Stanford University

NIH Research Projects · FY 2026 · 2022-02

Project Abstract. High-grade serous ovarian carcinoma (HGS-OvCa) is the most malignant form of ovarian cancer. Among the most aggressive HGS-OvCa tumors are those that harbor genomic amplification and overexpression of CCNE1, the gene that encodes for cyclin E1, a key cell cycle regulator. This challenging HGS-OvCa subset carries poor outcomes after standard cytotoxic chemotherapy, and is associated with high proliferative rate, rapid development of platinum resistance, and de novo resistance to poly ADP (ribose) polymerase inhibitors. Despite intense efforts, targeted therapies for the treatment of CCNE1-amplified HGS-OvCa remain elusive, in part, due to the paucity of druggable molecular targets. As such there remains an urgent unmet medical need for the development of new therapies for CCNE1-amplified HGS-OvCa and other cancers marked by CCNE1 overexpression. Promising preclinical evidence demonstrates that knockdown or inhibition of cyclin-dependent kinase (CDK2), the catalytic kinase partner of cyclin E1, selectively kills CCNE1-amplified ovarian cancer cell lines, highlighting a potential dependency associated with CCNE1 amplification. However, efforts to directly target CDK2 with pharmacological agents have been plagued by difficulties in achieving specificity for CDK2. We recently employed an alternative strategy of selectively inhibiting CDK7, a key upstream activator of CDK2, to achieve selective killing of CCNE1-amplified ovarian cancer cells. In proof-of-principle studies, YKL-5-124, a new CDK7 inhibitor with superior selectivity over existing inhibitors of its kind, led to pronounced tumor shrinkage in a human xenograft mouse models of CCNE1-amplified HGS-OvCa. The primary goal of the proposed research is to expand on our preliminary findings by elucidating the underlying principles governing CCNE1-amplified HGS-OvCa sensitivity to CDK7 inhibition. This knowledge will then be leveraged to guide further preclinical inquiry into targeting CDK7 in CCNE1-amplified HGS-OvCa. Herein we propose to identify (1) HGS-OvCa cancer cells and genetic backgrounds that are sensitive to YKL-5-124; (2) biomarkers that correlate with drug response; and (3) combination strategies that augment or expand drug response (Aim 1). While YKL-5-124 displays potent in vivo activity in mice, we will continue to optimize these CDK7 inhibitors for improved pharmacokinetics to further the preclinical development of this chemical series (Aim 2). Lastly, we will evaluate YKL-5-124 (or a further in vivo optimized analog) in mouse models of CCNE1-amplified and non-amplified HGS- OvCa (Aim 3). To accomplish these goals we have assembled a multi-disciplinary team with expertise in medicinal chemistry (Nathanael Gray, Stanford); cell and systems biology (Caitlin Mills and Peter Sorger, Harvard Medical School); mouse models in ovarian cancer (Panagiotis Konstantinopoulos, DFCI); and translational and clinical ovarian research (Ursula Matulonis, DFCI). This research describes a new approach to selectively target CCNE1-overexpressing tumors and identifies novel small-molecules that will enable the preclinical evaluation of this strategy for the treatment of CCNE1-amplified HGS-OvCa.

NIH Research Projects · FY 2026 · 2022-02

Project Summary/Abstract The human gut is a metabolic organ where anaerobic microbial pathways run at high capacity, expanding the biochemical landscape of the human body. Exploiting members of the gut microbiota and the metabolic pathways they encode represents an exciting new strategy to treat genetically encoded biochemical defects in humans such as Phenylketonuria. There are three reasons why identifying new microbiota probiotics should be a priority: 1) Gut microbes represent an important yet untapped resource for new metabolic pathways that influence human biochemistry. 2) Microbiota pathways are present in healthy individuals, and their metabolic end products are unlikely to be toxic to humans. 3) Native, non-genetically modified strains are likely to have a straightforward regulatory pathway for use in humans. While this proposal is focused on identifying microbes and pathways to reduce blood phenylalanine levels to treat phenylketonuria, our long-term goal is to lay the foundation for an entirely new approach to treat inborn errors in metabolism: controlling metabolic circuits via the gut microbiota. Our proposal is organized into the following two aims: In Aim 1, we will use gene cluster searches, transcriptomics, genetics, and metabolomics to identify and characterize anaerobic pathways for phenylalanine metabolism by gut bacteria. In Aim 2, we will assess the ability of microbial pathways to reduce plasma Phe levels in a gnotobiotic mouse model of phenylketonuria (PKU). Our results will not only provide new insights into how the microbiome expands the biochemical landscape of the host but will also lay the groundwork for a new approach to treating inborn errors in metabolism.

NIH Research Projects · FY 2025 · 2022-02

Advanced Imaging Tools to Assess Cancer Therapeutics in Pediatric Patients Imaging tests are essential for cancer diagnoses in children. However, computed tomography (CT) and positron emission tomography (PET)/CT are associated with considerable radiation exposure. Several large population studies have demonstrated that cumulative radiation doses from CT scans significantly increase the risk of children developing leukemia, thyroid cancer, and brain cancer later in life. Thus, there is an urgent need to reduce the radiation exposure of children caused by medical imaging procedures. To this end, we have developed a radiation-free imaging test for pediatric tumor staging, based on advanced whole body diffusion-weighted magnetic resonance imaging (WB-DW MRI) technology. Our team was the first to integrate tumor physiology information (proton diffusion) with an MRI technique for anatomical orientation (nanoparticle- enhanced T1-weighted MRI), based on the concept of integrated PET/CT scans (Lancet Oncology). While DW MRI has demonstrated high sensitivity and specificity for cancer detection, the value of DW MRI for cancer therapy monitoring in children has not yet been extensively studied. Current approaches for assessing tumor therapy responses in children often measure changes in tumor metabolism using PET after the injection of radioactive glucose (18F-FDG). Previous comparisons of WB-DW MRI and 18F-FDG PET for cancer therapy monitoring have been inconclusive, possibly because the scans were not obtained at the same time. Thus, apparent differences may be due to differences in the timing of the scan. We are in a unique position to address this problem by simultaneously measuring therapy-induced changes in tumor metabolism and diffusion in children. These measurements have only recently become possible due to novel integrated PET/MRI technology, which is available at our pediatric hospital. The overall goal of our project is to compare the diagnostic value of DW MRI with that of 18F-FDG PET-based imaging tests for tumor therapy response assessment in children with cancer. In our pursuit of an improved and immediately clinically applicable approach for a minimally invasive imaging test, we will first apply artificial intelligence algorithms to develop ultra-low-dose 18F-FDG PET/MR scans. Next, we will determine the value of ultra-low- dose 18F-FDG PET and DW-MRI scans for monitoring tumor response to classical chemotherapy: We will determine which pediatric tumor type should be monitored with 18F-FDG PET only, DW-MRI only or a combined approach. Finally, we will determine the value of ultra-low-dose 18F-FDG PET and advanced DW- MRI techniques for monitoring tumor response to immunotherapies. Both radiation-free WB-DW MRI and ultra- low-dose 18F-FDG PET/MRI entail significantly less radiation exposure than traditional 18F-FDG PET/CT scans. The results of this project will yield customized cancer therapy response assessments for children with substantially reduced radiation exposure compared with current clinical imaging tests, thus eliminating associated risks of secondary cancer development later in life.

NIH Research Projects · FY 2026 · 2022-02

Project Summary / Abstract It is known for more than 30 years that supporting cells in the normally quiescent avian hearing organ, the basilar papilla, can respond to sensory hair cell death with S-phase entry, mitotic division, and generation of replacement hair cells. The work proposed here utilizes several methodological advances for an unbiased, comprehensive, and systematic inventorying of changes in gene expression during aminoglycoside-induced hair cell death and in responding supporting cells. Pilot data obtained at multiple time points from single cells isolated from the chicken basilar papilla sensory epithelium revealed that activation of a novel pathway linked to protease-activated receptor-2, PAR2, is essential for S-phase entry of supporting cells and hair cell regeneration. The project puts forward a speci?c hypothesis for the activation pathway, which will be explicitly and thoroughly evaluated. Moreover, the identi?ed e?ector genes for the novel regeneration pathway will be assessed in a new adult mouse model for long-term hair cell loss. Here, the focus is on S-phase entry of mouse utricle and organ of Corti supporting cells four weeks after aminoglycoside-induced hair cell loss. Finally, the project will focus on the characterization of newly regenerated hair cells by reconstructing the temporal transcriptomic changes that happen when the new hair cells mature. In parallel, it is proposed to align transcriptomic data with in situ observations such as hair bundle formation, changes in planar cell polarity, reestablishment of synaptic connections, and electrophysiological features such as mechanoelectrical transduction and electrical tuning.

NIH Research Projects · FY 2026 · 2022-02

PROJECT SUMMARY According to the most recent U.S. Census, the elderly population will more than double to 80 million, encompassing 1 in 5 Americans by 2050. Aging is characterized by a decline in tissue function and regenerative capacity. Sarcopenia, also known as age-dependent loss of skeletal muscle mass and strength, is a major public- health problem that affects 15% of the elderly, leading to loss of mobility and diminished quality of life. Age- related muscle loss is paralleled by a loss in the function of muscle stem cells (MuSCs), key players in muscle homeostasis and regeneration. However, the mechanisms responsible for age-associated MuSC dysfunction remain elusive. Two major barriers to gaining mechanistic insights into MuSC aging are (1) the heterogeneity of the aged MuSC population, which renders standard bulk analysis ineffective, and (2) the lack of tools to resolve this heterogeneity, underscoring the need for single-cell studies. We previously demonstrated that aged MuSCs are a heterogeneous population comprised of functional and dysfunctional subsets. This key observation suggests a therapeutic strategy to regenerate muscle - boosting the activity of resilient functional MuSCs. Here we explore this possibility using a specific cell surface marker and a series of innovative single-cell technologies required to resolve MuSC subsets. Our preliminary data iden- tify CD47 as a cell surface marker whose expression level, not presence or absence, distinguishes functional CD47lo and dysfunctional CD47hi MuSC subsets. Known widely as a receptor for SIRPα, CD47 is also a receptor for thrombospondin-1 (THBS1). We found that CD47hi MuSCs accumulate in aged muscle and aberrantly ex- press THBS1. We hypothesize that during aging the accumulation of CD47hi MuSCs impairs the proliferation of CD47lo MuSCs through secretion of THBS1, hindering regeneration and contributing to sarcopenia. Here, our specific aims are to (1) determine how CD47 signaling goes awry in aging, (2) elucidate how post-transcriptional regulation of CD47 is altered during aging leading to the accumulation of dysfunctional CD47hi MuSCs, and (3) determine the effects of aberrant THBS1 secretion in the aged MuSC niche on regeneration. We capitalize on cutting-edge single-cell technologies, including multidimensional single-cell mass cytometry (CyTOF) and multi- plexed tissue imaging (CO-Detection by indexing (CODEX)). These technologies allow us to track simultane- ously 40+ distinct cell and signaling phenotypes in CD47lo and CD47hi MuSCs (CyTOF) and resolve how spatial changes in the architecture of the multicellular niche lead to MuSC dysfunction in aging (CODEX). We combine this knowledge with in vivo investigation of regenerative capacity and strength in aged mice. Finally, we perturb CD47 signaling in vivo using blocking antibodies to surmount the regenerative deficits in aged mice. The pro- posed analyses of newly identified aged MuSC subsets that can be prospectively isolated will provide fresh mechanistic insights into aging and inform therapeutic strategies to augment endogenous muscle repair.

NIH Research Projects · FY 2026 · 2022-02

The pathophysiological processes of Alzheimer’s disease (AD) –– betaamyloid plaques and neurofibrillary tangles –– begin decades before objective cognitive impairment and symptoms of clinical dementia are present. This “preclinical” disease stage offers a window to understand early disease mechanisms as well as the contributions of AD pathology to cognitive aging. Although work across different research programs highlights the utility of amyloid and tau measurements in aging cohorts as important predictors of future decline, it remains difficult to predict risk at the individual subject level and mechanisms associated with the initial consequences of AD pathology in aging remain unclear. Our research program involves cutting-edge neuroimaging (MRI, PET) and cerebrospinal fluid analysis to understand the neuronal correlates of memory decline. Specifically, we propose to leverage a pre-existing baseline cohort of 199 older clinically unimpaired (CU) older adults from the Stanford Memory and Aging Study (SAMS), and additionally increase the sample of this cohort with 30 new participants. SAMS participants previously completed lumbar puncture to collect cerebrospinal fluid (CSF), high-resolution functional MRI (at 3T) during a visual associative memory paradigm, ultra high-resolution structural MRI (at 7T) to assess medial temporal lobe subregion integrity, and extensive cognitive assessment including multiple measurements of hippocampaldependent memory. This proposal will extend SAMS to include a longitudinal visit 7 years after baseline (Wave 2) that repeats baseline modalities, and incorporates tau PET with a next generation ligand 18F-PI-2620. In addition to enriching the baseline sample, we anticipate data collection on 150 of the 199 eligible participants that completed the baseline visits for SAMS. A strength of our program is the emphasis on hippocampaldependent memory processes, given that neurofibrillary tangle pathology is common in entorhinal cortex and hippocampus, and the initial sites of cortical tau deposition are in cortical areas critical for visual associative memory recollection (angular gyrus and ventral temporal cortex). Thus, we are well positioned to understand how structural and functional measures that quantify (a) entorhinal and hippocampal integrity as well as (b) hippocampal-dependent mechanisms of memory (cortical reinstatement) predict memory decline (Aim 1) and relate to regional tau PET (Aim 2). Given that all MRI and biofluid measures previously collected at baseline will be repeated during the longitudinal Wave 2 visit proposed in this application, we will also examine regional structural and functional change in these innovative imaging measures over time (Aim 3). This proposed research program will yield critical insights regarding the specific mechanisms underlying memory failure and decline in aging and preclinical AD. The ultimate goal is to formulate comprehensive multivariate models that will combine our deep phenotyping metrics to determine the set of predictors most relevant for individual differences in memory in aging and the transition to pathological aging.

NIH Research Projects · FY 2026 · 2022-02

PROJECT SUMMARY Normal tension glaucoma (NTG) is characterized by optic neuropathy with progressive retinal ganglion cell (RGC) death and optic nerve (ON) degeneration but in the absence of intraocular pressure (IOP) elevation. All current glaucoma treatments are to lower IOP and are less effective in NTG patients. The primary reasons for the therapeutic vacuum are the limited understanding of the molecular mechanisms of IOP-independent glaucomatous degeneration and the lack of a practical and effective NTG animal model. Causal mutations in the optineurin gene (OPTN) have been found in familial and sporadic NTG. Interestingly, OPTN mutations also cause inherited forms of another CNS axonopathy, amyotrophic lateral sclerosis (ALS), indicating a common degenerative machinery that can be activated by dysfunctional OPTN in vulnerable CNS neuronal populations. However, although OPTN has been extensively studied and its various roles in autophagy, cytokine signaling, and vesicle trafficking have been found, the pathophysiology role of OPTN in CNS neurodegeneration are far from clear. We have recently established a highly efficient NTG mouse model by truncating OPTN gene in RGCs specifically, which presents significant RGCs and ON degeneration within weeks. Using this novel NTG model, we propose to investigate how OPTN mutation causes neurodegeneration in vivo through validating and characterizing OPTN-interacting proteins in RGCs and ON. Through these studies, we will generate essential information to uncover novel molecular mechanisms of glaucomatous neurodegeneration related with OPTN that may be also shared by ALS and other CNS axonopathies, identify novel modifiers of RGC/ON neurodegeneration, and provide valuable tools and animal models to develop neuroprotection therapies.

NIH Research Projects · FY 2025 · 2022-02

ABSTRACT/PROJECT SUMMARY Variants at ERAP1 modulate the risk for several forms of non-infectious uveitis in the presence of disease- associated HLA class I, strongly suggesting a so far unproven change in immunodominance with impact on disease causation and protection. The overall objective of this application is to determine through which mechanism ERAP1 allotypes cause and protect from HLA I-associated uveitis. Our long-term goal is to understand and therapeutically target HLA class I associated autoimmunity in uveitis. Our central hypothesis is that allotypic ERAP1 alters the HLA I-bound peptidome to include epitopes that are immunogenic when presented by disease-relevant HLA I, which induces or controls disease through a change in immunodominance. The rationale for this study is that mechanistic understanding of ERAP1-mediated pathogenesis in HLA I immunity will enable targeted therapy design aimed at specific ERAP1-HLA I-uveitis subsets. Based on these considerations we will implement two specific aims. In Aim 1 we will determine through which mechanism the allotype ERAP1 Hap10 initiates immune dysfunction in HLA-B*51+ Behçet’s uveitis (BU) but protects from HLA-B*27+ acute anterior uveitis (AAU) via a series of CRISPR/Cas9 genome editing experiments. These experiments will allow us to define its functional contribution to the HLA I restricted peptidomes relevant to each of these disorders and their effect on the generation of immunogenic or tolerogenic immune responses. In Aim 2 we will establish how HLA I restricted pathogenic epitopes depend on allotype- specific ERAP1 function through the exploitation of clonally expanded CD8 T cells from active BU and AAU patients for cellular cloning, genome-editing and functional assessment of immunogenicity. We expect the following outcomes 1) knowledge of the immunogenic and tolerogenic effects mediated by allotypic ERAP1 in two highly disease-relevant HLA restriction contexts: HLA-B27 and B51, 2) identity of epitopes that induce or prevent immunogenicity in these contexts, 3) proof of principle that allotypic ERAP1 regulates autoimmunity and that manipulation of its activity modulates pathogenicity providing irrefutable rationale for targeting ERAP1 enzyme activity pharmacologically, or through gene therapy. This will have a positive impact on the field through the identification of molecular targets allowing the design of therapy for patient groups defined by genotypes, and through mechanistic understanding extending beyond the scope of BU and AAU to additional MHC-I-opathies with immense impact on human health, such as IBD, psoriasis, and psoriatic arthritis.

NIH Research Projects · FY 2026 · 2022-02

PROJECT SUMMARY/ABSTRACT The most advanced malaria vaccine candidate, RTS,S, provides only partial efficacy against clinical malaria episodes when given to young children. Furthermore, efficacy wanes within 12-18 months post vaccination, with many children having low magnitude and/or rapidly waning immune responses, and booster doses are only partially efficacious. The immunologic mechanisms underlying sub-optimal and waning immune responses and vaccine efficacy remain unclear. We propose to utilize a multi-omics, systems biology approach to define baseline, and vaccine-induced signatures that predict immunogenicity and protection, following RTS,S vaccination of young children in Malawi. This project will take advantage of an extraordinary opportunity to comprehensively study baseline and vaccine-induced immune responses to RTS,S in young children through a collaboration with the Malawi International Centers of Excellence in Malaria Research (ICEMR). The Malawi ICEMR is studying the effectiveness of RTS,S to prevent malaria infection and transmission in a longitudinal cohort of children as part of a World Health Organization-sponsored implementation study. By leveraging our well-characterized cohort, detailed immunological characterization of host responses, and state-of-the-art computational models of immunity, we will in Aim 1 perform a systems analysis of baseline signatures that predict immunogenicity and protection from primary vaccination against Pf. We will use a multi-omics approach, using bulk RNA-seq, metabolomics of serum, cytometry by time of flight with epigenetic profiling (EpiTOF), and single cell epigenetic profiling to profile baseline signatures prior to primary RTS,S vaccination in 300 Malawian children. Our goal will be to perform an integrated analysis of these orthogonal datasets to define a baseline signature that can be used to predict the immunogenicity and efficacy of RTS,S vaccination. In Aim 2, we will perform a systems analysis of vaccine-induced signatures that predict immunogenicity and protection from primary and booster vaccination against Pf. We will use a multi-omics approach, using bulk RNA-seq, metabolomics of serum, multiplex analysis of serum cytokines and CSP-specific T cell assays, to comprehensively profile vaccine- induced signatures following RTS,S vaccination in Malawian children. We will perform an integrated analysis of these datasets to define vaccine-induced signatures that can be used to predict the immunogenicity and efficacy of RTS,S vaccination. The successful completion of these aims will provide deep insight into the molecular mechanisms underlying suboptimal immunity to RTS,S vaccination, and yield biomarkers of vaccine- induced immunity and protection.

NIH Research Projects · FY 2025 · 2022-02

A longstanding promise of biology is that with a deep enough understanding of the molecular rules of life, it should be possible to predict phenotypes from genotype and environment. This would enable rational genome engineering of crops and microbes for desirable traits and facilitate precision medicine through interventions based on an individual’s genetic variation and lifestyle. To make progress towards these goals, technologies are needed to comprehensively identify causal variants, their effects on genes and their interactions, directly in natural populations. Recent advances in high-throughput genome editing make this possible in the model eukaryote S. cerevisiae. This proposal will employ a high-efficiency, multiplexed genome editing system (MAGESTIC) where each cell in a pool receives a distinct edit for a natural variant and a corresponding barcode, which is integrated into the genome after editing. The barcode allows for building arrayed collections of validated strains by recombinase-directed indexing (REDI) and for tracking variant abundance in pooled growth assays by next-generation sequencing (NGS). To identify the basis for trait variation across the S. cerevisiae lineage, five distinct strain backgrounds derived from genetically and ecologically diverse wild isolates will be employed. Libraries will be designed for each strain to introduce >90% of the 85,000 variants observed in the other four strains, enabling studying the impact of the major and minor alleles and the background dependence of their effects. The variant pools will be assayed across a panel of conditions relevant to pathogenic fungi, wild isolates, and human disease, as many variants are expected to exert their effects only in certain environments. To understand how multiple variants modify multigenic traits, a sequential editing and barcoding technology (MARVEL) will be used to generate pairwise and higher-order combinations for up to hundreds of causal variants per trait. This approach will characterize the extent of non-additive effects between variants (i.e. genetic interactions) and genetic background dependencies. This effort will constitute the most comprehensive investigation of genotype-environment-phenotype relationships across a species, and of wild isolates, to date. Ultimately, the data and insights generated by our study will facilitate predictive models linking variants to pathways and phenotypes beyond the S. cerevisiae system, and in particular to other organisms where precision CRISPR editing at this scale is not yet feasible.

NIH Research Projects · FY 2026 · 2022-01

PROJECT SUMMARY The 22q11.2 microdeletion syndrome (22q11.2 DS), is associated with ~20-fold increased risk for psychosis. Brain imaging studies have consistently demonstrated abnormal functional connectivity in the cerebral cortex. Preclinical studies using cerebral cortex neurons derived from human induced pluripotent stem cells (hiPSC), revealed mitochondrial defects, neuronal hyperexcitability and calcium signaling defects, supporting the findings of cortical circuit dysfunction. However, the exact cellular and molecular mechanisms by which the 22q11.2 deletion leads to abnormal development and function of the cerebral cortex remain to be elucidated. The 22q11.2 chromosomal region contains 9 genes associated with mitochondrial energy production. Human blood samples revealed increased glycolysis, suggesting global metabolic alterations. Despite growing evidence for mitochondrial and neuronal dysfunction in 22q11.2 DS, the metabolic and mitochondrial dysregulations present in the human developing cerebral cortex remain unknown. We will test the hypothesis that human mitochondrial defects begin in proliferating neural progenitors and impair temporal mitochondrial maturation, induce metabolic reprogramming and subsequent neuronal function defects. We hypothesize these metabolic abnormalities are less pronounced in rodent models of disease. Aim 1 will identify mitochondrial defects during cortical development in hCS and forebrain sections from a mouse model for 22q11.2 DS, using advanced microscopy and functional assays. Aim 2 will assess for metabolic alterations during cortical development in hCS and forebrain sections from a mouse model for 22q11.2 DS, using untargeted metabolomics and cell type-specific metabolic profile. Aim 3 will identify metabolic pathways as targets for therapeutic interventions by testing the rescue potential of drugs known to enhance mitochondrial function (e.g. nicotinamide riboside, resveratrol, bezafibrate). Our multidisciplinary project will identify the initial timing and the exact metabolic and mitochondrial alteration during the development of the cerebral cortex in 22q11.2 DS and pinpoint metabolic pathways for drug discovery, (ii) assess for interspecies differences, and (iii) establish in vitro human neurometabolomics assays to study other metabolic disorders of genetic or environmental etiology.

NIH Research Projects · FY 2026 · 2022-01

PROJECT SUMMARY/ABSTRACT Myocarditis, pathologic inflammation of the heart, is a serious cause of sudden cardiac death affecting patients of all age groups. Immune checkpoint inhibitors (ICIs) are monoclonal antibodies to cytotoxic T-cell antigen-4 (CTLA-4) or programmed death-1 (PD-1)/programmed death-1 ligand (PD-1L) used as novel cancer therapeutics to release intrinsic brakes on T-cell cytotoxicity against tumor cells. Although ICIs are now relied upon to treat many advanced cancers, fulminant myocarditis has been reported as a life-threatening side effect of these drugs, leading to severe arrhythmias, heart failure and death. Under histopathology, an acute lymphocytic infiltrate is found in the heart, and multiple lines of evidence point to a T-cell and antigen-mediated phenomenon. In this proposal, Dr. Zhu’s preliminary data in ICI myocarditis patients and a germline PD-1 knockout mouse model of ICI myocarditis (MRL-Pdcd1-/-) demonstrates a population of clonally-expanded cytotoxic effector CD8+ T-cells thought to play a critical role in this disease, with upregulation of the chemokine RANTES (CCL5) and its receptor (CCR5). Dr. Zhu hypothesizes that ICI myocarditis is caused by the clonal expansion of cytotoxic effector CD8+ T-cells in the heart, whose pathogenesis is potentiated by signaling from CCL5, and she will aim to test this hypothesis using single-cell RNA-seq/single-cell TCR sequencing and T-cell adoptive transfer experiments (Aim 1), as well as and ex-vivo/in-vivo knockdown of CCR5 in MRL-Pdcd1-/- mice (Aim 2). Although T-cell clonal analysis of patient heart tissues suggest the existence of a cardiac-specific antigen in ICI-induced myocarditis, the identity of such antigen(s) remains elusive. Understanding the culprit antigens in this disease may lead to novel insights in T-cell mediated myocardial damage. In the second part of her proposal, Dr. Zhu hypothesizes that ICI-induced myocarditis is an autoimmune disorder caused by cardiac-specific auto- antigens that trigger the activation/clonal expansion of T-cells, leading to myocardial inflammation. In Aim 3, she will utilize the novel computational algorithm called GLIPH (Grouping Lymphocyte Interactions by Paratope Hotspots) to identify candidate pathogenic antigens in ICI myocarditis. Dr. Zhu’s work will bridge a major knowledge gap in the field of cardiac inflammation and identify culprit T-cell subsets and disease-causing antigens in ICI myocarditis and T-cell induced myocardial injury. The completion of this proposal will provide a platform for Dr. Zhu’s successful transition to an independent physician scientist investigating immune mechanisms in cardiac inflammation/toxicity.

NIH Research Projects · FY 2026 · 2022-01

Motor systems neuroscience seeks to understand the neural mechanisms behind voluntary movement. The last two decades have witnessed a transformation in this ?eld with the use of multielectrode recordings and statistical estimation and modeling techniques. These technological advances have yielded rich, low-dimensional neural dynamics that are suggestive of the mechanisms underlying behavior. To minimize confounds, the overwhelming majority of these studies utilize behavioral constraint to isolate just the behaviors of interest for study. While effective for generating many behav- iorally similar trials, this may have the unintentional consequence of arti?cially constraining neural dynamics to a subset of its full range. This project seeks to better understand whether and how neural dynamics change with respect to the behavioral context (constrained vs unconstrained) they occur in. This type of work has historically been challenging because capturing limb kinematics in an unconstrained setting is non-trivial. However, with recent advances in computer vision technology, accurate 3D cameras have become accessible tools for research. This study will leverage these new 3D cameras to capture unconstrained behavior in a large observational enclosure. Novel algorithms for the processing of these 3D datasets will be used to estimate the subject's pose. These limb kinematics will be synchronized and correlated against neural data recorded from one or more 96-channel Utah electrode array(s) implanted in motor regions of cortex. Low-dimensional neural dynamics can be generated from this synchronized data. The dynamics will be explored in the context of two behaviors in the enclosure: walking and reaching for food on the ?oor. The dimensionality of the dynamics in these two contexts will be compared, with the null hypothesis stating that there is no difference in dimensionality of dynamics between these behavioral contexts. A subsequent experiment will be to again construct low-dimensional neural dynamics, but this time include a context of behaviorally constrained reaching. The dynamics from these three contexts will be compared to ?nd a common subspace (subset of dimensions) shared among all. This subspace, if it exists (the null hypothesis is that there is no difference in the dynamics between the behavioral contexts), represents fundamental dynamics that are invariant of the behavioral context, suggestive of causal necessity of this subspace. Taken together, these studies will further our understanding of how low-dimensional neural dynamics drive motor be- havior. This insight has implications for the development of ambulatory brain-machine interfaces and may inform the treatment of individuals with motor disorders such as stroke.

NIH Research Projects · FY 2026 · 2022-01

Project Summary This K23 proposal will provide Calyani Ganesan, MD, MS with dedicated time, mentorship, training, and research experience to become an independent clinical investigator. Dr. Ganesan is a clinician-scientist with a long-term vision of improving the quality of care for patients with urinary stones. Under the guidance of a strong mentorship team, she will acquire skills in: 1) advanced statistical methods used in pharmacoepidemiology studies; 2) the collection and analysis of prospective patient data; and 3) a novel bone imaging technique, high-resolution peripheral quantitative computed tomography (HR-pQCT), to assess skeletal status. This grant proposes to improve the screening and management of patients with urinary stone disease and diminished bone strength. Despite an increased risk of fracture in patients with urinary stone disease, there are currently no guidelines regarding the screening and treatment of osteoporosis in these patients. It is also unclear which medications might provide dual benefit in decreasing skeletal fragility and reducing stone recurrence in these patients. This project aims to: 1) identify patients with urinary stone disease who are at high risk for osteoporosis or fracture; 2) determine the effects of medications used to treat osteoporosis on urinary stone disease; and 3) identify the underlying bone deficits in patients with urinary stone disease using HR-pQCT and bone biomarkers reflecting bone formation and resorption. The first two aims will be answered by analyzing national data from the Veterans Health Administration (VHA) and Optum datasets. For the third aim, Dr. Ganesan will recruit patients with urinary stone disease from the Stanford University and Veterans Affairs Palo Alto Health Care System Kidney Stone clinics. She will use HR-pQCT and serum biomarkers to measure and monitor bone parameters over time in these patients. The proposed work has potential to make a significant clinical impact. Successful completion will enable clinicians to identify which patients with urinary stone disease should be screened osteoporosis and inform clinicians how best to treat these patients so that fracture associated comorbidity and recurrent stones can be reduced. The proposed work is realistic and feasible within the award period, and the research infrastructure at Stanford is already in place. Dr. Ganesan is poised to build on her research skills, advance and disseminate scientific knowledge, create additional collaborative networks, and eventually compete for R01 or equivalent funding. In summary, the K23 award will provide the support to enable Dr. Ganesan to become a successful independent clinical investigator.

NIH Research Projects · FY 2026 · 2022-01

PROJECT SUMMARY/ABSTRACT Prostate cancer is the second deadliest cancer for American men. MRI is increasingly used to guide prostate biopsies and has potential to spare 500,000 men/year from the side effects of invasive biopsies. Yet, subtle differences in MRI appearance of aggressive vs. indolent (non-lethal) cancer vs. benign tissue creates three problems: missed cancers, high rates of false positives, and only moderate inter-reader agreement among ra- diologists. Selective identification of aggressive and indolent cancers is imperative for reducing cancer death while minimizing side effects from unneeded biopsies. We propose to develop and use pathology-based (pathomic) MRI biomarkers in rad-pathomic deep learning methods to assist radiologists in detecting and localizing aggressive vs. indolent cancers on prostate MRI. In addition, our proposed method will be the first to localize aggressive and indolent cancers when they coexist (76% of index lesions). We performed four preliminary studies in our unique dataset of matched radiology and pathology images. First, we found a high agreement in labeling aggressive vs. indolent cancers between the automated method and two pathologists. Second, we developed pathomic MRI bi- omarkers from MRI features that correlate with features derived from pathology images. Third, we used the biomarkers in rad-pathomic deep learning models to detect cancer (AUC: 0.86) and aggressive cancer (AUC: 0.85) on MRI. Fourth, we showed that combining radiologists and the rad-pathomic deep learning models helped identify 14% more aggressive cancers missed by radiologists. Three innovations will improve the localization of aggressive vs. indolent cancers on prostate MRI. First, we will develop 3D RAPSODI, a novel 3D registration method for 3D reconstructed MRI and pathology images to eliminate the need for slice-to-slice correspondences and map cancer labels from pathology onto MRI. Second, we will leverage our correlation learning method to identify pathomic MRI biomarkers. Third, we will use deep learning models to assist radiologists in localizing aggressive cancer on MRI. Our multidisciplinary team is uniquely positioned to test whether: (Aim 1) pathomic MRI biomarkers empha- size the visual differences of aggressive vs. indolent cancers on MRI; (Aim 2) rad-pathomic deep learn- ing models can reliably and automatically distinguish aggressive from indolent prostate cancers on MRI, and (Aim 3) radiologists assisted by deep learning models have increased detection accuracy and inter-reader agreement than unassisted radiologists. Impact: Our proposed rad-pathomic deep learning models have the potential to improve prostate cancer care in three ways: 1) detecting and targeting aggressive cancers that are currently missed in ~50,000 men/year; 2) eliminating up to 500,000 unnecessary biopsies/year in men with no cancer or indolent cancers; and 3) reduc- ing the number of biopsy samples needed to detect aggressive cancers (1-2 vs. 12-18 currently).

NIH Research Projects · FY 2025 · 2022-01

Project Summary Neuroscience research must be transdisciplinary, drawing on the knowledge and practices of such diverse fields as biology, psychology, engineering and computer science. This transdisciplinary approach is further supported by the innovative ideas that stem from a diverse population with a broad set of perspectives. As with many other science, technology, engineering, math, and medicine (STEMM) fields, the field of neuroscience needs to implement strategies that more effectively support individuals from diverse backgrounds to pursue further studies or careers in research. Underrepresented groups (URGs) are less likely to transition from graduate school to postdoctoral training and postdoctoral training to scientific positions in academia or industry. We posit that a sense of belonging promotes academic and professional success generally and that to support recruitment and retainment of URG trainees, issues like stereotype threat, lack of representation, microaggressions, and imposter phenomena need to be countered with mentorship and strategic access to professionals to build a diverse and representative network. Social capital is an additional necessary support to promote professional advancement of URG trainees. To maximize impact on professional advancement, we will focus on graduate students and postdocs and their potential transitions to postdocs and scientists, respectively. Our goal is to increase successful transitions and ultimately diversify neuroscience. Our program, Pathways to Neurosciences, was designed as a social sciences study, using a theory of action that describes psychosocial mechanisms that we hypothesize our program will activate that would lead to desirable outcomes. Our evaluation plan was designed in parallel to maximize useful data for others desiring to design similar programs, and to prioritize iterative data collection that will document how program elements operate over time, as a function of the interactions among elements and participants. In Aim 1 we will establish the Pathways to Neurosciences program and Subaim 1a will utilize a year 1 pilot cohort to optimize program components and processes, including refining ongoing participant surveys. A strength of our plan is that we will in Subaim 1b continue evaluations and adjustments throughout the project. Aim 2 is to determine whether and how the program leads to the hypothesized mechanisms in our theory of action, which are professional networks, safe spaces for interaction, and productive dynamics of interactions. In Aim 3 we will evaluate whether our desired short- and long-term outcomes are achieved by the program compared to control groups. Through this program and strong institutional support, we hope that Pathways to Neurosciences graduates will have increased opportunities and exhibit increased persistence in science and leadership. Ultimately, we will disseminate program design, processes, and insights to others to enable the creation of similar optimized programs at their own institutions.

NIH Research Projects · FY 2026 · 2022-01

SUMMARY In recent years, artificial intelligence has enabled automated systems to meet or exceed the performance of clinical experts across a wide variety of medical imaging tasks, in applications ranging from disease diagnosis using Chest X-Rays to survival analyses using histopathology slides. All current automated echocardiography systems – much like human echocardiography reads – are inherently reductionist in nature; a complex sequence and pattern of cardiac contraction is reduced to an outline of one or more chambers, from which a few global metrics of heart function are then calculated. Despite the staggering increase in usable data, the vast majority of information contained in time-resolved echocardiography videos remain woefully underutilized. As opposed to treating echocardiography studies as videos intended solely for visual interpretation, the ‘radiomics’ approach treats medical images as high-dimensional datasets to be mined with advanced computational tools. The overall goals of this project are to further develop and validate our novel, generalizable, multi-modal artificial intelligence (AI) platform for analyzing time resolved echocardiography studies, to address this underutilization. The impact of such an ECHO AI system is immediately perceptible in the field of heart failure. An estimated 6.5 million people suffer from heart failure in the United States. Across the spectrum of severity in this disease, echocardiography remains the cornerstone of screening and clinical diagnosis, a guide for medical management and pharmacotherapy, and an essential tool for planning acute lifesaving surgical interventions. We propose to build on our preliminary research and ready access to high quality paired echocardiographic and clinical datasets to achieve the following goals: 1) Develop a surgical decision support system for end-stage heart failure patients considered for left ventricular assist device (LVAD) implant. 2) Expand and generalize our ECHO AI tools to enable downstream prediction of long-term survival and development of heart failure, in both asymptomatic individuals and patients with pulmonary arterial hypertension 3) Cloud and hardware integration of our ECHO AI platform. The end result of our research will be a powerful ECHO AI tool with that is translatable, and integrated into clinical practice.

NIH Research Projects · FY 2026 · 2022-01

PROJECT SUMMARY Bladder cancer is the sixth most common cancer in the U.S., has one of the highest recurrence rates of all cancers, and is the most expensive cancer to treat from diagnosis to death. Current standard for bladder cancer diagnosis relies on clinic-based white light cystoscopy for initial screening, followed by transurethral resection of bladder tumor in the operating room for pathologic diagnosis and local staging. White light cystoscopy has several well recognized shortcomings, particularly incomplete detection, thereby leading to suboptimal resection and contributing to cancer recurrence and progression. Our goal is to improve outcomes for bladder cancer patients through integration of a deep learning algorithm to improve cystoscopic detection and enhance surgical resection. Artificial intelligence (AI)-based on deep neural networks have demonstrated remarkable capacity to learn complex relationships and incorporate existing knowledge into the inference model. We hypothesize that AI- augmented detection of bladder tumor will improve diagnostic cystoscopy in the clinic setting to identify suspicious lesions and improve the quality of transurethral resection in the operating room, thereby reducing overall cancer recurrence and outcome. Towards the goal of establishing a paradigm of AI-based framework for augmented detection of bladder cancer, we will leverage our strong preliminary data and outstanding environment in AI research. We propose three specific aims: 1) To curate a high-quality annotated cystoscopy imaging dataset to optimize deep neural network CystoNet; 2) To design and optimize CystoNet for real-time cystoscopic navigation and cancer detection; and 3) To conduct a prospective multicenter validation of CystoNet during bladder cancer surgery. Successful completion of the studies proposed here will serve to translate deep learning algorithm to the dynamic environment of cystoscopic surgery without the need for specialized instrumentaitons. We foresee our approach will improve the outcome of a major cancer and genearlizable to other organ systems amenable for endsocopic interventions.

NIH Research Projects · FY 2025 · 2022-01

Project Summary/Abstract Doxorubicin is a highly effective chemotherapy drug commonly used to treat multiple cancers, but its use is limited due to cardiotoxicity. Cardiotoxicity can range from asymptomatic reduction in left ventricular ejection fraction to highly symptomatic heart failure (Class III to IV). Acute doxorubicin-induced cardiotoxicity (DIC) occurs in ~11% of patients, and long-term cardiotoxic side effects can develop in ~36% of patients up to 10 years after treatment. Despite being the most effective class of anti-cancer drug and widely used since last five decades, the molecular mechanisms that underly DIC remain poorly understood. To date, three major inter- related mechanisms for cardiotoxic effects of doxorubicin have been proposed: (i) generation of reactive oxygen species (ROS) and subsequent membrane damage, (ii) inhibition of topoisomerase II-β (TOP2B) topoisomerase I mitochondrial (TOP1MT), and (iii) modulation of intracellular calcium release. However, as cardiotoxicity in DIC patients may not emerge for years or decades, a better understanding of the different mechanisms in DIC across different cardiac cell types and their crosstalk can have significant implications on the search for therapeutics. The endothelium is a critical component of the cardiovascular system that forms a protective barrier for CMs and releases paracrine factors to maintain CM health and function. It has been shown that DOX disrupts the normal endothelial physiology by damaging ECs that can lead to the development of severe chronic vascular diseases such as atherosclerosis, which often leads to cardiac dysfunction. With the knowledge that dysfunctional ECs can have a negative impact on CM function, we need a better understanding of the integral role of ECs in the development of doxorubicin-induced myocardial injury. Despite impressive progress, little attention has been given to the potential importance of cell-to-cell signaling between ECs and CMs, despite the fact that ECs serve a paracrine function to enhance signaling in CMs, especially in context to pharmacological stimulation. This knowledge gap impedes our comprehensive understanding of organ dysfunction at a multi- cellular level. The overarching goal of our proposal is to use a multidisciplinary approach that integrates human iPSCs, bioengineering tools, and NGS to gain novel insights into the pathogenesis of DIC. We will pursue three specific aims. In Aim 1: we will establish an experimental platform to study the role of ECs in DIC. For this, we will recapitulate the EC-CM crosstalk in DIC patient’s iPSC-derived cells with 3D engineered heart tissues (EHTs). In Aim 2: we will decipher the mechanism of EC-CM crosstalk in EHTs treated with DOX using single- cell approaches (scRNA-seq and scATAC-seq). In Aim 3: we will validate the key regulatory players of EC-CM crosstalk in an animal model of DIC. Our proposal is supported by compelling preliminary data from a multi- disciplinary team of investigators. We believe we are well positioned to achieve the project goals within five years.

NIH Research Projects · FY 2026 · 2021-12

Abstract Our central goal is to create a volumetric real-time system combining ultrasound (US) and photoacoustic (PA) tomography (USPAT) for high resolution structural and functional imaging. The recent development of high channel count ultrafast US systems creates the opportunity to capture volumes at a high frame rate. Tomography, defined as a technique for displaying a representation of a cross section through a human body, facilitates high resolution (lamba/2) imaging by effectively rotating the US point spread function to reduce the effect of diffraction. We have developed an ultrafast US capability mated to a tomographic ring of transducers and scanned in depth by motorized acquisition. Leveraging the ultrafast capability provides the opportunity for acquisition of volumetric, functional breast images within 1 minute. The acquisition is controlled by 1024 coherent channels of Verasonics imaging systems (to be increased to 2048) and includes embedded GPUs for real-time imaging and analysis. When operated at 5 MHz, the resulting spatial resolution is nearly isotropic in plane with resolution of ~ half a wavelength (in this case ~150 microns). Compared to US images acquired with conventional imaging, the image quality is far improved. Ultrasound methods are attractive for integration into breast management due to their utility in guiding biopsy and the very high sensitivity (97.3%) that can be achieve by combining ultrasound with conventional screening. Both transmission and reflection tomography modes will be evaluated in order to facilitate both high resolution reflective modes and highly quantitative transmission imaging. PA imaging (PAI) is particularly well suited to complement US and improve diagnostic imaging of the breast. Our immediate goal is to reduce the number of biopsies required in women undergoing breast screening. Photoacoustic tomography (PAT) enhances the signal to noise ratio and visualization of morphology over conventional PAI. Healthy breast tissue has low optical absorption and US scattering, allowing for highly efficient PAT. Since abnormally increased vasculature and hemoglobin at tumor sites produces strong intrinsic photoacoustic contrast, PAT is ideally suited for visualizing angiogenesis. Further, PAT can assess the relative oxygenation of a region. With our combined strategy, we will evaluate characterization algorithms based on each feature – blood flow, oxygenation and structural changes, assessing the sensitivity of individual and combined imaging features. With a first study of this technique in a mouse model of premalignant to malignant transformation and a human study of lesion characterization, we will determine whether USPAT can add to the sensitivity and specificity of lesion characterization by MRI. Our resulting specific aims are to: 1) implement and integrate blood mapping, US tomography, and PAT for breast imaging, 2) assess the sensitivity and specificity of the resulting system in a rodent model of breast cancer, and 3) apply these new capabilities to image patients with MRI detected abnormalities recommended for biopsy.

NIH Research Projects · FY 2026 · 2021-09

ABSTRACT The ultimate goal of this proposal is to verify an RNA liquid biopsy platform and transition it to have specific utility for the study, and non-invasive assessment, of the hematopoietic bone marrow (BM) niche. In addition to their reliance of material excreted from dying, diseased cells, cell-free DNA (cfDNA) platforms are limited to primarily detecting changes in somatic genomic sequence, copy number, or methylation status. Many biological and clinical events, such as hematopoiesis, fibrosis, and tumorigenesis, are executed via global changes in transcriptional regulation. My PhD advisor Dr. Daniel Kim and I have established a platform for exRNA liquid biopsy in human blood-plasma that has demonstrated significant diagnostic and monitoring potential in both Pancreatic cancer and COVID-19 patients. I propose to continue this research to complete my PhD by assessing diagnostic performance of the exRNA platform in a new cohort of 60 lung adenocarcinoma (LUAD) patients with matched controls. Furthermore, I will apply my expertise in exRNA liquid biopsies to assay the transcriptional dynamics of hematopoiesis and hematopoietic stem cells (HSCs) in the bone marrow (BM) niche. Currently, bone marrow biopsies or aspirations are used to determine bone marrow health and production. These techniques are costly and require sedation and/or pain relief for the subject and have the potential to lead to long term discomfort, infection, excessive bleeding, and other side-effects. BM aspirations remain a critical diagnostic and monitoring tool for HSC transplant recovery, leukemias and lymphomas, blood cell pathologies, and infections of unknown origin but the primary readout remains identification and counting of cell types. I hypothesize that hematopoietic lineages within the BM secrete exRNA that reflect cell state and identity that can be used in a non-invasive RNA liquid biopsy for detailed study of transcription and populations within the BM. I propose to identify exRNA expressed and secreted by HSCs and the remaining hematopoietic lineage in order to develop a platform to deconvolute peripheral blood into constituent hematopoietic cell types without the need for HSC mobilization.

NIH Research Projects · FY 2025 · 2021-09

PROJECT SUMMARY/ABSTRACT This K76 Career Development award will address the large unmet need for patient-specific decision support tools for surgical interventions in older adults and provide a platform for the applicant to become a leader in geriatric surgery. Primary hyperparathyroidism (PHPT) is a common endocrine disorder in older adults that is associated with serious long-term morbidity, including osteoporotic fractures, kidney stones, and chronic kidney disease (CKD). Parathyroidectomy can prevent these morbid sequelae. However, increased surgical risk associated with advanced age and frailty, in addition to the competing risk of death prior to achieving benefit from surgical intervention, raises the question of whether the short-term risks of parathyroidectomy outweigh long-term risk reduction in this group. Identifying older adults likely to benefit from surgical interventions, such as parathyroidectomy, while preventing overuse of surgery in this vulnerable group, is critical to improve patient-centered care. Currently, there is no standardized methodology for developing surgical decision support for older adults that incorporates individualized risk prediction and stakeholder input to facilitate informed, patient-centered decision-making. Therefore, this coordinated research and training proposal aims to: 1) compare the effects of parathyroidectomy vs. medical management on the incidence of fractures, symptomatic kidney stones, and CKD in older adults with PHPT; 2) develop and validate predictive models for the perioperative risks of parathyroidectomy and long-term risks of fractures, kidney stones, and CKD specific to older adults with PHPT; and 3) design and test with stakeholder feedback a PHPT decision support tool that describes the tradeoffs of parathyroidectomy vs. non-operative management. To accomplish these aims, Dr. Seib will obtain training in comparative effectiveness and predictive modeling, qualitative and mixed methods research to develop decision support, and implementation science. Upon completion of this research, Dr. Seib will have a user-tested risk/benefit calculator that will form the basis of a decision support tool to improve individualized, patient-centered treatment recommendations for older adults with PHPT. In addition, this research will establish a framework for developing patient-centered decision support that can be applied to other conditions to promote appropriate surgical management of older adults. This proposal is significant because it directly addresses the gap in decision support tools for PHPT and other surgically managed conditions in older adults. This project is innovative because it proposes a paradigm shift to incorporate geriatric principles to individualize treatment decisions in older adults with PHPT to optimize clinical outcomes. At the end of the proposed research, Dr. Seib will have the data and skills necessary to successfully pursue R01 funding for a hybrid type 1 effectiveness-implementation trial to test this decision support tool and apply this methodology to other conditions with surgical treatment options that affect older adults.

NIH Research Projects · FY 2024 · 2021-09

PROJECT SUMMARY Many adult organs--for instance, intestine, mammary gland, skeletal muscle, skin—respond to reduced levels of functional demand by shrinking their physical size. In these organs, cells are lost faster than they are made, leading to a reduction in total cell number. The intestine is a broadly conserved exemplar of demand- driven organ shrinkage. In wild animals, cyclic periods of starvation cause intestinal size to shrink by 60-75%. Humans also undergo healthy intestinal shrinkage, but excessive or dysregulated cell loss can quickly become pathological, as seen in enteropathies like celiac sprue, endotoxemia, and giardiasis. Yet—unlike the mechanisms that balance cell division/loss during everyday turnover—the mechanisms that tune cell imbalance for physiological shrinkage are virtually unknown. The roadblock to mechanistic investigation of intestinal shrinkage has been the lack of a tractable laboratory model, which must allow cells (and their dynamic behaviors) to be monitored across time and must possess cell-specific markers and other tools to facilitate mechanistic studies. Historically, studies used rodents, but modern research protocols cannot replicate natural famine/feast cycles. My lab has developed a new invertebrate model of intestinal shrinkage that is both tractable and genetically manipulable: the Drosophila adult midgut, akin to the vertebrate small intestine. We demonstrate that intestinal shrinkage is conserved in Drosophila, and we document that its underlying basis is the massive squeezing-out of now-superfluous enterocytes through active extrusion. Here, we investigate intestinal shrinkage from both sides of the equation for net cellular balance: mature cell loss (Aim 1) and stem cell capacity (Aim 2). Our studies leverage the midgut’s superlative toolkit of cell- specific genetic reporters and our own pioneering innovations for real-time and longitudinal imaging of functioning midguts inside live animals. In Aim 1, we ask how the gut senses loss of ingested food— mechanical compression, lack of nutrients, or both. We test if two known regulators of extrusion, the transcriptional co-activator YAP/Yorkie and intercellular Ca2+ waves, function during shrinking to increase extrusions. Third, we probe whether a shrinking gut regulates cell extrusions at the organ scale or at the level of individual cells. In Aim 2, we seek the mechanisms that cause a 75% culling of the stem cell pool during shrinkage—even as stem cell mitoses paradoxically increase. We will test if stem cells initiate non-self- renewing divisions, adopt terminal fates directly, and/or activate apoptosis. The fly gut’s digestive physiology, stem cell lineages, and molecular regulation are similar to humans. Hence by elucidating the cell-to-organ scale mechanisms that operate at this frontier of tissue biology, this project may yield leads for therapies to treat cellular imbalances in human disease.

NIH Research Projects · FY 2025 · 2021-09

PROJECT SUMMARY Cancer is primarily a disease of the old. While this is due in part to the sequential acquisition of genomic alterations, aging is also associated with a constellation of changes that could impact tumor initiation and growth. These “hallmarks of aging” involve diverse pathways that impinge on carcinogenesis and lead to systemic changes. However, despite the very close association between aging and cancer, or perhaps because of it, very little is known about how cancer cell-intrinsic, microenvironmental, and systemic age-related changes impact cancer initiation and growth. Genetically engineered mouse models uniquely enable the introduction of defined genetic alterations into normal adult cells with defined temporal control. Human lung cancer has been modeled using genetically engineered mouse models, and these tumors recapitulate many features of early-stage human lung adenocarcinoma. To increase the scope and precision of in vivo cancer modeling, we integrated conventional genetically engineered mouse models, CRISPR/Cas9-based somatic genome engineering, and quantitative genomics with statistical approaches. Tumor barcoding coupled with CRISPR/Cas9-mediated gene inactivation and high-throughput barcode sequencing (Tuba-seq) enables quantitative analysis of the effects of large panels of genes on tumor initiation and various facets of autochthonous tumor growth. These models can thus distinguish the effects of aging from mutational events while affording a level of precision that allows us to detect differences in tumor suppressor function across age contexts. In Aim 1, we will quantify the interaction between age and tumor suppressor gene function. Our in vivo experiments will define whether aging increases or decreases the absolute efficiency of tumor initiation and uncover the impact of aging on the importance of diverse tumor suppressor genes on tumor initiation and growth. In Aim 2. we will determine how the lung tumor microenvironment and lung cancer cells themselves change with age. We will elucidate the impact of tumor genotype on the microenvironment across age and determine whether age-dependent changes in growth are accompanied by dramatic differences in cancer cell state. In Aim 3, we will disentangle cell-autonomous differences in tumors developing in young and aged mice from effects on tumor suppressor function driven specifically by aging of the local tissue and systemic host environments. These experiments will provide insight into whether age-dependent genotype-specific effects are largely cancer cell-intrinsic or driven by the shifts in the microenvironment or whole organism environment. By permuting cancer cell age and genotype, as well as microenvironment and host age, we will gain an unprecedented understanding of the contribution of these factors to multiple aspects of lung carcinogenesis. Ultimately, these findings could have important implication for cancer prevention, detection, and treatment.

NIH Research Projects · FY 2025 · 2021-09

ABSTRACT The most widely used and most general-purpose knowledgebases in biomedicine are the ontologies that define the entities and the relationships that are central to different aspects of health care or the life sciences. When ontologies are brought together in an integrated framework—enabling terms denoting the same or similar entities to be related to one another and allowing computational infrastructure to operate across ontologies—then the aggregate collection of ontologies provides additional capabilities for users. Our group has developed an enormously popular knowledge resource, known as BioPortal, that provides a unique knowledgebase of nearly all the world’s publicly accessible biomedical ontologies. Adoption of BioPortal has been remarkable. Each month, more than 75,000 unique users from academia, government, and industry access the contents of our ontology repository, and our API services more than 16 million requests. At least 7,850 scientific papers mention the use of BioPortal, according to Google Scholar. We propose the following four specific aims: (1) We will continue to govern and maintain the BioPortal knowledgebase by enhancing the services that the knowledgebase provides to the biomedical community. (2) We will improve the efficiency with which users interact with the knowledgebase by creating the BioPortal Knowledge Graph. The graph—which will be a queryable knowledge resource in its own right—will integrate all ontologies in the knowledgebase and it will provide links to other knowledge sources, allowing users to perform expanded queries across all ontologies. (3) We will enable users of BioPortal to make requests for changes or extensions to ontologies, and to view the history of changes and change requests when ontologies are managed in Git repositories. The result will be a much more efficient workflow for making suggestions to ontology developers and the ability to monitor the change history of component ontologies to inform ontology selection for particular end-user tasks. (4) We will provide enhanced mechanisms to enable users of BioPortal to search, access, and browse knowledge in remote BioPortal servers. Users no longer will need to know in advance where particular knowledge might be stored and they will have the ability to easily integrate knowledge from complementary scientific disciplines stored across a network of servers. The result will be the world’s most widely accessed knowledgebase of biomedical concepts and relationships— supporting all other work in semantic technology in biomedicine and making all biomedical knowledgebases more standardized, more easily integrated, and more FAIR.