Brigham And Women'S Hospital

NIH Research Projects · FY 2025 · 2023-09

IL-1α and β were discovered more than 50 years ago, and knowledge about these pleiotropic cytokines has grown exponentially since that time. Therapeutic use of these molecules has been limited by toxicity, and their blockade has had limited success in inflammatory disorders. In 2017, the CANTOS trial using canukinumab and antibody to IL-1β, showed an unexpected association of blocking IL-1β and a dose dependant reduction in the incidence of cancers, rekindling interest in the role of IL-1’s molecules in tumor immunity. However, our data thus far suggests unappreciated complexity. We and others have shown that the of blocking IL-1β in mouse models of cancer, and our published and preliminary data suggest that the mechanism by which anti- tumor immunity evolves is not straightforward. For example, blocking IL-1β permits immunostimulatory T cell activation by IL-1α to proceed unopposed, favoring tumor immunity. In parallel, blocking IL-1β’s effects on myeloid cells modifies the tumor microenvironment independently to favor CD8 T cell mediated anti-cancer immunity. The immunotherapeutic effects of IL-1α appear to be operating through the CD8 T cell IL-1R1, and we propose that by understanding this pathway more completely, we can greatly enhance its activity. Moreover, IL-1R1-driven effects on myeloid cells appear to inhibit anti-cancer immunity in the TME, and we hypothesize that understanding these effects will allow us to target this pathway with precision. To visualize these changes histopathologically, we have employed the powerful CyCIF platform. To test hypotheses in vivo, we will use novel molecules known as AcTakines. These compounds target cytokine activity or blockade precisely to specific cell types in vivo without systemic toxicity and will help us to dissect these immune pathways in tumor-bearing animals. Because these drugs are being developed for future use in humans, our work will serve as a basis for a better mechanistic understanding of their future use in cancer immunotherapy. We believe this is a new and unexplored pathway through which we can greatly enhance anti-tumor immunity, contributing fundamentally to immunotherapy of cancer.

NIH Research Projects · FY 2025 · 2023-09

SUMMARY Metastatic brain tumors are the most commonly observed intracranial tumors. Patients with advanced breast cancer have a high propensity to metastasize to the brain with human epidermal growth factor receptor (EGFR) positive and triple-negative breast cancer (TNBC; estrogen and progesterone receptor and Her2 negative) subtypes showing the highest incidence of brain metastases. To effectively treat multiple highly aggressive brain metastatic breast tumors (BMBT), there is an urgent need to develop therapeutics that target aberrant signaling pathways in tumor cells and the immune cells in the tumor microenvironment (TME) of BMBT. Recently, we have shown that intrathecal (IT) and intracarotid artery (ICA) injection of adult allogeneic “off the shelf” mesenchymal stem cells (MSC) expressing bifunctional protein simultaneously targeting EGFR and (DR)4/5), EvDRL have therapeutic efficacy in mouse models of BMBT that mimic clinical settings. These findings although promising, have raised fundamental questions on the potential of combining MSC released EvDRL induced tumor cell killing with therapeutic agents that simultaneously activate immune effector functions against BMBT. Our recently published studies indicate that DRL (TRAIL) component of EVDRL is the key driver of EVDRL mediated cell death in patient derived BMBT cells. Previous studies have shown that in addition to tumor cells, DRL induces apoptosis in myeloid derived suppressor cells (MDSC) and CD4+ CD25+ FoxP3+ Tregs and simultaneously increases recruitment of CD8+ T cells in the TME. Furthermore, clinical and pre-clinical studies using combined cytotoxic therapy and immune checkpoint (ICI) blockade have shown increased efficacy in breast metastatic tumors thus offering the potential to combine of MSC-EVDRL with immunomodulatory agents to treat BMBT. In this proposal, we will first evaluate the efficacy and influence of MSC-EVDRL induced tumor cell death in the TME in humanized (hu) NSG breast to brain metastatic tumor models generated from BMBT cells that have varying response to EVDRL mediated apoptosis. Next, we will create bimodal MSC releasing EVDRL and anti- programmed cell death protein (PD)-1 nanobodies (Nb-PD1) and evaluate the mechanism combined efficacy of engineered MSC in huNSG breast to brain metastatic tumor models. We hypothesize that human MSC-EvDRL will lead to specific killing of local and widely disseminated BMBT cells and Nb-PD1 will target T cells recruited to the TME. To ease clinical translation, we will incorporate activatable kill switch/PET imaging agent, herpes simplex virus-thymidine kinase (HSV-TK) into MSC and assess their fate by PET imaging and selective eradication mediated by HSV-TK activation. Given that engineered MSC are in phase I clinical trial in non-small cell lung cancer patients; a phase I/II trial using IT of anti-PD-1 antibody is currently ongoing in leptomeningeal metastatic patients; and our MSC-DRL therapy in primary brain tumor (GBM) patients is under consideration by FDA; successful execution of the proposed studies will facilitate translation of our strategy into clinics. We anticipate that our findings will have a major contribution towards developing novel mechanism based targeted therapies for BMBT and thus a major impact in saving the lives of many metastatic breast cancer patients.

NIH Research Projects · FY 2025 · 2023-09

NUT carcinoma (NC) is an aggressive squamous carcinoma driven by the BRD4-NUT fusion oncoprotein. NC affects all ages and is highly lethal (>90%), a median survival of 6.5 months. There are no effective treatment options for NC; thus, this disease represents an extreme unmet need. The overarching goal of this proposal is to improve survival of these patients through mechanism-driven identification and testing of therapeutic targets. In response to the need for an immunocompetent animal model in which both NC cell-autonomous and -non- autonomous pathogenic mechanisms can be investigated rigorously, we have developed the first genetically engineered mouse (GEM) model of NC. As a squamous carcinoma, NC serves as a paradigm for fusion oncoprotein-driven solid tumors. The NC GEM will expand that paradigm to understanding how tumor intrinsic and extrinsic interactions sustain NC growth. Mechanistically, BRD4 is a BET family protein whose dual bromodomains bind acetyl-histones that when fused to NUT, recruits the histone acetyltransferase, p300, forming enormous super-enhancers called megadomains. BRD4-NUT megadomains maintain expression of pro-growth, anti-differentiation transcription factors including MYC, SOX2, and TP63. Our demonstration that treatment with BET bromodomain inhibitors (BETi), small molecules that competitively inhibit binding of BET bromodomains to chromatin, can inhibit growth of NC in humans, led to a new field investigating the role of BRD4 in cancer. However, it has become clear that monotherapy with BETi does not fully address NC biology. We have recently found that repression of tumor suppressor gene expression, such as that of CDKN2A/B, by the histone methyltransferse, EZH2, highly complements oncogenic activation by BRD4-NUT in maintaining NC growth. Targeting of this pathway with tazemetostat (taz) is highly synergistic with BETi and will be explored in the proposed GEM model. It is now recognized that NC harbors an immune-evasive tumor microenvironment (TME) and can respond to immune modulation therapy. Epigenetic modulators such as taz and BETi are known to promote an anti-tumor immune TME. Thus, pre-clinical animal models with intact immune systems such as our NC GEM are needed to fully evaluate effects of epigenetic modifiers, and the role of immune therapy in this disease. Our GEM has a tamoxifen-inducible, conditional knock-in fusion of murine Brd4 with human NUTM1, encoding a BRD4-NUT fusion oncoprotein. Invasive tumors formed in our NC GEM (`mNC') have provided the most definitive evidence that BRD4-NUT is the sole driver of this cancer. mNC closely mimics human NC, demonstrating rapid growth, metastatic spread, and an indistinguishable histopathology and immunophenotype. Moreover, the immune cell composition of the mNC TME also resembles that of human NC. We will make use of our novel GEM to address the following aims: 1. establish the applicability of the NC GEM (mNC) to human NC (hNC) biology.; 2. devise improved primary therapy for NUT carcinoma.; 3. explore approaches to prevent relapse of NC.

NIH Research Projects · FY 2025 · 2023-09

Project Summary/Abstract Whole-genome sequencing of population biobank cohorts holds great promise for enabling accurate prediction of genetically-mediated risk for heritable human diseases and traits. Such information has the potential to be a powerful resource for precision medicine, informing preventative and therapeutic decisions. To more fully realize this potential, new statistical methods are needed to incorporate all genetic variants – including structural variants, blood-derived somatic mutations, and rare SNPs and indels – into genetic risk models. These classes of genetic variation, which are known to include many variants with large effects on disease risk, can be detected in high-coverage whole-genome sequencing data now being generated at biobank scale. However, such variants have not been accessible from previous genetic data sets (which have relied on SNP- array genotyping and imputation). Consequently, existing methods for polygenic prediction have typically considered only common inherited SNPs and indels. We propose to develop a suite of statistical methods to enable these additional classes of genetic variants to be incorporated into models of genetic risk, thereby improving predictive power. For variant types that are currently difficult to ascertain even from whole-genome sequencing data – including somatic mutations and some types of structural variants – we will develop new genotyping algorithms that improve statistical inference by harnessing information across large sequenced cohorts. We will efficiently integrate information across all variant types into genetic risk models using fast Bayesian regression methods. We will apply these approaches to train genetic risk models for common diseases using data from very large biobank sequencing projects. This project will have three specific aims. First, we will develop and apply methods for incorporating structural variants into polygenic scores. Many structural variants are known to confer substantial disease risk but are at imperfectly modeled by existing polygenic scores, such that directly including such variants will increase prediction accuracy and cross-ancestry transferability. Second, we will develop and apply methods for incorporating somatic mutations detectable in blood-derived DNA into genetic risk models. Such acquired mutations, often indicative of clonal expansions of blood cells, provide an orthogonal source of risk compared to the inherited variants considered by standard polygenic scores. Third, we will develop and apply efficient computational methods for training polygenic score models on biobank-scale sequencing data. These methods will allow model-fitting to be performed on individual-level genetic data, optimizing prediction accuracy. We anticipate that these efforts will significantly improve performance of genetic risk models trained on current and future population-scale whole-genome sequencing data sets.

NIH Research Projects · FY 2024 · 2023-09

Project Summary Abstract Buprenorphine reduces overdose mortality by up to 70%, making it one of the most critical interventions to combat the opioid overdose crisis. With the increasing prevalence of illicit fentanyl, patients with opioid use disorder (OUD) attempting to initiate buprenorphine now routinely report experiencing precipitated withdrawal despite waiting for withdrawal symptoms to first emerge. In response, clinicians and patients alike are increasingly recommending a novel strategy called “micro-dosing” or “low-dose” buprenorphine induction, where a dose significantly lower than the typical 4mg is administered. With this strategy, precipitated withdrawal does not occur despite buprenorphine is administered before the emergence of withdrawal symptoms. A variety of low-dose induction protocols have been reported—some use low dose (≤0.5mg) SL buprenorphine, while others initially use transdermal (Butrans®) or buccal (Belbulca®) formulations. Regardless of the specific approach, the requirements for a successful low-dose induction appear to be the low initial dose, the slow up-titration of the buprenorphine, and continuation of the full agonist opioid during the induction. However, these approaches can be problematic—low-dose SL buprenorphine requires the medication to be cut which is often prohibited in inpatient settings; transdermal and buccal formulations are costly and often not on formulary; and their use violates US federal laws that prohibit them to be prescribed to outpatients seeking treatment for OUD. Therefore, there is an urgent need to research strategies for buprenorphine low-dose inductions that avoid having to cut the medication, or use prohibited and costly formulations. To meet this need, we propose to study the safety and feasibility of utilizing orally (PO) administered buprenorphine. Buprenorphine undergoes extensive first-pass effect when taken PO, hence the bioavailability is estimated to be significantly less than the SL route of 30-50%. As such, using the existing SL dose formulations (e.g. 8mg) via the oral route may allow low-dose induction without having to split the medication. However, research on the safety and feasibility of PO buprenorphine is largely absent. We therefore propose to conduct a two-phased study: in the first phase, we will conduct a randomized cross-over trial with healthy human volunteers to receive low-dose SL and PO buprenorphine to determine the pharmacokinetic parameters. The results will inform the dose needed for a successful low-dose induction using PO buprenorphine. In the second phase, we will conduct a pilot feasibility trial among individuals with OUD to undergo a low-dose induction using PO buprenorphine in a controlled laboratory setting. Given the importance of buprenorphine in improving clinical outcomes for individuals with OUD and preventing overdose deaths, research that aims to identify safe approaches to treatment initiation are urgently needed. Results from this study will be the basis for a safe, accessible, and evidence-based approach to buprenorphine initiation, and lay the groundwork for a randomized controlled trial comparing the efficacy of low-dose induction strategies using PO buprenorphine with standard inductions.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY Older adults with multiple chronic conditions (MCCs) are often hospitalized and are at risk for adverse events (AE) during acute episodes of care. Fragmented care across different healthcare providers is common for these patients and increases risk of AEs, especially in elderly patients with unrecognized geriatric conditions. Patient safety research that attempts to address these risks are limited by available data and often lack of access to electronic health records (EHR) from providers external to the health system conducting the research. We propose leveraging emerging interoperability standards, and public policies that require their adoption, to empower patients to locate, retrieve, and share their EHRs with our research team. We will partner with two studies that will enroll older adults and aim to reduce AEs during acute care episodes. Both studies have timelines suitable to consenting participants for this demonstration study. To accomplish our aims, we will leverage and enhance our existing digital infrastructure (a web-based application and secure backend cloud technology) that we have developed and implemented in past and ongoing work. Leadership of both partner studies will play major roles in this work, ensuring strong coordination. In Aim 1, we will enhance our existing digital infrastructure using two open-source projects. We will leverage infrastructure from the Sync-for-Science (S4S) Procure project (used in the All Of Us Research Program) enabling patients to use HL7 FHIR Services to find and share their EHRs with research teams. The MCC e-Care Plan project will supply clinical information models and value sets, ensuring that data collected can be used for both research and clinical care. User needs and requirements for identifying prior sites of care and sharing EHR data from those sites with the research team will be elicited via a rigorous user-centered design process. In Aim 2, we will implement and iteratively refine workflows defined in Aim 1 using mixed methods. In Aim 3 we will develop analytic methods for harmonizing aggregated EHR data and metrics relevant to our partner studies. These metrics will reflect care fragmentation based on EHR data aggregated using FHIR services, including unrecognized geriatric syndromes identified by applying natural language processing to unstructured text in retrieved clinical notes. In Aim 4, we will use these metrics as risk factors in a multivariable regression model to assess their effect on the safety outcomes of our partner studies. This analysis will demonstrate how novel assessments of care fragmentation and conditions common in geriatric populations contribute to AEs during acute care episodes. Our expert advisors, consultants, and software developers will assist with all clinical and technology aspects of this work. Our demonstration study will produce foundational knowledge regarding how to empower patients to collect and share their data with research teams, best practices for collecting data in this manner, and the value of using such data in studies of patient safety that are relevant to older adults. Finally, our contributions to two open-source projects will be made available for broad adoption to benefit other studies.

NIH Research Projects · FY 2025 · 2023-09

Abstract The dynamic tumor microenvironment (TME) where cells continuously communicate, migrate, and react to each other and the signals that are secreted, is critical for inducing tumor progression and aggressiveness of most forms of cancer. We have special interest in glioblastoma (GBM) that displays a dynamic and complex TME for which we have developed the necessary tools to dissect it, understand it, and have a positive impact on its treatment. As such, it is necessary to understand the underlying biology in a dynamic and relevant environment. With various degrees of limitation pertaining to currently available in vitro and in vivo models, in this proposal, we aim to leverage our expertise to optimize a unique three-dimensional (3D) human mini-GBM model through the utilization of a light-based bioprinting technology and taking advantage of primary neuronal, vascular, and GBM cells, to more precisely replicate the brain TME in human patients. It is anticipated that, construction of an in vitro 3D human mini-GBM model mimicking not only the cellular compositions but also the extracellular matrix (ECM) properties and importantly, tissue architecture of its in vivo counterpart, will allow us to precisely assess proliferation, migration, and transformation of GBM cells, similar to those already proven in ex vivo GBM organotypic cultures but at much higher availability and throughput for potential drug screening in the future.

NIH Research Projects · FY 2025 · 2023-09

Project Summary Despite treatment with neoadjuvant chemotherapy, 30-40% of patients diagnosed with early-stage triple- negative breast cancer (TNBC) develop metastasis and die of their cancer. Part of standard treatment for TNBC includes anthracycline-cyclophosphamide and taxane-based (AC-T) chemotherapy, radiation, and surgery. Combination chemotherapy with immune checkpoint inhibitors (ICI) that target T-cell inhibitory receptors are now FDA-approved for early stage and metastatic TNBC. Despite promising trial results, most patients with metastatic TNBC do not experience durable long-lasting benefits, particularly those whose tumors progressed on prior chemotherapy. Although proliferating cancer cells are the intended targets, systemic chemotherapy clearly impacts other organ systems. This includes detrimental effects on the immune system, such as elimination of cytotoxic T cells. Thus, if chemotherapy impairs anti-tumor immune cells, it would limit ICI efficacy. A major challenge to the field is that we do not understand how chemotherapy impacts immune function or tumor cell fitness in metastatic microenvironments. In our TNBC mouse models, AC-T chemotherapy reduced primary tumor growth and lung metastasis. Surprisingly, liver metastasis, which is a predominant metastatic site in TNBC patients, was significantly enhanced in the AC-T-treated mice. We also observed markers of immunosuppression in the liver after chemotherapy in both our mouse models and clinical samples. We hypothesize that the liver is specifically immunosuppressed by chemotherapy, thus making the liver more hospitable for TNBC metastasis and reducing ICI efficacy. Our objective is to understand how chemotherapy impacts the liver immune microenvironment and TNBC liver metastasis, and to identify pre-clinical strategies that prevent liver immunosuppression and metastasis. We will use our TNBC lung metastasis models and our highly sensitive molecular barcoding method for metastasis detection. We will identify tumor cell clones that grow in metastatic sites and if the clonal composition changes in response to chemotherapy treatment. We will also determine whether those clones are inherently sensitive/resistant to chemotherapy or if their response to chemotherapy relies on the metastatic microenvironment. We will perform high dimensional immune-profiling of primary tumors and liver from chemotherapy-treated tumor-free and tumor-bearing mice using single cell multi-omic approaches. We will assess immune function of cells derived from metastatic sites in the mouse models. We will validate our findings by multiplex immunofluorescence staining on patient biopsy samples taken from metastatic sites. We will identify treatment regimens that target tumor cells while protecting anti-tumor immune cells. Our proposed studies will deepen our understanding of the systemic effects of chemotherapy, not only on breast cancer cells that spread to various organs but also on immune cells in those organs. Success in our line of investigation will identify new treatment approaches that are effective against breast cancer but do not diminish critical immune cells.

NIH Research Projects · FY 2024 · 2023-09

PROJECT SUMMARY Sleep irregularity is highly prevalent and linked to downstream adverse cardiometabolic health outcomes, but the upstream drivers of sleep irregularity are not well characterized. Adverse health outcomes associated with irregular sleep timing mirror those linked to shift work, and irregular sleep may represent a driver of circadian misalignment and related disease in the general population. Notably, sleep timing is modifiable and could serve as an inexpensive, non-invasive way to promote health, but further research on environmental factors influencing sleep regularity is required to inform successful interventions. Understanding the environmental drivers and molecular markers of irregular sleep are critical gaps that would aid public health intervention and disease prevention efforts. The recent 2021 NIH Sleep Research Plan highlights research on the effects of environmental exposures on sleep and on epigenetic mechanisms underlying sleep and circadian health as top priorities. Therefore, to address these gaps in knowledge and stated research needs, I propose to apply acquired training in sleep epidemiology, chronobiology, and advanced statistical analysis and epigenetics to: 1) investigate which dimensions of light exposure impact sleep regularity and moderation by factors such as age and sex (K99), 2) develop biological markers of sleep regularity (K99), 3) validate and establish the temporality of resulting findings with prospectively collected data (R00), and 4) expand measurement of light and environmental factors with prospectively collected data (R00). My goal is to establish an independent research program centered around how light and other environmental exposures affect sleep and chronobiology in population health. This proposed study and career development plan logically builds upon my training in environmental health, vision research, and molecular epidemiology, to gain expertise in light data analysis and collection, sleep epidemiology, chronobiology, advanced statistical modeling, and omics integration. With expert mentored guidance provided by Dr. Tamar Sofer, Dr. Susan Redline, and Dr. Frank Scheer, I will establish a unique interdisciplinary research program focusing on the interactions of the light environment and sleep. This award will provide key training in four areas: 1) sleep epidemiology; 2) clinical chronobiology; 3) integration and analysis of high-dimensional light and actigraphy data with omics data; and 4) professional development. This data collected during the R00 phase will also provide a strong foundation for R01 applications. This support provided by this award will allow me to launch a novel independent research program and address inherent gaps in our understanding of the role of naturalistic light exposure on sleep health in the general population.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY Diagnostic and treatment approaches for non-small cell lung cancer (NSCLC) have evolved over the last decade from primarily empirical methodologies to objective strategies that rely on clinical characteristics of the patient and morphological features of the nodule. Following recommendations by the United States Preventive Service Task Force (USPSTF), high-risk individuals are screened yearly with low-dose computed tomography (LDCT) as this provides high sensitivity with acceptable specificity for lung cancer. However, the introduction of LDCT as the primary screening modality for lung cancer has increased detection rates of indeterminate pulmonary nodules that then require invasive investigation. This decreases the quality of life for at-risk individuals through repeated follow-ups and procedures, and greatly increases anxiety over what usually turns out to a benign nodule. In this proposal, we aim to improve upon these outcomes by determining the features that convolutional neural networks (CNNs) utilize when classifying lung nodules as either or benign. We will also determine if providing CNNs with pre-specified histologic image features known to be associated with lung cancer improves their ability to generalize to novel images outside the image set used to train them. The central hypothesis of this proposal is that increasing the attention of a CNN on LDCT image features that are accepted as being pathophysiologically relevant will improve its generalizability to novel images and thus its ability to accurately distinguish between malignant versus benign nodules. In the F99 Aim of this proposal, we will address this hypothesis by utilizing LDCT images from the National Lung Screening Trial (NLST) together with concept activation vectors to determine which parenchymal and tumor-specific features are used by CNNs to classify lung nodules. In the K00 aim, we will determine if endophenotypes extracted from the COPDgene LDCT image set can be used to improve CNN generalizability. Completion of these aims will lead to an increased understanding of the morphologic biomarkers of lung cancer inherent in LDCT images of the lung that are most important for accurate diagnosis. This will have potential application to the improvement of CNN classification performance in other medical domains. In addition, by adhering to the training program outlined in this proposal I will gain high levels of expertise in image biomarkers, early cancer pathogenesis and detection, genetic networks, and genomics. These will collectively serve as a solid foundation for my future career as an independent biomedical investigator.

NIH Research Projects · FY 2025 · 2023-09

Although there is increasing recognition that viruses are an important cause of sepsis, most clinicians and policymakers tend to equate sepsis with bacterial infection. As a result, current guidelines emphasize immediate administration of antibiotics for all patients with suspected sepsis, even though antibiotics will not benefit patients with pure viral sepsis and may cause harm at both the individual and population levels. However, the contribution of viruses to the overall burden of sepsis is poorly characterized, and important questions remain about antibiotic utilization patterns and potential antibiotics-associated harms in this population. The proposed project seeks to provide rigorous estimates of the proportion of community-onset sepsis caused by respiratory viruses, describe current antibiotic utilization in viral sepsis, quantify potential antibiotic-associated harms, and identify patient subgroups in whom antibiotics can safely be limited. The ultimate goal of the proposal is to promote safer, more nuanced treatment for patients with sepsis, in line with AHRQ’s stated priorities of improving patient safety and avoiding harm. The proposed research will accomplish this goal by using large datasets with rich clinical data from electronic health records (EHR) at over 200 diverse hospitals, a validated EHR-based approach to identifying sepsis, and sophisticated causal inference statistical techniques to address confounding in order to pursue three aims: (1) Determine the fraction of community- onset sepsis attributable to respiratory viruses and characterize antibiotic use and predictors of prolonged antibiotics in this population; (2) Assess hospital-level variation in antibiotic prescribing for patients with viral sepsis and evaluate risk-adjusted outcomes for patients at high- versus low- utilization hospitals; and (3) Assess the potential utility of the combination of positive viral assay and low procalcitonin level as an indicator that antibiotics can safely be withheld or rapidly discontinued. The candidate, Dr. Claire Shappell, is an advanced research fellow and Pulmonary and Critical Care physician at Brigham and Women’s Hospital (BWH) with experience using EHR data to gain actionable insights into problems in critical care. Dr. Shappell’s goals during the K08 period are to develop advanced proficiency with the acquisition, preparation, and handling of large healthcare datasets; learn statistical methods for causal inference analysis including propensity score methods and approaches to multilevel data; acquire expertise in the use of R statistical computing software; and strengthen abilities in scientific communication. To achieve these goals, she has assembled a multidisciplinary mentorship team led by Drs. Michael Klompas and Chanu Rhee, experts in the use of EHR data for sepsis surveillance and outcomes research. By the completion of the K08 award period, Dr. Shappell will be well-positioned to achieve her long-term goal of becoming an independent physician-scientist with expertise using large clinical datasets to improve care for critically ill patients, with a particular focus on sepsis and antibiotic utilization.

NIH Research Projects · FY 2025 · 2023-09

Project Summary / Abstract Cumulative evidence from large-clinical and neuroimaging studies suggests that the pathophysiology of schizophrenia involves an increased vulnerability to premature aging. However, this knowledge has not been translated into clinical practice due to the lack of understanding of the biological underpinnings of premature aging in schizophrenia. Additionally, there remains a current lack of diagnostic tools for detecting and monitoring individuals who experience premature aging in a clinical setting. This lack establishes the critical need to develop in vivo biomarkers of premature aging in schizophrenia to provide a novel avenue toward diagnosis and neuroprotective treatment. The current proposal provides a step to tackling this challenge through a large, multimodal study of schizophrenia. The central hypotheses state that individuals with schizophrenia are more vulnerable to premature aging, as indicated by an increased expression of senescence-associated secretory phenotype (SASP) proteins, and that the increased expression of SASP proteins explains abnormalities in physical health, cognition, and brain structure in schizophrenia. The applicant, Dr. Johanna Seitz-Holland, has access to several cross-sectional datasets, including clinical, cognitive, blood, structural, and diffusion data, spanning the schizophrenia lifespan. In the K99 phase, she will utilize data from 80 individuals with early schizophrenia and 80 matched healthy individuals to establish the increased expression of SASP proteins as a biomarker for increased vulnerability to premature aging in early course schizophrenia. In the R00 phase, Dr. Seitz-Holland will include data from over 700 individuals (18-85 years) and characterize the role of the increased expression of SASP proteins as a mediator between schizophrenia, physical health, cognition, and structural brain abnormalities across the lifespan. Successful completion of these aims will yield several impactful outcomes. The findings will inform the development of a clinically feasible, minimally invasive, and low-risk biomarker for premature aging. In addition, the findings will allow the development of a parsimonious hypothesis that accounts for aspects of brain and physical health deficits. Lastly, they will provide a scientific basis for developing novel neuroprotective treatments. Dr. Seitz-Holland’s long-term goal is to conduct translational research to increase the life quality of those with psychotic disorders. This application builds on her postdoctoral training in multimodal trajectory schizophrenia studies and complements it with training from world-class experts in the use and analysis of blood biomarker data and geriatric science. This award will thus provide her with a unique opportunity to develop into an independent researcher who can effectively conduct multimodal psychiatric studies and translate findings into the evidence-based diagnosis and treatment strategies needed in clinical science.

NIH Research Projects · FY 2025 · 2023-09

Environmental endocrine disrupting chemicals (EDCs) are associated with multiple adverse health effects. Early- life exposures alter neurobehavioral trajectories, with increased risk for depression and poorer cognitive functioning in childhood. However, very few studies have evaluated the effects of EDC exposure in later life despite ongoing exposure. Patients with a history of major depression (MDD) are at increased risk for neuropsychiatric symptoms such as low mood and cognitive dysfunction and this risk is further elevated in women during the menopausal and post-menopausal period. Preclinical and clinical evidence suggests that EDCs affect a number of biological variables implicated in mood and cognitive disorders, including inflammatory biomarkers, brain-derived neurotrophic factor (BDNF), and sex hormones. The proposed study seeks to better understand how EDCs affect mood and cognition in postmenopausal women with and without depression. We propose to evaluate 1) the association between EDCs and neuropsychiatric symptoms (mood and cognition) in postmenopausal women with MDD and without mood disorders (healthy controls; HC), 2) the relationship between EDCs and biological variables including inflammatory cytokines like C-reactive protein, interleukin-6, and tumor necrosis factor-α, BDNF, and sex hormones including estradiol and testosterone, and 3) whether the biological variables of interest mediate any potential association of EDCs with neuropsychiatric outcomes. We will leverage ongoing data collection of an existing study of postmenopausal women including depression rating scale scores and detailed cognitive assessment data, as well as serum cytokine, BDNF, and sex hormone levels. Urinary bisphenols and a suite of 16 phthalate metabolites will be measured in 75 MDD and 75 HC subjects. Urine will be collected at 2 timepoints (at recruitment and 1 month later), then pooled for EDC assessment. Regression and statistical methods for mixtures will be used to assess relationships between individual EDCs and their mixtures with neuropsychiatric outcomes and biomarkers. Mediation analysis will explore the direct and indirect effect of EDCs on outcome measures via the biological variables. Career development plan goals include gaining competency in the interpretation of environmental exposures and inflammatory data and in biostatistics, especially mixture methods of analysis. Training goals will be met by coursework, workshops/seminars, conferences, lab experience, readings, and mentorship. Training experiences will mostly take place at Harvard- affiliated BWH and HSPH. This project will identify emotional and cognitive effects and relevant biological effects of EDCs in a population vulnerable to neuropsychiatric symptoms. Enhanced mechanistic understanding will allow novel interventions targeting contributory biological variables, such as lifestyle changes or medications that reduce inflammation, increase BDNF, and regulate sex hormones to mitigate adverse effects of EDCs. Increased awareness of neuropsychiatric implications of EDC exposures may also guide public policy changes that regulate or reduce their omnipresence in consumer products and the environment.

NIH Research Projects · FY 2024 · 2023-09

ABSTRACT Protein replacement therapy has been a cornerstone in treating genetic diseases (e.g., hemophilia) with loss or reduction of the function of a particular protein, by using recombinant proteins or recombinant engineered proteins. However, most protein therapeutics have short circulation lives, and thus require frequent invasive infusion to maintain their therapeutic efficacy. For example, the most common treatment for hemophilia A caused by a deficiency of blood clotting factor VIII (FVIII) is factor concentrate replacement, which is associated with burdensome frequent intravenous infusion. To address the unmet medical need of hemophilia, the major objective of this project is to develop a non-invasive oral mRNA delivery nanoplatform for durable protein replacement therapy requiring infrequent dosing. Synthetic mRNA has shown enormous potential for biomedical applications, with mRNA vaccines already clinically approved for COVID-19. Various delivery strategies have been developed to improve mRNA translation; however, an ongoing challenge of mRNA therapy is managing the transient efficacy due to its relatively short half-life, and oral mRNA delivery remains elusive. In my previous work, I have identified a unique poly(zwitterion)-lipid-based micelle platform that can cross the intestinal epithelial barrier and lead to a very potent oral bioavailability of biomolecules such as insulin. Recently, I have also discovered a new type of ionizable lipids that can extend the duration of mRNA-mediated protein expression. In this F32 project, I propose to combine the epithelium-crossing poly(zwitterion)-lipids and the unique ionizable lipids to develop an innovative mRNA delivery platform for oral, durable replacement therapy of bleeding disorders. In Aim 1, I will synthesize new poly(zwitterion)-lipids and ionizable lipids and generate a series of new mRNA lipid nanoparticles (LNPs), and systematically investigate their effects on oral transcytosis and the durability of protein expression in vitro and in vivo. In Aim 2, we will select the top-performing LNPs for oral delivery of FVIII mRNA and evaluate the FVIII mRNA nanotherapy in healthy and hemophilia mouse models. With the successful completion of this project, we expect that the oral durable mRNA delivery strategy will provide a more effective and robust therapy for hemophilia and other bleeding disorders.

NIH Research Projects · FY 2025 · 2023-09

Project Summary In lower-and-middle income countries (LMICs) like Thailand, family members providing care for adults with chronic disorders experience similar levels of mental health risks to that of their counterparts in higher income countries but have significantly more limited access to mental health care. Despite comparable rates of mental health illness, family caregivers in LMICs have significantly more limited access to mental health care—due to a number of factors, including the limited number of providers trained in mental health care delivery, as well as sociocultural factors, such as stigma and privacy concerns, that may prevent those in need from seeking care, and/or result in significant delays in obtaining diagnosis and treatment. Lack of treatment for caregiver mental health can lead to other adverse consequences for both caregivers and patients, including caregiver burnout, patient abuse, losing employment and elevated financial strain. Despite an accumulating body of evidence of the promise of mobile technology-based innovations for the treatment and prevention of mental health disorders in LMICs, as well the promise for improving mental health outcomes for family caregivers, there is limited research on the development, refinement, and implementation of evidence-based digital health technology into the routine care of family caregivers. The goal of the 5-year project is to develop and test a culturally informed Caregiver Mental Health Mobile Application (hereafter referred to as CAMMA) program that will deliver an evidence-based intervention to reduce mental health problems in family caregivers of adults with chronic disorders in Thailand. The R21 project will allow us to systematically adapt evidence-based self-care intervention contents developed originally in the Western world, and to make it compatible with the sociocultural and technological context in Thailand through qualitative and formative research. The R33 will allow us to test the usability and effectiveness of the mobile technology-enabled intervention in reducing negative mental health outcomes in Thai family caregivers. Specific Aims of the R21 include: 1) Conducting qualitative research on family caregivers of persons with chronic disorders, focusing on their experience with mental health problems, caregiving practice, coping mechanisms, and socio-technological barriers to technology acceptance and use; 2) Develop a mobile application for the delivery of the CAMMA intervention; 3) Conduct a formative evaluation of the CAMMA Intervention Components to ensure acceptability and usability of individual intervention components . Specific aims for the R33 include: 1) Assessing usability of the CAMMA intervention (including all intervention components); 2) Conducting a randomized clinical trial (RCT) to evaluate effectiveness of the CAMMA intervention in reducing psychological distress (depression, anxiety, psychological stress). We hypothesize that participants who receive the CAMMA intervention will show significantly higher reduction in depressive symptoms, anxiety, and psychological stress, compared to controls.

NIH Research Projects · FY 2025 · 2023-09

Project Summary Regular physical activity induces specific adaptive responses in various tissues, and ultimately improves health and reduces the risk for cardiovascular diseases and type 2 diabetes. To improve the efficacy of physical activity, many studies have examined the impacts of intensity, duration, frequency, and modality of physical activity on the adaptive responses. However, timing of physical activity has been rarely considered. It is well-established that the circadian timing system modulates cardiovascular function and metabolism, generating ~24-h rhythms in these physiological functions that are in synchrony with the day-night cycle. Thus, physical activity at different times of day may have different impacts on health due to the circadian control. Indeed, recent breakthrough animal studies have shown time-of-day dependent effects of exercise on transcripts and metabolites enriched in metabolic pathways that are related to exercise adaptations and, importantly, linked to the circadian system. We hypothesize that timing of physical activity modulates the adaptive responses, and can thus be used to enhance physical activity-induced health benefits. To address this hypothesis, during the K99 phase, I will first utilize the dataset from an NIH-funded randomized controlled trial with lifestyle intervention and 1-week accelerometer recording among approximately 2,200 patients with type 2 diabetes to examine 1) the association between timing of physical activity and markers of cardiometabolic health at baseline, and 2) the association between timing of physical activity and the improvements in markers of cardiometabolic health at 1-year follow-up. In the R00 phase, I will test the causal relationship by experimentally manipulating the timing of physical activity in individuals with prediabetes using a randomized, cross-over design with two highly controlled in-laboratory protocols. This innovative project will advance our knowledge in the interaction effects of the circadian timing system and physical activity and may help in designing evidence-based lifestyle interventions incorporating timing of physical activity. I am well suited to perform this research based on 1) my expertise in chronobiology, physiology, and human experimental research; 2) the exceptional multi-disciplinary mentoring team comprised of leaders in their respective fields; and 3) the unparalleled research environment to support my career development. Through this study, I will further my training in circadian physiology, as well as expand my expertise in exercise physiology, epidemiology, and clinical trial design. The proposed research and training will help achieve my long-term goal of launching an independent research program dedicated to understanding the interaction of the circadian system and physical activity from observational towards experimental study design. The findings of this research have the potential to frame the basic strategy and recommendation of chronobiology-based exercise interventions.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY/ABSTRACT Obstructive sleep apnea (OSA) is a highly prevalent disorder characterized by repetitive upper airway obstruction that has major deleterious effects on health. However, the leading treatment, continuous positive airway pressure (CPAP), is poorly tolerated by many individuals. Thus, new treatment strategies are needed. We recently discovered that neural ventilatory drive plays a far greater role in upper airway obstruction than previously appreciated: In a 60% majority of patients, ventilatory drive falls in tight synchrony with the loss of airflow during respiratory events; these patients—referred to as “drive-dependent”—no longer exhibit respiratory events when drive rises at some time during the night (<10% likelihood at 200% resting drive). By contrast, patients who exhibit “classic” events (reduced airflow despite rising drive), continue to exhibit events regardless of drive. The current proposal will address major clinically-relevant questions that emerged from this discovery: ● In Aim 1, we will show that the role for ventilatory drive in OSA is experimentally reversible (i.e. causal). Falling ventilatory drive during events will be mitigated with carefully-timed inspired carbon dioxide stimulation (2% for 3-4 breaths). We expect to show that events are prevented, and the characteristic loss of airflow and pharyngeal muscle activity (genioglossus EMG) is spared, when falling drive (per intraesophageal diaphragm EMG) is averted. Benefits are expected exclusively in patients with “drive-dependent OSA” (N=18) but not “classic OSA” (N=18). ● The discovery also has major implications for which patients may respond to the promising ventilatory drive stimulant acetazolamide. In Aim 2, in a randomized placebo-controlled mechanistic crossover trial, we will assess whether drive-dependent OSA (N=18) is more amenable to ventilatory drive stimulation with acetazolamide than classic OSA (N=18). Physiological measurements of ventilation and ventilatory drive will describe how acetazolamide, by mitigating dips in drive, raises airflow and prevents events exclusively in “drive- dependent OSA”. An open label extension will confirm subgroup differences over a longer period (4-weeks). ● In a translational aim (Aim 3), we will refine our clinically-applicable method to identify drive-dependent OSA from routine sleep studies, and thereby predict responders to acetazolamide therapy (precision medicine). Our preliminary model using five clinically-recognizable characteristics already demonstrates potential clinical utility. Further methods development will utilize non-invasive ventilatory drive surrogates to improve the preliminary model, which will be prospectively validated using the new physiological studies in Aims 1 and 2. Overall, our proposal will establish that mitigating ventilatory drive decline is a promising therapeutic strategy for a large recognizable subgroup of OSA patients. The work will also provide the necessary background knowledge for further trials of acetazolamide or other drive interventions in selected patients with drive-dependent OSA (precision medicine), and has great potential to improve OSA-related adverse health outcomes for those without a tolerable therapy for their untreated disorder.

NIH Research Projects · FY 2025 · 2023-09

Project abstract The widespread sequencing of healthy babies is imminent: at least a dozen research projects have recently launched, and several companies offer newborn genetic screening panels. A newborn’s genome can contain health information of relevance across their lifespan — as a baby, later in childhood, and in adulthood. This poses a timing issue: if babies are sequenced near birth, when should this information be revealed? A proposed vision for the future of genomic medicine is to reveal information as it becomes relevant, to the child’s parents and later, if desired, to the individual. This would necessitate the genome being kept “on file,” to be used as a resource over time. This strategy may promote the ethical rollout of lifelong genomic medicine by promoting the developing child’s autonomy and optimizing the balance of benefits to risks. However, the feasibility of this strategy, the details of its implementation, and its implications have yet to be explored in a rigorous and empirical manner. Perhaps other approaches are preferable. A second, simplified, strategy would reveal all childhood-relevant information at birth and then give the individual the option of receiving adult-onset information at age 18. A third strategy would reject using the genome as a resource over time, and just generate one report for a baby, potentially including adult-onset information. This strategy may be preferable because the use of the genome as a resource raises complex ethical, legal, and social implications (ELSI), including data control, privacy, consent, legal obligations, and decision making about when information becomes relevant. For these different strategies, this project will 1) Determine their feasibility, 2) Assess the ELSI, 3) Understand the preferences of parents, using two cohorts that have already consented to their healthy babies being sequenced, and 4) Develop consensus on the necessary and desirable features for a strategy to sequence babies near birth, possibly using the genome as a resource over time. The project will have impact by producing concrete, evidence-based and ethically-framed recommendations for implementers of newborn sequencing.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY The prevalence of diabetes has rapidly risen during the last decades at an alarming rate, and more than 54.9 million Americans (15.3% of the population) are predicted to suffer from diabetes by 2030. Diabetic patients are highly susceptible to bone infections (osteomyelitis) and have poor bone regeneration capacity, placing them at a risk of amputations that dramatically impacts the quality of life. Even though osteomyelitis is one of the oldest diseases in human history, the existing medical approach to treat infected bone still has serious limitations while encountering new challenges. The effectiveness of the current treatment approach of debridement of the bone followed by antibiotics application is critically limited by (a) the formation of strongly assembled bacteria (biofilm) that are difficult to remove, (b) evolution of bacterial resistance to existing antibiotics, and (3) non-degradability of polymethylmethacrylate (PMMA) bone cement, which is used to locally deliver antibiotics but requires additional surgery to remove it afterward and is bioinert with potential toxicity of unreacted monomers. Therefore, there is a significant unmet medical need for the development of a next-generation antibiotic and an advanced antibiotic delivering system that can effectively cure the infection and improve the recovery of bone tissue. To solve this important problem, in this project, we aim to develop an innovative drug-device combination based on a novel dual-targeting antibiotic that can effectively retard bacteria resistance and an advanced biodegradable nanostructured bone cement that can induce a sustained release of antibiotics and enhance bone regeneration. We propose (1) to use whitlockite (WH) nanoparticles to develop a next-generation biodegradable bone cement, leveraging the excellent bone regeneration capacity and biodegradability of WH nanoparticles. WH also has a highly functionalized surface and can form nanostructured cement that can provide a large binding site for antibiotics; (2) to rationally develop next-generation antibiotics to have enhanced bactericidal capacity and compatible with our new degradable bone cement via computer-aided design and multiple screening processes. This is a significant advance from currently used antibiotics, which were originally never developed for bone infection or delivery from bone cement. We have already demonstrated that our preliminary model of dual-action antibiotics can significantly retard the evolution of bacterial resistance and is effective against biofilms; and (3) to validate the therapeutic efficacy of our dual-targeting antibiotic-impregnated WH bone cement in a diabetic osteomyelitis model in vivo by evaluating bone regeneration rate and conducting a comprehensive toxicological test. We envisage that this project will generate the first rationally designed antibiotic-delivering biodegradable cement that can treat biofilms, overcome drug resistance and regenerate the bone, thereby addressing a major clinical need. This research will also be beneficial for inhibiting infections in general orthopedic surgeries and thus, can lead to a paradigm shift in the treatment of bone infection.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY/ABSTRACT Chronic obstructive pulmonary disease (COPD) is a progressive, debilitating disease in critical need of disease- modifying treatments. Emphysema, progressive lung destruction commonly encountered in subjects with COPD, portends a poor prognosis. This project will leverage two large well-phenotyped, NHLBI-funded studies (the COPDGene and Lung Tissue Research Consortium (LTRC)) and the team’s extensive expertise in modern imaging techniques, multi-omics data analysis, machine learning approaches, and in vitro functional validation. The overall objective of this application is to identify novel multi-omics biomarkers and molecular subtypes of centrilobular, panlobular, and paraseptal emphysema patterns utilizing a systems biology approach to understand relationships between the multiple omics data types. In Aim 1, we will apply the local histogram (LH) chest computed tomography (CT) quantification method to generate imaging phenotypes of centrilobular, panlobular, and paraseptal emphysema in each lung lobe. We will cluster these lobar LH data to identify distinct groups of subjects with similar LH patterns. We will then test for single-omics associations of the identified emphysema clusters with genetic variants, DNA methylation marks, telomere length, gene expression, and proteomics in peripheral blood and lung tissue samples. Aim 2 will develop and evaluate a lung-tissue informed, blood-based multi-omics machine learning model for reliable clinical prediction of emphysema patterns. Timely diagnosis calls for a blood-based predictive model as it may identify emphysema in subjects where CT scans are not clinically indicated. This would also overcome the issues of radiation exposure and false positive findings associated with CT scans. Aim 3 will discover molecularly-informed emphysema subtypes by applying an innovative, interpretable, machine learning algorithm that captures directional feature interactions and provides network representations of the molecular determinants of emphysema subtypes. We will then perform cluster analysis on the Bivariate Shapley network representations to identify distinct subgroups of subjects based on their graph similarity. To confirm the critical regulators of the identified pathways, we will conduct targeted gene silencing and overexpression investigations in airway epithelial cells and lung fibroblasts. Genes will be prioritized for functional validation utilizing existing biological knowledge and network analyses. Through a combination of innovative, cutting-edge data generation, analytic approaches, and functional validation, this project will make a significant contribution by enhancing emphysema phenotyping and multi-omics profiling for a more robust prediction and a better understanding of disease pathobiology. Such knowledge will pave the way for the development of much-needed novel and personalized therapeutic strategies.

NIH Research Projects · FY 2024 · 2023-09

PROJECT SUMMARY/ABSTRACT The widespread sequencing of healthy babies is imminent: at least a dozen research projects have recently launched, and several companies offer newborn genetic screening panels. A newborn’s genome can contain health information of relevance across their lifespan — as a baby, later in childhood, and in adulthood. This poses a timing issue: if babies are sequenced near birth, when should this information be revealed? A proposed vision for the future of genomic medicine is to reveal information as it becomes relevant, to the child’s parents and later, if desired, to the individual. This would necessitate the genome being kept “on file,” to be used as a resource over time. This strategy may promote the ethical rollout of lifelong genomic medicine by promoting the developing child’s autonomy and optimizing the balance of benefits to risks. However, the feasibility of this strategy, the details of its implementation, and its implications have yet to be explored in a rigorous and empirical manner. Perhaps other approaches are preferable. A second, simplified, strategy would reveal all childhood-relevant information at birth and then give the individual the option of receiving adult-onset information at age 18. A third strategy would reject using the genome as a resource over time, and just generate one report for a baby, potentially including adult-onset information. This strategy may be preferable because the use of the genome as a resource raises complex ethical, legal, and social implications (ELSI), including data control, privacy, consent, legal obligations, and decision making about when information becomes relevant. For these different strategies, this project will 1) Determine their feasibility, 2) Assess their ELSI, 3) Understand the preferences of parents from diverse backgrounds, and 4) Develop consensus on the necessary and desirable features for a strategy to sequence babies near birth, possibly using the genome as a resource over time. The project will have impact by producing concrete, evidence-based and ethically framed recommendations for implementers of newborn sequencing. The candidate was originally trained as a computational biologist, was formerly employed in the genomics industry, and is currently an ELSI scholar. Her goal is to become an independent investigator working in the context of clinical research informing the adoption of genomic medicine to identify, assess and address ELSI questions, ultimately to ensure that genomic medicine works to the benefit of all. To accomplish this goal, this proposal focuses her training efforts on a) developing skills in conducting surveys, b) developing expertise in Delphi methods, and c) refining skills in conceptual and normative analysis. The project will leverage the BabySeq cohorts, the world’s first empirical studies of comprehensive genomic sequencing in healthy newborns, directed by members of her mentorship team. The proposed training will make the candidate a well-rounded ELSI researcher able to deploy mixed methodologies while leveraging her technical background, preparing her to contribute to NHGRI’s goal of developing and assessing strategies for implementing the use of genomic information at the population level.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY: Type 2 diabetes (T2D) is caused both by genetic and environmental factors, such as diet, as well as the complex interactions between them. While diet is the cornerstone for T2D prevention, dietary interventions are often difficult to implement and monitor due to limitations in dietary assessment techniques and strategies to produce dietary changes. Despite efforts to reduce SSB consumption, SSBs remain the largest single source of added sugar in the US. SSB consumption has been linked to a higher risk of T2D and related risk factors, but the underlying biological mechanisms are not completely understood. Proteomic profiling and multi-omic integration allow for more detailed phenotyping that may provide a broader view of diet-associated metabolic changes and their functional interpretation. Examination of plasma proteomic and integrative omic profiles that reflect SSB intake and a common alternative beverage, artificially sweetened beverages (ASB), may enhance current dietary assessment methods and unveil novel biological pathways linking diet to T2D and related risk factors through identification of novel dietary biomarkers. Discovery of plasma proteomic and multi-omic profiles of SSB and ASB consumption has immense potential to provide an objective assessment of individual beverage intake and enable informed beverage choices, which is in line with the precision nutrition approaches emphasized in the National Institute of Health’s (NIH) 10-year strategic plan. This proposal cost-effectively leverages existing proteomics profiling among the Nurses’ Health Study II and Health Professionals Follow-up Study cohorts (n=648). It also examines repeated assays in the ongoing NIH-funded SUBstituting with Preferred OPtions trial, a randomized parallel-arm 6- month beverage trial testing the effects of substituting SSBs with ASB or water among daily SSB consumers. We will utilize proteomic and multi-omic network and machine learning analyses to identify discriminatory profiles between SSB and ASB consumption levels and evaluate the associations of these profiles with T2D risk factors. The central hypothesis is that distinct proteomic and omic profiles reflect habitual SSB or ASB intake and that changes in their omic biomarkers are associated with changes in T2D risk factors, revealing novel biomarkers of beverage consumption and biological pathways modified by beverage consumption. This K01 career development award expands on the applicant’s experience in nutritional epidemiology, omics, and biostatistics to gain proficiency in the design and management of intervention studies, implementation of cutting-edge multi-omic statistical analysis techniques, and scientific leadership for precision nutrition applications for T2D prevention. With mentorship from a renowned multidisciplinary research team, the applicant will gain the crucial skills necessary to advance T2D prevention and refine a framework for the utilization of innovative multi-omics techniques in complementary interventional and epidemiological study designs to inform precision nutrition initiatives and transition to an independent investigator.

NIH Research Projects · FY 2025 · 2023-09

Our microbiomes, or the collections of trillions of micro-organisms that live on and within us, are highly dynamic and have been implicated in a variety of human diseases. Sophisticated computational approaches are critical for analyzing increasing quantities and types of microbiome data, including time-series, assays for non-bacterial components of the microbiome, and multiple measurement modalities such as metabolite and gene expression levels. Another exciting recent trend in the field has been translational applications, particularly live bacterial therapies for treating human diseases. In parallel, the field of machine learning has been advancing with deep learning technologies that have dramatically improved applications such as speech and image recognition. My lab develops novel machine learning methods and experimental approaches for understanding the microbiome, with a particular focus on microbial dynamics and bacteriotherapies. In the past five years, we have developed new computational methods and released open-source software tools for assessing the consistency of changes in the microbiome induced by therapeutics, forecasting population dynamics of microbiomes, and predicting the status (e.g., presence of disease) of the human host from changes in the microbiome over time. I have also led experimental efforts to delineate the role of bacteriophages in microbiome dynamics and to develop gut metabolite-based biomarker assays to predict recurrence of C. difficile infection. Additionally, with collaborators, we have developed candidate bacteriotherapies for C. difficile infection and food allergies. My overall vision for my lab in the next five years is to leverage deep learning technologies to advance the microbiome field beyond finding associations in data, to accurately predicting the effects of perturbations on microbiota, elucidating mechanisms through which the microbiota affects the host, and improving bacteriotherapies to enable their success in the clinic. I plan to accomplish this by developing new deep learning models that address specific challenges for the microbiome, including noisy/small datasets, highly heterogenous human microbiomes, the need for direct interpretability of model outputs, complex multi-modal datasets, and constraints imposed by biological principles. My plan is to directly couple computational models and biological experiments through reinforcing cycles of predicting, testing predictions with new experiments, and improving models. Approaches I will pursue include incorporating into deep learning models probability, embeddings of microbes and other entities using rich information (such as gene content or chemical structure), decomposition of multi-modal data into interpretable and interacting groups, and automated design of new biological experiments in gnotobiotic mice that seek to maximize information for computational models and ultimately improve engraftment and efficacy of candidate bacteriotherapies. An important objective will also be to make computational tools that my lab develops widely available to the research community, through release of quality open-source software.

NIH Research Projects · FY 2025 · 2023-09

Project Summary About 40-45% of people living with Alzheimer’s disease and related dementias (ADRD) reside in a skilled nursing facility (SNF). Behavioral and psychological symptoms of dementia (BPSD) occur in ~80% of older adults with ADRD living in an SNF. Antipsychotic medications (APMs) are the most commonly used pharmacological treatment for BPSD. Because APMs are associated with numerous adverse events, clinical guidelines recommend that their use should be limited to managing acute episodes and discontinued as soon as possible. However, studies have shown that APMs are often used in individuals with ADRD for sustained periods (≥6 months). Small randomized controlled trials (RCTs) comparing withdrawing vs. continuing APMs used for BPSD have yielded conflicting and confusing results that suggested deprescribing APMs had little or no benefits for adverse events. These RCTs were clearly underpowered, and they severely underrepresented frail and complex older adults with ADRD in routine care. There was also a lack of non-randomized studies addressing this critical knowledge gap because deprescribing APMs for behavior disturbance is highly informed by symptom severity, and confounding by disease severity can be very difficult to control unless detailed clinical information is available for research. Our objective is to assess the health effects of different APM deprescribing strategies for managing BPSD in an SNF. To provide solid evidence guiding the deprescribing process, we will assess the effects of discontinuing APMs with vs. without gradual dose reduction and different rates of dose tapering. We will integrate Medicare claims data with electronic health records (EHR), Minimum Data Set (MDS), and Certification and Survey Provider Enhanced Reporting (CASPER), covering >370,000 older adults with ADRD living in an SNF from 2013 to 2026. We will employ the clone-censor-weight approach, high-dimensional machine-learning-aided proxy adjustment methods, external adjustment, and instrumental variable analysis to minimize measured and unmeasured confounding. We will address three specific aims: 1) To evaluate the prescribing and discontinuation patterns and determine the barriers to APMs deprescribing among older adults with BPSD in an SNF. 2) To determine comparative health outcomes of different discontinuation strategies vs. continuation of APMs used for BPSD in older adults who reside in an SNF. 3) To determine the treatment effect heterogeneity by key clinical phenotypes when comparing continuation vs. different discontinuation strategies of APMs used for BPSD in older adults who reside in an SNF so that such deprescribing decisions can be tailored according to patient characteristics. The impact of this proposal is high because it will generate direct evidence to inform optimal management of psychotropic medications in older adults with ADRD living in an SNF. It will also yield a scalable analytical framework specializing in comparative safety and effectiveness analyses of deprescribing psychotropic treatments for behavioral and psychological symptoms of dementia.

NIH Research Projects · FY 2024 · 2023-09

Project Summary In lower-and-middle income countries (LMICs) like Thailand, family members providing care for adults with chronic disorders experience similar levels of mental health risks to that of their counterparts in higher income countries but have significantly more limited access to mental health care. Despite comparable rates of mental health illness, family caregivers in LMICs have significantly more limited access to mental health care—due to a number of factors, including the limited number of providers trained in mental health care delivery, as well as sociocultural factors, such as stigma and privacy concerns, that may prevent those in need from seeking care, and/or result in significant delays in obtaining diagnosis and treatment. Lack of treatment for caregiver mental health can lead to other adverse consequences for both caregivers and patients, including caregiver burnout, patient abuse, losing employment and elevated financial strain. Despite an accumulating body of evidence of the promise of mobile technology-based innovations for the treatment and prevention of mental health disorders in LMICs, as well the promise for improving mental health outcomes for family caregivers, there is limited research on the development, refinement, and implementation of evidence-based digital health technology into the routine care of family caregivers. The goal of the 5-year project is to develop and test a culturally informed Caregiver Mental Health Mobile Application (hereafter referred to as CAMMA) program that will deliver an evidence-based intervention to reduce mental health problems in family caregivers of adults with chronic disorders in Thailand. The R21 project will allow us to systematically adapt evidence-based self-care intervention contents developed originally in the Western world, and to make it compatible with the sociocultural and technological context in Thailand through qualitative and formative research. The R33 will allow us to test the usability and effectiveness of the mobile technology-enabled intervention in reducing negative mental health outcomes in Thai family caregivers. Specific Aims of the R21 include: 1) Conducting qualitative research on family caregivers of persons with chronic disorders, focusing on their experience with mental health problems, caregiving practice, coping mechanisms, and socio-technological barriers to technology acceptance and use; 2) Develop a mobile application for the delivery of the CAMMA intervention; 3) Conduct a formative evaluation of the CAMMA Intervention Components to ensure acceptability and usability of individual intervention components . Specific aims for the R33 include: 1) Assessing usability of the CAMMA intervention (including all intervention components); 2) Conducting a randomized clinical trial (RCT) to evaluate effectiveness of the CAMMA intervention in reducing psychological distress (depression, anxiety, psychological stress). We hypothesize that participants who receive the CAMMA intervention will show significantly higher reduction in depressive symptoms, anxiety, and psychological stress, compared to controls.