University Of Chicago

NIH Research Projects · FY 2026 · 2026-04

Abstract This developmental research grant award (R21) requests funds to characterize the social and transmission networks of Rosebud Sioux Tribe (RST) (Sicangu Lakota Oyate) community members to mitigate ongoing and future syphilis epidemics among American Indian/Alaska Native (AI/AN) populations and move towards elimination of congenital syphilis: A Sicangu-driven social network strategy for syphilis prevention (S4). In addition to classic transmission networks, we include social networks that confer influence, social support, diffuse information/innovation and can lead to syphilis prevention interventions that the team has experience implementing. AI/AN individuals in South Dakota (SD) are one of the most syphilis impacted communities and are at increased risk of syphilis transmission, including congenital syphilis. In 2020, 3% of all AI/AN babies born in South Dakota had congenital syphilis. AI/AN individuals are more likely to live in rural areas with limited access to prenatal care and hospital obstetric units, creating barriers to timely identification and treatment of syphilis. Earlier this year, The Great Plains Tribes requested emergency assistance from the federal government to declare a public health emergency and address the syphilis epidemic. Critical to public health is improving syphilis prevention among AI/AN communities and their larger social networks. Network analysis traditionally focuses on transmission dynamics and potential for future epidemics. Contact tracing and other strategies do not, however, fully include the larger social network and data can be limited due to the stigma associated with providing names, as well as mistrust in government and healthcare providers, particularly for AI/AN individuals. Social network analysis that this team has expertise in, can illuminate multiple networks and develop metrics tied not only to disease transmission but to diffusion of information, and among highly marginalized groups such as people who use substances. The PI has a track record of collaborative work implementing participant network recruitment protocols such as the Social Network Strategy to be used in S4. The PI and site-PI are joined by additional experts in AI/AN Health, Indigenous community leaders and local community members engaging in Talking Circles in these contexts. Accordingly, we aim to: Characterize the social networks of RST community members and measure features of their network structure— assortativity, density and bridging – most relevant to syphilis transmission and network intervention; Explore individual (ie. age, education), contextual (ie employment type), network and structural (stigma, health care access) factors associated with syphilis seropositivity. We will collect survey data and biologic samples to model potential factors associated with historic and recent syphilis transmission and; Determine individual and social network level factors associated with syphilis prevention behaviors (ie. condom use, drug treatment, doxyPEP) and network intervention (ie. information sharing, proportion approving of syphilis prevention), which could lead to future network interventions.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY/ABSTRACT The hallmark of systemic lupus erythematosus (SLE) is the production of antibodies (Abs) to nuclear antigens such as ribonucleoproteins and chromatin. High-affinity IgG Abs to double-stranded DNA (dsDNA) are associated with more severe disease and the development of lupus nephritis (LN). DNASE1L3 is a unique secreted DNase that is capable of digesting DNA packaged in chromatin, and null mutations in human DNASE1L3 cause monogenic early-onset SLE with anti-DNA reactivity. Similar to humans, DNASE1L3-deficient mice rapidly develop Abs to chromatin and dsDNA. Our analysis of this model revealed that the anti-DNA Ab response arises from the short-lived extrafollicular plasmablasts supported by the extrafollicular helper T cell (EF-TFH) subset. Confirming the relevance of DNASE1L3 to human patients with sporadic SLE, we demonstrated that many SLE patients with LN have reduced DNASE1L3 activity, which is associated with the presence of blocking Abs to DNASE1L3. These results suggest that by blocking D1L3, anti-D1L3 Abs facilitate autoreactivity to chromatin and thereby contribute to SLE pathogenesis. In this cycle of the award, these advances will be leveraged to dissect the mechanisms and clinical consequences of anti-DNA responses in SLE patients with autoantibody-mediated D1L3 deficiency. The project will continue the team effort that includes experimental immunology (Reizis), human SLE phenotyping and biobanking (Buyon), and immune repertoires analysis (Ippolito). In Aim 1, we will further characterize the dynamics and clinical correlates of anti-DNASE1L3 Abs in SLE patients, and analyze their role in the control of cell-free DNA. In Aim 2, we will clone anti-DNASE1L3 Abs from SLE patients and analyze their reactivity spectrum and pathogenic potential in vivo. In Aim 3, we will use single-cell TCR sequencing to characterize the autoreactive T cell clones in SLE patients with Ab-mediated blockade of DNASE1L3, and in DNASE1L3-deficient mice. Collectively, these studies should provide insights into the origin and mechanisms of the pathogenic anti-DNA responses in SLE, and facilitate more targeted approaches towards their therapeutic blockade.

NIH Research Projects · FY 2026 · 2026-04

Project Summary/Abstract The SLC4A2 (or Anion Exchanger2, AE2) is a ubiquitous membrane transporter that mediates the sodium (Na+)- independent and electroneutral exchange of chloride (Cl−) and bicarbonate (HCO3−) ions, and participates in the regulation of intracellular pH (pHi). AE2-deficiency in humans has been linked with the development of Primary Biliary Cholangitis (PBC), which is a chronic cholestatic liver disease associated with autoimmune phenomena. Supporting these findings, mice with whole-body deletion of SLC4A2 develop a spontaneous and progressive autoimmune cholangitis that resembles the human PBC. However, the cell intrinsic requirement of SLC4A2 in immune cells and/or biliary epithelial cells and the mechanisms underlying the loss of tolerance against biliary epithelial cells in the absence of SLC4A2 are unknown. Here, we aim to dissect the role of SLC4A2 in the development of liver inflammation and autoimmune cholangitis using SLC4A2 conditional knockout (cKO) mice. To this end, we have generated tissue-specific SLC4A2-cKO in the liver (i.e., Slc4a2fl/flAlbCre+ mice) as well as in T cells (i.e., Slc4a2fl/flCd4Cre+ mice) to study the requirement of this membrane transporter in immune tolerance in the liver. We aim to: 1) characterize the immune cell composition in the liver and lymphoid organs of Slc4a2fl/flCd4Cre+, Slc4a2fl/flAlbCre+ mice and their control littermates, and ii) study the alterations that occur in T cells and liver cells in the absence of SLC4A2. Investigating the mechanisms underlying the autoimmune cholangitis and liver inflammation caused by SLC4A2 deficiency in mice will be an important step forward to better understand the etiopathogenesis of PBC and to the development of future therapeutic approaches to treat these patients.

NIH Research Projects · FY 2026 · 2026-04

Project Summary/Abstract HLA-F is a nonclassical class I MHC (Ib) molecule that has been found expressed on a variety of cancers, shown to play a role in HIV and adenoviral infection, the neurological autoimmune disease ALS and, relevant to this application, is expressed throughout pregnancy. Despite the potential importance of this protein in these conditions, little is known about this molecule in terms of its function or even in which conformational state it is expressed. We have recently shown that, in addition to being expressed as a heavy chain only state, or open conformer (HLA-FOC), HLA-F can also be expressed as a bon fide peptide presenting molecule, associated with the β2m subunit (pHLA-F). Peptides are presented in an unconventional way, with the N- terminus not anchored within the groove and the potential for post-translational modifications featuring in peptide anchoring. Despite these advances, there remains much unknown about how these conformer states are regulated, how it engages its various receptors in each of these conformer states, and the role of HLA-F in reproduction. Thus, the aims of this proposal focus on addressing these questions and are: Aim 1: To identify which cell types express HLA- F during early and late gestation in normal, healthy pregnancies, and determine which extracellular conformer states and splice isoforms that HLA-F adopts on these cells. Using conformer-specific antibodies, we will determine what cell types express which (or both) forms. We will also pursue peptide elution studies from relevant cell lines to determine if the peptide differs from non-reproductive tissue. Aim 2: To identify and analyze the factors that regulate the production or interchange of HLA-F conformers and splice forms during gestation. We will explore the cellular factors that may play a role in switching HLA-F between peptide-loaded and HLA-FOC as well as an intriguing splice variant of HLA-F of unknown function. Finally, in Aim 3 we seek to establish the cellular and receptor repertoire that engage HLA-F in its various conformer states in human pregnancy and study the functional consequences of their engagement. Together these aims will provide an atlas of HLA-F expression, receptor engagement and provide insight into its role in human pregnancy.

NIH Research Projects · FY 2026 · 2026-04

PROJECT ABSTRACT Obesity affects more than 40% of U.S. adults (more than 100 million) and contributes to substantial morbidity, mortality, and over $170 billion in annual healthcare expenditures. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) offer a transformative opportunity to improve obesity-related outcomes, including significant weight loss, cardiovascular risk reduction, and emerging effects on metabolic comorbidities. However, real-world responses to GLP-1RAs vary widely. Many patients experience suboptimal weight loss and adverse effects. Current prescribing relies on body mass index (BMI) thresholds, failing to account for individual heterogeneity driven by biological, genetic, behavioral, social, and environmental factors yet to be uncovered. This contributes to trial-and-error prescribing, avoidable harm, inappropriate resource allocation, and unnecessary spending. This K23 proposes to develop and validate clinically actionable tools to individualize GLP-1RA prescribing and improve obesity care delivery. In Aim 1, machine learning will be applied to electronic health record (EHR) data (N=25,212) to develop and validate predictive models that estimate the likelihood of (1) achieving 12-month weight loss and (2) experiencing treatment-related adverse events, categorized as intolerances (e.g., nausea, constipation) or complications (e.g., gallbladder disease). In Aim 2, the expected value of individualized care (EVIC) framework will be used with a validated microsimulation model to quantify the clinical and economic value of individualized GLP-1RA prescribing versus a treat-all approach. In Aim 3, a conversation aid will be designed to communicate individualized risks and benefits, followed by usability testing to optimize its effectiveness. The overarching goal is to build tools that support scalable, individualized obesity treatment strategies grounded in real-world evidence. The candidate will receive mentored training in (1) predictive analytics and machine learning, (2) economic evaluation using the EVIC framework, and (3) user-centered design and usability testing. Completion of this training will position the candidate to lead an independent research program focused on the implementation and evaluation of cost-effective, individualized obesity care.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY: The ability to tolerate the complete loss of water is a rare but conserved trait observed across the diversity of life, including in many bacterial species. While desiccation is a common stress experienced by bacteria that primarily inhabit the natural environment, there are rare examples of host-associated bacteria that can tolerate extended periods of water loss. Acinetobacter baumannii is an emerging opportunistic bacterial pathogen that frequently colonizes mammalian hosts after contact with contaminated surfaces. The long-term persistence of A. baumannii on abiotic surfaces is well-documented and has been attributed to the extreme desiccation tolerance of this organism. Moreover, the ability of A. baumannii to readily transition between environmental and host-associated niches implies the evolution of sophisticated regulatory mechanisms that sense and transduce environmental stimuli, including water loss. However, the precise regulatory mechanisms A. baumannii employs to sense and respond to desiccation stress are not well understand. Previously, we uncovered two central regulatory proteins that interface to control desiccation tolerance in A. baumannii, the two-component transcriptional regulator BfmR, and the conserved protease Lon. We discovered that the regulatory processes controlled by these proteins are tightly linked, suggesting a mechanism whereby A. baumannii coordinates the regulated turnover of protein content with a global transcriptional response as a mechanism to program its long- term survival on abiotic surfaces. Over the next five years, we build on these recent discoveries to further delineate the precise regulatory networks and signal transduction pathways controlled by Lon and BfmR to facilitate adaptation to water loss. First, we will explore how regulated turnover of protein cargo by a newly discovered adaptor of Lon protease influences adaptation to desiccation. Second, we will elucidate the signal transduction mechanisms governing transcriptional control of desiccation tolerance by the BfmRS two- component system. Finally, we will define the mechanisms by which transcriptional and post-translational regulatory processes crosstalk to control desiccation tolerance in this organism. Together, this work will reveal novel factors mediating the extreme desiccation tolerance of A. baumannii and will create a framework for better understanding how bacteria induce multi-faceted regulatory processes to transition between host and environmental reservoirs.

NIH Research Projects · FY 2026 · 2026-04

Abstract Complex multicellular organisms develop through hundreds of precisely orchestrated genetic interactions that can lead to deleterious phenotypes like disease when disrupted. Many genes require conservation in their expression to perform their specific functions and activities in concert with other genes. Yet, developmental programs and regulatory networks are frequently modified to produce beneficial, novel, or alternate phenotypes. The most obvious example of this is the evolution of adaptive sexual dimorphisms within and across species. This suggests a degree of flexibility in highly conserved developmental systems that can be accessed by genetic variation. However, the molecular mechanisms by which genetic variation relates to developmental pathway architecture remains poorly understood. A powerful and natural system in which to investigate this is phenotypic polymorphism, defined as the co-occurrence of multiple distinct phenotypes within a species. Alternate phenotypes, including adaptive polymorphisms and disease states, arise from distinct developmental programs that are shaped by underlying genetic variation, often at just one or a few genes. My proposed work aims to characterize the link between genetic variation and developmental variation by using a natural system of female- limited polymorphism in Papilio butterflies. In several species, the gene doublesex controls the development of female-limited wing pattern polymorphism. Ancestrally, doublesex (dsx) regulates sex differentiation by controlling the development of multiple secondary sexual traits across insects. In addition to performing its role in sexual differentiation, alternate dsx alleles cause females to develop either a derived mimetic or an ancestral non-mimetic color pattern. Both female forms co-occur, with males developing a single phenotype. My proposed work will uncover and experimentally test what mutation(s) caused dsx to be reused to control the color pattern development switch, and characterize the cis-regulatory architecture controlling allele-specific expression. Additionally, I will characterize how dsx integrates with or modifies the wing development program by identifying the direct targets of DSX and associated gene expression changes. Collectively, these experiments will tease apart genetic interactions underlying developmental program modifications at a novel level of precision. Moreover, by contrasting results from closely-related species with and without female-limited polymorphism, my proposed work will highlight the common and unique elements of wing development programs that have been modified by the insertion of the same reused gene. This will bear directly on our understanding of how shared genes and programs are (re)deployed across diverse genetic and genomic backgrounds to produce new phenotypes.

NIH Research Projects · FY 2026 · 2026-04

Project Abstract/Summary: Aggregates of amyloid peptides are highly cytotoxic and associated with human illnesses such as Alzheimer's disease, Parkinson's disease, and systemic amyloidoses (e.g., AL and AA amyloidosis). These toxic amyloid aggregates form through cross-β-sheet formation between amyloid peptides. The process begins with the slow and reversible formation of small multi-peptide amyloid seeds and advances with rapid and largely irreversible elongation into amyloid fibrils. Monomeric amyloid peptides are crucial in both initiation and progression. Presequence protease (PreP, a metalloprotease from the M16C clan) and angiotensin-1 converting enzyme (ACE, a metalloprotease from the M2 clan) are structurally distinct enzymes that degrade monomeric amyloid peptides, such as amyloid β (Aβ), preventing the formation of toxic amyloid oligomers and fibrils. PreP, located in the mitochondrial matrix, degrades mitochondrial targeting sequences (also known as presequences) and imported Aβ, maintaining mitochondrial proteostasis. Loss-of-function mutations in PreP are linked to human disorders like mental retardation and psychosis; gene deletion in mice is embryonic lethal. In contrast, ACE is found on the plasma membrane or in the extracellular space and processes various bioactive peptides. Though small-molecule ACE inhibitors are a first-line treatment for hypertension, global ACE inhibition can disrupt multiple pathways and cause significant side effects. A deeper understanding of substrate selection mechanisms by these enzymes could lead to improved therapies with fewer off-target effects. Our lab has determined the structures of human PreP in both closed and open states and human full-length ACE dimer. Our integrative structural analysis suggests that these enzymes utilize significant, coordinated motions between separate domains to capture and degrade substrates of diverse sequences. However, the molecular basis for how these metalloprotease families use conformational dynamics to selectively degrade their preferred substrates remains unclear. We hypothesize that these proteins share mechanisms for recognizing and degrading amyloid peptides despite their structural differences. Our long-term goals are to elucidate the mechanistic basis for substrate selectivity by these amyloid peptide-degrading proteases and to understand the role of inter-domain dynamics in their catalytic cycles. To achieve this, we will employ structural, biochemical, and biophysical analyses, molecular dynamics simulations, and cellular assays to identify the molecular determinants governing the recognition and degradation of clinically important bioactive peptides by these enzymes. This work is significant because it will structurally define the key conformational states of clinically relevant metalloproteases and provide novel approaches for manipulating their activities, thereby opening new avenues for treating human diseases. It is innovative because it integrates diverse yet complementary methods of structural analysis to decipher how amyloid peptide-degrading proteases function.

NIH Research Projects · FY 2026 · 2026-04

Project summary The human population has complex genetic structure because of past and ongoing patterns of migration and admixture, because of nonrandom mating based on heritable traits, and because of selection on disease and non-disease traits. These processes have profound consequences not only for our understanding of human genetic variation, but also for our understanding of the genetic architecture of human traits, including those relevant to human health and flourishing. The genetic architecture of a trait can be summarized as: (i) the loci that underlie variation in the trait; (ii) the effects and frequencies of the alleles at these loci; and (iii) the correlations between alleles across these loci. My work seeks to understand how these features of genetic architecture interact with one another, and how they—and our ability to learn about them from genomic data— are impacted by the processes that determine the genetic structure of human populations. In the first part of my proposed research, I will develop a novel genomic test for stabilizing selection (selection against extreme values of traits) and assortative mating, based on the different effects of recombination on the allelic correlations across loci—long-range linkage disequilibria (LD)—that stabilizing selection and assortative mating generate. I will use this LD–recombination signature to jointly estimate the strengths of stabilizing selection and assortative mating in genome-wide association study (GWAS) data for human traits. I will furthermore characterize how the long- range LD generated by stabilizing selection slows down the allele-frequency dynamics expected under stabilizing selection. I will interrogate the consequences of this slowdown for the equilibrium distribution of allele frequencies and effect sizes, and for the portability of genomic studies across populations. Second, I will seek to understand how demographic processes and selection impact the GWASs that we often use to learn about genetic architecture. Traditional methods to control for the biases that population structure can generate in GWASs have recently been shown to be inadequate in important test cases. I will use coalescent-based approaches to reframe these methods in terms of their ability to capture long-range LD structure in the genome. I will then use this theoretical framing to understand the shortcomings of these methods by studying the consequences of various demographic and selective forces on long-range LD. Then, I will develop and analyze a GWAS design that uses chromosome-specific polygenic scores to control for non-random mating and selection—which traditional methods struggle to control for—in population-based GWAS. Third, I will show that stabilizing selection on complex traits generates selection against the minor-parent ancestry in admixed populations. Using comprehensive simulations of demographic and selective scenarios relevant to Neanderthal–human introgression, I will explore whether this novel mechanism of selection against introgression can explain the extent and pattern of Neanderthal ancestry in the human genome. Finally, I will characterize the genomic signals of introgression unique to this mechanism of selection, and test for them in biobank-scale data.

NIH Research Projects · FY 2026 · 2026-04

ABSTRACT Recurrence from early-stage (stage I/II) primary melanomas, often undetected until symptomatic metastasis, accounts for most melanoma mortality. Current prognostic tools are inadequate to identify patients at high risk of recurrence and metastasis at the time of diagnosis, limiting the ability to provide improved surveillance and personalized treatment. Additionally, biological mechanisms driving early-stage melanoma recurrence remain understudied, as previous studies have extensively focused on advanced melanomas. I hypothesize that the states, interactions, and spatial relationships of cells within the melanoma microenvironment, as well as transcriptomic signatures, play a critical role in early-stage melanoma recurrence and can further inform predictive models. To test this hypothesis, I propose to leverage recent advances in multiplexed tissue imaging, spatial transcriptomics, and artificial intelligence to robustly quantify known prognostic factors, discover new biomarkers, and develop explainable machine-learning models for predicting early-stage melanoma recurrence. The integration of clinical, histopathologic, and multiplexed imaging single-cell data will facilitate the development of accurate and reliable prognostic tools, which can be deployed in clinical settings following independent validation. In Aim 1 (K99), I will develop computational approaches to identify and quantify both known prognostic features and novel biomarkers through multi-modal analyses of multiplexed imaging and spatial transcriptomic data. In Aim 2 (K99), using an efficient multi-modal imaging method, I will generate multiplexed and conventional histopathologic hematoxylin and eosin-stained images of the same tissue section for a large cohort and integrate them with electronic health records to build interpretable machine-learning models for predicting melanoma recurrence. In Aim 3 (R00), I will validate the machine learning models in an independent cohort and apply the pipeline for stage III melanoma recurrence and other cancer settings. My Ph.D. in computer science and ongoing postdoctoral training in computational biology, genomics, and biomedical informatics put me in a unique position to accomplish this proposed research. During the K99 phase, I will be supported by an outstanding and interdisciplinary team of mentors, advisors, and collaborators (Dr. Semenov, Dr. Sorger. Dr. Yu, Dr. Shalek, Dr. Tsao, Dr. Lian, and Dr. Nemeth) with expertise in all aspects of the proposed research. I will acquire new knowledge and skills in (a) cancer biology and immunology and (b) computational analyses in multiplexed tissue imaging and spatial transcriptomics. Together with formal coursework, professional training, and institutional support from the Massachusetts General Hospital and Harvard Medical School, I will bridge my knowledge gap in computational cancer biology, professional skills, and leadership vital to transition into an independent position and establish a highly impactful laboratory, focusing on the development of computational methodologies to systematically decipher mechanisms of cancer development and progression.

NSF Awards · FY 2026 · 2026-04

Systemic inflammatory responses are dramatic immune flares that can be triggered by causes such as infection and surgical trauma. These inflammatory responses can be lethal. The goal of this project is to develop a mathematical framework to describe these responses, using a combination of experiments in a model biological system (zebrafish) and novel Artificial Intelligence (AI) and Machine Learning (ML) tools. This project will use methods from genomics to trace the organism’s immune response and develop computational tools to simulate these responses and design interventions to alter their course. This work contributes to biotechnology via development of high-throughput quantitative genomics methods. The project will also develop new computational tools that contribute to the integration of AI with traditional analytical methods in physics and biology. The project has broader impacts on researchers in adjacent fields, including the biomedical research community. The investigators will also engage in educational initiatives that strengthen the future scientific workforce, especially at the expanding interfaces of physics, computational sciences, and biology. Systemic inflammatory responses are dramatic, organism-wide immune flares, characterized by high levels of immune signaling proteins (“cytokine storms”) and infiltration of immune cells into tissues and organs. A unifying feature of these responses is that they are driven by interactions within the immune system itself, as opposed to external stimuli. It remains a major challenge to understand how these apparent runaway responses emerge from interacting components. While immunologists have long discussed immune states, we lack concrete characterizations of the phase space. Thus, many fundamental questions remain open: to what extent are responses canalized onto predictable paths? What variables control key tipping points to runaway responses? The project will combine theory with experiments in larval zebrafish to develop a dynamical systems framework for systemic inflammatory response in this tractable model system. In Aim 1, the investigators will perform high-throughput RNA sequencing-based measurements to map phase portraits of systemic inflammatory responses. In Aim 2, the investigators will use machine learning to model the responses and test interventions in silico. In Aim 3, the investigators will use live imaging zebrafish to trace response dynamics and test model predictions. This project will yield the first measurements of the basins and attractors governing the behavior of systemic inflammatory responses and provide a predictive mathematical framework for altering the course of these responses. This project integrates broader impacts in both research and education. The project will provide proof-of-principle for a dynamical systems framework that enables prediction and manipulation of systemic inflammatory responses. This framework can be applied by researchers in adjacent fields, including the biomedical research community, to human disease, ultimately improving societal well-being. The investigators will also broaden and strengthen participation in the physics major at the collegiate level via two initiatives: developing collaborative learning sections for the University of Chicago Honors Introductory Physics sequence and integrating physics of living systems problems into our first-year curriculum. These initiatives will strengthen retention and training for physics students in our physics major and introduce students to the power of physics for biological systems. 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 · 2026-04

PROJECT SUMMARY The myriad connections between oral and systemic health, the relevance of those connections for population health, and the fact that medicine and dentistry are highly siloed from each other with respect to both research and practice justifies efforts to expand the pool of researchers with the necessary skills to conduct cross- disciplinary research at the intersection of oral and systemic health. Although interdisciplinary research across dentistry and medicine will be important to improve health outcomes on individual, community, and population levels, the rigorous clinical training provided in medical and dental school leaves little time for training in clinical research and social science-based methods. Moreover, the siloed graduate education systems for training medical and dental providers impede cross-training and collaboration of clinician-scientists in these disciplines. To develop innovative research across professional silos, it is critical to develop research education programs for future clinician-scientists that instill interest and build skills in interdisciplinary oral health related clinical research at an early stage. To address these gaps in research training integrated across professional disciplines, we propose a research education program, Cultivating Researchers in Oral and Systemic health Studies (CROSS), to train a next generation of researchers to be leaders in cross-disciplinary clinical research at the intersection of oral health, systemic health, and the social sciences. The CROSS program will offer undergraduate students enrolled in colleges across the Chicago area the opportunity to gain hands-on experience in research that integrates oral health, medicine, and the social sciences while learning about the overall relationship between oral health, systemic health, and well-being on an individual and at population level. The program will take place over 15 weeks in one academic year, with a full-time summer experience and part-time engagement during the academic year. CROSS will be based at the University of Chicago within the Center for Health and the Social Sciences’ Program on Oral Health, Systemic Health, Well Being and the Social Sciences and will build on a robust infrastructure for undergraduate training programs in CHeSS, including successful strategies for recruiting and retaining students across the Chicagoland area. CROSS will embed trainees in one of several longitudinal, clinical research studies related to oral health and systemic health in outpatient settings and among hospitalized patients. Participants will complete a training curriculum to learn about clinical research, to consent patients to their respective studies and collect data that is used to assess oral and systemic health outcomes. Participants will be paired with faculty mentors to conduct a faculty-mentored research project using data they helped collect with a final poster presentation at an end-of-year symposium. In addition, students will participate in complementary research education activities, including oral and systemic health related didactics, clinical shadowing in dental or medical settings, and exposure to data analytics, scientific writing, and presentation skills.

NIH Research Projects · FY 2026 · 2026-03

This research project aims to develop innovative computational methods to enhance causal inference in genetics and genomics, addressing key challenges in understanding how genetic variation drives disease mechanisms. Advances in sequencing technologies, biobanks, and multi-omics datasets have provided unprecedented opportunities to study the genetic and molecular basis of diseases. However, significant barriers remain, including pervasive biases in Mendelian Randomization (MR), technical noise in pooled CRISPR screens, and limitations in the generalizability of genomic findings across populations. Over the next five years, this program will focus on creating statistical and machine learning tools that integrate experimental data with large-scale external datasets to overcome these challenges. For MR, the research will develop new methods to address confounding arising from family and environmental structure and to improve estimation of ancestry-specific causal effects. These methods will leverage family-based data, data integration and transfer learning, and Bayesian frameworks to strengthen robustness and improve the reliability of causal estimates, particularly for complex neuropsychiatric and related health outcomes. For single-cell CRISPR screens, the program will create advanced models that combine in silico predictions from unperturbed cells with experimental data to improve the accuracy of perturbation effect estimates and optimize experimental designs. The central goal of the project is to unify population-scale genomic studies with cellular-level experimental data to identify causal pathways linking genetic variants, cellular responses, and disease outcomes. This work will address data heterogeneity and refine causal models of genetic regulation and disease progression. The project will also support training and mentorship of students and researchers in quantitative genomics, causal inference, and computational biology. By addressing these challenges, the research will contribute to fundamental advancements in genomics, improving the understanding of complex traits and informing the development of more precise and effective therapeutic strategies.

NIH Research Projects · FY 2025 · 2026-03

Project Summary Research in evolutionary biochemistry has provided insights into how new enzyme functions evolve through changes in substrate specificity. However, much less is known about how new catalytic mechanisms evolve. What molecular mechanisms and processes shape the emergence of new catalytic mechanisms? To what extent is this evolution repeatable? And how might evolutionary history shape the evolution of new catalytic mechanisms? Addressing these questions provides a mechanistic understanding how extant enzymes catalyze diverse chemical reactions, functioning as molecular machines to produce the diversity of life. This research is often challenging because diverse catalytic mechanisms are usually found across protein superfamilies, beyond the phylogenetic horizon of what can be reconstructed. Here, I propose to work with Dr. Joseph Thornton at the University of Chicago to investigate the repeated evolution of a new catalytic mechanism, monooxygenase-catalyzed light production, in the bioluminescent proteins of octocorals (Cnidaria) and brittle stars (Echinodermata). This is an especially powerful system, since these bioluminescent proteins repeatedly evolved in distantly related taxa, each time by recruiting homologous haloalkane dehalogenases with ancestral hydrolase activity. A mechanistic understanding of how these bioluminescent proteins repeatedly evolved may provide fundamental information useful for bioengineering these proteins, which already serve as invaluable biological tools for reporting gene expression, monitoring protein-protein interactions, and activating molecules in optogenetics. In this project, I will investigate the molecular mechanisms underlying the repeated functional transition between hydrolase to monooxygenase activities. First, I will use computational phylogenetic methods to reconstruct the genetic trajectories to evolving monooxygenase activity for each taxon. Then, I will then use experimental methods to functionally identify causal mutations between protein sequence and function. Finally, I will perform manipulative experiments to test the effects of functional mutations at deeper nodes within each lineage and across lineages. Altogether, these findings will illuminate the molecular mechanisms and factors underlying the repeated evolution of monooxygenase-catalyzed light production from haloalkane dehalogenases. More broadly, this research will provide insight into how historical contingency and lineage specific evolutionary processes shaped the emergence of new catalytic mechanisms. The research training plan proposed here will prepare me for my future career as an independent principal investigator by strengthening my expertise in experimental evolutionary biology and computational molecular phylogenetics. This training will allow me to develop a robust framework in molecular evolution, supporting my integrative and interdisciplinary research program that combines organismal evolutionary biology with biochemistry to understand the evolution of novel phenotypes across biological scales.

NSF Awards · FY 2026 · 2026-03

Quantum mechanics shapes how systems behave at every scale, from the dynamics of neutron stars to the rearrangement of electrons during chemical reactions. The PI and her group will use ultracold quantum gases—atoms or molecules cooled to nearly absolute zero temperature—to study and simulate such systems. A major recent advance has been the use of ultracold polar molecules, whose strong, tunable interactions provide powerful control over their internal states and motion. The next step is to produce more strongly interacting types of polar molecules (silver-potassium), to reach the interaction strengths needed to explore exotic new states of matter. With this leap in technology, the PI and students will advance ultracold polar molecules as a quantum platform for discovery on an equal footing to the successes of ultracold atoms, leading to better understanding of the microscopic origins behind exotic phases of matter. The work will train graduate and undergraduate students on quantum hardware and technologies. Furthermore, the PI will develop a new graduate-level course on atomic physics to train PhD students and advanced undergraduates, incorporating modern elements of quantum information science with atomic systems. The PI and her team will create and use a new molecule—potassium-silver (KAg), which is predicted to have an 8.5 Debye electric dipole moment in its ground state. Due to the multiple isotopes of potassium, KAg can be prepared either as a composite bosonic or fermionic molecule. Compared to existing ultracold molecules assembled from alkali elements, KAg will also be created by preparing the individual atomic constituents to sub-microkelvin temperatures, transferring to a bound state via a magnetic Feshbach resonance, and forming the ground state molecule through two-photon coherent Raman transfer. The molecule will inherit the ultracold temperature of the atoms. The benefit of KAg lies in its exceedingly high dipole-dipole interaction strength, an order of magnitude or more compared to the dipolar fermionic molecules currently created. A high dipole moment aids in (1) further evaporative cooling of the molecular gas to quantum degeneracy, requiring “shielding” techniques to prevent inelastic collisions, (2) higher critical temperatures for dipolar superfluid phases to be stabilized, and (3) novel mechanisms to load large, low-entropy molecular tweezer arrays. The creation and control of “ultrapolar” molecules represent a viable pathway for molecule-based quantum simulation to reveal microscopic details of the topological p+ip superfluid phase, dipolar supersolids, and dipolar quantum spin liquids. 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 · 2026-03

PROJECT SUMMARY The number of disease-associated variants identified by GWAS continues to increase with larger sample sizes, but our ability to interpret these variants appears to have reached a plateau: the mechanisms underlying most GWAS loci still remain unclear. Importantly, a comprehensive list of causal genes and biological pathways is missing for nearly all complex diseases. Without such a list, progress in understanding human traits and in our ability to design effective treatments for complex diseases is greatly stunted. To link GWAS loci to causal genes, there have been large efforts to map the effects of non-coding genetic variants on the expression levels of nearby genes (i.e. cis-eQTLs) in many tissue- and cell-types. However, recent work pointed out that GWAS loci are less likely to have large cis-eQTL effects, especially those that harbor functionally important genes. Therefore, to comprehensively interpret GWAS loci and identify disease genes, we propose to move beyond cis gene regulation, and focus on 1) trans regulation of genes and proteins and 2) genetic regulation of the epigenome (such as histone modification). We will continue developing novel statistical approaches for large-scale data generated from cutting-edge technologies. We will (a) comprehensively map trans-regulatory signals the proteome and characterize trans regulatory mechanism of the proteome; (b) develop statistical approaches to identify trans-perturb-QTLs in perturb-seq data; (c) develop statistical methods to identify cis-by-trans genetic interaction effects and advance understanding of the genetic architecture of gene expression; (d) identify core disease genes of immune-related disorders and neurodegenerative disorders, by leveraging trans regulatory signals; (e) experimentally validate core disease genes through collaborations and reveal the trans regulatory networks of core genes; (f) develop statistical approaches to map chromatin-QTLs in bulk and single cell datasets. Applying these methods to large-scale proteome datasets, scRNA-seq, scATAC-seq, CUT&TAG and perturb-seq data of disease relevant tissues and cell types will improve the interpretation of disease-associated variants, reveal disease mechanisms, and unlock the full potential of genomics for translational research.

NIH Research Projects · FY 2026 · 2026-03

Project Summary: Evolution builds biological systems that can self-assemble and display complex information and material processing capabilities that rival or exceed the performance of man-made systems. For example, protein molecules fold spontaneously into ordered structures and exhibit the ability for specific binding, catalysis, signal transmission, and allosteric regulation. They can do all these things while remaining robust to random variation and adaptive to changing conditions of selection in the environment. The central goal of our work is to deduce the design principles by which high-performance, robustness, and adaptability are realized in the process of evolution. Past work using sequence-based statistical models give us clear hypotheses to test, and new technologies for deep generative models, protein dynamics, and forward evolution experiments open up for the first time a definitive opportunity to test the hypotheses. In this proposal, we describe a new generative model that enables text-prompted protein engineering of functional artificial proteins with great diversity. We also describe new experiments for dynamics and evolution that allow us to conceptually understand the information encoded by generative models. Since this work represents a unification of modern AI approaches with cutting- edge experimental methods, the proposed studies promise to provide an excellent training environment for young quantitative scientists working in biology. Overall, the outcomes of this research program will be experimentally validated models for protein engineering, new concepts of how proteins work and evolve, and a technology platform that can empower the scientific community to advance model-driven approaches for biological systems.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY/ABSTRACT Growing evidence suggests that exposure to environmental toxicants contributes to Alzheimer’s disease and related dementias (AD/ADRD). Understanding how environmental exposures contribute to disease onset and progression enables policy and/or pharmacological interventions that prevent, mitigate, or potentially even reverse the deleterious consequences of exposure to environmental toxins. Most studies to date are limited by the inability to measure early life or cumulative exposure to toxicants, availability of brain and other tissues at scale (especially from diverse subgroups that are more likely to encounter pollution and develop AD/ADRD), and/or knowledge of the molecular processes through which toxicants act. To overcome these limits, we will leverage the established infrastructure of the Pathology, Alzheimer’s and Related Dementias Study (PARDoS), a major AD/ADRD cohort study that examines neuropathologic and clinical AD/ADRD traits, to study 2,500 deceased Brazilians of European and African ancestry that lived in and around São Paulo, Brazil. Our overall goal is to examine the association of both particulate matter (PM) and metal exposure with AD/ADRD clinical and pathologic traits. This project tests the hypothesis that humans accumulate pollutants in multiple tissues over their lifespan that alter genetic pathways in the brain that can lead to AD/ADRD. Aim 1 will measure toxicant levels in the lung, bone, brain (late life exposure), and teeth (early life exposure) and relate these exposures to brain pathology to determine relationships between environmental pollution exposures across the lifespan and AD/ADRD neuropathological traits. Aim 2 will focus on brain regions known to be important in AD/ADRD (olfactory bulb and dorsolateral prefrontal cortex) to characterize pollutant deposits at the subcellular level and assess the impact of these exposures on transcriptome profiles in relation to AD/ADRD pathogenesis. Importantly, the scale of our data collection effort enables integrative analyses across aims and the opportunity to examine differences by sex and race. These analyses, and the composition of the population from which data will be collected, will provide novel insights relevant for understanding health disparities. The project is led by experts in neurology, olfaction, epidemiology, toxicology, genomics, and environmental health and it will, for the first time, use direct, quantitative measures of PM exposure and metals to identify genes and molecular networks that connect toxicants to AD/ADRD pathology. Policy and therapeutic interventions informed by an improved understanding the relationship between environmental exposures and AD/ADRD, including molecular mechanisms, could benefit brain health worldwide.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY / ABSTRACT The development of targeted RNA degradation (TRD) technologies, such as RNA-degrading chimeras, holds significant promise for antiviral therapies by reducing viral RNA levels to prevent viral replication. Despite their potential, current TRD mechanisms exhibit limited potency. In various literature reports, ribonuclease- targeting chimeras (RIBOTACs) exemplify this problem, achieving only ~75% maximum degradation of distinct RNA targets in cells. For example, our preliminary work optimized a synthetic RNA ligand, C34, which binds robustly to an RNA G bulge in the 5’ untranslated region of SARS-CoV-2’s RNA genome with a low nanomolar dissociation constant. However, the minimum inhibitory concentration (MIC) of the C34-based RIBOTAC remained moderately high at 20 μM in SARS-CoV-2-infected cells, indicating insufficient cellular potency. RIBOTAC is a drug-induced TRD modality utilizing an endogenous ribonuclease, RNase L. Interestingly, this RIBOTAC potency cap in cells was not observed in cell-free assays using purified recombinant RNase L, indicating the possible presence of cellular factors that inhibit the RNase L degradation complex. To address this potency limitation, the proposed project aims to discover novel cellular targets to significantly enhance RNase L activity and brand-new TRD mechanisms for future drug development through two specific aims. Aim 1 focuses on identifying cellular determinants that inhibit RNase L-dependent RIBOTAC activity using a genome-wide CRISPR knockout screen. By targeting these inhibitory genes, we aim to significantly boost the efficacy of existing RIBOTACs. Aim 2 focuses on discovering novel cellular targets for TRD by performing genome-wide screenings in three screening platforms. We will utilize a plasmid library comprising ~14,000 open reading frames from the human genome to identify candidate genes that can effectively induce TRD through drug-induced proximity. Both aims will leverage a SARS-CoV-2 cellular model and our optimized RNA ligand C34 to validate these new mechanisms in antiviral research. Ultimately, this project seeks to expand our chemical genetics toolbox by unveiling new mechanisms and targets for RNA degradation. These advancements will be pivotal in developing new chimeric molecules designed to combat a variety of infectious diseases, significantly enhancing our capacity to address global health challenges. 1

NIH Research Projects · FY 2026 · 2026-02

Project Summary/Abstract Mucosal healing is an essential physiological response to inflammatory and injurious diseases of the bowel, including ischemic colitis, radiation-induced mucosal injury, and inflammatory bowel diseases (IBD). In fact, successful and complete mucosal healing in IBD is associated with better clinical outcomes, longer remission, and lower risk of complications like fibrosis, bleeding, and colorectal cancer1. Yet, few therapies in current use are designed to target this important aspect of patient care in large part because of gaps in knowledge about the mediators and mechanisms underlying intestinal epithelial cell (IEC) restitution following injury. We recently discovered that the poorly understood small molecular weight heat shock protein, Hsp25 (the human form is Hsp27) which is highly regulated by colonic anaerobic microbiota, is essential for intestinal mucosal restitution following mucosal injury. We show scientific merit for the hypothesis that IEC Hsp25/27 is key to two essential events necessary for wound healing, (1) promoting IEC restitution through changes in gene transcription that promote IEC proliferation and maturation, and (2) ensuring directional cell movement to injured areas. The former is highly dependent on the interactions of phospho-STAT3 and Hsp25/27 and the latter appears to involve Hsp25/27-dependent formation of actin-stress filaments and assembly and stabilization of focal adhesion plaque (FAP) complexes necessary for directional cell movement. Whether the Hsp25/27- dependent mucosal restitution and directional cell movement are dependent or independent will also be addressed. To better understand the intricacies and mechanistic basis of these pathways, three specific aims are proposed: (1) to test the hypothesis that IEC Hsp25/27 is essential for gut mucosal healing using multiple in vitro and in vivo models of colonic injury and to disentangle the potential role of Hsp25 in mitigation of mucosal inflammation from a direct role in promoting mucosal restitution; (2) to define the role of pSTAT3-Hsp25 interaction in the assemblage and stabilization of nuclear transcriptional complexes and their genetic programs that promote IEC proliferation, differentiation and function; and (3) to determine the mechanisms underlying IL- 22 stimulated, Hsp25-dependent directional cell movement and if they are independent of Hsp25-dependent activation of pSTAT3. The studies will employ unique conditional and inducible murine models and organoid lines derived from them where IEC Hsp25 gene expression has either been deleted, constitutively expressed, or induced to provide mechanistic insights into the organismal, cellular, molecular, and genetic bases that are essential to the mucosal healing response in chronic inflammatory diseases of the bowel. The insights gained will create opportunities to restore elements of the host-microbe mucosal healing circuitry that can lead to better and sustained clinical outcomes and opportunities for wound healing drug discovery and interventions.

NIH Research Projects · FY 2026 · 2026-02

ABSTRACT Systemic autoimmune diseases such as systemic lupus erythematosus (SLE) are mediated by autoantibodies against key tissue constituents, accompanied by the activation of innate immune system. In addition, systemic autoimmunity is frequently associated with hematological complications such as lymphopenia, anemia and/or thrombocytopenia, which can be debilitating and even life-threatening. These abnormalities are typically considered as isolated symptoms caused by autoantibodies against the respective blood cell types, and are treated by immunosuppressive therapies. On the other hand, it is possible that frequent hematopoietic abnormalities in SLE may reflect a defect in the source of hematopoiesis, i.e. hematopoietic stem cells (HSC) and/or progenitors. This model has important implications for the pathogenesis and treatment of SLE; however, it remains to be supported by genetic and mechanistic evidence. Our preliminary studies suggest that the bone marrow from mice with SLE-like disease showed impaired ability to reconstitute irradiated recipients. Moreover, HSCs in moribund mice showed increased proliferation and upregulation of transcripts associated with stem cell exhaustion. We therefore hypothesize that clinical SLE-like disease impairs the activity of HSC, which may further exacerbate hematological abnormalities and inflammation. This hypothesis will be tested using two Specific Aims. In Aim 1, we will characterize HSC impairment in a mouse model of SLE-like disease, including its cell-extrinsic nature and relevance to endogenous hematopoiesis. In Aim 2, we will characterize the epigenome of disease-affected HSC, as well as molecular pathways that cause HSC impairment in this model. Collectively, these studies may support the paradigm of impaired HSC activity as a source of hematopoietic abnormalities and "trained autoimmunity" in SLE. As such, they may pave the way for future studies of stem/progenitor function in human SLE and of its potential therapeutic modulation.

NSF Awards · FY 2026 · 2026-01

Recent research on the aggregate economy tries to understand how aggregate economic performance has differential impacts on individual economic units. This is partly due to increased access to detailed micro-level data sets, increased and cheaper computing power, and partly due to growing concerns about whether economic growth benefits all of society. While much progress has been made in both theory and empirical methods of understanding the links between the distribution of income and aggregate economic activity, current statistical methods used by academic researchers, central banks, and other policy institutions are not designed to account for these differences. This proposal consists of four projects that will develop new and modern econometric tools to improve empirical research on differential effects of macroeconomic outcomes. The project will also develop video materials to educate students and practitioners in these econometric tools. This CAREER research proposal will use three projects to develop new econometric methods for studying heterogeneity in macroeconomics. The first project develops an inference procedure with formal coverage that guarantees visualizing multi-dimensional cross-sectional heterogeneity, such as heterogeneity in dynamic response profiles across groups of households or firms. The second project provides a causal reinterpretation of popular methods for estimating temporal heterogeneity and nonlinearities in time series data, such as state- or sign-dependence of impulse responses. The third project runs a large-scale simulation study of impulse response estimators and provides quantitative recommendations on how to choose between the many available procedures. Finally, the project proposes a plan for developing a collection of interactive educational materials on modern macro-econometric methods. 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 · 2025-12

Project Summary Cells within epithelial sheets undergo collective cell migration during a variety of processes including tissue morphogenesis, organ homeostasis, and cancer metastasis. However, we lack a clear understanding of how epithelial collectives sense directional cues and translate them into polarized movements, particularly in vertebrates. Through the proposed project, I will establish the zebrafish pronephros (embryonic kidney) as a tractable, in vivo model of epithelial collective cell migration. During development, the epithelial cells of the pronephric duct undergo a collective cell migration towards the anterior of the embryo, driving morphogenesis of the proximal tubule. This migration is powered by the extension and retraction of migratory protrusions at the basal cell surface. Lumenal fluid flow has been identified as the directional cue that dictates migration orientation: pronephric epithelial cells migrate against flow. Therefore, the pronephric epithelium is an ideal yet underutilized system to study how cells sense and relay directional information during collective migration, as both the upstream polarizing cue (fluid flow) and the downstream readout (anterior migration) are known. Collective migration is often regulated by polarity modules that coordinate cytoskeletal behaviors. I hypothesize that the zebrafish Fat1a/LarA/LarB proteins function as a migratory polarity module that orients pronephric protrusions in a flow-dependent manner. In Aim 1, I will test this hypothesis through live-imaging of migration in wildtype and polarity-mutant embryos. By mechanically altering pronephric flow in embryos expressing tagged polarity proteins, I will determine if the Fat1a/LarA/LarB polarity module localizes in a flow-dependent manner. Cilia have long been speculated to function as flow sensors in the pronephric epithelium. I hypothesize that mechanosensitive sensory cilia detect flow via the Pkd2/calcium signaling pathway. In Aim 2, I will test this hypothesis by performing live calcium imaging to ascertain if there is a population of cilia exhibiting flow- responsive calcium signaling. Engineering of a ciliary-trafficking Pkd2-mutant will reveal if ciliary Pkd2 is required for migration. Together, the proposed experiments will shed light on how epithelial cells translate directional cues into polarized migration, advancing our understanding of kidney development. Moreover, through completion of these aims, I will learn essential skills in zebrafish genetics, molecular cloning, and live-imaging that will be key to the success of my future independent research lab.

NSF Awards · FY 2025 · 2025-10

Speciation is a fundamental evolutionary process by which new species form. Behavioral isolation often plays a key role in speciation and occurs when mismatches in mating traits (signals and/or discrimination of those signals) prevent closely related species from interbreeding. The relative importance of changes in signals versus changes in signal discrimination to behavioral isolation remains unknown, as studying cognitive traits such as discrimination has traditionally posed a major challenge in natural populations. It is now feasible to develop genomic resources and functional genetic tools in non-model systems, providing an exciting opportunity to investigate the neural and genetic basis of behavioral isolation. This project will integrate cutting-edge transcriptomic and genomic approaches to connect patterns of behavioral, neural, and genomic variation within species to large-scale processes of species diversification. This innovative work will move the field forward by identifying how selection acts on cognitive traits underlying behavioral isolation, providing novel insight into the mechanisms that generate and maintain biodiversity. The broader impacts of this project focus on developing initiatives aimed at supporting underrepresented groups in biology. First, training opportunities and curriculum resources centered on inquiry-based learning will be provided to secondary education teachers from minority serving schools. Second, curriculum on coding for biology will be designed and implemented through an existing local program for girls interested in gaining experience in coding. Third, students from underrepresented groups will be recruited for paid research positions on this project. Interspecific reproductive interactions can play a key role in species diversification by favoring the evolution of enhanced behavioral isolation in sympatry relative to allopatry between closely related species, resulting in a pattern of character displacement. Understanding how traits underlying behavioral isolation evolve is central to understanding how species diversify in this manner. Yet, strikingly little is known about the proximate mechanisms of behavioral isolation, representing a critical gap in our understanding of speciation. This project will test the hypothesis that selection on cognitive traits underlying species discrimination plays a key role in the evolution of behavioral isolation. Well-documented patterns of character displacement in darters, a diverse group of North American stream fishes, will be leveraged in combination with recently developed genomic resources and functional tools in this system. The mechanistic basis of enhanced behavioral isolation in sympatry will be investigated using three approaches: (1) individuals from sympatric and allopatric populations will be raised in the lab to investigate the genetic and environmental contributions to species discrimination, (2) molecular profiling of behaviorally relevant neurons will uncover the neurogenomic basis of species discrimination, and (3) population genomic analyses in sympatric and allopatric populations will identify the genetic basis of behavioral isolation and speciation. This integrative project will provide unprecedented insight into the mechanistic basis of behavioral isolation. Furthermore, this project will support the development of educational, training, and mentorship resources for secondary and post-secondary students from groups underrepresented in STEM and for high school biology teachers from minority-serving schools. This project is jointly funded by the Behavioral Systems Cluster in the Division of Integrative and Organismal Systems, and the Evolutionary Processes program in the Division of Environmental Biology. 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 2025 · 2025-10

Many critical scientific challenges, from understanding complex diseases to designing innovative materials, rely on sophisticated computer simulations. However, scientists often encounter a "silicon ceiling," where current computational power restricts their ability to model these intricate real-world phenomena accurately enough to achieve major breakthroughs. The SINAPSE project directly addresses this issue by developing a powerful, open-source software toolkit that combines Artificial Intelligence (AI) with High-Performance Computing (HPC). This integration promises to enhance simulation capabilities, effectively offering significant orders-of-magnitude performance gains. SINAPSE will provide foundational software that benefits the broader AI-HPC research community, advancing the field itself. The project is also dedicated to supporting education and training for students in these cutting-edge computational methods, fostering the next generation of STEM professionals. By making advanced simulations more powerful and accessible, SINAPSE serves the national interest by driving innovation and enabling solutions to pressing scientific challenges. The project aims to overcome the "silicon ceiling" limiting complex simulations by developing a Scalable Infrastructure for AI-driven Predictive Simulation Enhancements (SINAPSE), delivering an open, sustainable Software Development Kit (SDK) that seamlessly couples Artificial Intelligence (AI) with High-Performance Computing (HPC) workflows. The project will provide functional capabilities through new and enhanced core software elements for AI-coupled HPC and integrated problem-solving frameworks for common scientific discovery patterns. The methodology begins by convening the SDK with a community focus. The SDK will then be populated by creating several novel core software elements and significantly enhancing existing tools like Colmena and RHAPSODY to support diverse AI-HPC coupling needs, including dynamic and asynchronous execution. These components will be assembled into problem-solving frameworks such as "Muse" for online surrogate model training, "Music" for model-directed sampling, and "Melody" for multi-scale campaigns. Finally, the entire SINAPSE SDK and its frameworks will be validated and strengthened through applications in biophysics, focusing on viral glycoprotein dynamics, and materials engineering, specifically for catalyst design. 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.