Texas A&M University

NSF Awards · FY 2024 · 2024-08

This award funds the research activities of Professors Katrin Becker and Ergin Sezgin at Texas A&M University. String theory is a leading candidate for providing a framework that unifies the two pillars of our understanding of the laws of nature at the fundamental level, namely quantum theory and gravity. Its main premise is that all elementary particles in nature are different vibrations of an incredibly small fundamental string. Remarkably, the consistency of string theory requires extra dimensions, and the shape of these extra dimensions determines the spectrum of observed particles and the forces between them. For phenomena at low energies, string theory involves an extension of Einstein's theory of gravity, known as supergravity. As part of this research project, the PIs will explore fundamental aspects of string theory and supergravity. This is expected to enrich the interface between string theory and mathematics as well. This project advances the national interest by promoting the progress of science in the US and seeking new physical laws describing uncharted territories involving not only the extreme conditions that existed in the era of the early universe but also the black hole environment where the gravitational force is extremely large. The PIs will also provide critical training to postdocs and students. They also intend to give public lectures on their research results and organize workshops. More technically, Professors Becker and Sezgin aim to address moduli stabilization in type IIB string theory compactified on Gepner models. The goal is to determine if the shape and size of the extra dimensions of string theory, which in four space-time dimensions are described by fields called moduli fields, can be predicted. If successful, this will increase the predictive power of string theory and could open the door to answering questions such as determining why we live in four space-time dimensions or why there are three families or generations of quark and leptons among the elementary particles. Further, the PIs will study the higher derivative couplings of supergravity theories and their spontaneous compactifications. Moreover, the PIs will determine candidate effective theories of quantum gravity that can in principle admit a UV completion. They will also study the geometrical description of duality symmetries of strings and branes in a powerful framework known as exceptional field theory. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Forecasting how our environment will change into the future requires the scientific community to understand the processes that shape Earth's surface environments through time. To do this, geoscientists collect images of Earth with satellites, run simulations, and test hypotheses with laboratory experiments. All of these methods improve our understanding of landscape change, but scientists using each of these tools struggle to bring their research together to make new insights. This project establishes a framework of interoperable hardware and software tools, called sandpiper, that enables research products from different teams and approaches to integrate with one another more easily than ever before. Major efforts of the project team include (1) designing and implementing an affordable open-source hardware-firmware system for data acquisition, (2) forging a community-backed data standard, (3) developing a flexible and interoperable data-analysis software library, and (4) establishing a sustainable community of practice. The project team is also advancing science and technology education by creating science museum exhibits that demonstrate fundamental principles in geomorphology and reach a wide audience through an interactive web interface. Recent strides in geomorphology have been fueled by widely available satellite imagery, powerful numerical modeling toolkits, and decades of physical laboratory experiments. Customized algorithms lie at the heart of the discipline because raster data—e.g., photographs, topography—form a fundamental bridge between these complementary modes of inquiry. Transformative insights can arise when researchers apply tools from one mode of inquiry to data from another. However, most innovation at the forefront of geomorphology currently proceeds in silos via ad-hoc algorithms that accumulate “mutations” as they traverse laboratories and graduate-student generations. The problem is particularly acute for experimental geomorphology, where technological barriers have prevented FAIR (Findable, Accessible, Interoperable, Reusable) and OS (open-source) principles from integration into the research process. At present, there is no unifying framework to support collaboration between modelers, observationalists, and experimentalists. The team for this project is creating such a cyberinfrastructure framework and solving these problems at every level. (1) To break down experimental silos, the project team is designing and implementing a modular and extensible open-source hardware–firmware system to affordably and uniformly make measurements and generate reproducible data products in labs across the world. (2) To promote and simplify data sharing, the project team is organizing a community effort to forge a data standard. (3) To mitigate algorithm drift, the project team is developing a flexible analysis library that integrates with this data standard. (4) To establish a community of practice, the project leaders are engaging researchers in their own laboratories and computing environments to facilitate reusing and contributing algorithms to the library. This acquisition-to-analysis toolchain, called sandpiper, will enable the next generation of collaborative research in geomorphology, sedimentology, and stratigraphy; advances could also influence seemingly unrelated fields like dendrochronology, hydrology, and seismology. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information Science and Engineering and by the Geosciences Directorate’s Research, Innovation, Synergies, and Education and Earth Sciences divisions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Earthquake swarms are complex clusters of seismic events that can significantly impact communities and infrastructure. They often occur in volcanic and geothermal areas and regions affected by human activities such as fluid injection or extraction. Identifying and understanding the factors that influence earthquake swarms is crucial for improving our ability to assess and mitigate seismic hazards. This research project aims to unravel the complex behavior of earthquake swarms by examining how they are affected by internal factors, such as the properties of the fault zones, and external factors, such as the slow fault and fluid movements. By studying major earthquake swarms in Hawaii and California, the research team will gain insights that can help improve earthquake forecasting and risk assessment. This project also provides training for graduate students, a postdoctoral researcher, and undergraduate students at two US institutions while fostering collaborations between scientists from the US and Japan. The research team will investigate earthquake swarms in volcanic and geothermal areas using a combination of seismic and geodetic data analysis and computational modeling. They will examine earthquake source characteristics, spatial and temporal clustering patterns, and slow deformation processes to understand the relationships between earthquake sequences, aseismic slip, fluids, and fault zone properties. High-resolution data from well-instrumented areas will be used to characterize multi-scale faulting processes during major swarms. The team will develop physical models of fault zones based on laboratory friction laws and heterogeneous stability and strength conditions. These models will simulate the interplay between seismic and aseismic slip, fluid diffusion, and stress transfer, enabling realistic representations of fault behavior during swarms. By integrating observations with these models, the project will advance our understanding of why earthquake swarms start, grow, and decay and what they reveal about underlying fault zone conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY The goal of this career development award is to provide me with the training necessary to develop an independent aging research program focusing on neuroimaging biomarkers and socioeconomic risk factors associated with differential aging trajectories and vulnerability to Alzheimer’s disease (AD), with the long-term goal of informing and improving risk prediction for AD and potential interventions and treatment to reduce AD risk. To achieve these goals, I propose to examine how alterations of brain functional and structural connectomes underlie cognitive aging and its longitudinal trajectories, and contribute to individual vulnerability to AD in cognitively normal individuals. Moreover, I propose to investigate how socioeconomic status may modulate the associations of brain connectomes with cognitive aging and risk for AD. My training to date has provided me with a strong methodological and theoretical foundation in cognitive neuroscience and neuroimaging, and advanced computational analyses to examine brain connectomes. My proposed training plan complements my existing expertise by providing training in the study of cognitive and brain aging, socioeconomic risk factors and biological processes and markers associated with aging and AD. Completion of the proposed research and training will enable my transition to independence and allow me to build an interdisciplinary aging research program that integrates neuroimaging, cognitive, and biological assessments. Research Project: Converging evidence has shown that connectivity and topological properties of brain connectomes underpin cognitive aging and contribute to the accumulation and spreading of AD pathologies. However, connectome-based (i.e., connectomic) biomarkers of cognitive aging trajectories have not been characterized in large-scale longitudinal studies, especially with regard to their potential in indexing early vulnerability to AD in cognitively unimpaired individuals. Moreover, there has been a lack of research on these topics in socially disadvantaged groups with high AD prevalence. The proposed project will address these critical research gaps using two large-scale longitudinal studies with cognitive, neuroimaging, and biological measures: Health and Aging Brain Study-Health Disparities and Vietnam Era Twin Study of Aging. Specifically, I will examine whether longitudinal changes in brain connectomes are associated with changes in multiple cognitive domains, serving as putative connectomic biomarkers of cognitive aging trajectories (Aim 1). I will then examine whether longitudinal changes in brain connectomes are associated with changes in levels of AD- related pathologies as measured from PET and plasma (Aim 2). Finally, I will examine whether low socioeconomic status (SES), an indicator of social disadvantage, modulates the associations between brain connectomes, cognition, and AD-related pathologies (Aim 3). The project will inform efforts aimed at establishing neuroimaging biomarkers for early identification of individuals at risk for cognitive impairment and AD, as well as understanding the role of SES in cognitive and brain aging and AD risk.

NSF Awards · FY 2024 · 2024-08

Among the four fundamental forces in nature the strong nuclear force is the least understood. It is responsible for many important processes in our Universe. One particularly important aspect is the existence of quark-gluon plasma. If ordinary matter is heated up to temperatures of about 1,000,000,000,000 degrees, hotter than the core of the sun, atoms cease to exist and protons and neutrons inside nuclei melt. The resulting plasma of quarks and gluons filled the very early Universe. We can recreate this plasma in the laboratory by colliding heavy nuclei at high energies. Experiments at the Large Hadron Collider in Europe and the Relativistic Heavy Ion Collider in the US study quark gluon plasma. The PI and his group carry out research that improves our understanding of properties of quark-gluon plasma in nuclear collisions. A particular focus lies on the mechanism of quarks and gluons arranging themselves back into protons and other bound states, a process called hadronization. It is those latter particles that are measured in experiments. Therefore, it is critical to develop a quantitative understanding of hadronization, in order to study quark-gluon plasma in experiments. This project provides training for graduate students in nuclear science. This project seeks systematic improvements to the modelling of hadronization using the Hybrid Hadronization model which has recently been developed. It addresses the treatment of spin and angular momentum in hadronization which is currently understood rather poorly. There are immediate applications of results to nucleus-nucleus collisions, where a significant amount of angular momentum is present in off-center collisions, but also to proton-proton and electron-proton collisions, where the study of the spin structure of the proton is a key goal of the experimental programs. The project also improves the treatment of baryons in Hybrid Hadronization by implementing the full spectrum of excited baryons, as recent experimental results involving baryons have created challenges to existing hadronization models. Another key component is the introduction of an important new layer regarding the study of jets, which are used to probe quark-gluon plasma and cold nuclear matter. By studying, for the first time, the hadronic interactions of jets in an ambient medium missing physics is added to state-of-the-art simulations. This will improve the accuracy of any quantitative conclusions extracted from jet measurements. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professors Wooley and Darensbourg of Texas A&M University will develop synthetic methodologies that harness the chemical diversity of natural products and consume carbon dioxide for the sustainable production of high value macromolecular materials. These materials will possess properties that allow them to replace current fossil-fuel-based plastics, while also being capable of undergoing degradation or depolymerization to regenerate the natural building blocks once they have completed their useful function. The expected significance for the proposed work includes an advancement of knowledge of synthetic methodology for sustainable polymer production that leads to societal benefits by transforming natural products into polymer materials, thereby overcoming issues with both a lack of sustainability of petrochemical feedstocks and environmental persistence of most current commodity polymers. Moreover, the proposed materials would address an ever-pressing issue of the loss of health, welfare and life to birds, fish and other wildlife that ingest non-degradable and non-digestible plastics. Translation of fundamental academic research advances to achieve ambitious practical broader societal and environmental impacts will be facilitated by entrepreneurial activities. Primary outcomes of the proposed work will be (1) diverse and extensive education, training and recruiting of the next generation STEM workforce with strong foundational knowledge in chemistry, (2) advances to synthetic polymer chemistry techniques, (3) creation and translation of novel materials that have the potential to positively impact society, and (4) education and outreach to the broader community. The proposed work is intended to advance knowledge of chemical methods that afford naturally-derived polymers, which possess properties that may allow them to displace current commodity polymers, while being sustainable and having mechanisms for degradation. The complementary expertise of the PIs in organic, organometallic and polymer chemistry and catalysis is combined to employ methods to convert carbohydrates or nucleic acids into cyclic ether, carbonate or thiocarbonate monomers that retain high degrees of functionality, followed by their ring-opening copolymerization, ring-opening polymerization, and in-situ structural metamorphoses that result in transformation into functional macromolecular structures across the platforms of polycarbonates and sulfur analogs. Rigorous characterization studies will probe the ability to access polymer materials possessing properties that are unique, including through exploration of stereochemical effects and regiochemical outcomes, and finally, their chemical modification, depolymerization and/or degradation will be investigated. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Researchers in the social sciences increasingly utilize event data sets when studying crime, protests, and terrorism. These data sets provide information on each incident, including where it occurred, who was involved, what the consequences were, etc. Unfortunately, the recorded location of incidents in these data sets are often inaccurate, due to limitations in the available information from which they are drawn (ex. incomplete media reports). Left unaddressed, these geolocation errors impair one’s ability to effectively learn about the underlying process of interest from these data. For example, geolocation errors may cause researchers to infer spatial patterns from these data that would not be found with the correct locations. In this research, investigators will develop statistical methods to better account for geolocation errors in these kinds of data. The statistical methods developed will then be applied to data on political violence, demonstrating their importance for improved understanding of real-world problems. The multidisciplinary project will also provide training for the next generation of researchers at the intersection of statistics and the social sciences. This collaborative project includes support and mentorship for graduate students. Spatial point processes are a natural approach for modeling event data. However, geolocation errors produce two distinct, but related, problems for these methods: i) duplicate event locations, and ii) inaccurate spatial coordinate information. In this project, investigators will address both issues, developing a computationally efficient statistical inference method to account for geolocation error in spatial point pattern data within the Log-Gaussian Cox Process framework. Various geolocation error structures will be considered, including nonstationary errors, to better reflect complex real-world applications. The project will include research on both the finite-sample performance and asymptotic behavior of the estimators from the developed inference methods. These methods will be used to analyze real-world political violence data from various sources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Isoperimetric inequalities are foundational principles in mathematics, explaining numerous phenomena across the sciences. At the core of the theory is the classical inequality, which asserts that the circle has the smallest perimeter among all shapes of fixed area. A novel approach to such inequalities, developed by the PI and collaborators, employs empirical sampling. An empirical form of the classical inequality states that uniformly sampling points from a domain results in the expected perimeter of the convex hull being greater than if the same number of random points were sampled uniformly within a disk of equal area. This innovative approach opens new avenues for applying isoperimetric inequalities to random structures. The proposal's interplay between geometry and probability leads to applications to various problems in learning theory and algorithmic complexity. Progress is expected to enhance our grasp of complex structures in machine learning and refine algorithms used in neuroscience, computer vision, and signal processing. The PI will mentor students and early-career researchers in this new direction, presenting the ideas at international conferences and seminars. Until recently, this research has been within Brunn-Minkowski's theory, the classical mathematical theory that explains isoperimetric inequalities through properties of projections or shadows. The project suggests a new direction that includes the rapidly growing "dual theory," rooted in domain sections and inspired by Geometric Tomography. The primary objective is to develop a comprehensive set of techniques that bridge fundamental conjectures in Brunn-Minkowski theory and dual Brunn-Minkowski theory. A key focus will be directed toward intersection bodies and their higher-dimensional generalizations, built on methods in the empirical approach to isoperimetry. New connections to functional and harmonic analysis, as well as tools from matrix analysis, are proposed to transform methods tailored to the classical theory into the dual theory. These connections also offer a novel perspective on several problems on metric embedding and the role of convexity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Vast stretches of the ocean, covering almost 60% of its surface, are ‘deserts' where life struggles to survive due to nutrient scarcity. Located in the central gyres of the subtropical ocean, these regions are too remote to receive significant nutrient inputs from land or from deeper waters by vertical mixing. The low supply of nutrients should result in reduced levels of photosynthesis and less complex food webs. However, observations instead indicate that life grows at rates similar to regions which receive higher nutrient supplies. Our project seeks to determine what the missing sources of nutrients are that help sustain these ecosystems. This work will focus on nitrogen bound within dissolved organic molecules that are produced in nutrient-rich regions, including coastal California, and then transported laterally to the ‘ocean desert’ of the subtropical North Pacific Ocean. A one-month research expedition will leave from San Diego, CA and end in Honolulu, HI USA to study the production, accumulation, chemical composition, and utilization of dissolved organic nitrogen (DON) and link it to photosynthesis occurring across the North Pacific. The new knowledge gained about the role of DON in satisfying the nutrient requirements of the subtropical North Pacific will be used to construct computer models that gauge the global importance of this nutrient source to subtropical ocean ecosystems. Additional nitrogen (N) sources beyond subsurface nitrate (NO3-) and N2 fixation fluxes are required to explain observed net community production (NCP) within subtropical ocean ecosystems. Numerical models indicate that laterally supplied allochthonous DON may support 10-60% of NCP in oligotrophic subtropical gyres, but with large uncertainties. The proposed work will field test hypotheses concerning the biological production and consumption of marine DON, and whether allochthonous DON is a significant organic nutrient source sustaining NCP. A North Pacific cruise will use observations of DON concentration, chlorophyll a concentration, and N isotopes [DON's 15N/14N ratio, and NO3-'s 15N/14N ratio] to identify regions of net DON production and consumption in the context of net and gross biological productivity estimated using O2/Ar and triple Oxygen isotopic measurements, respectively. Novel organic geochemistry tools will be used to identify the molecular composition of the allochthonous DON. The influence of depth on DON consumption will be investigated using ship-board incubations that expose surface DON collected across gradients in surface NO3- to microbial communities from several depth intervals. Finally, a new semi-labile DON tracer will be encoded in a global ocean biogeochemistry model, fashioned to behave like the allochthonous DON characterized from the field observations. The DON and isotopic data sets will serve to constrain model parameterizations of semi-labile DON cycling. The state-of-the-art model of semi-labile DON cycling will be used to quantitatively assess the portion of NCP sustained by allochthonous DON delivery to the North Pacific subtropical gyre and extend this assessment across the global ocean. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-07

Project Summary/Abstract: The degree to which behavioral variation is shaped by experiences or determined by heritability has long captivated biologists and non-biologists alike. It is now clear that behavior often has a genetic component, but mapping specific genetic variants underlying differences in behavior remains a major challenge. Recent advances in “-omics” technologies and analytical tools provide an exciting opportunity to uncover connections between genetic and behavioral variation. Parental care is a complex behavioral trait that has independently evolved many times across the animal kingdom. The neural, physiological, and molecular mechanisms of maternal (female-provided) care have been widely studied and appear to be highly conserved across deep evolutionary time. Yet, there remains a critical gap in our understanding of the mechanisms of paternal (male-provided) care. The small stream fishes commonly called darters provide a unique opportunity to investigate the genetic and evolutionary drivers of paternal care. Darters comprise the most diverse group of vertebrates in North America and exhibit ample variation in reproductive behavior. Paternal care has evolved repeatedly among evolutionarily independent darter lineages and has also been secondarily lost in at least one species. Our goal over this five-year project is to use the darter system we developed to study the biological basis of evolved differences in paternal care. We will leverage natural replication in this system to test the hypothesis that similar genetic and molecular changes underlie the evolution of this behavior in darters. Our lab has pioneered the development of genomic resources and functional genetic tools for darters, which will serve as a platform for the proposed work. First, we will take advantage of the fact that closely related darter species with and without paternal care can form viable crosses to produce fine-scale genetic maps for paternal care behavior. Second, will use our previously developed bioinformatic pipelines to conduct genome scans for selection and identify genes repeatedly under positive selection only in species that have evolved paternal care. Additionally, we will ask whether these same genes show signatures of relaxed selection in a species that secondarily lost paternal care. Third, we will investigate the neural and transcriptional basis of paternal care using an approach for molecular profiling of behaviorally relevant neurons that we recently developed in darters. The proposed work will be complimentary to other lines of research in the lab aimed at applying population genomic, quantitative genetic, and functional genetic methods in darters and other fish models to test fundamental evolutionary hypotheses and uncover genotype-phenotype connections. This innovative project will bridge behavioral neuroscience and evolutionary genomics – fields which have historically remained largely siloed and limited to model systems – to investigate the mechanistic basis of evolved differences in behavior in natural populations, setting the stage for a highly integrative, cutting-edge research program.

NSF Awards · FY 2024 · 2024-07

This project pursues the contemporary problem of statistical network integration facing scientists, practitioners, and theoreticians. The study of networks and graph-structured data has received growing attention in recent years, motivated by investigations of complex systems throughout the biological and social sciences. Models and methods have been developed to analyze network data objects, often focused on single networks or homogeneous data settings, yet modern available data are increasingly heterogeneous, multi-sample, and multi-modal. Consequently, there is a growing need to leverage data arising from different sources that result in multiple network observations with attributes. This project will develop statistically principled data integration methodologies for neuroimaging studies, which routinely collect multiple subject data across different groups (strains, conditions, edge groups), modalities (functional and diffusion MRI), and brain covariate information (phenotypes, healthy status, gene expression data from brain tissue). The investigators will offer interdisciplinary mentoring opportunities to students participating in the research project and co-teach a workshop based on the proposed research. The goals of this project are to establish flexible, parsimonious latent space models for network integration and to develop efficient, theoretically justified inference procedures for such models. More specifically, this project will develop latent space models to disentangle common and individual local and global latent features in samples of networks, propose efficient spectral matrix-based methods for data integration, provide high-dimensional structured penalties for dimensionality reduction and regularization in network data, and develop cross-validation methods for multiple network data integration. New theoretical developments spanning concentration inequalities, eigenvector perturbation analysis, and distributional asymptotic results will elucidate the advantages and limitations of these methods in terms of signal aggregation, heterogeneity, and flexibility. Applications of these methodologies to the analysis of multi-subject brain network data will be studied. Emphasis will be on interpretability, computation, and theoretical justification. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Logistics planning, including optimal distribution of products, leads to questions about maps with weighted distances, and routes that minimize these distances. Transportation cost spaces, also known as Lipschitz-free spaces, Wasserstein spaces, Arens-Eals spaces, and Earthmover spaces, have been used to model such problems. They can be viewed as a framework to study nonlinear metric spaces by embedding them isometrically and linearly densely into Banach spaces and provide powerful tools to study the nonlinear geometry of Banach spaces using well-known linear techniques for nonlinear problems. These spaces play a fundamental role in many areas of applied mathematics, engineering, physics, computer science, finance, and social sciences. Finding an optimal embedding is known to be a computationally hard problem and it has become a central problem in computer science to find low distortion embeddings. Using methods from the structure theory of Banach spaces and computational graph theory, the investigator’s goal is to achieve more precise estimates of these embeddings. He will obtain a deeper understanding of the structure of these spaces, which will result in several applications to the areas mentioned above. The principal investigator plans to organize conferences as well as mentor Ph.D. students as a part of this project. A crucial connection exists between the L1-distortion of Transportation Cost Spaces and stochastic embeddings of the underlying metric space into trees. The investigator will further study this connection to obtain lower and upper estimations on the distortion. The second part of the project represents a contribution to Lindenstrauss’s program in determining Banach spaces that are primary, and that cannot be decomposed into essentially different subspaces. The investigator will continue to determine primary function spaces. This project concentrates on studying the primarity and related factorization properties of function spaces in two parameters, combining methods from Functional and Harmonic Analysis and Probability Theory. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

As the scientific community is moving into a data-driven era, there is an unprecedented opportunity for the integrative analysis of network and functional data from multiple sources to uncover important scientific insights which might be missing when these data sources are analyzed in isolation. To this end, this project plans to transform the current landscape of integrating network and functional data, leveraging their combined strength for scientific advancements through the development of innovative hierarchical Bayesian statistical models. The proposed work holds transformative promise in vital scientific domains, such as cognitive and motor aging, and neurodegenerative diseases. It will enhance scientific collaborations with neuroscientists using multi-source image data for targeted investigations of key brain regions significant in the study of motor and cognitive aging. Moreover, the proposed research will facilitate the prediction of images, traditionally acquired via costly imaging modalities, utilizing images from more cost-effective alternatives, which is poised to bring about transformative changes in the healthcare economy. The open-source software and educational materials created will be maintained and accessible to a wider audience of statisticians and domain experts. This accessibility is anticipated to foster widespread adoption of these techniques among statisticians and domain scientists. The PI's involvement in conference presentations, specialized course development, curriculum expansion, graduate student mentoring, undergraduate research engagement with a focus on under-represented backgrounds, and provision of short courses will enhance dissemination efforts and encourage diverse utilization of the developed methods. The proposed project aims to address the urgent need for principled statistical approaches to seamlessly merge information from diverse sources, including modern network and functional data. It challenges the prevailing trend of analyzing individual data sources, which inherently limits the potential for uncovering innovative scientific insights that could arise from integrating multiple sources. Hierarchical Bayesian models are an effective way to capture the complex structures in network and functional data. These models naturally share information among heterogeneous objects, providing comprehensive uncertainty in inference through science-driven joint posterior distributions. Despite the potential advantages of Bayesian perspectives, their widespread adoption is hindered by the lack of theoretical guarantees, computational challenges, and difficulties in specifying robust priors for high-dimensional problems. This proposal will address these limitations by integrating network and functional data, leveraging their combined strength for scientific advancements through the development of innovative hierarchical Bayesian models. Specifically, the project will develop a semi-parametric joint regression framework with network and functional responses, deep network regression with multiple network responses, and Bayesian interpretable deep neural network regression with functional response on network and functional predictors. Besides offering a novel toolbox for multi-source object data integration, the proposed approach will advance the emerging field of interpretable deep learning for object regression by formulating novel and interpretable deep neural networks that combine predictive power with statistical model interpretability. The project will develop Bayesian asymptotic results to guarantee accurate parametric and predictive inference from these models as a function of network and functional features and sample size, an unexplored domain in the Bayesian integration of multi-object data. The proposed methodology will significantly enhance the seamless integration of multimodal neuroimaging data, leading to principled inferences and deeper comprehension of brain structure and function in the study of Alzheimer's disease and aging. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-07

Type IV pili (TFP) are captivating bacterial nanomachines that are broadly conserved and exhibit a diverse array of physiological functions. In the opportunistic pathogen Pseudomonas aeruginosa (PA) for example, TFP are implied in numerous virulence associated factors such as biofilm formation, mechanosensing, and motility. The physiological function of TFP relies on repetitive cycles of extending and retracting of a short polymeric fiber into the extracellular space, for example to adhere to a substrate and pull the cell forward. These dynamic cycles are facilitated by a complex molecular machinery consisting of dozens of different proteins that work together in a concerted manner. Although the constituents of TFP are known, the intricate molecular processes governing their interaction and how these interactions enable the physiological function of TFP remain largely elusive. Current limitations of observing the molecular interactions that drive the functions of TFP are intrinsic to the experimental techniques that have been used to far: these techniques cannot resolve individual proteins or TFP complexes. Single molecule techniques have the potential to fill this gap and to gain invaluable insight into the interactions among the proteins of the TFP system and how they function together as multi-molecular complexes. The power of single molecule studies is to resolve and observe individual TFP complexes, their constituents, and to probe the rapid interactions between TFP and their effector molecules, which happens on the ten to hundreds of milliseconds timescale. The long-term vision of my research program is to leverage the potential that single molecule light-microscopy based techniques offer to understand TFP and their physiological function at the molecular level: we will reveal how TFP machines are assembled molecule-by-molecule, how individual molecular motors and regulatory proteins interact with the static components of TFP, and how these interaction kinetics are changed and trigger downstream signaling pathways during the physiological function of TFP. Specifically, firstly, we will use super-resolution microscopy to resolve individual TFP complexes and their components and map their precise locations and assembly states molecule-by-molecule. Combined with timelapse fluorescence microscopy to track the formation of new TFP components through the cell cycle, this will reveal the first step to the physiological function of TFP: when, where, and how the different components of new TFP are assembled and controlled. Secondly, we will use single-molecule Forster resonance energy transfer (smFRET) to investigate the dynamic interactions between specific pairs of proteins of the TFP system. This will reveal how molecular effectors enable and tune TFP dynamics to regulate the physiological functions of TFP. Long term, we will couple these experiments with single-molecule force microscopy techniques to reveal how the physiological functions of TFP feed back to the molecular dynamics of its constituents. The impact of this program extends beyond Pseudomonas' TFP and encompasses the broader TFP superfamily, including secretion, adhesion, and flagellar systems in gram-positive and -negative prokaryotes, and archaea.

NIH Research Projects · FY 2025 · 2024-06

PROJECT SUMMARY/ABSTRACT Acute myeloid leukemia (AML), a blood and bone marrow malignancy, manifests as an aggressive disease that requires immediate treatment. A key player in AML is the mixed-lineage leukemia gene (MLL or KMT2A), found on chromosome 11q23. Mutations in MLL frequently correlate with poor prognosis, prompting a need for therapeutics that can effectively target these genetic alterations. MLL is known to combine with over 80 genes, creating chimeric proteins implicated in AML. Among these, ENL, a component of the YEATS protein family, has been identified as essential for maintaining the dysregulated gene expressions that drive leukemogenesis. Targeting the ENL YEATS domain, a unique structure that forms an "open-end" epigenetic reader pocket, offers a promising therapeutic avenue. Despite significant strides made in developing small molecule inhibitors targeting ENL YEATS, these compounds suffer from limited efficacy, lack of selectivity, and metabolic instability. The PI’s team has previously developed a number of potent small molecule ENL inhibitors. By coupling a newly designed NanoBRET system for their cellular potency analyses, they identified a promising compound (Cmpd 13) that demonstrated favorable characteristics for further in vivo pharmacokinetic and animal efficacy studies. Notably, Cmpd 13 significantly improved survival time for MOLM-13 xenografted mice, establishing it as a potential orally administered therapy. In parallel, the PI’s team has also used a uniquely developed phage display technique, in combination with medicinal chemistry, to identify a potent peptidic ENL YEATS inhibitor, tENL-S1f, that has high cellular permeability and significant in vitro anti-leukemia effects. Additionally, the PI’s team capitalized on the (Proteolysis Targeting Chimera) PROTAC technology, a breakthrough in targeted protein degradation. Using this strategy, they designed a potent PROTAC, Gxj-47, that successfully eliminated AML cells while demonstrating low cellular toxicity. Built on strong preliminary results, the PI’s team propose to significantly expand their multiplatform strategy for the development of ENL inhibitors and PROTACs by pursuing three primary objectives: 1) To develop more potent, stable, and selective small molecule ENL inhibitors and PROTACs; 2) To develop more potent, stable, and selective peptidic ENL inhibitors and PROTACs; 3) To perform rigorous pharmacokinetic/ pharmacodynamic analyses on these molecules, aiming to identify potential Investigational New Drugs (INDs) for MLL-rearranged AML. If successful, this research holds the potential to revolutionize AML treatment, providing a significant shift in treatment modalities, increasing survival rates, and improving patients' quality of life. This research could also serve as a pioneering blueprint for drug development in diseases with similar genetic alterations, thus having broader implications in the field of targeted therapy development.

NIH Research Projects · FY 2026 · 2024-04

PROJECT SUMMARY/ABSTRACT Clostridioides difficile is a Gram-positive, spore forming and strictly anaerobic and pathogenic bacterium that is the leading cause of antibiotic-associated diarrhea worldwide. C. difficile infections (CDI) are highly contagious, difficult to treat and prevent recurrence of the infection. There are several critical gaps in our current knowledge of the molecular mechanisms of persistence of C. difficile spores during CDI-treatment that lead to recurrence of the infection. Our long-term goals are to elucidate how C. difficile spores interact with the intestinal mucosa to facilitate spore-persistence and how these interactions lead to disease recurrence. Our previous studies revealed that C. difficile spores internalized and adhered in intestinal epithelial cells act as a reservoir for the recurrence of the infection. These results lead to our central hypothesis: that C. difficile spores combine intracellular and adherence mechanisms to persist in the intestinal mucosa during infection and treatment, to drive disease recurrence. Towards this hypothesis, we found that C. difficile spores internalize into intestinal epithelial cells (IECs). During vancomycin treatment of C. difficile-infected mice, we observed in ileal, cecum, and colonic samples, significant persistence of adhered and intracellular C. difficile spores. We observed that C. difficile spores interact, in a concentration dependent manner, with the extracellular matrix proteins fibronectin (Fn) and vitronectin (Vn), and uses them as molecular bridges to gain entry into IECs in an a5b1 and avb1 integrin- dependent manner. The spore surface protein, BclA3, is essential for spore entry into IECs via the Fn/Vn-integrin pathway. BclA3 contributes to disease recurrence, apparently by enhancing spore adherence to the intestinal mucosa. Administration of the cholesterol-sequestering agent, nystatin, strongly attenuated spore entry into IECs in an ileal and colonic loop mouse model, while administration of nystatin during vancomycin treatment of C. difficile-infected mice diminished disease recurrence in mice. Our results also show that E-cadherin interacts with C. difficile spores and plays a role in spore-adherence to IECs, and that E-cadherin-mediated spore- adherence to IECs is enhanced by toxin-mediated damage, which may also play a role in recurrence of CDI. Guided by our strong preliminary data, we propose to pursue three Specific Aims to characterize the mechanism underlying the interactions between C. difficile spores and the intestinal mucosa that contribute to recurrence of the infection: (1) Dissect the mechanism of internalization of C. difficile spores into intestinal epithelial cells; (2) Determine the role of adhered and intracellular spores in C. difficile persistence and disease recurrence; (3) Investigate the impact of toxins in E-cadherin-mediated C. difficile persistence and infection recurrence. Completion of these aims may expose novel targets for decolonization, therapeutics, and vaccine strategies to combat this pathogen.

NSF Awards · FY 2024 · 2024-01

Researchers will undertake both underwater and terrestrial research to understand how foraging peoples in the Americas made decisions that enabled them to adapt to the rapid environmental changes that occurred over multiple millenia. During this time, dozens of animal species had recently gone extinct or were dying out or migrating to new lands, plant communities completely transformed, lakes were forming, rivers were flooding, and sea levels rose at least 40 meters. Nevertheless, archaeological data show that people not only adjusted to these transformations, but seem to have thrived, as there are ever-increasing numbers of sites and artifacts appearing throughout this span. Although these groups were entirely reliant upon hunting, gathering, and managing resources available in the world around them, they seem to have met the challenge of extremely rapid and dramatic environmental changes with aplomb. Researchers have long wished to understand how human social, economic, and environmental systems are or are not resilient, and archaeology is particularly well placed to provide relevant insight because it can trace human systems over centuries and millennia. Nuanced understanding of how these forager societies managed to adjust to near-constant change over nearly 5,000 years of rapid environmental fluctuations can provide insight into ways to make human systems more resilient. However, nearly all the known sites contain only lithic artifacts, often in semi-disturbed contexts, severely curtailing what can be learned about social resilience. Some submerged Florida sites however are an exception. Hundreds of osseous and lithic tools have been recovered from the river, and some mid-channel sinkholes have extensive archaeological remains within intact, dateable deposits. The research team will conduct fieldwork at three sites: two adjacent submerged sinkholes and one interior terrestrial site. The two submerged sites have dateable organics and intact sediment sequences. The terrestrial site will likely not have good organic preservation compared to the sinks, but it will provide data about an area where people were not maximizing access to freshwater. Exploring human relationships with the dynamic land and waterscape entails three research and one pedagogical component: 1) creating a diachronic model of resource availability through time; 2) generating predictions for site distributions by modeling potential resource maximization strategies; and 3) assessing the archaeological record of the basin in light of these frameworks. Data from prior excavations will be combined with the new excavation data to test the utility of central place foraging models . Macro-level (geospatial modeling and paleoenvironmental reconstruction) and micro-level (intrasite analysis of features, lithic artifacts, and preserved organics) will be combined to discuss human use during the terminal Pleistocene and early Holocene. Equally important, this project will train some of the next generation of geoarchaeologists, teaching them how to investigate landscapes in their totality and see the waterline as an opportunity, rather than a boundary, giving them the tools to understand and manage submerging and submerged cultural resources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-01

Proposal Summary Neural regulation of sleep, appetite and energy homeostasis is critical to an animal’s survival and under stringent evolutionary pressure. Despite the prevalence of disorders associated with metabolism and sleep, the neural and genetic processes that regulate interactions between these two systems is unclear. This proposal will investigate how genes and neurons modulate sleep in response to changes in metabolism. Flies, like mammals, potently modulate rest/activity cycles in accordance with their nutritional needs. Specifically, flies and mammals suppress sleep in response to food deprivation, presumably to initiate food-seeking behavior. Powerful genetics in the fruit fly allow for precise characterization of genes regulating behavioral and metabolic processes. We recently identified a single pair of neurons that express the neuropeptide Leucokinin and are required for sleep- metabolism interactions. This proposal will examine the neural signatures of food deprivation within these neurons, and how they are regulated by intrinsic and extrinsic cues associated with feeding state. We will combine single cell physiology and sequencing to define the molecular changes within these neurons associated with sleep and food deprivation. Finally, we will examine how the peripheral adipose tissue communicates energy stores to neurons in the brain that regulate sleep-metabolism interactions. Functional investigation of genes regulating sleep-metabolism interactions will provide the groundwork for understanding metabolic regulation of behavior and further our understanding of obesity, sleep disorders and diabetes.

NSF Awards · FY 2024 · 2024-01

The goal of the international GEOTRACES program is to understand the distributions of trace chemical elements and their isotopes in the oceans. This project will generate a dataset of 40 trace elements on suspended particles and surface sediment samples collected on the GEOTRACES GP17-ANT cruise to the Amundsen Sea, West Antarctica. The Amundsen Sea hosts the most productive polynya per unit area in all of Antarctica, with biological carbon uptake ten times higher than the average for the Southern Ocean. Over the past 30 years, this region has become a primary locus of increased freshwater input, as the fastest melting glaciers in West Antarctica deliver huge and increasing volumes of freshwater to the Amundsen Sea. The major contribution of this region to global sea level rise is well documented, but the impact of accelerated additions of meltwater and associated chemical constituents on the biogeochemistry of the Antarctic shelf waters, and in particular on the cycling of trace elements, has not received comprehensive investigation. Hypotheses addressing four key components of the biogeochemical system in the Amundsen Sea will be tested, and results will closely mesh with complementary efforts proposed by other GP17-ANT investigators. The project will support a graduate student and several undergraduate interns, with a focus on broadening participation in STEM. The investigators will also work with established programs to create meaningful out-of-school science experiences for middle and high school students. The aim of the project is to quantify and interpret the distributions of particulate trace elements in approximately 500 samples covering a large swath of the Amundsen Sea shelf, including waters influenced by five major ice shelves, and in the adjacent iron-limited Southern Ocean waters bounded by 100°W and 135°W, and south of 67°S. The investigators will use size-fractionated sample collection, total acid digestion and weak acid leaching, and well-established mass spectrometric methods to determine concentrations and probe the physico-chemical state of the particulate trace elements. The team will use the new data to investigate the following issues: 1) the role of phytoplankton, with a focus on Phaeocystis and diatoms, dominant taxa on the Amundsen Sea shelf, in driving element cycling in the upper water column while experiencing variable degrees of iron stress; 2) the “meltwater pump” which generates vigorous and particle-rich outflow from ice shelf cavities; 3) the bottom nepheloid layer of resuspended sediments as a reaction zone that determines the composition of the sedimentary paleo-record and also modulates of chemical fluxes at the sediment-water boundary; and 4) the rare earth elements (REE), which the team proposes carry unique geochemical information about terrigenous particle provenance among the geologically diverse glacial drainage regions, and also includes a labile particulate fraction whose magnitude and inter-element ratios may serve as a relative index of element scavenging intensity that can be applied to predict regions of maximal scavenging for other particle-reactive elements. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2023-09

Cognitive deficits such as learning and memory impairments are common in people subjected to chronic disturbance of the circadian cycle due to shift work, travel, or genetic dysregulation of the circadian clock. Epidemiological studies have revealed a global rise in cognitive disorders with circadian disruptions comorbidity such as depression, and Alzheimer’s disease, stressing the need to identify the causal relationship between these phenomena. However, the molecular mechanisms linking the circadian cycle and cognitive performance in health and disease remain largely unresolved. Neuronal synapses are the cellular basis for learning and memory processes. Synapse number, activity, and expression levels of synaptic proteins show rhythmic time- of-day-dependent changes, yet how these changes are regulated by the circadian clock is poorly understood. A growing body of work supports a critical role for the glial cells, astrocytes in normal clock function. Astrocytes are important synaptic regulators, and key for establishment and maintenance of memory and learning. Yet, how the astrocytic clock regulates synaptic rhythmicity and related cognitive performance has not been thoroughly examined. This critical gap in knowledge must be addressed in order to understand not only the fundamental functions of the astrocytic clock, but also to characterize the regulatory mechanisms that control circadian changes in synaptic levels. This application will define the role of astrocytic clocks in regulating synaptic rhythmicity and subsequent learning and memory behaviors in three aims. Aim 1 investigates how the astrocytic clock expressed in brain regions responsible for cognitive processes (e.g., cortex, and hippocampus; outside the central clock located in the suprachiasmatic nucleus (SCN)), affects time-of-day-dependent changes in synapses and cognitive performance. Aim 2 investigates how the astrocytic clock is regulated by calcium activity to influence synaptic rhythmicity. In Aim 3, we test the hypothesis that astrocyte-derived synapse-regulating factors are rhythmically produced to facilitate time-of-day-dependent modulation of synapses. Successful completion of these aims will uncover the role of astrocytic clock in regulating synaptic and cognitive rhythms, and reveal strategies for future manipulation of synaptic rhythmicity through astrocyte-targeting, to restore clock-associated cognitive deficits prevalent in neurological disorders.

NIH Research Projects · FY 2024 · 2023-09

PROJECT SUMMARY/ABSTRACT Catheter-related bloodstream infection (CRBSI), also called catheter-related sepsis, is one of the most frequent, lethal, and costly complications of central venous catheterization. CRBSI affects hundreds of millions of people worldwide; in the U.S. alone, it affects more than 250,000 patients yearly. These infections are mostly caused by the migration of microorganisms found on the patient's skin flora at the catheter insertion site. Tremendous efforts have been undertaken to reduce catheter-related sepsis, including improvements to the catheter insertion guidelines and the development of dressings impregnated with antibiotics. These methods help reduce the number of bacteria on the patient's skin but do not eliminate them. No available catheter dressing enables automated and early detection of bacterial growth at the catheter insertion site. Such catheter dressing is a critical need for early detection of CRBSI, allowing for the removal/replacement of the catheter, and, as needed, for early treatment of patients with tailored antibiotic therapy. In addition, it remains a clinical challenge to detect bacterial colonization on the skin at early stages without catheter removal due to the human skin's highly flexible and topographical nature. Flexible biosensors that provide conformal and seamless adherence to the skin can help, but previous studies on the merits of wearable and flexible sensors to detect bacterial infection have been limited to wound infections measured by indirect parameters (e.g., pH) that are subject to change with dietary restrictions and not specific to bacterial infection. Therefore, a significant knowledge gap exists in the use of wearable and flexible sensors integrated with electronics for real-time monitoring of direct bacterial growth at the catheter insertion site for the early detection of CRBSI-related infection risks. The overall objective of this application is to address this need and knowledge gap by developing a fully integrated, wirelessly operated catheter dressing that is capable of monitoring bacterial growth at the catheter insertion site in real-time and non- invasively to enable automated early detection of infection originating from the skin. The central hypothesis is that the electrochemical activity of live bacteria at the catheter insertion site can be directly measured, and acquired data can be classified using machine learning, thereby allowing precise monitoring of extraluminal contamination in real-time. To attain the overall objective, the following two specific aims will be pursued: Aim 1: Develop an integrated catheter dressing (ICD) capable of real-time monitoring of bacterial growth at the catheter insertion site. Aim 2: Validate and optimize the ICD for early detection of catheter-related sepsis on a skin phantom and an animal model. These aims will be accomplished by a team of skilled experts and excellent resources. The proposed research is significant because the ICD can transform the current point-of-care practices, ultimately has the potential to reduce infection risks, health care costs, and morbidity and mortality rates related to CRBSI, and monitor the infection status in real-time, non-invasively, and at the point of care.

NIH Research Projects · FY 2024 · 2023-09

Project Summary/Abstract While the vast majority of antiviral efforts to combat severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) focus on essential viral proteins, emerging evidence shows that conserved viral RNA (vRNA) structural elements are compelling targets with the potential for pan-antiviral activity. Despite this promise, however, selective targeting of RNA using drug-like small molecules remains challenging. In particular, methodologies for screening small molecule libraries against RNA remain underdeveloped, and do not adequately address the central problem of target specificity. As a result, RNA-targeted screens often fail to yield efficacious compounds. The proposed study takes direct aim at this technological gap through the development of a novel RNA-targeted screening technology using L-aptamers composed of mirror-image L-DNA. The PI previously established that L- aptamers can be evolved to bind native D-RNA structures, including SARS-CoV-2 vRNAs, with high affinity and selectivity. He now proposes to develop L-aptamers into RNA-specific competitive displacement probes for identifying small molecules with analogous properties. The general utility of nucleic acid aptamers, combined with the unique RNA-binding properties of L-aptamers, impart the proposed L-aptamer-displacement assay with several advantages over current RNA-centric screening technologies, and is hypothesized to facilitate the discovery of small molecules with unprecedented RNA-binding capabilities. The PI has already prepared an L-aptamer targeting a conserved RNA element with the SARS-CoV-2 genome, which will be developed into a biochemical assay that couples competitive displacement of the L- aptamer from the vRNA target with an optical readout (Aim 1). Using this assay, the PI will initiate a high- throughput screen to identify potent ligands targeting the corresponding vRNA. The most promising lead compounds will be evaluated for antiviral activity against SARS-CoV-2 infected cells (Aim 2). Parallel efforts will be undertaken to generate L-aptamers against additional SARS-CoV-2 RNA structures (Aim 3), which will be shuttled through this same pipeline. Successful completion of this project will signify a major advance in the area of RNA-targeted drug discovery. While combatting SARS-CoV-2 is the immediate goal, technologies developed herein are readily adaptable to target any RNA virus. By targeting essential RNA structures that are conserved across β-coronaviruses, the PI envision that this approach will allow for identification of antiviral compounds with broad-spectrum activity that might quickly pivot to address future outbreaks.

NIH Research Projects · FY 2025 · 2023-08

Project Summary Animals have evolved circadian (near-24 h) rhythms to anticipate and adjust their behavior to daily opportunities and challenges such as mating, food availability, and predation. These behavioral rhythms are synchronized to the solar day by the central circadian pacemaker, the suprachiasmatic nucleus (SCN). SCN neurons exhibit daily rhythms in firing rate and clock gene expression that communicate circadian time to the rest of the brain and body. However, critically, we do not know how SCN signals interact with molecular and neuronal clocks in downstream neurons to generate circadian outputs. Our lab’s overarching goal is thus to understand how circadian input from the SCN is encoded by target neurons to ultimately generate diverse behavioral rhythms that peak at different times of day. To address this, over the next five years, our research program will focus on several interrelated but independent themes, including defining the “transfer function” for circadian output circuits, determining how molecular clocks in target neurons contribute to behavioral rhythmicity, and understanding how target neurons integrate diverse inputs to generate behavioral rhythms. We propose that endogenous rhythmicity in downstream neurons and daily input from SCN neurons are each required to drive appropriately timed circadian behavioral outputs. Here, we will use multi-level analysis at the molecular, circuit, and behavioral levels including targeted genomic editing of clock genes, in vivo and ex vivo imaging of rhythmic neurons, and machine learning analysis of behavior to dissect circadian output circuitry in two complementary species, the nocturnal laboratory mouse and the diurnal African striped mouse. Curiously, molecular and neuronal activity rhythms in the SCN peak at similar times in diurnal and nocturnal animals. How does an ostensibly identical SCN rhythm determine these dramatically different temporal niches? Our approach will allow us to address this and other long-standing questions in chronobiology by identifying both the mechanisms that temporally organize behaviors and the differences in molecular and neural function that decide an animal’s temporal niche preference. Identifying the genes, neurons, and circuits that regulate the timing of behavior in both laboratory mice and striped mice will also provide a novel framework for understanding the biological basis of chronotype in humans and the etiology of circadian rhythm sleep disorders. The discoveries we will make through our research program can generalize beyond circadian biology to reveal fundamental mechanisms linking genes and circuits to behavior.

NIH Research Projects · FY 2025 · 2023-08

Project Summary Social interactions are essential for animal health. Prolonged isolation from social environments profoundly affects animal behavior, physiology, and wellness, expressed during the COVID-19 pandemic as increased levels of sleep disruption and eating disorders, among other population-wide behavioral problems. The underlying mechanisms through which chronic social isolation is processed and impacts health-critical behavior are unknown. A brief disconnection from the social environment is not detrimental. Social isolation, by its very nature, is a continuous and prolonged process, yet how animal brain constructs an evolving state recording this process remains an outstanding problem in understanding social isolation biologically. To address this challenge, I established a Drosophila melanogaster model and discovered the molecular differences between physiological states associated with acute and chronic social isolation. This novel approach has enabled the dissection of underlying mechanisms by using “isolation timing” as a parameter, thereby allowing the identification of cells that signal the chronic status of social isolation for the first time in any model system. My previous research has shown that manipulating the identified cells alters the perception of social isolation duration and social isolation-induced behavioral outcomes, including sleep loss and hyperphagia. In this proposal, we plan to carry out three complimentary projects that capitalize on our recent results to further uncover the timekeeping mechanism modulating physiological effects during chronic social isolation. First, we will elucidate the genetic and molecular pathways that contribute to timekeeping and mediate health-critical behavioral alterations induced by chronic social isolation, with a special focus on the cross talk with the circadian clock. Second, we will identify the molecular substrates underlying “isolation timing” during chronic social isolation and interrogate how an “isolation timer” signals the sleep/wake regulatory network. Third, we will investigate how chronic social isolation drives insatiable hunger and impacts metabolism. To achieve these goals, we will employ a multidisciplinary approach including neurogenetics, high throughput and high-resolution behavioral measurements, transcriptome profiling, functional imaging, and metabolomic analysis. The proposed study, using an innovative framework to investigate the mechanisms by which chronic social isolation is processed on long-time scales and impacts health-critical behaviors at the molecular and cellular levels, will ultimately lead to a deeper understanding of the biology of social isolation and potential interventions/treatments to alleviate the suffering and diseases caused by chronic social isolation.

NIH Research Projects · FY 2025 · 2023-08

Summary Translation of mRNA is a central cellular process, but the mechanisms that control it are not fully understood. Up to 50% of eukaryotic mRNAs contain predicted upstream open reading frames (uORFs), but the roles of the vast majority of these uORFs remain undetermined. In some cases, we know that this additional translational capacity is evolutionarily conserved and serves critical functions in controlling gene expression. A special class of these conserved uORFs encodes peptides that stall protein synthesis in response to the presence of small metabolites. These nascent regulatory peptides act within the ribosome tunnel to arrest translation; by doing so, they control the production of enzymes important in metabolism. However, there remain important gaps in knowledge of the functions of uORFs. First, the mechanisms by which uORF-encoded peptides recognize small molecules and stall eukaryotic ribosomes to control gene expression remain unclear. Second, the extent to which uORFs are translated in cells under different conditions, the extent to which uORFs are evolutionarily conserved, and how and why the translation of particular uORFs controls gene expression, or if their translation serves other functions, is not known. To help bridge these gaps, we will determine the functions of a newly discovered conserved fungal uORF peptide named the inositol regulatory peptide (IRP). Our data indicate that the IRP regulates the expression of the first enzyme necessary for the synthesis of the important molecule inositol. We know that the IRP, while fungal in origin, can regulate reporter genes in mammalian cells as well as the fungus Neurospora crassa, in which we first discovered it. Using fungal and mammalian cell-free translation systems, we obtained direct evidence for translational control by the IRP. We will use both in vivo and in vitro approaches to determine the mechanism of action and physiological consequences of IRP function. In Aim 1, we will perform functional analyses to determine the mechanism of action of the IRP. In Aim 2, we will perform structural analyses to determine the mechanism of action of the IRP. Successful completion of the proposed work will provide mechanistic information that should significantly increase our understanding of translational control mechanisms that are generally important. It will provide new insights into regulatory and metabolic pathways that could be important for developing strategies to manipulate metabolism to improve human health and welfare.