Stanford University

NIH Research Projects · FY 2025 · 2023-09

ABSTRACT How to ensure adherence to computerized cognitive training in unsupervised circumstances (e.g., at-home, self- administered) in older adults at risk for Alzheimer’s disease (AD) or AD related dementia (AD/ADRD) is understudied. The objective of the R61/R33 is to refine and test a novel facial expression-based personalization engine (FPE) for monitoring and modulating real-time effective engagement, with an ultimate goal of enhancing long-term adherence in unsupervised cognitive training in older adults at risk for AD/ADRD. Here, Effective engagement is defined as the extent to which someone is actively engaged and performing with significant attention and enjoyment while training, addressing a balance between adherence and cognitive gains/plasticity from the training. Based on previous work, including ours, we hypothesize that (1) mental fatigue revealed in facial expressions will reflect a trainee’s degree of effective engagement, which can be modified by modulating task novelty; (2) our proposed FPE will ensure the effective engagement in cognitive training by monitoring trainee facial expressions and modulating training in response, promoting the trainee’s long-term adherence to the training and cognitive plasticity. In R61 (Y1-Y2), we will generate the FPE for monitoring and modulating real- time engagement in cognitive trainings in older adults at risk for AD/ADRD by refining our established application programming interface using a Stage I design. In R33 (Y3-Y5), we will conduct a Stage II intervention efficacy study comparing effective engagement and adherence in unsupervised cognitive training between training programs with vs. without FPE in older adults at risk for AD/ADRD. We will address milestones proposed in both stages to (a) ensure the readiness of the proposed FPE for R33 (R61 milestone) and (b) evaluate and further revision of FPE for future implementation test (R33 milestone). Impact: the proposed FPE may assist in monitoring and improving effective engagement and adherence in older adults with unsupervised cognitive training. In the current application, we will test FPE in a cognitive training program called speed of processing training. However, such FPE may be embedded to any computerized cognitive training in future studies to help address adherence related issues.

NIH Research Projects · FY 2026 · 2023-09

Acute injury to articular cartilage is common and can significantly increase an individual’s risk for developing osteoarthritis, yet effective regenerative therapies remain lacking. Cartilage has a limited capacity for self- regeneration due to low cellularity and lack of vasculature. One promising strategy for cartilage repair is the use of mesenchymal stem cells (MSCs). Injectable hydrogel carriers are particularly desirable for MSC delivery as they can be applied to cartilage defects in minimally invasive procedures. Hydrogel design can be inspired by native cartilage tissue properties such as stiffness and biochemical ligands, which have been extensively studied in the context of MSC chondrogenesis in 3D. Cartilage is also viscoelastic, demonstrating stress relaxation behavior in response to applied stresses. Using alginate hydrogels as a model system, it has been recently shown that faster stress-relaxation enhances chondrocyte-based cartilage production. However, the way viscoelasticity modulates MSC-based cartilage regeneration remains largely unknown. Our lab has previously reported sliding hydrogels (SG) with mobile crosslinks that can slide along the PEG polymer backbone, which significantly accelerated MSC chondrogenesis in 3D compared to non-mobile, covalently crosslinked hydrogels. Unlike alginate hydrogels, SG is crosslinked by irreversible covalent bonds and does not exhibit stress relaxation. Based on previous findings in both the SG and alginate hydrogel systems, I hypothesize that introducing viscoelasticity to SG would further accelerate MSC-based cartilage regeneration in a dose-dependent manner through enhanced mechanotransduction in vitro and in vivo. To test this hypothesis, I propose to: (1) Develop and characterize adaptable sliding hydrogels (ASG) with tunable stress relaxation as a 3D stem cell niche through the incorporation of dynamic crosslinks; (2) Evaluate the effect of stress relaxation in ASG on MSC chondrogenesis in vitro and elucidate the underlying mechanisms by characterizing mechanotransduction signaling; (3) Validate the efficacy of ASG with optimized stress relaxation in accelerating MSC-based cartilage regeneration in vivo using a rat osteochondral defect model. Compared to alginate hydrogels, the proposed PEG-based ASG is a cleaner system that presents cells with highly controlled niche cues. The outcomes will fill a critical gap in knowledge about the way viscoelasticity influences MSC chondrogenesis in 3D and pioneer the in vivo translation of dynamic hydrogels with viscoelasticity for cartilage regeneration. I will be mentored by a team of basic and clinical scientists with complementary expertise in biomaterials and tissue engineering, polymer chemistry, mechanotransduction, imaging and animal models. The outcomes will fill a critical gap in knowledge about the way that viscoelasticity influences MSC chondrogenesis in 3D and validate adaptable sliding hydrogels as a new biomaterial for accelerating MSC-based cartilage regeneration.

NIH Research Projects · FY 2025 · 2023-09

Project Summary/Abstract Major depressive disorder (MDD) is among the most prevalent, recurrent, and functionally debilitating of all psychiatric disorders. The incidence of MDD rises sharply during adolescence, and individuals who have an onset of MDD in adolescence tend to have a more chronic and severe course of depression than do those with a later onset. Thus, there is an urgent need to develop effective approaches for early identification, prevention, and intervention for MDD. Experiences of early life adversity (ELA), which affect over 40% of children, are a strong predictor of MDD. Research suggests that one pathway by which ELA increases risk for MDD is through alterations in the structural and functional development of frontolimbic regions implicated in stress reactivity and regulation; however, the direction of these effects and how they unfold over time are not known. Moreover, the biobehavioral mechanisms by which ELA influences neurodevelopment and risk for MDD are not well understood. In this context, sleep disturbances is a significant risk factor for MDD across the lifespan and is an underexplored pathway by which ELA might increase risk for MDD during adolescence. Sleep disturbances tend to increase during adolescence due to a combination of normative biological and psychosocial changes; indeed, over 70% of high-school students report getting insufficient sleep. Emerging research suggests that adolescents with greater sleep disturbances have both attenuated white matter development in tracts that connect frontolimbic regions and heightened frontolimbic reactivity to stress. The overlapping neurobiological and health effects of ELA and sleep disturbances suggest that sleep disturbance is a critical pathway that links ELA to frontolimbic alterations and increased risk for MDD. The proposed research addresses critical gaps in the literature by examining the multivariate and longitudinal effects of ELA, sleep disturbances, and frontolimbic connectivity during adolescence and how these factors predict risk for depression in young adulthood. Leveraging data from a multimethod longitudinal study, the proposed project investigates sleep disturbances as a pathway linking ELA with alterations in frontolimbic development and risk for MDD across adolescence and young adulthood (9-20 years of age). The results of this project will not only increase our understanding of the neurobiological mechanisms by which ELA relates to increased risk for MDD, but will also provide insight into sleep disturbances as a potential target of intervention during adolescence to ameliorate the effects of ELA. Moreover, the proposed training plan will enable the applicant to gain theoretical and methodological expertise in studying the relations among ELA, sleep quality, frontolimbic development, and psychopathology during adolescence, and to develop professional skills necessary to transition to an independent research career. Stanford University, the institution at which the applicant will be training, has experts and resources relevant to all domains of the proposal, making it an ideal training environment for the applicant.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY Solid cancers are a leading cause of cancer related death in children and there is great interest in harnessing recent progress in immunotherapy for the treatment of pediatric solid tumors. Immune checkpoint inhibition (ICI) is the most active form of immunotherapy for adult solid cancers, but ICI is not effective in pediatric solid tumors. This discrepancy is explained by the low mutational burden of pediatric solid tumors, since neoantigens arising from tumor specific mutations are the target of the most potent ICI induced immune responses. Overexpressed non-mutated self-antigens, that are not expressed on normal vital tissues, can serve as the basis for effective immune therapies, but immune tolerance must be overcome to induce potent immune responses to this class of molecules. This project focuses on Ewing Sarcoma (EWS) a prototype low mutation burden solid tumor, for which progress has stalled. Standard therapies for EWS rely on dose intensive regimens largely developed in the 1970s and 80s which leave survivors with severe, lifelong late effects. No targeted therapeutics have been demonstrated to be effective. Few patients with metastatic or recurrent EWS survive. Using immunopeptidome profiling, we discovered novel peptides from lipase-1 (LIPI) and IGF2 binding protein 1 (IGF2BP1) that are presented by HLA-A2+ on EWS. These non-mutant proteins are overexpressed at high levels in the vast majority of EWS and are essentially absent from vital normal tissues, thereby demonstrating a very favorable profile for immune targeting. To translate this discovery into a therapeutic application for EWS, this project applies a workflow we developed to discover, characterize, and engineer T cells receptors (TCRs) targeting these peptides. The major overarching challenge that the project addresses is determining the optimal approach to identify and/or engineer high potency TCRs capable of targeting self-antigens without incurring cross-reactivity that would result in unacceptable toxicity. In Aim 1, we test the hypothesis that TCRs targeting LIPI- and IGF2BP1-derived peptides will be identified in HLA-A2+ hosts but will manifest low potency due to immune tolerance. We will simultaneously discover and compare antigen reactive TCRs present in HLA-A2– hosts, which we predict will be more potent, but may be unsafe due to cross-reactivity. In Aim 2, we use next generation approaches to engineer natural TCRs, identified in HLA-A2+ hosts, into more potent, but safe antigen-specific TCRs, through affinity maturation or catch bond engineering. Given the known risks for cross reactivity of high potency TCRs, next generation engineered TCRs developed here will be closely vetted across several platforms for cross-reactivity. In Aim 3, we use fitness enhancements developed in the Mackall lab to enhance the potency of CAR T cells to enhance the potency of T cells expressing our lead candidate LIPI- and IGF2BP1-reactive TCRs. The work conducted in this project will deliver state-of-the-art therapeutics ready for clinical testing in EWS and provide general understanding regarding the optimal approach to engineer TCRs targeting self- antigens, which will provide value in pediatric oncology and low mutation burden cancers beyond EWS.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY Prescription opioid misuse is a significant burden on adolescent public health in the United States. Opioid misuse often starts with prescribed opioids, with surgery representing a key pathway by which adolescents are first prescribed opioids for the management of acute pain. Yet, little is known about the critical period following surgery during which adolescents initiate prescription opioid misuse or the modifiable behavioral mechanisms contributing to this process. These are critical gaps in our knowledge impeding our ability to identify adolescents at increased risk for opioid misuse and to develop interventions aimed at reducing prescription opioid misuse. Sleep deficiency (including sleep deprivation, noncircadian sleep, sleep disorders, and poor sleep quality) is an important proximal risk factor for prescription opioid misuse. Sleep is often disturbed during the perioperative period, a time when many adolescents are exposed to their first opioid prescription. Indeed, in our own preliminary study, we found that sleep deficiency present both before surgery and during the immediate postsurgical period was associated with increased opioid use. However, this pilot study did not allow us to characterize aspects of sleep most strongly related to opioid use and did not allow us to evaluate mechanisms, such as pain and psychological factors, underlying the sleep – opioid use relationship. Furthermore, data are urgently needed to determine how sleep deficiency prospectively predicts the development of opioid misuse behaviors in the context of other putative factors, such as a history of substance use, pain intensity, psychosocial (e.g., depression), peer, and family factors. Given that sleep deficiency is modifiable, it is a critical focus of research aimed at reducing the development of adolescent opioid misuse behaviors. Therefore, this project aims to 1) test the direct and mediation pathways of sleep deficiency, pain, psychological factors, and opioid use following sports-injury surgery, and 2) develop and validate a multivariable prediction model to identify adolescents at increased risk of prescription opioid misuse over the 24 months following surgery. To address these aims, we propose a prospective, observational study of N= 400 adolescents (10-19 years) who receive their first ever opioid following sports injury surgery. Presurgery, participants will undergo comprehensive multimodal sleep assessments (surveys and actigraphy monitoring) to measure sleep deficiency. Participants will also report on previous substance use, pain intensity, psychosocial, peer, and family factors. Adolescents will then be followed over the first 14 days after surgery using ecological momentary assessment to capture real-time daily data on sleep, pain, psychological factors, and opioid use. We will use an innovative electronic medication monitoring methodology to accurately measure opioid use (total number of doses and duration) following surgery. Follow-up assessments at 3-months, 6-months, 12- months, and 24-months will track opioid misuse developing over time. We will apply modern machine learning algorithms to develop and validate models predicting adolescent prescription opioid misuse.

NIH Research Projects · FY 2025 · 2023-09

Project Summary: The majority of lived experience depends on neural activity conveying sensory information about the world. Neural trauma and stroke are leading causes of disorders such as coma and spatial neglect, which severely damage visual experience, and there are no viable treatment options. Similarly, life saving medical treatments depend on the ability for general anesthesia to temporarily disconnect patients from the sensory world. Current anesthetics do so by inhibiting the entire brain, including the brainstem, which is a significant health risk. Even so, for unknown reasons, general anesthesia sometimes fails to prevent experiences during surgery, resulting in severe trauma for patients. These problems persist, creating negative health outcomes, in part because despite substantial research into the mechanisms of sensory encoding, particularly in vision, it remains unclear how neural activity transforms sensory information into conscious experience. There are many theories, each suggesting different mechanisms. Visual experience may emerge from the activity of higher-order neural ensembles, or depend on hidden, complex interactions built into network structures. Experience may involve local, recurrent network interactions, long-range computations, or global events that subsume and unify network activity. Unfortunately, concrete evidence supporting any of these theories is limited. Existing technologies lack either the specificity to identify the microscopic encoding properties of individual neurons and subtypes, or the necessary scope to detect activity simultaneously across sizable visual networks. New emerging technology in the Schnitzer lab overcomes these technical limitations. Advanced microscopes and complimentary optical techniques now make it possible to simultaneously record thousands of neurons across the entire visual network, with Ca2+ imaging revealing activity related to neural firing, and voltage imaging revealing subthreshold wave dynamics associated with neural communication. In this project, we will 1.) develop a task designed to isolate visual experience in mouse models to optimize the benefits of neural recording techniques; 2.) use state-of-the- art optical instrumentation to resolve the dynamics of thousands of individual neurons of specific types across all visual cortical areas, characterizing activity patterns that differentiate seen from unseen percepts; 3) use chemogenetic manipulations to test mechanisms of perception by inhibiting the pulvinar, a subcortical area that modulates visual networks. The results of this work, as preliminary data supports, will reveal detailed evidence of neural mechanisms associated with conscious visual perception that can differentiate predictions made by current theories. This will drive the field towards a data-driven consensus and illuminate mechanisms that will be instrumental in treating disorders.

NIH Research Projects · FY 2025 · 2023-09

Project Summary Neuropsychiatric disorders are the single greatest cause of disability due to non-communicable disease worldwide, accounting for 14% of the global burden of disease. The current standards of care suffer from subjectivity, inconsistent delivery, and limited access with growing waitlists. Data science solutions, in particular artificial intelligence (AI) that can port to more ubiquitous mobile tools and that are not restricted for use in clinical settings, have great potential to complement or even replace aspects of the standards of care. We propose to develop a novel data science solution for one of the most pressing mental health burdens, autism, which is up in prevalence by more than 200% since 1990, among the fastest growing pediatric concerns today, and highly representative of many other mental health conditions. We have invented a prototype mobile system called Guess What (GW) that noninvasively turns the focus of the camera on the child through a fluid social engagement with his/her social partner in a way that reinforces prosocial learning while simultaneously measuring the child’s developmental learning progress. At its simplest level, the GW app engages and challenges the child to imitate social and emotion-centric prompts shown on the screen of a smartphone held just above the eyes of the individual with whom the child is playing. Preliminary work to-date resulted in positive user feedback, evidence of high engagement for both the parents and children, and meaningful gains in socialization in the child. A single session produces 90 seconds of enriched social video and sensor data, opening up an exciting opportunity for the game play to passively generate labeled training libraries that enable the development of novel models that are extremely difficult to build without sufficient amounts of domain- relevant training data. Our grant plan will explore this opportunity by designing and optimizing game modes, creating a reusable active learning framework for growth of domain-relevant training libraries, and by creating at least 3 “autism-feature-aware” neural networks that detect child emotion, eye gaze, and hand gestures. Our project will show that GW can not only gamify crowdsourced construction of novel AI models that automatically classify important features of child development – providing a way to address many challenges with AI in medicine today -- but that it can also serve as a mobile therapy for repeat use to target core autism deficits while also tracking progress at the same time.

NIH Research Projects · FY 2025 · 2023-09

7. Project Summary/Abstract The success of precision medicine continues to rest on our ability to measure the genome, the environment, and the physiological state of patients; then choose interventions that maximize efficacy and minimize adverse effects. A key component of precision medicine is to understand pharmacogenomics (PGx) — the genetic influences on interindividual drug response variability. Two million adverse drug reactions occur annually in the United States, which results in roughly 100,000 deaths and costs upwards of $30 billion dollars each year. Approximately 15%-20% of FDA-approved medications are impacted by common germline genetic variation, and their effectiveness and safety can be improved by using genetic tests to guide prescribing. Many of these hospitalizations and deaths are preventable, prompting many health care organizations, community, and academic medical centers to invest in genomic medicine implementation by supporting PGx decision support and returning PGx results. For over 10 years, CPIC has provided critical resources to translate patient genotypes into evidence-based prescribing recommendations for specific drugs. Based on these important clinical guidelines, the Pharmacogenomics Clinical Annotation Tool (PharmCAT) provides the scientific and clinical communities the ability to annotate raw genetic test data (genotypes, DNA sequence) with standardized PGx calls, knowledge from the clinical guidelines and report the subsequent haplotypes, diplotypes and phenotypes with appropriate clinical guidance via a user-friendly software pipeline. CPIC coupled with the PharmCAT software enable the global clinical implementation of PGx in precision medicine programs. In this proposal, we outline our plan to develop the Clinical Implementation Resources for Pharmacogenomics (CIRP) to accelerate clinical implementation of PGx research discoveries. We will accomplish this plan by continuing the development of CPIC clinical guidelines and supporting evidence which are then applied to genetic test results by PharmCAT for result translation and the subsequent clinical implementation of PGx. The track record of CPIC (2009) and PharmCAT (2017) in collaboration with the PharmGKB (2000) in serving the broad scientific and clinical community is exceptional. In this proposal, we request support to advance the CIRP in support of genome-informed medicine and outline a plan to (1) develop and utilize innovative approaches to create, expand, and update CPIC guidelines (2) integrate CPIC, FDA, and other publicly available guidelines through PharmCAT and (3) disseminate PGx clinical implementation content and tools to the greater scientific community for local and cloud-based usage.

NIH Research Projects · FY 2025 · 2023-09

Up to 93% of young adult female (YA-F) cancer survivors report fertility distress; 30-46% meet criteria for moderate-severe fertility-related trauma. Gonadotoxic cancer treatments can cause infertility, early menopause, or problems getting pregnant and carrying a pregnancy to term. Family building after cancer often requires in vitro fertilization, surrogacy, or adoption, which have medical/physical, psychosocial, financial, legal, and logistical barriers. Prior work shows that YA-Fs are unprepared for the challenges of family building after cancer, have unrealistic expectations (such as overestimating the likelihood of success), and risk missing their narrowed reproductive window and experiencing greater difficulty, distress, and higher costs than expected. The long-term goal of the proposed research is to improve oncofertility care in post-treatment survivorship. We propose to test the efficacy of the Roadmap to Parenthood software, an interactive web-based decision aid and planning tool for family building after cancer for YA-F survivors (18-45 years old) and explore its implementation potential. First, we will conduct a rigorous 12-month randomized controlled trial (Aim 1). YA-F cancer survivors (N=256) will be randomized into the (a) Roadmap intervention condition or (b) time and attention control condition that includes a web-based young adult cancer survivorship informational booklet. Surveys will be administered at baseline (pre-intervention) and 1-, 6-, and 12-month follow up timepoints. We hypothesize the intervention group compared to the control group will report lower decisional conflict about family building (primary outcome), more planning behaviors aligned with family-building goals (e.g., fertility testing, financial planning), and improved quality of life (secondary outcomes). Then, we will test mediators and moderators of intervention efficacy (Aim 2). We hypothesize age, partnership status, fertility preservation history, and engagement with the decision aid will moderate the relationship between the intervention and decisional conflict, and increased levels of knowledge and self-efficacy and improved communication with providers will mediate the intervention effect on decisional conflict. Finally, we will evaluate future implementation potential of the Roadmap tool in clinical settings (Aim 3). Guided by the Capability-Opportunity-Motivation Behavior (COM-B) implementation model, we will conduct qualitative interviews with providers representing four specialties (N=32; 8 from each) to understand barriers and facilitators to the implementation of the Roadmap tool across clinics. Providers working in diverse settings in oncology, primary care, gynecology, and reproductive medicine, all of whom address reproductive health clinically, will be included. Themes related to barriers/facilitators within the categories of ‘capability’ (e.g., skills, knowledge), ‘opportunity’ (e.g., resources), and ‘motivation’ (e.g., emotion, beliefs) will be identified. This research will comprehensively respond to the identified needs of YA-F survivors that hope to have a child after cancer through a novel intervention that provides information and support for decision making and early planning.

NIH Research Projects · FY 2024 · 2023-09

PROJECT SUMMARY Millions of individuals around the world are exposed to arsenic, mostly from contaminated drinking water sources, including many areas in the U.S. Arsenic is a known human carcinogen, and exposure has been consistently associated with other chronic diseases including diabetes, cardiovascular and respiratory disease risk with emerging evidence highlighting its immunomodulatory effects. There is evidence that arsenic exposure influences epigenetic programming and proposed to be a potential link between arsenic exposure and the latency of many associated health effects, including cancer. The leading hypothesis that arsenic’s toxicity might involve epigenetic dysregulation has been tested mostly in adult cross-sectional and birth cohorts with limited follow-up of participants to test for persistence or clinical relevance of epigenetic changes. The proposed project will leverage samples and data from a large epidemiological study in Antofagasta, the largest city in Northern Chile, where extensive arsenic water concentration records exist. In 1958, two rivers with high arsenic concentrations were diverted into the study region as the primary source of drinking water and this high exposure period ended in 1970 when an arsenic water treatment plant was installed. As a result, there was a thirteen-year period in which average arsenic concentrations were 860 µg/L, with much lower levels (<10 µg/L) before and after the period. This tragic scenario provided a natural experiment to study the latency of health effects among people exposed to high levels of arsenic with valid comparison populations from the rest of Chile. Studies from this region have reported strong prospective associations and evidence that early-life arsenic exposure is associated with increases in lung, bladder, and kidney cancers as well as increased risk of myocardial infarction, chronic renal disease, bronchiectasis, and respiratory symptoms. These associations were only evident decades after the peak exposure period and persisted among the exposed population decades after mitigation measures were taken. We are leveraging already collected samples from individuals exposed in early-life and unexposed matched study participants to test for persistence of epigenetic disruption decades later in mid-life (median age ~ 50 years). We will evaluate if exposed individuals have accelerated epigenetic aging across multiple epigenetic clocks that reflect different aspects of biological aging, morbidity, and mortality risk. Additionally, we will test if exposed individuals have different estimates of leukocyte composition and DNA methylation signatures. We will match and control for key covariates, such as current urinary arsenic levels, historical arsenic exposure in adulthood, diet, smoking, BMI, sex, and socioeconomic status. This approach will enable us to test for latency of epigenetic disruption captured in DNA methylation of leukocytes independent of recent and current arsenic exposure. If successful, our study will demonstrate that exposure to arsenic during early-life can persistently program epigenetic biomarkers that are strongly associated with morbidity and mortality decades later.

NIH Research Projects · FY 2025 · 2023-09

Neurofibrillary tangles (NFTs) of the microtubule-associated protein tau are a universal feature of the aging brain. The extent of tau pathology throughout the brain correlates with both synapse loss and severity of cognitive impairment in age-related tauopathies. The ability to maintain cognitive function with a brain accumulating NFTs relies on the preservation and maintenance of synaptic networks. Therefore, understanding the mechanism by which tau contributes to network and synapse vulnerability is critical for developing preventative therapeutics. The goal of this proposal is to determine the cellular mechanism(s) by which pathologic tau drives neuronal network dysfunction. The experiments proposed in this application will uncover these mechanisms and provide intellectual and technical training for a successful transition into a postdoctoral position. The F99 phase (Aim 1) will test mechanistic cell biological hypotheses to better define how abnormal tau accumulation induces synaptic dysfunction, while providing opportunity to develop intricate experimental design and execution skills through cutting-edge biochemistry, microscopy, and electrophysiology techniques. The K00 phase (Aim 2) will build upon the F99 studies with computational strategies to integrate proteomics data from humans and experimental models to identify co-existing molecular changes that are highly relevant to age-related tauopathies.

NIH Research Projects · FY 2025 · 2023-09

Abstract The retina and visual cortex represents visual information in the form of a complex set of electrical signals to support visual behavior and memory. Although we have learned a great deal about how simple visual patterns such as striped gratings lead to neural activity in the early visual system, we know little about how natural visual scenes are represented during behavior, and how the active process of gathering visual information through body and eye movements influences this process. Visual processing becomes progressively more complex towards higher levels in the brain. Compared to primates, mice combine strong motor input with visual input at an earlier level in the visual stream, the primary visual cortex. This makes the mouse visual system an accessible to system to understand how natural scenes are represented and influenced by active sensation at a level in the visual system where computational models of the neural code for natural scenes are more tractable. This proposal has two primary goals. First to determine how the neural code changes for natural scenes from the retina to the cortex with an accurate computational model that can be analyzed to determine how specific retinal cell types contribute to cortical activity for ethological computations such as determining motion direction and speed, adaptation and object motion detection. Second, to test alternative theoretically grounded hypotheses as to how motor activity influences the representation of natural scenes, including the subtraction of expected visual stimuli to create a more efficient representation, known as predictive coding, predictive or Bayesian feature detection that adjusts the detection threshold to the prior probability that visual features are present, and simple adaptation to the strength of combined signals to avoid saturation. Using high channel count silicon probes, computational models that combine known biophysical and circuit level properties with interpretable cutting edge machine learning approaches and virtual reality systems, we will gain new insight into visual processing for natural scenes in the early visual system. These results will give a quantitative picture of how the retina and visual cortex function, which will be essential in understand how diseases that affect central visual processing such as amblyopia, strabismus and schizophrenia, and reveal general principles of cortical sensory processing. The computational models established here will also be directly applicable for use in retinal and cortical visual prosthesis systems.

NIH Research Projects · FY 2026 · 2023-09

PROJECT SUMMARY/ABSTRACT Radiculopathy is a common spinal condition resulting from compression and irritation of the spinal nerve roots, leading to sensory deficits, muscle weakness, and pain. Dermatomal maps are a key component of the clinical exam and provide information on the correspondence between cutaneous sensations and the nervous system. Dermatomal sensory deficits can help localize neurological injury in spinal conditions and guide treatment. Dermatomal maps, however, are limited—they contain uncertainty in the neuroanatomy mapped (spinal nerve, dorsal root ganglion, dorsal horn, or spinal cord (SC) segment), assume left-right symmetry and no sex differences, provide no information on between-subject variability, and remain to be validated. SC functional magnetic resonance imaging (fMRI) permits the non-invasive in vivo spatial mapping of human SC activity. Here we will use SC fMRI to test hypotheses central to dermatomal maps, investigate the effects of neurological injury on SC sensory processing, develop markers of SC sensory activity, and test their diagnostic value in cervical radiculopathy while improving our SC fMRI methods. To accomplish this, we will first enhance our existing SC fMRI methods by building a research-grade 64-channel head-neck coil, testing a novel spatial normalization method that accounts for SC segment location, and exploring the use of surface electromyography to monitor and remove motor-related noise during fMRI experiments. We will compare the improved SC fMRI methods against our currently operational methods while characterizing the SC correlates of sensory stimulus intensity encoding using electrocutaneous sensory stimulation of the third digit of the right hand (C7 dermatome) in 30 healthy volunteers (HV) (20-79 years old, 15 females, 15 males). Then using the enhanced SC fMRI methods, we will quantitatively map the spatial distribution of SC activity in 120 right- handed HVs (20-79 years old, 60 females, 60 males, stratified by age) during electrocutaneous sensory stimulation of the first, third, and fifth digits (C6, C7, and C8 dermatomes, respectively) of the left and right hands. We will develop probabilistic maps of the spatial distribution of SC activity, assess the superior-inferior localization of activity, contrast the activity between left and right stimulation and sexes, and quantify between- subject variability. We will use machine learning algorithms to develop normative SC sensory markers by predicting the stimulation site. Finally, 40 right-handed patients with right-sided C7 cervical radiculopathy (30– 79 years old, 20 females, 20 males) and 40 age- and sex-matched HVs will also undergo the same SC fMRI experiment, and we will investigate group differences in SC activity to uncover the effects of neurological injury on SC sensory processing and then assess the diagnostic value of the SC sensory markers. Completing our aims will improve SC fMRI methods, validate/refute hypotheses central to dermatomal maps to better inform their use in clinical practice, advance our scientific knowledge of SC sensory processing in healthy and injured states, and provide preliminary validation of MRI-based diagnostic markers for cervical radiculopathy.

NIH Research Projects · FY 2025 · 2023-09

Kacper B. Rogala | NIGMS R35 MIRA-ESI (PAR-20-117) | Project Summary | October 3, 2022 The focus of this project is on the mechanisms of signal transduction by the large macromolecular supercomplex called GATOR, which is made of three individual sub-complexes with distinct roles: GATOR1, GATOR2, and KICSTOR. The GATOR supercomplex was previously shown to be responsible for receiving information from various cellular sensors, and then passing that information down to a large protein kinase called mTORC1, the role of which is to regulate cellular metabolism in response to the environment. One of the key signals that must be relayed to mTORC1 is availability of nutrients in the cell — amino acids and sugars. Monitoring how much of every individual nutrient the cell has at its disposal is critical for making rational decisions that will determine the future activities of the cell — whether it should grow when nutrients are available, or stand-by and maintain itself when nutrients are in short supply. This project will specifically focus on amino acids as signaling molecules. Our goal is to decipher the molecular chain of events that accompany changes in cellular concentration of three critical amino acids — leucine, arginine, and methionine. Indeed, out of twenty different amino acid types, only three of them are directly monitored by the mTORC1 pathway to inform cellular growth decisions. Each one of these amino acids also appears to signal to the GATOR complex — via three distinct mechanisms. Yet, despite extensive research in this field, we still know very little about how these mechanisms propagate amino-acid availability signals to either activate or deactivate mTORC1. Is the presence of leucine, arginine, and methionine equally important, or perhaps one amino acid dominates the rest? Are there any large conformational changes that accompany binding of amino-acid sensors to GATOR proteins? Does the composition and localization of the GATOR supercomplex change upon amino acid supplementation or withdrawal? We built this research project around three main themes that will begin answering these (and many more!) fascinating questions. In Theme #1, we will focus on the GATOR complex itself — in its apo form. Theme #2 will explore the mechanism of leucine and arginine signal transduction to GATOR2. And in Theme #3, we will attempt to decipher the enigmatic methionine-availability effects on GATOR1. Our ambition is to inspire deeper protein-mechanism-centered thinking in this field. And by providing novel protocols, protein complex structures, and a set of validated structure-guided mutants, we will lay the foundation that will enable new research directions, while also contributing to the development of therapeutics against devastating diseases of growth, such as cancer.

NIH Research Projects · FY 2025 · 2023-09

Abstract Osteoarthritis (OA) is a major disease affecting 1 in 6 adults above 60 years of age in US that significantly impairs quality-of-life by impacting movement and function. Tissue health and disease is frequently governed by a complex and non-linear interplay of cell-intrinsic and systemic factors including both biochemical and biophysical cues. The overall aim of this project is to understand how the changes in ECM (extra cellular matrix) viscoelasticity affect cartilage homeostasis in health and during disease initiation and pathogenesis in OA. Recent studies by our team have elegantly demonstrated that ECM viscoelasticity governs cell volume in cartilage cells i.e. chondrocytes. Previous studies of cartilage biology had only examined the impact of ECM elasticity (i.e. “stiffness”), and the role of viscoelasticity had been mostly ignored. We found that viscoelastic hydrogels that exhibit fast stress relaxation, or were more viscous, could provide a microenvironment that is more conducive to anabolic gene expression in human chondrocytes resulting in increased ECM production, promoting a healthy chondrocyte phenotype. The underlying cause was observed to be the ability of chondrocytes to expand their volume in the fast relaxing gels, an ability that was restricted in the slow relaxing gels, which are more elastic. Understanding the optimal ECM viscoelasticity for healthy and human induced pluripotent stem cell derived chondrocytes can guide ideal scaffold preparation for cartilage tissue engineering. The aim of this proposal is therefore to optimize hydrogel viscoelasticity for engineering inflammation- suppressive cartilage constructs. We will firstly optimize development of cartilage constructs in fast relaxing hydrogels in the presence of dynamic mechanical loading. Secondly, these constructs will be tested in human and rat models of cartilage defects. Thirdly, we aim to gain an understanding of the molecular pathways underlying the relationship between mechano-transduction and inflammation in cartilage health and disease. The experimental outcomes from these studies have the potential to enhance therapeutic strategies for cartilage regeneration and OA that remain unmet clinical needs.

NIH Research Projects · FY 2026 · 2023-09

PROJECT SUMMARY Atherosclerotic cardiovascular disease (ASCVD) is the main cause of morbidity and mortality worldwide, and affects 18+ million adults nationally. However, 80% of ASCVD deaths may be prevented with prompt intervention following early screening for ASCVD risk – a powerful rationale for the unmet need of accurate subclinical ASCVD diagnoses. Thus, in this study we assess whether a deep learning (DL)-based analysis of pre-existing abdominal computed tomography (CT) scans paired with electronic medical records (EMR) improves prediction of cardiovascular death, myocardial infarction, and stroke in a large multi-site primary prevention population. We will conduct this study in a large, real-world population with an external validation to ascertain whether we can improve upon the clinically-utilized pooled cohort equations (PCE) that have numerous shortcomings. 20+ million abdominal CT scans performed annually in the US. While these scans answer specific clinical questions, quantitative information related to tissue phenotypes associated with cardiometabolic risk is simply not evaluated. DL algorithms can be used to quantify body composition metrics for adipose tissue, muscle, bone, liver, and vascular calcifications, which can all be used to improve upon the PCE for determining cardiovascular events. In aim 1 of our proposal, we will build automated segmentation algorithms with a built-in quality control mechanism to extract these body composition metrics in 125,000+ subjects to ascertain population-level normative values of tissue size and radiodensity. In aim 2, we will augment the PCE covariates with these body composition values and additional EMR features for predicting ASCVD risk with advanced DL models. Moreover, we will devise new algorithmic approaches for improving accuracy of the models using distributionally robust optimization. In aim 3, we will build a new ASCVD risk estimator that directly uses 3D imaging data. We will augment this end-to-end prediction approach by integrating multi-modal models that leverage both imaging data and EMR data. Realizing the need for improved explainability of DL solutions, we will build digital twins of each subject to describe why model predictions are being made and what changes a patient could make to lower ASCVD risk. We will train all models on data from Stanford (25k patients), test on data from Stanford (8k patients), and externally validate the models on data from three Mayo Clinic sites (20k+ patients) to assess the generalizability of our tools. We have assembled an inter-disciplinary MPI team of DL experts, cardiologists, and abdominal radiologists to build such ASCVD risk models. We develop innovative tools to improve accuracy, generalizability, and explainability of DL-based ASCVD risk models. Our long-term goal is to enable early detection of silent atherosclerosis and trigger interventions that may ultimately prevent over 800,000 death, myocardial infarction, and stroke events in Americans annually.

NIH Research Projects · FY 2026 · 2023-09

Lung cancer is the leading cause of death in the United States, with over 125,000 patients predicted to die of lung cancer in 2024. Despite improvements to treatments, such as advancements in immune therapy, patients diagnosed at late-stage diseases have low response rates to therapies across the board. Aside from immunotherapies, there are few therapies that target the stromal components within the tumor microenvironment. Macrophages and fibroblasts have been shown to be some of the most abundant stromal cell types in tumors and have been found to contribute to immune suppression in the tumor microenvironment. To better understand how macrophages and fibroblasts contribute to immune suppression, In Aim 1, I will adapt CRISPR/Cas9 mediated gene editing to inactivate ~50 genes in macrophages in vivo and track macrophage phenotypes throughout lung cancer tumor progression. In Aim 2 I will inactivate ~50 genes in fibroblasts in vivo, tracking phenotypes and identifying important pathways in fibroblast that drive tumor progression. In Aim 3 I will determine how our top candidate genes in macrophages and fibroblast impact the large tumor microenvironment and functionally determine how they contribute to immune suppression. This study will elucidate stromal cell phenotypes of importance at various stages of tumor progression and has the potential to reveal new stromal targets to improve immunotherapies in lung cancer.

NIH Research Projects · FY 2024 · 2023-09

Project Summary / Abstract Alzheimer’s disease (AD) and related dementia are one of the most prevalent neurodegenerative disorders in the aging population, affecting more than 1 in 10 people aged 65 and older each year in the United States alone. Since AD is a slow and complex progressive brain disease, early identification of brain changes before the onset of clinical symptoms is critical for minimizing brain tissue damage and improving the effectiveness of clinical interventions. Diffusion magnetic resonance imaging (MRI) is a promising tool for the early detection of AD because of its non-invasiveness, wide availability, and sensitivity to subtle brain microstructural changes. Unfortunately, current diffusion MRI techniques can be inadequate for several reasons, including (1) insufficient spatial resolution to accurately delineate important but fine-scale white matter pathways in the medial temporal lobe (MTL), a critical region in the early propagation of AD; and (2) a lack of specificity to reveal the underpinning brain microstructural changes from early neuronal injury and dysfunction. The innovation of this proposal mainly lies in the joint use of SNR-efficient high-resolution diffusion acquisition and advanced diffusion encoding strategies to effectively address the accuracy and specificity limitations of current diffusion MRI methods. Specifically, this project will (1) develop a high-resolution MRI protocol for more accurate segmentation of white and gray matter regions that are first affected by and play critical roles in AD propagation; (2) develop advanced diffusion encoding waveforms for more sensitive and specific characterization of subtle brain microstructural changes at the early stage of AD; and (3) use existing AD molecular biomarkers to rigorously evaluate the efficacy of the new diffusion biomarkers for early AD detection. The outcomes of this proposal will lay the foundation for studying the longitudinal AD progression, elucidating the underlying AD pathogenesis, and evaluating new intervention strategies to prevent and/or slow down AD onset and/or progression. The candidate has a strong background and extensive training in MRI physics, engineering, image processing, and brain imaging. With the additional training in cognitive neuroscience, biostatistics, and multimodal neuroimaging provided by this program, he will be well equipped as an independent researcher focusing on (1) developing advanced neuroimaging techniques with a strong focus on MRI; and (2) employing a multimodal toolset to address fundamental questions in basic neuroscience research and neurodegenerative disease studies.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY/ABSTRACT Circulating tumor DNA (ctDNA) which mirrors the parental tumor, is present in the plasma of non-small cell lung carcinoma (NSCLC) patients. Liquid biopsy analysis of ctDNA allows early detection and genetic monitoring. However, current PCR-based liquid biopsy methods often have difficulty detecting ctDNA due to low copies in circulation. In contrast, the electric field-induced release and measurement liquid biopsy (eLB) platform, detects mutant EGFR (L858R, exon19del, T790M) ctDNA with a >90% tissue-genotype concordance in <50ul of unprocessed plasma of NSCLC patients. Preliminary data demonstrate that the ctDNA detected by eLB may be ultrashort single-stranded ctDNA (usssctDNA) which contrasts the typically described 160-basepair internucleosomal double-stranded ctDNA. This new subpopulation is undetectable through PCR-based liquid biopsy methods due to size-selection bias. The goal of the F99 Phase is to use usssctDNA-seq, a novel NGS pipeline, to explore if NSCLC plasma contains high quantities of ctDNA that are ultrashort and single-stranded. This pipeline consists of a usssctDNA-specific extraction, library preparation, deep sequencing, and bioinformatic analysis. To verify, 80 late-stage NSCLC EGFR-mutated plasma samples will undergo usssctDNA-seq. The discovery of usssctDNA will open up a wealth of previously unnoticed information leading to improved treatment and survival of NSCLC patients. Afterward, during the K00 Phase, the trainee hopes to explore the biological origins of biofluid ctDNA and the incorporation of usssctDNA into modern multi-analyte approaches for pan- cancer detection. This will involve training in new skills such as organoid modeling, methylation and leukocyte sequencing, and bioinformatic machine learning at a world-leading cancer-oriented institute. Ultimately, the goal of this proposed F99/K00 project will to prepare the trainee for a career in cancer research with an expertise in liquid biopsy and cell-free DNA biology.

NIH Research Projects · FY 2024 · 2023-09

Heart failure affects 2-3% of the US population and remains the single largest cause of mortality. Despite the large unmet need, heart failure drug development is notoriously difficult, and few first-in- class drugs have been approved in the past decade. Human induced pluripotent stem cell (hiPSC)- based models of heart disease are widely considered to hold tremendous potential for the development of heart failure drugs because they faithfully model disease phenotypes and reflect individual patient genetics. However, their utility for drug screening is limited because the technology for assessing disease modifying effects are too cumbersome and low throughput for large-scale screens. Visualizing disease-modifying activity of genes and drugs for heart failure requires kinetic read outs of cardiomyocyte function that correlate calcium (Ca2+) cycling with contractile force and resting tension to reveal systolic and diastolic heart dysfunction. The lack of off-the-shelf solutions for simultaneous measurement of these parameters is a critical gap that has hindered the pace of basic research into disease mechanisms and drug development. Although modern high content imaging systems can acquire the requisite fast kinetic datasets, the major roadblock is that available data analysis tools lack key capabilities, are too low throughput, and/or require substantial coding expertise to implement in large-scale genetic studies and drug discovery. Resolving this roadblock will be transformative by placing powerful tools in the hands of scientists without coding expertise, enabling them to develop gene and drug screens using iPSC and adult cardiomyocyte models of heart failure. We will deliver an integrated toolbox of software, reagents, and standardized protocols for contemporaneous measurement of intracellular and subcellular Ca2+ dynamics with contractile force and resting tension that can be overlaid with subcellular feature detection – all compatible with 384-well plate format – to model systolic and diastolic heart function. The software will have a user-friendly graphical user interface to fully automate measurements of Ca2+- contractility force curves (in absolute µM and nN terms) from beating cardiomyocytes. A key feature will be individual, cell-by-cell analysis that will increase dynamic range and allow the recognition of cellular heterogeneity in the preparations making possible realistic culture models with multiple cell types. We will also develop a toolkit of viral vectors to deliver genetically encoded sensors of absolute intracellular Ca2+ concentrations in cardiomyocyte populations without generating stable cell lines. The integrated platform will be validated and benchmarked against current software in pilot drug and gene screens that demonstrate ability to quantify disease-modifying activities for systolic and diastolic heart disease. Quantitative performance measures will evaluate assay readiness.

NIH Research Projects · FY 2024 · 2023-09

Tachycardia, or abnormally fast heart rate, is an important risk factor for cardiovascular morbidity and mortality. Prolonged tachycardia is known to induce cardiomyopathy in patients who have no prior structural heart diseases. Moreover, transient tachycardia, frequently observed in heart failure patients, can exacerbate the cardiovascular outcome. However, very little is known about the molecular drivers underlying tachycardia-induced cardiac dysfunction. This gap in our knowledge hinders the development of more effective heart failure treatment, especially for patients with hard-to-control tachycardia. This K99/R00 proposal will leverage recent advances in induced pluripotent stem cell (iPSC), tissue engineering, and multiomics technologies to uncover the molecular signaling pathways critically involved in the pathology of tachycardia-related heart disease. The applicant, Dr. Chengyi Tu, has established and validated an in vitro tachycardia platform using engineered heart tissue (EHT). In Aim 1, Dr. Tu will perform metabolomic and transcriptomic profiling of EHTs with or without tachypacing. To validate the physiological relevance of the EHT model, canine samples from tachypacing-induced heart failure will also be profiled. Preliminary data from the EHTs and the canine samples coherently indicate that the disruption of glycolysis homeostasis may underly the impairment of cardiac function by tachycardia. Metabolomics analysis shows that tachypacing in EHTs resulted in a selective accumulation of glycolysis intermediates such as glyceraldehyde 3-phosphate (GA3P) and 3-phosphoglycerate (3PG). Interestingly, promotion of fatty acid metabolism accelerated the recovery of cardiac contractility in tachypaced EHTs. Based on these novel results, Aim 2 will focus on elucidating how different glycolysis intermediate metabolites affect the function of cardiomyocytes, which has yet to be systematically examined. Lastly, Aim 3 (R00 phase) will employ state-of-the-art mass spectrometry workflow to screen for novel binding targets of glycolysis intermediates in cardiac cells, and examine the potential therapeutic benefits of manipulating these targets. This K99/R00 proposal will be guided by an excellent mentoring team with diverse expertise, including mentor Dr. Joseph Wu (iPSCs and cardiac biology), co-mentor Dr. Sanjiv Narayan (arrhythmia), advisors Dr. Michael Snyder (genetics and multi-omics), Dr. Yuqin Dai (metabolomics), Dr. Stanley Qi (CRISPR interference) and Dr. Beth Pruitt (bioengineering), as well as collaborators Dr. Fabio Recchia (canine model) and Dr. Donald Bers (cardiac physiology). To sum up, the completion of the proposed study will significantly advance our mechanistic understanding of how tachycardia adversely affects the heart, thereby creating new opportunities for therapeutic interventions. The proposed training will significantly strengthen and expand Dr. Tu’s research expertise, providing substantial momentum to his transition toward an independent cardiovascular researcher.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY/ABSTRACT Epstein Barr virus (EBV) is a broadly disseminated gammaherpes virus that, in immunosuppressed or immunocompromised individuals, can cause serious, life-threatening B cell lymphomas. In solid organ transplant (SOT) recipients these EBV+ B cell lymphomas are the most serious manifestation of the group of heterogeneous lymphoproliferations termed post-transplant lymphoproliferative disease (PTLD). Predisposing factors for PTLD include primary EBV infection, reactivation of EBV in recipient B cells, and impaired T cell immunity due to immunosuppression. There are major gaps in our understanding of how specific viral genes contribute to lymphomagenesis in the context of EBV+ PTLD and whether there are specific alterations in the immune response to EBV in SOT that develop EBV+ PTLD compared to those that do not. Prior work from our group has focused on latent membrane protein 1 (LMP1), the major oncogene of EBV, to better understand EBV+ PTLD pathogenesis. In a recent prospective, multicenter clinical trial in SOT recipients we demonstrated that specific gain of function mutations in LMP1 significantly correlate with the development of EBV+ PTLD. We’ve also demonstrated that EBV alters the host cell microRNA profile and that this has direct effects on survival of EBV+ B lymphoma cells. Building on our previous innovative studies of the bidirectional interactions between EBV and host immunity, and using our unique Biorepository of samples from SOT recipients that developed EBV+ PTLD and matched SOT controls that did not develop EBV+ PTLD, we propose to define the impact of viral genetic diversity on protective immune responses to EBV. We hypothesize that EBV genetic diversity leads to alterations in viral gene function and immune recognition that contribute to the pathogenesis of EBV+ PTLD. To test this hypothesis we propose the following Specific Aims:1) Determine the genetic diversity of EBV in PTLD and the impact on host cell function 2) Determine the effect of EBV+ PTLD-associated genetic diversity on host immunity to EBV and 3) Determine how extracellular vesicles and microRNA contribute to the development of EBV+ PTLD. We anticipate these studies will identify novel mechanisms underlying the EBV-driven pathogenesis of B cell lymphomas in PTLD and will reveal new opportunities for therapeutic strategies to prevent and treat EBV+ B cell lymphomas in immunosuppressed and immunocompromised individuals.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY Genome-wide studies have now identified hundreds of thousands of associations between genes or genetic loci and human phenotypes, each of which could reveal mechanistic insights about disease biology. Yet, the cell-type specific functions of most of these genes remain unknown, and we currently lack the ability to connect these genes into cellular programs and thereby reveal the pathways important for disease. To address this limitation, our proposed Data Analysis and Validation Center aims to work together with the MorPhiC Consortium to build a Catalog of Cellular Programs — i.e. a map of which genes work together in biological pathways and their corresponding multimodal molecular and cellular phenotypes, in defined cell types or states. Our team brings a diverse set of expertise in computational genomics, methods and technology development, experimental design, interdisciplinary collaboration, consortium organization; and includes new junior investigators who will bring new forward-thinking ideas and tools to MorPhiC. We have developed a wave of innovative methods integrating CRISPR, single-cell, imaging, and human genetics data that will enable building such a Catalog of Cellular Programs and applying this Catalog to understand the genetics of human disease. The goals of our Center are to: (i) Define single-layer phenotypes, by applying a suite of computational state-of-the-art approaches for analysis and modeling of RNA, ATAC, and imaging data; (ii) define a multi-modal representation of molecular and cellular phenotypes, by identifying modules of features that co-vary across perturbations and single cells; (iii) build a Catalog of Cellular Programs that links genes to the molecular and cellular phenotypes they control, by inferring causal gene regulatory networks from perturbation data; (iv) apply the Catalog of Cellular Programs to demonstrate its utility identifying causal genes and programs for human diseases; and (v) participate in Collaborative Activities with MorPhiC, including to guide experimental design and ensure utility, robustness, and interoperability of Phase 1 datasets. Together, these aims will develop novel computational toolkits to infer causal gene regulatory networks from multi-modal perturbation data; construct a Catalog of Cellular Programs as a foundational resource for MorPhiC and the broader community; and demonstrate the utility of this Catalog through application to understand the genetics of human diseases.

NIH Research Projects · FY 2025 · 2023-09

Project Summary Up to 42% of Americans with gastroesophageal reflux disease (GERD) experience uncontrolled, chronic symptoms that reduce health-related quality of life and heighten the risk of long-term complications. My long- term goal is to improve health outcomes for patients with esophageal disorders. This proposal aims to improve health outcomes for patients with GERD, by harnessing patient activation and motivating behavioral change as a framework for a mobile health (mHealth) Question Prompt List (QPL) intervention. Patient activation emphasizes patients’ knowledge, skills, and confidence in managing their health care and predicts patients’ level of health success. However, almost half of patients with GERD demonstrate low patient activation scores. Low patient activation leads to poor adherence to medical therapy. Remarkably, very little research has focused on developing an intervention to improve patient activation and motivate behavior change in GERD. Emerging evidence suggests effective patient-physician communication interventions, such as disease-specific QPLs, increase patient activation. The objective of this proposal is to apply a 5-step user-centered Design Thinking model to develop, refine, and test a mobile app based QPL specific for GERD patients titled “Esophagus-Qs.” The central hypothesis is that Esophagus-Qs will be usable and has potential to harness patient activation and motivate behavior change. Our specific aims include: 1) Develop the design of Esophagus-Qs by empathizing, defining the problems patients face, and ideating to generate solutions; 2) Refine the design of Esophagus-Qs by prototyping and testing usability; and 3) Collect preliminary data on Esophagus-Qs in a randomized controlled pilot study to estimate the effect size of the intervention. We will pursue these aims using an innovative combination of qualitative and quantitative methodologies and implementation science to inform app development. The proposed research is significant, because it has potential to harness patient activation and motivate behavior change, that can translate into improved health outcomes. It is also significant because it will develop a platform that can be extended to develop mHealth QPLs in other esophageal disorders. The proximate expected outcome of this work is to develop a usable Esophagus-Qs and collect preliminary data on differences in patient activation over time, medication adherence, health-related quality of life, and GERD symptom severity between Esophagus-Qs and standard of care. Pilot data from this career development award will be the foundation for an R01-funded clinical trial to rigorously test the efficacy of Esophagus-Qs among a large, diverse population.

NIH Research Projects · FY 2025 · 2023-09

ABSTRACT Spatial mRNA organization plays a fundamental role in diverse cellular processes and disease. In large, compartmentalized cells (e.g., neurons and embryos), subcellular mRNA localization offers a core mechanism for the spatiotemporal regulation of protein synthesis. Since the initial discovery of subcellular mRNA distribution in 1983, high-throughput imaging and sequencing methods have revealed that, in many cell types, thousands of RNAs are localized to distinct compartments. For example, many axonal-related mRNAs in neurons will transport to the “site of needed” along the very long (>100μm) axon, which likely play an important role in axon development and local synaptic activities. Furthermore, mounting evidence shows a correlation between aberrant spatial RNA organization and an increasing number of diseases, including amyotrophic lateral sclerosis (ALS), fragile X syndrome (FXS), and spinal muscular atrophy (SMA). However, due to a lack of technologies that allow for the tracking and manipulation of the spatial localization of endogenous mRNAs in primary cells and in vivo, the mechanism and functional relevance of spatial organization has only been explored for a small number of mRNAs. In this proposal, we seek to establish a set of technologies as a new foundation to study spatial RNA biology, by developing an integrated framework that allows for sophisticated computational analysis, real-time RNA tracking, and programmable spatial manipulation of any endogenous mRNA(s) in situ and in vivo, on a high-throughput (>1,000 mRNAs in parallel) scale. To achieve this goal, we will start by building a deep learning framework that can analyze spatially localized RNAs in different cell types and predict their associated regulatory factors (e.g., RNA motifs, RNA binding proteins). This will provide an atlas of spatial RNA organization as well as candidate RNAs for functional studies. Next, we will develop two novel approaches, RNA live-cell fluorescent in situ hybridization (RNA-LiveFISH) for single-molecule, real-time dynamic tracking, and CRISPR- mediated transcript organization (CRISPR-TO) for programmable manipulation of any target mRNA localization. The two approaches form a new framework that enables us to study the regulatory mechanism and functional relevance of subcellular mRNA localization with unprecedented ease and spatiotemporal resolution. Third, we seek to apply this framework to study the function of mRNA localization in primary neurons, via high-throughput manipulation of >1,000 mRNAs to uncover functions for axon guidance, growth cone development, and synaptic activities. Selected functional mRNAs (>100) will be verified in vivo. Finally, we will apply the framework to investigate the pathological mechanisms of aberrant RNA localization underlying the neurological disease spinal muscular atrophy (SMA) in vitro and in vivo. We will not only dissect the relationship between mRNA organization and SMA pathology, but also explore the strategy of modulating RNA localization for potential therapeutics. We envision that the proposal will lead to new groundbreaking insights into the mechanism and functional roles of whole-cell mRNA spatial organization for cellular and physiological functions that has not been revealed before.