The overarching goal of this proposal is to investigate the spatiotemporal coding of acetylcholine (ACh) and dopamine (DA) with high-resolution and precision in the striatum using state-of-the-art genetically encoded biosensors combined with modern optics in awake animal imaging. The striatum is crucial for movement, learning and flexible behavior, with striatal DA and ACh both playing key roles in these functions. While the role of DA is relatively well established, the role of ACh in natural behavior still remains enigmatic. Cholinergic interneurons (CINs), the major source of striatal ACh, are involved in processing contextual information that guides flexible behavior. Locally, CINs also exert control over striatal DA release, hijacking DA axons and making them release ACh by activating nicotinic receptors near their terminals. We propose to image the spatiotemporal dynamics of striatal DA and ACh using two-photon microscopy and endoscopy in awake mice engaged in tasks requiring behavioral flexibility. To image DA and ACh simultaneously during behavior we will extend the color-spectrum of DA and ACh biosensors. We will also further optimize the performance of these biosensors to make them suitable for robust in vivo application. Our combined interdisciplinary but complementary expertise – in biosensor engineering, imaging, modelling and behavior – is essential for our aims. We will ensure a coherent, interactive approach by sharing procedures, behavioral tasks, and biosensor technology, with regular planning sessions and feedback of results. A successful outcome of this program will reveal, for the first time, the spatiotemporal coding of neuromodulatory signaling by DA and ACh and how it shapes the function of striatal circuits during flexible behavior. We will also obtain a mathematical understanding of the genesis of the spatiotemporal dynamics. The newly engineered sensors developed in the program will have further broad applications in various biological systems of interest, which will ultimately pave the way toward a more complete understanding of brain function at synaptic, microcircuit, and behavioral levels.

The evolution of animal sociality has been driven, in part, by the balance of cooperation and conflict. Extensive studies of dyadic (one-on-one) conflicts across taxa have revealed how opponents achieve safe resolution by assessing competitive ability. While conflicts between groups of social animals are equally important, little is known about how competing groups assess ability. Studying intergroup assessment can reveal principles that extend across taxa, influencing our understanding of the evolution of sociality.
I will adapt the theoretical and experimental framework of dyadic assessment to test principles of intergroup assessment. I will first use a database of over 600 banded mongoose contests to test central components of conflict dynamics: how group composition predicts competitive success, how territory ownership confers an advantage, and if groups of similar ability have more dangerous contests. I will also experimentally test how banded mongooses use scent markings and collective “war cry” calls in intergroup assessment, including if and how “leaders” of conflicts differ from other group members. Finally, working in the extremely tractable wood ant system, I will use automated tracking and network analysis to test how individual-level behavioural variation influences overall group contest dynamics.
This project integrates big data, behavioural ecology, and state-of-the-art individual tracking to reveal principles of intergroup assessment that extend across taxa and levels of social organisation. The outcomes of this work can influence research in fields like animal contests, social evolution, and human psychology, among others.

Mitochondria are dynamic, multifunctional organelles that are a defining feature of eukaryotic cells. Maintaining mitochondrial homeostasis is essential for normal cellular and organismal physiology. Mammalian cells have roughly ~1,500 mitochondrial proteins, however the vast majority are encoded in the nuclear genome. Therefore, in response to stress, mitochondria must signal their internal states to the nucleus, which can mount a compensatory transcriptional response. Despite the fundamental importance of mitochondrial regulation, the full range of perturbations that disrupt homeostasis, the subsequent signalling cascades and the resultant nuclear responses remain poorly defined in higher eukaryotes. One consequence of mitochondrial stress is the activation of the integrated stress response (ISR), though the mechanism of this remains obscure. I propose to address these aspects of mitochondrial homeostasis in human cells using complementary genetic and biochemical strategies. i) First, I will use a CRISPRi screen to identify factors involved in activation of the ISR. ii) I will use Perturb-seq technology to objectively and systematically establish the full spectrum of stress induced transcriptional responses needed to maintain mitochondrial homeostasis. I will then use the approaches in (i) to explore the exact mechanism for key responses. iii) Finally, I will dissect the role of a new factor TMA7, identified through a previous genetic screen aimed at uncovering genes that sensitize mitochondria to proteotoxic stress. In all cases, the ultimate goal is to delve into the biochemical mechanism of how promising hits are involved in maintaining mitochondrial homeostasis.

Proteins emerging from previously non-coding DNA regions are becoming increasingly appreciated as an important path to creating completely novel functions. However, their path of emergence is very poorly characterized. Comparative genomics studies are finding numerous genes that have emerged from non-coding sequences, providing deep insight into gene evolution. However, these studies provide no insight into the transition from non-coding to coding because these genes have already been shaped by selection. For instance, de novo gene emergence could be frequent but mostly deleterious, or rare but mostly advantageous. In our project, we propose to go beyond classical comparative genomics and force novel proteins to emerge to measure their fitness effects and biochemical properties, and establish the relationship between the two. We will measure the fitness effect of these proteins, confirm their existence and localization, as well as their propensity to interact with other proteins. We will identify protein properties such as length and intrinsic disorder that define the fitness effects and thus the likelihood for a novel protein to emerge. Because many of these biochemical properties are largely defined by nucleotide sequences, our findings will lead to models that directly link de novo gene sequences to fitness effects, allowing us to model gene emergence from sequence composition alone. This will be the first project that is poised to answer the major question of how novel proteins can emerge from previously non-coding sequences.

Neuronal circuits store information through the mechanism of synaptic plasticity, a process where synaptic transmission is strengthened or weakened. Long-term potentiation (LTP) is a major form of synaptic plasticity. It requires both activation of CaMKII and subsequent trafficking of receptors and other proteins to the postsynaptic site. Despite extensive research, the causative relationship linking these two processes is still unknown. Here, Hayashi (live imaging and electrophysiology), Zhang (structure biology), and Lucic (cryoelectron tomography) will team up and take a unique minimalist approach to reconstitute synaptic plasticity from purified proteins. We will reconstitute postsynaptic density (reconstituted PSD or rPSD) on a glass substrate using a group of key scaffold proteins (PSD-95, SynGAP, SAPAP, Shank, and Homer) and receptor such as NR2B. Once a key process is found in minimal system, we will test if the same mechanisms work in intact neurons. Finally, we will investigate the persistent modification of the rPSD induced by the activation of CaMKII, which is expected to act as a hub for trafficking of various proteins. The network organization of the resulting complexes in vitro and in situ will be determined by cryo-electron tomography. The final goal of this proposal is to understand the minimum essential machinery for activity dependent delivery of postsynaptic proteins.

Is evolution possible in the absence of template-based replication?
Evolution, in life as we know it, relies on the copying of DNA with errors, providing the basis of reproduction with heredity and variation. Likewise, in the hypothetical RNA world, copying of RNA with errors is thought to have played the same role. However, the high complexity of RNA polymerases suggests that replication must have been preceded by reproduction and evolution based on simpler catalysts. Reproduction can occur by autocatalytic synthesis of single ribozyme species from RNA fragments, or by collective autocatalysis of multiple ribozyme species, all denoted AutoCatalytic Systems (ACS). For ACS to evolve in an open-ended way, theory indicates that there must exist a large diversity of such ACS throughout the sequence space. Further conditions for evolution are that ACS must amplify within compartments (enabling reproduction with heredity), propagate to other compartments as a function of their differential amplification (selection), and that rare events trigger the appearance of novel ACS (variation).
We aim to test the hypothesis that RNA reproduction based on ACS is widespread in the sequence space, and from this diversity, demonstrate that evolution in ACS is indeed possible.
For this, we will generate a large landscape of self-reproducing molecules using the natural group I intron family as an input for statistical inference methods. Self-reproducers will be constructed by fragmentation of these ribozymes by generalizing the strategy formerly applied to a ribozyme from the Azoarcus bacterium, and developing innovative in vitro screening methods with droplet microfluidics. We will show how the autocatalytic dynamics of individual self-reproducers, and their propensity to catalyse the formation of other self-reproducers or self-reproducing networks, allow to implement the properties of reproduction with heredity and variation underlying evolution.
Evolution will be tested in bulk and in compartmentalized populations, by submitting ACS to cycles of incubation and propagation. Finally, the probability of emergence of evolution in ACS and its open-ended character will be assessed based on the density of functional reproducers and their evolutionary accessibility in the sequence space.

We propose to use new tracking technology and computational modeling to determine how vocal communication influences collective behavior in animal societies. Canonical examples of collective movement such as bird flocking and fish schooling involve cohesive groups making short-term decisions in a shared context. However, many animals form stable social groups that coordinate and cooperate over extended time spans, across varying distances, and in diverse contexts. In these stable animal societies, group members must make decisions despite varying access to information and exposure to the costs and benefits of coordinating. Moreover their decisions are likely to be shaped by the long-term social relationships among group members. To achieve coordination in such systems, many species use sophisticated signaling systems, such as vocal communication, that transfer information among group-mates. Animals can flexibly control the vocalizations they produce independent of their movements, resulting in a complex interplay between signaling and movement that ultimately drives group-level outcomes such as collective decisions and coordinated actions.
To understand the mechanisms underlying coordination in animal societies, we will record movements and vocal signals concurrently from all members of wild animal groups at a high resolution, and across varying degrees of spatial dispersion. We will compare three mammal species that face a common set of coordination task, but differ in cohesiveness: meerkats form highly cohesive groups, coatis are moderately cohesive, and spotted hyenas live in fission-fusion societies. In each species, we will 1) fit at least one entire social group in the wild with tags that continuously record fine-scale movements and vocalizations, 2) combine supervised and unsupervised machine learning to identify animal calls and movement states, 3) develop modeling approaches to reveal how animals integrate spatial and acoustic information, how information flows through groups, and how social interactions give rise to collective outcomes, and 4) conduct audio playback experiments to isolate causal factors driving collective dynamics. Combining these approaches with long-term data from field studies will shed light on both unifying features underlying coordination mechanisms across animal societies and differences imposed by distinct constraints.

Octopuses have highly complex brains and are capable of many advanced behaviors that involve cognitive abilities. However, their brains and nervous system evolved completely independently from those of vertebrates, and it is largely unknown how the brains of such seemingly “alien” animals perform vertebrate-like sensory and cognitive functions with this distinct brain organization. In this proposal, we will study how visual sensory information is processed and stored in the octopus memory system. In order to overcome the technical obstacles to achieve this, we will bring together two labs with complementary expertise. The Niell lab studies the visual system of mouse, using calcium imaging of neural activity to understand how cortical circuits perform the computations that underlying visual perception and behavior. The Hochner lab studies learning and memory in the octopus vertical lobe. They have used electrophysiological tools and behavior to show that the vertical lobe is organized in a simple fan-out fan-in architecture and demonstrates robust activity-dependent synaptic plasticity. However, these current experimental methods are not sufficient for understanding how learning and memory networks store sensory features that are likely represented sparsely in the activity of many individual neurons.

Together, we will implement two-photon calcium imaging techniques for the octopus brain, to directly observe how sensory information from the eye is processed and represented in the visual system as it is conveyed into the central brain. We will then measure how this information is stored in patterns of activity across the large population of small neurons in the memory centers of the octopus brain, within a learning paradigm. In other words, we will watch memories being formed from a visual input. We will also perform manipulations that will allow us to determine the role of synaptic mechanisms and neuromodulation that enable this storage and its modulation by reward and punishment signals. The result of this collaborative endeavor will be a comprehensive view of neural information processing, from sensory input to memory formation, in the unique and enigmatic brain of the octopus.

Understanding the cellular and molecular mechanisms on memory formation is one of the most important topics in life science and will be highly effective for development of remedies of memory disorders such as Alzheimer’s diseases and dementia. The modification mechanism of the neural network in the brain is a key factor for memory formation, but its understanding is not sufficient yet. The main reason is that direct observation of the neural network modification during memory formation in intact animal brains is still very difficult today. Voltage imaging, which enables direct observation of membrane potential, is a powerful technique for visualization of neural activities ex vivo. In contrast, in vivo voltage imaging in intact mouse brain is highly challenging despite the importance. The difficulty is derived mainly from the following requirements; (i) high signal-to-background ratio in the deep brain of the intact mouse, and (ii) high temporal resolution (<5 ms) for the voltage imaging. In the proposed study, three-photon excitation with near-infrared femtosecond laser will be applied to achieve the condition (i). Three-photon excitation is highly effective because NIR (1300-1700 nm) shows very low scattering from tissues, thus high signal-to-background ratio will be attained. To achieve (ii), an adaptive femtosecond laser source will be applied, making the laser light irradiated only to the regions of interest. The proposed in vivo voltage imaging will be applied to monitor neural network modification in the intact mouse brain during memory formation.

Hearing loss, ranging from mild to severe, is a detrimental condition linked to decreases in quality of life for those affected. The causes of hearing loss have been extensively studied previously, and few options are available in rescuing hearing loss. Hearing aids have become the standard for treating hearing loss; however, they have only been partially successful due to their limitations when processing sounds in noisy environments. This is due to the fact that hearing loss causes complex distortions in neural activity patterns of the auditory system, which are not fully understood to date. The aim of this project is to identify key features of the neural code for speech that are distorted by hearing loss, and as a result, be able to assess the ability of current hearing aids to correct these distortions. Using recent developments in technology, we will use custom designed multi-channel electrodes to perform large-scale recordings in the inferior colliculus (IC) of the Mongolian gerbil. We will perform these recordings in both anaesthetized as well as awake-behaving gerbils under control and induced hearing loss conditions. We will then assess the neural activity patterns of IC neurons and determine the key distortions caused by hearing loss under differential stimulus conditions mimicking noisy situations. Standard analysis of coherence of speech information will be assessed to understand to what extent certain sounds become distorted under hearing loss. The results of this study will reveal novel insights into how hearing loss creates distortions in the neural code along the auditory pathway, and provide ideas for the efficacy of current and future hearing aids.

The cerebral cortex is composed of distinct subtypes of neurons organized in circuits allowing high-order functions such as integration of sensory stimuli and sensorimotor transformations. These different neuronal subtypes are connected with neurons located both within and outside of the cortex. Intracortical connectivity is mostly mediated by layer (L) 2/3 neurons, which form synapses with other cortical neurons within and across areas; instead neurons located in L5B project to sub-cerebral targets and are responsible for cortical output.
While the molecular diversity of cortical neurons and their circuit organization is increasingly understood, it is still difficult to genetically manipulate cortical neurons based on which circuits they belong to; the ability to do so would, however, be a critical skill to repair circuits when they are affected by injuries or neurodegenerative diseases. To address this challenge, here we combine our expertise in developmental neurobiology (DJ) and in bioengineering (WL) to develop a strategy to manipulate gene expression in cortical neurons in a circuit-dependent manner. We do so by engineering artificial synaptic contact-dependent signaling cascades to drive new cellular features.
Specifically, we will:
1. Assess the in vitro molecular identity and connectivity of pure populations of L2/3 and L5B cortical neuronal types and manipulate these cellular features by direct reprogramming of L2/3 neurons into L5B neurons (Aim 1).
2. Manipulate gene expression and cellular features of L2/3 neurons in vitro in a synaptic-contact dependent manner by developing a synaptic version of the synthetic notch (synNotch) receptor system (synsynNotch) (Aim 2).
3. Manipulate axonal projections of specific populations of intracortically-projecting neurons in vivo using the synsynNotch system (Aim 3).
Together, these experiments will increase our understanding of the mechanisms controlling cell-type specific circuit assembly and allow us to functionally interrogate this process through circuit-specific manipulation of gene expression.

After fertilisation in the early embryo, the epigenetic reprogramming of heterochromatin is thought to be necessary for development. Notwithstanding, the mechanisms driving the de novo formation of heterochromatin are unclear. In somatic cells, transposable elements (TE) are largely heterochromatic and transcriptionally inert. However in the pre-implantation embryo many TE families are highly expressed. Recently, the expression of several TE families has been shown to be necessary for development. However, how TEs drive the developmental program mechanistically is unknown. I propose that their expression is essential for the establishment of heterochromatin, through the formation of long-range chromatin interactions during development. To address this hypothesis, I will establish a targeted DAM-ID protocol to map the genome-wide long-range DNA interactions formed by TE families in vivo, dynamically throughout development. I will develop novel technologies to analyse their chromatin composition and nuclear organisation to uncover the relationship between TE organisation and the establishment of heterochromatin and higher order chromatin structures. To determine the function of TE expression in development, I will perturb their expression and chromatin composition and investigate the ensuing effect on their higher-order organisation, nuclear localisation, and more broadly on heterochromatin formation and development. Finally, I will use the datasets generated in this study to generate a comprehensive model on the mechanisms driving higher order chromatin formation at TEs, and how they influence the local and higher order chromatin architecture in development.

It has been well established that eukaryotic genomes produce thousands of short and long non-coding RNAs. Many studies have uncovered the biosynthesis, processing, and functions of small non-coding RNAs. However, whether long non-coding RNAs (lncRNAs) are functional molecules or not has not been clear. Although supporting evidence of functional lncRNAs have been accumulated for the last decade, the working mechanisms of functional lncRNAs are still largely unknown. In this proposal, I aim to 1) identify functional lncRNAs in C. elegans by performing in-depth phenotyping with a large collection of lncRNA knockout mutants, and 2) characterize working mechanisms of the functional lncRNAs by using unbiased genetic and biochemical screen approaches. This study will help unveil mechanisms by which lncRNAs exert functions, and provide insights into how and why eukaryotic genomes contain a large number of the non-coding genetic elements that produce lncRNAs.

Protein sequencing remains a challenge for small samples. A sensitive sequencing technology will create the opportunity for single-cell proteomics and real-time screening for on-site medical diagnostics. We will use our expertise of single-molecule protein detection and material sciences to develop novel sequencing tools. In particular, we will use graphene mass sensors to measure the mass of proteins with sub-Dalton sensitivity. Utilizing this high sensitivity, we will measure the mass of protein fragments and identify the sequence of the fragments. We will also apply this method for detecting post-translational modifications of single proteins. Ultimately we aim to achieve sequencing of full-length proteins. This proof of concept will open the door to single-molecule protein sequencing and pave the road toward the development of a new, fast, and reliable diagnostic tool.

How our brain learns the relationships between movements and the associated sensory feedback is an important question facing neuroscience. Since such sensorimotor relationships are subject to change, sensorimotor learning is crucial for the development of flexible sensory-guided behaviours. One elegant potential solution to this problem would be that the brain generates an internal model of the world that is used to make predictions about the sensory feedback given a certain motor output. Discrepancies between predicted and actual feedback could then be represented as prediction errors, which in turn could be used to update the internal model used to make the predictions. Recent experiments in which sensory feedback was manipulated during movement have revealed neural responses consistent with prediction errors in primary sensory areas of cortex. In primary visual cortex, these responses have been shown to depend on the precise tuning between visual and motor input that is learned with experience. However, both the mechanisms underlying this learning, and the effect of the error-like responses are unknown. In this project, I propose experiments employing a range of techniques, including calcium imaging, in vivo whole cell patching, and optogenetics in order to investigate the driving forces of sensorimotor learning. I will manipulate the activity of single cells in the visual cortex and probe neuromodulatory plasticity signals during visuomotor learning. This will provide a fundamental mechanistic insight into how sensorimotor learning takes place.

Social interactions between individuals lead to emergent collective behavior, whereby locally acquired information yields decentralized collective decisions, implying a flow of information within a social network. However, since social interactions are generally not directly accessible, and network structure changes according to different social and ecological contexts, this flow is difficult to observe and little is known about the principles governing its dynamics. We hypothesize that network structure shapes the dynamics of information flow and its characteristics, which can adapt according to the social context. Based on this concept, we suggest a novel and experimentally tractable system of self-organized crowded plants interacting via mutual shading while competing for light. This system is amenable to a social network analysis where nodes, representing individual plants, are connected via edges representing unidirectional and deleterious shading interactions which can be observed. The flow of information is represented by cascades of growth-driven morphological changes in neighboring plants as a response to shading manipulations, where the kinematics of individual responses to shade are described mathematically. We aim to uncover the dependence of information flow on network structure by considering mathematical properties of observed flow dynamics resulting from perturbations of the network structure, and interpret these results in terms of ecological and selective consequences. Capitalizing on the advantages of this unorthodox model system, we will tackle this goal through the following complementary and interdisciplinary lines of investigations: (i) run experiments on the system of mutually shading plants, designed to probe the dynamics of information flow; (ii) analyze experimental data in terms of social networks, (iii) combine simulations and experiments to analyze mathematical properties of observed flow dynamics as a function of network structure. These steps will allow us to interpret ecological aspects of social networks in terms of efficiency of information flow and network structure. This work will impact the fields of social ecology, plant science, and collective behavior, providing a quantitative understanding of dynamics of social information as never done before, and suggesting solutions for the optimization of agricultural crop stands.

During early embryonic morphogenesis, naïve epithelia undergo structural changes like bending, folding, stretching, convergent-extension, growth, or even epithelial-to-mesenchymal transitions. Various species show evolutionary divergences in these morphogenetic processes. I propose that such differences in epithelial morphogenesis depend, in part, on physical parameters like tissue area, cell packing, cell aspect ratio, and the pliability of mechanical connectivity between cells. In fly embryos, the blastoderm epithelium undergoes morphogenesis after being defined from a zygote syncytium, in a process called cellularization. The process of cellularization is well described, very stereotypical, and the genetic and molecular players establishing basic cellular properties are known in Drosophila.

I will take advantage of natural diversity that accumulated over 250 million years of fly evolution and resulted in highly different blastoderm epithelia that vary substantially in tissue area, cell packing and cell aspect ratio. The host lab maintains lab cultures for 10+ fly species, including mosquito-related midges and moth flies, scuttle and hover flies, and various drosophilid species, which collectively sample a broad range of natural diversity in the insect order. I propose to compare cell properties and tissue morphogenetic behavior of the embryonic blastoderm in these species, with the aim to identify key genetic innovations underlying the origin of novel tissue properties and divergence in morphogenesis. I will further test how the introduction of these novel genetic elements into more rudimentary systems consequently changes cell properties and epithelial morphogenesis.

Post-transcriptional mRNA modifications have emerged as important mechanisms in eukaryotic gene expression control. The most abundant epitranscriptomic mark, N6-adenosine methylation (m6A), affects the processing, translation, and degradation of mRNAs and thus is involved in diverse biological processes including stem cell self-renewal, sex determination, and immunity. Uncovering the m6A molecular mechanisms is crucial to understand its function in gene regulation and shed light on its implicated roles in cancers and psychiatric disorders. Numerous studies have revealed the cellular m6A landscape, defined by writer and eraser enzymes and interpreted by reader proteins. Nevertheless, specific aspects of the m6A pathway, including how mRNAs are chosen for methylation, are not fully understood. Although the m6A mark occurs within a consensus sequence, not all such sites are modified in the transcriptome. It is unclear how the core METTL3/METTL14 methyltransferase complex recognizes its RNA substrates and how additional factors, such as the recently identified MACOM complex, contribute to this mechanism. To address these questions, the proposed research will aim to: (i) characterise the substrate RNA recognition mechanism of the METTL3/METTL14 complex, (ii) define the molecular architecture of the MACOM complex, and (iii) determine the structural and functional links between METTL3/METTL14 and additional m6A writer factors. To achieve these goals, I will combine structural biology with biochemical, biophysical, and functional cell-based assays. Together, these studies will provide fundamental insights into the molecular mechanisms of m6A in epitranscriptomic gene regulation.

The world is characterized by an unequal distribution of resources. To cope, many organisms evolve symbiotic trade partnerships to exchange commodities they can provide at low cost, for resources more difficult to access. Such trade partnerships allow species to colonize extreme environments and survive resource fluctuations. While the ubiquity and importance of trade partnerships has been established, we do not understand the chemical, physical, and environmental stimuli mediating trade strategies, nor how organisms integrate this information to execute trade ‘decisions’. This is largely because of the lack of tools to quantify symbiotic trade across space and time.
Combining biophysics, fluid mechanics, network theory and evolution, we will develop techniques to track, quantify and predict trade strategies in symbiotic networks formed between plants and their arbuscular mycorrhizal fungal partners – a globally ubiquitous trade partnership fundamental to all terrestrial ecosystems. By visually monitoring the trade of nutrients tagged with fluorescent quantum-dot nanoparticles across scales - from within individual fungal hyphae up to complex plant-fungal networks - we will ask: (1) how do oscillatory flow patterns within fungal networks act to regulate fungal trade decisions; (2) can the fungus manipulate its chemistry and physical architecture to maximize nutrient transport and trade benefits; (3) can trade strategies be predicted by environmental stimuli; (4) what is the influence of the external microbiome on trade behaviors.
Using high-resolution video to track fluorescently tagged nutrients within hyphae, we will be the first to test how the fungal symbiont regulates internal flows to mediate trade. We will develop 2D and 3D time-lapse imaging of network topologies to test the factors driving the optimization of fungal transport routes. We will use transformed in-vitro root systems with precisely controlled nutrient landscapes to correlate specific trade strategies with environmental conditions. We will push the frontiers of tracking trade in whole plant mesocosms by growing plant-fungal networks on transparent farming film, characterizing how synthetic microbiomes affect trade strategies. By integrating the state of the art in imaging, fluid mechanics, and ecological manipulations, we will achieve a quantitative and predictive understanding of organismal trade.

Cells face a phosphate challenge. Growth requires a minimal concentration of this limiting resource because intracellular phosphate (Pi) is a compound of nucleic acids modifies most cellular proteins, and is crucial to maintain the cellular ATP/ADP balance. At the same time, cytosolic Pi may not rise much, because elevated cytosolic Pi can stall metabolism. It reduces the free energy that nucleotide triphosphate hydrolysis can provide to drive energetically unfavorable reactions. Cells should hence coordinate their systems for uptake, export and storage of Pi in order to strike the delicate balance between the biosynthetic requirements for Pi and the risks of elevated cytoplasmic Pi. Exploring the signaling mechanisms that guarantee Pi homeostasis is challenging, because intracellular Pi is tightly linked to other metabolites: ATP/ADP, inositol pyrophosphates, and inorganic polyphosphate. These four metabolite classes influence each other's concentrations. I want to dissect this apparently conserved "metabolic cluster" and render the signaling pathways that regulate it accessible. To this end, I will create tools and methods to uncouple these parameters and fix one or several of them to an invariant value. This will allow me to "isolate" the signaling pathways responsible for the homeostasis of these essential parameters and possibly elucidate novel regulatory loops of Pi and energy metabolism.