and engineering. We aim to: 1) Develop novel techniques in fabricating different, well-defined curved geometries such as tubular and spherical surfaces with nano-patterns; 2) Identify biomolecules and proteins involved in CCM on curved surfaces; 3) Develop computational models to perform 3D imaging and analysis so as to better monitor and measure complex tissue dynamics; and 4) Develop a universal theory to better explain and unify experimental observations.
Together, the unique combination of these different approaches will enable us to better understand the fundamental mechanisms and impact of surface curvature on cell behaviour and tissue organization. These will shed light on related biological functions such as in organ development and tissue repair, as well as contributing towards better tissue engineering or regenerative medicine applications.

As intracellular, obligate parasites viruses rely on a diversity of strategies to survive. Some, like most RNA viruses, cause acute “hit-and-run” infections and require large susceptible host populations. Others, mostly DNA viruses, persistently infect their hosts. Morbilliviruses, like measles virus (MV) and canine distemper virus are examples of RNA viruses that can cause persistent infections. The exceptional circumstances which converge to drive RNA virus persistence remain unclear, since these viruses need to replicate continually in vivo. Bats are evolutionary and ecologically unique mammals standing out in their ability to tolerate viral infections. We recently showed that bats host major mammalian paramyxoviruses and described an ancient morbillivirus in vampire bats. This discovery provides a unique opportunity to explore RNA virus persistence in an important reservoir host that has high rates of contact with humans and domestic animals through its blood feeding habits. The primary objective of the proposed research is to unravel fundamental mechanisms that govern morbillivirus persistence and understand the consequences for immunity and infection. We hypothesize that within-host persistence in canonical morbilliviruses is a vestigial relic of a more common strategy that evolved in bats. Research activities comprise a multidisciplinary program involving investigations of natural and experimental infection of bats, reverse genetics to resurrect viruses from infectious clones, comparative studies of canonical morbilliviruses and hosts together with investigation of pathobiology and immunity underpinned by mathematical modeling. Existing materials, a unique field network for long-term monitoring vampire bats, novel approaches and critical insights from systematic field and laboratory studies make this project both exciting and feasible. This study represents an unusual integration of approaches and ideas from disease ecology, evolutionary biology and synthetic biology. We expect this fusion to resolve fundamental knowledge gaps in our understanding of how RNA viruses persist in hosts; a critical component for the understanding of rare disease manifestations, the consequences of viral persistence on the host immune system and for identifying patterns in risk of virus transmission between species.

Cells achieve reproducible outputs while relying on intrinsically stochastic molecular processes. In plants, cell division orientation is accurately predicted before mitosis by a microtubular ring, the preprophase band (PPB), through an unknown mechanism. Using high-throughput microscopy, quantitative image analysis, biomechanics and stochastic modeling, we will investigate how the stochasticity of microtubule (MT) self-organization is used, or filtered out, during PPB formation to sense temporal, geometric and mechanical cues in order to generate a robust placement of cell division planes. In short, we will test whether the PPB acts as a macromolecular mechanosensor. Using statistical mechanics such as Monte Carlo, event-based modeling and mean-field theory, we will assess how stochasticity leads to distinct dynamical states for MT arrays. We will develop biophysical models of dynamic MTs in 3D cells to explore how stochastic MTs self-organize into PPBs and how spatiotemporal cues modulate that process. Using the Arabidopsis shoot apical meristem, we will identify correlations between MT array dynamics, PPB behavior, cell shape, growth and mechanics, cell cycle progression, and cell division plane from statistically representative sample involving hundreds of dividing cells. To challenge the robustness of PPB and cell division in vivo and in silico, we will globally and locally increase variability in these cell parameters with mutants (MT dynamics, wall mechanics, cell cycle, mechanotransduction), inducible lines, mosaics, and micromechanical perturbations. Last, we will unravel the molecular mechanism processing molecular stochasticity to channel cell divisions: we will investigate how the TTP (TON1-TRM-PP2A) complex, a key regulator of PPB formation in connection with the cell cycle, contributes to generate reproducible divisions by monitoring MT self-organization, integrating geometric, mechanical and temporal cues. Implications of this project go beyond cell division robustness: while many cellular pathways are adapted to respond to rapid and discontinuous changes, we expect here to unravel mechanisms managing slow and continuous signals, like shape, growth or tissue-stress. We will also gain insight into the mathematical properties of stochastic extended objects like microtubules, and we will propose a mechanism for cells to perceive directional cues.

Ribonucleoprotein (RNP) granules are "membraneless organelles" found in eukaryotic cells. Common to RNP granules is enrichment of RNA-binding proteins harboring intrinsically disordered domains of low sequence complexity (LC) that may self interact, stabilizing granules via liquid-liquid phase separation (LLPS). Importantly, several LC domains of stress granule (SG) proteins are aggregation-prone and associated with inclusions observed in many neurodegenerative diseases. Although the constituent proteins and RNA are being enumerated by mass spectroscopy and sequencing approaches, and the ~100 nm-scale features are being resolved by fluorescence microscopy techniques with improving resolution, a fundamental conformational/physical-chemical understanding of structures and protein-protein interactions stabilizing SGs remains to be directly interrogated. Our goal is to observe with, residue-by-residue detail, the protein-protein interactions and structural changes caused by disease-causing missense mutations in human SGs both in vitro and in cells. Using the well-studied protein FUS to benchmark our spectroscopic tools and the SG marker and nucleator TIA-1 as a focal point, we will provide the first analysis of protein secondary structures and stabilizing interactions in granules. We will apply in-cell vibrational and Förster resonance energy transfer (FRET) imaging combined with NMR spectroscopy to determine the molecular details of LC domain structure in LLPS assemblies in vitro and SGs in cells. By examining disease-associated variants known to form intracellular aggregates or impair SG dynamics, we will probe the underlying physico-chemical protein changes underlying disease-associated SG biophysical changes. Our novel molecular toolbox and experimental protocols to probe protein structure in cellular (and in vitro) SGs will be immediately applicable to other RNP granules and phase separated structures and their disease-associated changes.

Muscles are producing the active forces that enable all our voluntary body movements. The cellular components of muscle are called muscle fibers and are very large cells that can be several centimeters in length in humans. Inside all muscle cells are millions of tiny molecular machines, called sarcomeres that produce the active forces of muscles. These sarcomeres are arrayed in highly regular long chains called myofibrils that span from one muscle end to the other. This arrangement ensures that the force produced by the myofibrils is transmitted to the connected tendons and bones at the muscles ends. The highly regular periodic organization of the myofibrils results in the cross-striated appearance of skeletal muscles under the microscope.
We assembled an international team of researchers from France, USA and Germany to study how myofibrils are built during muscle development. For this we combine two different experimental model systems, the fruit fly Drosophila muscles and human muscle cells generated in vitro from stem cells, together with mathematical modeling of myofibril assembly. Our starting hypothesis is that mechanical forces, which are generated early during muscle development, are an important factor for the polymerization of myofibrils. We will test this hypothesis in fly as well as in human muscles produced in vitro by following the localization of sarcomeric protein components with high-resolution microscopy techniques. We will measure when a sarcomeric protein pattern can be first detected and how this pattern is refined. These data will be used to generate a computer model that can produce periodic sarcomere-like structures that are linked together from homogenous components. We will further measure the strength of mechanical forces present during myofibril formation in fly muscles in vivo and in human muscles in culture. Finally, we aim to modify these forces and to observe the effect of these manipulations on the developing muscles and myofibrils. These data will be used to produce a refined computer model of myofibrillogenesis. Together, our research should lead to a better understanding of how the contractile apparatus of human muscle is built, which could eventually be helpful to treat muscle injuries or aged dependent myopathies.

Perceiving and interpreting our sensory environment feels subjectively effortless, yet, it is one of the computationally most challenging tasks the brain needs to solve. Critically, our brain constantly fuses current sensory input with expectations derived from prior experience in order to be able to reliably interpret our environment in spite of ever-changing conditions, such as lighting, pose, or configuration, making every single experience unique. That prior expectations are constantly shaping our percepts has been well known for over a century from behavioral studies, but how neural circuits in sensory areas of the brain achieve this sensory-prior fusion has remained largely unknown. Progress in several research fields make it possible now to address the neural bases of sensory-prior fusion: novel recording techniques provide an unprecedented opportunity to track the activity of large populations of neurons over extended periods of time; advances in artificial intelligence and machine learning provide powerful new tools to analyse complex data; and advances in computational theories allow us to formulate experimentally testable hypotheses and compare their predictions to data. These techniques are now being used to great effect in sensory neuroscience, but almost exclusively to characterise how sensory input alone impacts neural responses along different stages of the visual processing system of the brain. Our project aims to bring these advances to bear on understanding the effects of prior expectations on neural responses, and their fusion with sensory information. We will rely on complementary approaches that provide insights into different aspects of sensory-prior fusion: experts in mouse experiments and monkey experiments will train complex tasks and record from brain areas that contribute to sensory-prior fusion, while experts in machine learning-based data analysis and computational neuroscience will develop cutting-edge analysis techniques and models to guide experiments and interpret data in a formal theoretical framework. Our project will thus help us understand how a brain that is composed of unreliable components and prone to a variety of perceptual distortions and illusions can efficiently process rapidly changing, cluttered, and noisy scenes to achieve feats such as driving us home safely in busy traffic.

Understanding how out of single cells functional tissues and organs develop is a major challenge of biology. Recent progress allows us to grow organ-like cell assemblies (organoids) from stem cells in vitro. Organoids offer great potential for studying diseases and development. However, in many cases we do not yet understand how these complex tissues emerge out of progenitor stem cells. A common feature in the initial growth phase of many organoid systems is the formation of a polarized epithelial cyst with a single or multiple internal apical lumen. This initial transition into an epithelial cyst establishes a tissue template that on the one hand enables maintenance of progenitor/stem cells (niche) and on the other hand guides the patterning of differentiated cells into a functional tissue. Our aim is to understand how the interplay between proliferation (cell divisions), polarization (epithelial transition) and differentiation (patterning) leads to self-organization of this epithelial progenitor template and how this structure facilitates correct patterning into functional organoids. To this end, we will systematically control and characterize the early growth phase of two organoid systems (pancreatic and neural tube) using microfabrication and micro-patterning approaches. We will quantify evolution of cell shapes, adhesion and cortical forces, apical-basal polarization and differentiation as a function of initial cell contact patterns. This approach will provide the means to find rules how local cell interactions (cell-cell, cell-matrix, cell-lumen) are connected to tissue growth and differentiation. We will then test sufficiency of the hypothetical rules to generate the observed organoid structures using an in silico mechano-chemical model. Taken together, by dissecting the early growth phase of two organoid systems, we aim to uncover the common rules on how progenitors establish a polarized epithelial template, and how this template is then differentially used to generate organ specific differentiation patterns.

Life on earth depends upon the photosynthetic conversion of H2O, CO2 and light into fixed carbon (C) compounds including polysaccharides and lipids, and yet the biosynthesis, regulation and many aspects of the function of the photosynthetic apparatus are still not well understood. A major challenge for photosynthetic organisms is to capture light energy for growth while at the same time eliminating excess absorbed excitation to prevent photodamage, which can be energetically costly and potentially lethal. A major photoprotective mechanism in plants and most algae is qE (quenching energy), a quenching mechanism that dissipates absorbed excitation energy as heat, ensuring survival even under adverse conditions. In the green alga Chlamydomonas reinhardtii (Cr), two LHCSR proteins (Light Harvesting Complex Stress Related; LHCSR1 and LHCSR3) are required for qE function.
The LHCSRs are nucleus encoded, and their expression is governed by transcriptional and post-transcriptional processes that are impacted by CO2, nutrient availability, photosynthetic electron transport (PET), blue light perception and calcium(Ca2+)-elicited signalling. Despite their fundamental importance, mechanisms that regulate LHCSR accumulation are still not well understood and complicated by multiple inputs. For example, our findings demonstrate that LHCSR control is impacted by the metabolism of fixed C compounds. The combined use of physiology, biochemistry, genomics and mathematical modeling will allow us to elucidate transcriptional, post-transcriptional and epigenetic processes that link photosynthesis, C metabolism and photoperception to qE activity, and help establish a systems level view of photosynthetic dynamics.

The complexity of cellular machineries has been forged by the motors of evolution (mutation, natural selection, and genetic drift), with boundaries that are determined by the properties of biological molecules and the biophysical limits of the cell. Our goal in this project is to determine what are the relative contributions of biophysics and evolution in shaping this complexity. Our model of interest is cell signaling, which is rapidly evolving and involves highly interconnected networks of proteins that sense and process signals and make real-time decisions to satisfy cellular needs and promote survival and proliferation. Because typical cells contain from millions to billions of protein molecules, signals can reach out to unwanted proteins in an unspecific manner. How much can evolutionary forces optimize this process so that cells can reach biophysical optimality by minimizing unspecific interactions is currently unknown. This question can be answered neither from physics nor from biology alone, and thus requires a multidisciplinary team. Our international team with expertise in theoretical physics (Gasper Tkacik), evolutionary biology (Christian R Landry) and biochemistry (Judit Villen) is poised address this question. We will generate a mathematical model of signaling network evolution that includes key biophysical and evolutionary driving forces to examine how each contributes to create unspecific signaling interactions in organisms with finite population sizes. This model will guide experiments in which we will manipulate the amount and extent of unspecific interactions and measure its biochemical impacts and its effects on fitness using experiments and bioinformatics data integration to calibrate parameters of the model. In biophysics, it is often assumed that biological systems have reached an optimum attained by natural selection and are only limited by their physical characteristics. Evolutionary biology considers that natural selection works against genetic drift and mutations to bring biological networks near their optimum, which is unknown. Our work between these two fields will reveal how close to the biophysical limits can signaling networks get and how biophysical and evolutionary forces are integrated to shape accurate signal transduction.

Heat produced by living species has been known for centuries but viewed merely as a factor contributing to body temperature. Recent progress in ultra-local thermometry at a cellular scale has challenged this concept. Reports on heat release by active ion channels and pumps and other pioneering experiments and theoretical estimates have fuelled discussion about the significance and the magnitude of endogenous heat. Discoveries of the faster neurite growth aligned with a thermal gradient and heat-pulse induced muscle contractions without Ca2+ influx suggest that cell hot spots may channel signalling and thus contribute to cell and tissue differentiation. We will study the role of heat in three-dimensional cell shaping and differentiation and test the hypothesis that localized heat signals can affect morphology in cells other than neurons. We will aim to resolve the current controversy of intracellular thermometry, the drastic mismatch between theoretical estimates based on near-equilibrium thermodynamics and reported experimental values of temperature rise. We will investigate the signalling activity of artificial hot spots introduced into or close to cells and measure thermal activity of natural hot spots on cell membranes. We will develop a reliable intracellular nanothermometry system and an ultra-local heat-shock trigger-and-measure method to create and control the ultra-local hot spots with light. The method will use a synthetic complex of several nanoparticles, one as a heater and the others as temperature sensors. For the heater, we will use a gold nanorod whose heating can be controlled by the wavelength and polarization of light focussed on the rod. The temperature will also be measured optically, with nano-sized crystals of diamond doped with photoluminescent defects (nitrogen-vacancy centres being the primary candidate fro this role). The expertise of the applicants provides the required synergy between advanced nanotechnology, biophysics and cell biology to achieve these goals. The project starts simultaneously at four nodes and efforts will increasingly integrate as the investigation progresses.

Basic biological processes such as cell migration, tissue morphogenesis, and hearing rely on mechanotransduction, i.e., the conversion of mechanical forces into electrical and biochemical signals that can be used by individual cells or multicellular organisms to survive. Adhesion complexes bearing mechanical force are often needed in mechanotransduction, with several protein families that include integrins and cadherins directly transmitting force signals to sites of transduction. Classical cadherins, key for tissue morphogenesis and integrity, provide calcium-dependent bonds involved in cell-cell contact formation, in signaling to the actomyosin cytoskeleton, and in mechanical stabilization of cell-cell adhesion. Non-classical members of the family are also involved in mechanotransduction, with cadherin-23 and protocadherin-15 forming an adhesive “tip link” essential for the vertebrate senses of hearing and balance. Multiple structural and biophysical studies have elucidated the unbinding, unbending, and unfolding strength of single molecules and adhesive complexes, including those formed by cadherins. However, to the best of our knowledge, nothing is known about how the mechanical properties of adhesion complexes have evolved. Here we aim to study the mechanical evolution of adhesion complexes using inner-ear cadherin tip links as a model system. Tip links initiate sensory perception by directly conveying tension to inner-ear mechanotransduction channels on a microsecond time scale. Fast and effective transmission of mechanical force must be one of the selective factors that dictate the molecular evolution of tip links. We will sequence, synthesize, and study tip-link proteins from diverse extant species and will carry out evolutionary analyses both to reconstruct the sequences of ancestral, “resurrected” tip links, and to identify potentially stronger tip links from species in which positive selection is reported. In parallel, we will use biophysical techniques to determine equilibrium binding affinities and structural properties of modern tip links from various species and ancestral tip links at different evolutionary stages. High-speed force spectroscopy and molecular dynamics simulations will be combined to probe tip link mechanics and strength across species and during evolution. Our interdisciplinary work will provide a unique evolutionary view of adhesion mechanics.

The aim of this proposal is to gain insight into the mechanisms of chromatin reprogramming to totipotency in mammals. Totipotency is the potential of a cell to generate all cell types of an organism including extraembryonic tissues. Whilst induced reprogramming to pluripotency by the Yamanaka factors is relatively inefficient and takes days to weeks, natural reprogramming to totipotency occurs within hours in the one-cell embryo or zygote. How zygotic reprogramming is achieved is poorly understood. We will approach this fundamental question by studying the chromatin state of totipotent cells and their precursors. We will determine how higher-order chromatin organization changes during epigenetic reprogramming in zygotes and primordial germ cells, using both mouse embryos and human/mouse primordial germ cell-like cells. We will use our understanding of the zygotic chromatin state to identify candidate pioneer factors and to test their role in promoting totipotency and genome activation. The results of the project will provide insights into how reprogramming alters genome organization and promotes establishment of a totipotent chromatin “ground state”. This project is led by a strong multi-disciplinary team bringing together expertise in polymer physics (Mirny), ovary organoids (Saitou), biochemistry and single-molecule imaging (Peters) and zygote biology (Tachibana-Konwalski).

Breast cancer frequently metastasizes to bone where it leads to osteolytic bone degradation and poor clinical prognosis. Nevertheless, therapeutic options to interfere with this process are scarce as the underlying mechanisms remain unclear. Most current research focuses on the cellular and molecular signaling underlying bone metastasis, but breast cancer cell interactions with the bone mineral matrix, a composite of collagen fibers reinforced with nanoscale crystals of hydroxyapatite (HA), may be similarly important. However, in the field, there is an ubiquitous lack of (i) high resolution techniques to characterize the multiscale structure of bones as a function of breast cancer progression, (ii) microscopy techniques to identify the underlying changes in bone mineralization pathways in fully hydrated samples, and (iii) model systems to evaluate the functional consequences of varied bone mineral properties on cell behavior largely prevent such studies. Here, we will address these shortcomings and capitalize on the interdisciplinary background of the proposed team in cancer biology and tissue engineering (Fischbach), high resolution structural analysis of hierarchical materials (Fratzl), bone biology and biomineralization pathways (Addadi), and bioinspired materials synthesis (Estroff) to test the hypothesis that breast cancer modifies the hierarchical structure of bones by altering bone mineralization pathways and that the resulting changes in HA nanoparticle characteristics are critical to the pathogenesis of breast cancer bone metastasis. To address this hypothesis, we will pursue three specific aims: Aim 1 will analyze the multiscale structure of bones in mouse models mimicking pre- and post-metastatic stages of breast cancer. Aim 2 will determine the corresponding differences in bone mineralization pathways and Aim 3 will synthesize biofunctional bone-mimetic materials to study the effect of bone mineral matrix properties on tumor and bone cell behavior. This work will broadly impact our understanding of breast cancer bone metastasis and challenge the conventional paradigm of bone metastasis of this disease as solely initiated by cellular and molecular mechanisms. Insights gained from our studies may yield novel therapeutic targets to treat or possibly even prevent bone metastasis in the future.

Some four and a half billion years ago, primitive proteins spontaneously phase separated from their environment, like oil from water, to form liquid droplets, which became crucibles for the evolution of the chemical processes that underlie life to this day. This is how the Russian biochemist Alexandr Oparin postulated that life began to evolve, almost a century ago. Very recently, the droplets that Oparin predicted have been discovered and they are composed of molecules resembling the primitive proteins he imagined would have been found in the “primordial soup”. These droplets have been shown to serve a number of important functions and proteins that compose them underlie neurological diseases, including Parkinson’s and Huntington’s diseases and ALS, among others. How many such structures, called non-membrane organelles (or NMO), could exist in the cell and what are their functions? What are the physical rules and chemical information encoded in the sequences of MNO-forming proteins that dictate their unique properties? What happens if NMOs are disrupted? These are the questions we have set out to address in this proposal. The central aim of our proposal is to create a compendium of NMOs in a model of the eukaryotic cell; the bakers yeast. Yeast is an ideal model because it shares many genes with humans, notably those that are most primordial and those that become dysfunctional with age and in diseases such as cancers, and neurodegeneration. We will use massively parallel and automated experimental and computational and theoretical approaches to determine protein sequences that code for their ability to form MNOs of different types and predict the biological function of specific MNOs. We have shown how certain types of cell regulation including gene function and spatial organization of large cell structures depend on NMOs. The compositions of NMOs are surprisingly rich in protein molecules that when mutated, are known to underlie neurodegeneration. We hope to define how these mutant proteins cause dysfunction, perhaps by disrupting the normal functions of NMOs, and what kinds of strategies could re-stabilize them. Finally, the rules of NMO formation and function we elucidate in yeast will allow us to make predictions about similar structures throughout the tree of life, including in humans, ultimately establishing another axis of order that defines life.

In this project, we will provide new insights into the behavioral strategies, biomechanics and motor control animals use to perform complex versatile and adaptive functions and provide novel bio-inspired robot technology for solving complex motor control problems.
Despite the constraints imposed by their miniature brains and bodies, dung beetles are capable of the most impressive
feats of motor control and navigation. Beetles of the species Scarabaeus galenus are able to move large objects – dung balls that can exceed 10 times their own weight – accurately between a food source and their burrow by combining celestial compass orientation with step-counting. In addition, they are able to use their specialized body and legs to dig burrows, manipulate different dung types and move them over different types of terrain (e.g. sand or grass) using a variety of locomotor patterns. How do they achieve these complex versatile and adaptive behaviors when moving over difficult terrain? Our project aims to address this through our integrative approach which combines behavior (EB), biophysical, kinematic and biomechanical analyses (SG) of S. galenus, and computational motor control and robotic modeling (biorobotics, PM).
Research on behavior and neuro-biomechanical functions of insects has, until now, largely dealt with few behavioral modes – like walking, climbing, or navigating – in isolation. We will launch a major research effort aimed at uncovering behavioral strategies, biomechanics, and computational motor control that enable the miniature brains and bodies of insects to effectively generate multiple adaptive behavioral modes. Our behavioral experiments, both in the field and in the lab, will investigate locomotion, navigation, food transportation, and their combination in S. galenus. The data from these experiments will lay the foundation for novel biophysical, kinematic and biomechanical analyses, and computational motor control development. Simultaneously, biomechanical principles underlying the different behavioral modes will be used to shape the behavioral experiments and guide the structural design of robotic modeling. We will develop a biorobotic model that will enable us to test our hypotheses about the relationship between structural and mechanical features and how they interact with motor patterns and neural mechanisms to form complex behaviors.

The ribosome is the universal translation machine, which makes all the proteins in the cell, including its own. Much is known about ribosome structure and assembly pathway. However, cell-free synthesis and assembly of ribosomal proteins and rRNA into functional ribosomes has not been reconstituted, which would be essential for establishing a protein-based self-replicating model system of artificial cells, and provide a new means to develop antibiotics. We will address the challenge of cell-free ribosome biogenesis by combining the reconstitution of E. coli translation from purified components developed by Shimizu and Ueda (PURE system), with the miniaturized on-chip DNA compartments introduced by Bar-Ziv's lab. We address following three questions: i) What are the minimal requirements for cell-free ribosome biogenesis?; ii) How is ribosome biogenesis affected by excluded volume interactions, segregation, and macromolecular gradients in the DNA compartment?; and iii) Can DNA brushes coding for rRNA and ribosomal proteins mimic spatial ‘operons’ and promote efficient assembly coupled to synthesis? For these questions, we will attempt to 1) develop a methodology to label and localize cell-free synthesized ribosomal proteins and rRNA; 2) tailor the PURE system to support cell-free ribosome biogenesis reactions; 3) tailor DNA compartments geometrically to direct ribosome biogenesis and measure synthesis and assembly or ribosomal proteins and rRNA using time-lapse fluorescence microscopy; and 4) develop approaches to resolve, physically and biochemically, new from old ribosomes. The combination of molecular biology and biochemistry with soft matter physics and materials science are expected to provide favorable conditions for in vitro ribosome biogenesis and novel means to separate new from existing ribosomes.

A fundamental challenge in biology is to understand how brain cells change in response to experience and how these changes contribute to memory and establishment of other long-lasting behaviors. Changes in neuronal gene expression are important for memory formation, maintenance and retrieval. Recent advances in genome biology have found that three-dimensional (3D) genome organization—genome topology—is critical for transcriptional regulation. We hypothesize that neuronal genome topology provides special “topology codes” for activity-driven transcriptional modulation in neuroplasticity, and abnormal topology codes contribute to cognitive disorders. This project will examine genome topology and transcriptomics in mouse hippocampus and explore how the dynamics of genome topology contribute to the rapid and highly coordinated transcriptional response during learning and the long-lasting changes in gene expression that underlie memory, epilepsy and other enduring forms of neuroplasticity. Our multidisciplinary team with complementary skills in neuroscience, genomics and bioinformatics will take an integrated approach that includes a novel 3D genome mapping strategy for small numbers of cells (Ruan), advanced genetic labeling and biochemical isolation techniques for transcriptional and epigenomic profiling of neuronal ensembles (Barco), and state-of-the-art, multimodal microscopy for nuclear imaging (Wilczynkski). We will develop and optimize our platform in cultured neurons subjected to different stimuli (Aim 1), and we will examine the 3D epigenomic dynamics of the nuclear response to synaptic activity in two well-established in vivo paradigms: epileptic seizure (Aim 2) and the formation of associative memory (Aim 3). The large volumes of high-quality and multiplex genomic data representing dynamic changes in neurons in response to activation will be integrated by comprehensive computational analysis (Aim 4). Predicted genetic loci that have important roles in chromatin topology and transcriptional regulation will be validated structurally by super-resolution nuclear imaging and functionally by genome editing (Aim 5). The results of this effort will open new chapters in learning and memory and epilepsy research by laying a foundation for understanding the dynamic and topological mechanisms of genome regulation in neuronal plasticity in health and disease.

This work leverages cutting-edge genomic and shape analysis methods to study the evolution of mammalian cooperative societies. Current approaches to studying vertebrate cooperative behavior work in the classical, but mechanism-free, frameworks of behavioral ecology and life history theory. Thus, while cooperation itself is of long-standing interest, we know little about how animals that occupy distinct roles in cooperative societies differ at the molecular level.
Here, we propose to integrate new molecular and computational approaches with a 24-year field study of the most cooperative nonhuman mammal yet described, the meerkat. Like cooperative insects, meerkat societies are characterized by a division of social roles: adults are dominant breeders or morphologically and physiologically distinct helpers, who feed and guard the breeders’ young. However, all helpers retain the capacity to transition to breeder throughout life. Meerkats thus present an exceptional opportunity to study alternative phenotypes in cooperative societies. We will first investigate how helpers and breeders are differentiated at the gene regulatory level, including whether steroid hormone signaling generates these differences. Second, we will test social role-driven differences in growth and immune defense. We will track growth by developing computational geometry-based approaches to perform 3D skeletal reconstructions from X-ray data and immune defense using experimental pathogen challenges. Finally, we will test the hypothesis that the competing demands of growth, reproduction, and immune defense create “competition” at the transcriptional level, which is resolved differently by helpers and breeders. Our experiments will not only quantify phenotypic transition-associated trade-offs, but also identify the genes and pathways that mediate them.
The methods required for these analyses either do not exist or will need to be generalized to field studies for the first time. Thus, this study requires collaboration across behavioral ecology, genomics, immunology, and computational image analysis. Together, it will contribute a powerful model for applying modern tools to long-standing puzzles in evolution and behavior. It will also yield new insight into the molecular changes involved in the evolution of cooperative societies—a subject of fascination and controversy for almost two centuries.