Arizona State University-Tempe Campus

NIH Research Projects · FY 2025 · 2023-07

Project Summary/Abstract Lyme Disease (LD) is a tickborne illness with markedly increasing prevalence in the United States. While more than 80% of the LD cases can be effectively treated using established antibiotic treatments, 10-15% of patients develop post-treatment, long-term sequalae manifested as fatigue, cognitive impairment, joint pain, and other symptoms and termed Post Treatment Lyme Disease Syndrome (PTLDS) causing patients to experience substantial loss in quality of life and resulting in a marked financial burden on both patient and health care system. The absence of approved molecular diagnostic tests leads many physicians to dismiss the notion of PTLDS entirely, leaving patients with few or poorly defined treatment options. While currently no approved treatment for PTLDS exists, emerging evidence suggests substantially better clinical outcomes from early intervention with targeted therapies in a number of chronic and autoimmune disorders. Early risk assessment of developing PTLDS offers a window of opportunity by alerting both the patient and the physician to anticipate a long-term symptomatic result and adjust symptom-based treatment. The proposed study focuses on the urgent need to identify immunologic biomarkers for predicting the risk of a patient developing PTLDS early during the acute phase of the disease and has the potential to markedly improve clinical outcomes through early intervention. The proposed approach derives disease-specific antigen information from a comprehensive binding profile of the patient’s circulating antibody repertoire. The novelty of the approach is in representing an entire binding space of a donor’s circulating antibody repertoire, instead of simply focusing on a priori known antigenic targets. The approach relies on using machine learning models trained on the antibody binding profile to a diverse, random library of 126,050 unique peptides with an average length of 9-10 amino acids as a sparse representation of all possible combinatorial 9-mer sequences. Predictive models are then used to identify disease-associated pathogen epitopes with high predictive power that can be combined into a potential panel for PTLDS risk assessment. It is hypothesized that B. burg. antigens and/or self-antibodies from the human proteome are involved and that there is a set that can be used as biomarkers to predict progression to PTLDS early in the disease. The antibody response over time will be profiled in a set of longitudinally collected patient samples as they progress from confirmed acute LD, through treatment to disease outcome. This approach applied will enable the breadth of the antibody response, including a potential response/cross- reactivity to human proteins to be examined along with the heterogeneity of antibody responses across a cohort of patients. In silico predictions of protein antigenicity will be confirmed using solution-based immunoassays. The proposed work is expected to identify a set of B. burg. and potentially human autoimmune antigens that are associated with progression to PTLDS. Such knowledge is expected to serve as the basis for future diagnostics, therapeutics or in the generation of hypothesis that can be tested in disease models.

NIH Research Projects · FY 2025 · 2023-07

PROJECT SUMMARY Inflammation has been implicated in nearly every neuropsychiatric and degenerative disease, yet neuroimmune cells remain nearly intractable by every available therapeutic strategy. Small molecule drugs consistently fail clinical trials, as neuroimmune signaling pathways have essential pleiotropic functions in neighboring cell types. Meanwhile cell type selective therapies remain elusive, as microglia, and other neuroimmune cells, are intrinsically resistant to gene therapy vectors. Overcoming these unique challenges demands a new approach to medicine, drawing from an unlikely source of neuroimmune specific bioactivity. In nature, the Zika virus (ZKV) infects microglia, suppresses inflammation, and stimulates autophagy so expertly that almost half of infections go unnoticed. If this bioactivity could be safely refined, it would offer relief for neurodegenerative disorders from Parkinson’s to Alzheimer's disease. Parsing therapeutic from pathogenic mechanisms of the ZKV genome presents much greater complexity than ever previously addressed, but recent advances make it possible. Recombinant ZKV vectors, already in use, provide starting material for synthetic biology, while new viral assisted and continuous evolution methods allow bioengineering at scales capable of reshaping whole genomes. We can harness ZKV’s microglia specific immunosuppressive mechanisms into therapies with potential beyond any current technology. This proposal presents the first steps along the path towards an entirely new kind of therapy for neurodegenerative disorders.

NIH Research Projects · FY 2025 · 2023-07

Project Summary Protein-protein interactions (PPIs) drive countless processes in biology. The ability to block these interactions with high specificity is crucial for probing the basic science of these processes, as well as for developing imaging agents or novel therapeutics. However, most traditional molecules for blocking protein-protein interactions—like small molecules, peptides, or antibodies—rely on the precise targeting of the crucial interface or binding pocket, which can be difficult for some targets. Furthermore, none of these approaches can be easily tuned to match the valency or size of the target, and binding to patches on the protein not directly involved in PPIs can fail to block activity. Here, we propose to develop a nanoscale synthetic antibody (“nano-synbody”) consisting of a tunable DNA nanostructure bearing 2-3 peptide/protein ligands that can bind to distinct surfaces of a target protein and block its association with its partner through the steric bulk of the DNA structure. The individual peptide/protein binding agents will be derived from either known molecules, or found independently through methods like phage display. Critically, our method merges computational simulation—and in silico “evolution”—of these hybrid protein-DNA nano-synbodies, creating a library of structures and probing their association with the target. We aim to create a feedback loop, whereby the computational simulations yield candidate nano-synbodies that can be experimentally tested, further informing the next round of simulations. We will first apply this pipeline to a homo-trimeric nano-synbody against the SARS-CoV-2 spike protein trimer (Aim 1). This test bed will allow us to optimize the process and find a high-affinity blocking structure. Then, we will apply our method to nano-synbodies for blocking the assembly of fibrinogen into fibrin clots (Aim 2). The second Aim will involve phage display against fibrin to find novel binding agents, and thereby convert them into high-affinity hetero-trivalent structures. In both Aims, we will demonstrate the advantage of nano-structuring ligand presentation over simple oligomerization with flexible linkers. Taken together, our work will generate a new computational-experimental paradigm for the design of tunable, user-defined nanostructures that can present three or more peptides/proteins for binding to any protein, and blocking its association with its target. Crucially, our approach does not require binding directly to the interface, which should enable it to target a much larger range of proteins that may not be amenable to traditional approaches, large protein complexes, or mutants/variants of the targets that might escape single binding agents.

NIH Research Projects · FY 2025 · 2023-07

Project Summary Approximately 12 million people in the United States have been diagnosed with a visual impairment. These individuals face unique challenges in our modern environment, where much critical information related to education, employment, entertainment, and community is presented in the form of digital videos. Inaccessible information can result in social exclusion or become life threatening if individuals require access to it in order to make decisions related to their health and safety. For example, in a personal or global health crisis, individuals may need to access the mass amounts of information conveyed via videos or dynamic infographics in order to make informed decisions. To address this need, the online platform YouDescribe allows blind and low vision (BLV) users to request amateur volunteers to create video descriptions, also referred to as audio descriptions (AD), of YouTube videos. However, the platform has been unable to keep up with the overwhelming demand, and 92.5% of videos on the YouDescribe user wish list remain undescribed. The overall objective of this proposal is to build an AI-driven system, suitable for use on a wide-scale, to automatically generate descriptions of online videos, as well as answer questions asked by BLV users about the content of videos. The rationale for this project is that AI-based tools are necessary to facilitate timely access to the deluge of new videos appearing on the Internet every day. The proposed work encompasses three specific aims: 1) develop an AI-based tool in collaboration with sighted describers that more efficiently produces video descriptions and increases the availability of accessible videos. The goal is to create an AI-driven NarrationBot that will decrease the time required for novice volunteers to produce video descriptions by 80%; 2) develop an AI-based tool in collaboration with BLV individuals that offers user-driven access to visual information in online videos. The goal is to develop an AI-driven QABot that allows users to pause a video, ask questions about content, and receive immediate answers (e.g., “What breed is the dog?”, “German shepherd”) that are accurate 80% of the time; and 3) develop and publicly release large-scale datasets to improve machine learning for video accessibility. These novel datasets will be used to increase the quality and accuracy of NarrationBot and QABot until AI-generated descriptions and answers need minimal intervention from human volunteers and can serve BLV users directly. The proposed research is innovative because it focuses on videos, whereas existing AI-driven efforts to address this problem have focused primarily on static photos or images. It is also one of only a few efforts to directly partner with BLV individuals to develop AI-driven systems that produce visual descriptions or answer visual questions. The proposed research is significant because it will result in open-source, AI-driven tools that will give BLV individuals unprecedented control over their ability to independently navigate the information-rich world of online videos, thus improving their health and wellbeing.

NIH Research Projects · FY 2026 · 2023-06

PROJECT SUMMARY Chronic pain affects more than one third of the U.S. population, incurring an annual health care cost of $1 trillion. Some of the most prevalent “idiopathic” pain conditions that frequently co-occur, such as chronic low back pain (cLBP), temporomandibular disorders, and fibromyalgia, are referred to as Chronic Overlapping Pain Conditions (COPCs). Among various COPCs, cLBP has the highest prevalence and is one of the top 10 most disabling conditions worldwide. Unfortunately, having multiple COPCs can further interfere with individuals' adaptive pain coping and exacerbate pain-related disability. However, much focus to date has been placed on individual anatomically-based chronic pain conditions and/or one COPC at a time, while little is known about the mechanisms underlying progression to multiple COPCs. A paradigm shift from anatomy-based approaches to mechanism-based approaches recognizing common and modifiable risk factors of multiple COPCs is crucial in developing effective interventions to treat and prevent multiple COPCs. Emerging evidence suggests that sleep and circadian rhythm disturbances—common clinical features across COPCs—that are modifiable, may serve an important role in progression to multiple COPCs via enhancement of pain amplification and psychological distress, which are two critical proximal determinants of multiple COPCs. For the present study, we propose to recruit a total of 300 participants with cLBP (i.e., 200 with cLBP only vs. 100 with cLBP and other COPCs) at baseline. Among these, 200 participants with cLBP only will be followed for 12 months. Sleep and circadian rhythms will be assessed longitudinally (i.e., baseline and 6-month follow-up) using a cutting- edge wireless EEG sleep monitoring device, 24-hour evaluation of the rhythm of urinary 6-sulfatoxymelatonin (aMT6s), wrist worn actigraphy, and daily sleep diaries. Pain amplification (measured by quantitative sensory testing), psychological distress (measured by well-validated self-report measures), and the number of pain sites (measured by a well-validated pain body map) will be assessed at baseline, and 6- and 12-month follow- up visits. The proposed study will (1) comprehensively characterize the severity of sleep and circadian disturbances using ecologically valid multimodal ambulatory assessments among individuals with single and multiple COPCs; (2) prospectively examine whether baseline and changes (0 to 6 months) in sleep and circadian disturbances are associated with changes (0 to 6 months) in pain amplification and psychological distress; and (3) whether pain amplification and psychological distress at 6 months and their changes (6 to 12 months) are related to progression (6 to 12 months) in the number of pain sites (i.e., a valid proxy measure of multiple COPCs), while controlling for the effects of baseline sleep and circadian disturbances. The findings from this proposed work will provide novel insights into potential mechanistic pathways underlying progression to multiple COPCs, which may inform treatment and prevention strategies for these challenging conditions.

NIH Research Projects · FY 2025 · 2023-06

Project Summary The long-term goal of the proposed Team-Based Design and Clinical Immersion Enhancements to the ASU Biomedical Engineering Design Education Program is to prepare innovative and entrepreneurial biomedical engineering professionals who possess state-of-the-art product development skill sets and best practices relied upon by the MedTech Industry to develop and commercialize innovative medical device technologies to meet the global health care needs of the 21st Century. In order to achieve this objective, two specific aims are proposed. Specific Aim 1 intends to strategically tune selected biomedical engineering design courses and program curricula to produce highly competent and high performing, innovative and entrepreneurial MedTech product design and development team members and teams that meet 21st Century Workforce Needs. Proposed programmatic enhancements include (a) the expansion of coverage of an existing BME 214 FDA regulatory course to include coverage of emerging medical device technologies and applications, (b) the piloting of a junior level BME 300 design course that has been restructured to serve as a pre-capstone feeder to the yearlong senior BME capstone design experience, (c) the assessment of team formation and team performance in pursuit of high performance ‘Dream Teams’ (d) expansion of BME senior capstone design to include a MedTech manufacturing foundation for all graduates of the BME program, (e) supplemental support of capstone design projects having promising clinical impact and commercialization potential of either Biomedical Devices and Biological Devices nature (e) support to further develop one or two promising capstone projects having potential to have significant clinical impact and commercialization potential. Specific Aim 2 entail (f) the addition of a summer, contextual inquiry mini-workshop is planned to help onboard BME students selected for a clinical immersion, as well as, (g) the creation of a clinical immersion component to our BME design program for selected upper division BME design students with partnering medical institutions that include the Mayo Clinic Arizona, Phoenix Children’s, Barrow Neurological Institute and the Creighton University Medical School at the Health Science Campus-Phoenix.

NIH Research Projects · FY 2026 · 2023-05

Project Summary Most RNA molecules transcribed in mammalian cells do not encode for protein sequences. Among these noncoding RNAs (ncRNA) is a vast family of long noncoding RNAs (lncRNAs) that are larger than 200 nt. LncRNAs can modulate cellular protein expression patterns by influencing the transcription of many genes, the post-transcriptional fate of mRNAs and ncRNAs, and the turnover and localization of proteins. Telomerase RNA is a unique class of lncRNA that functions as an integral component of the telomerase ribonucleoprotein complex which maintains genomic stability and cellular immortality in cancer and stem cells. The overarching goal of this project is to understand the mechanism and regulation of a novel mRNA-derived biogenesis of telomerase RNA (lncRNA) in Ustilago maydis, a basidiomycete fungus. Telomerase RNA in Ascomycete yeasts and animals, telomerase RNAs are transcribed by RNA polymerase II and share biogenesis pathways with small nuclear RNA (snRNA) and box H/ACA small nucleolar RNA (snoRNA), respectively. In contrast, telomerase RNAs in ciliates and plants are transcribed by RNA polymerase III. These distinct biogenesis mechanisms employed by these homologous telomerase RNA molecules from different eukaryotic kingdoms or groups provide unparalleled opportunities for understanding the fundamental principles underlying the biogenesis and evolution of the vast varieties of noncoding RNA species in biology. We have recently identified the first Basidiomycete telomerase RNA from U. maydis, a fungal model organism. Animal and yeast TRs are transcribed by RNA polymerase II with a protective cap at the 5’-end. intriguingly, the U. maydis TR lacks a protective 5’ cap and is processed from the 3’-untranslated region of a protein-coding mRNA precursor. In this research program, we will identify determinants in the TR precursor that regulate the biogenesis of the mature U. maydis TR from the protein-coding mRNA. We will also study the function and expression of the protein encoded in the mRNA precursor. Lastly, we will identify telomerase accessory proteins in U. maydis and determine their roles in regulating the U. maydis TR biogenesis. Successful outcomes of these specific aims will provide comprehensive and exciting details needed for understanding the unprecedented mRNA-derived biogenesis mechanism of telomerase lncRNA.

NIH Research Projects · FY 2025 · 2023-05

Project Summary/Abstract Rapid and specific histopathologic diagnoses are critical for cancer treatment. Tumor tissue biopsy is routinely performed to detect and monitor cancer progression. Current test biopsies require surgically-collected tissue samples from detectable primary or metastatic tumors. Several difficulties, such as patient inconvenience, multistep complicated procedure, partial samplings, and non-specific findings, make this process slow, invasive, expensive, unfit for screening large sample sizes, and error-prone. Non-invasive selections of biomarkers in body fluids, known as liquid biopsy, offer great promise in complementing or even substituting surgical tissue biopsy in the diagnosis and prognosis of cancer patients. Recent studies have indicated exosomal microRNAs (exmiRs) as promising liquid biopsy biomarkers in detecting cancer progression and efficacy of therapy with high sensitivity and specificity. However, current technologies for ex-miR detection, such as qRT-PCR, and microarray screening tests, require high sample volume, are expensive, slow, tedious, requiring highly specialized skills and resources such as ultracentrifuge, expensive RNA extraction kits, etc. Single-exosome level studies can significantly circumvent these problems. However, the few single-molecule ex-miR quantification attempts lack amplification strategy, thus limiting their applications to resource-heavy research settings. To address these problems, we have developed a molecular beacon-based Transmembrane Nano-Sensor (TraNS) that inserts itself into the membrane of lipid vesicles and signals the presence of a DNA target by an increase in fluorescence. We have successfully demonstrated the ability of the TraNS device to spontaneously insert into the lipid membrane and sense membrane-enclosed nucleic acid biomarkers with high specificity. In this study, we propose to (1) optimize the TraNS device to sense cancer- specific ex-miRs from biofluids, (2) harness the transmembrane structural reconfiguration of TraNS to develop an isothermal signal amplification method to improve the sensitivity of detection significantly, and (3) integrate the TraNS device with our patented DNA origami-based biomarker detection array to improve the throughput, specificity, and sensitivity of digital quantification of ex-miR stoichiometry with low sample volume. We will use the platform’s sensitivity, specificity, and throughput on clinical samples from pancreatic cancer patients against their healthy controls. This effort’s potential impact can help physicians and clinicians with rapid, ultrasensitive, precise, and cost-effective cancer diagnostics without a surgical tissue biopsy.

NIH Research Projects · FY 2026 · 2023-05

ABSTRACT The endogenous opioid system is strongly implicated in the rewarding, reinforcing, and motivational effects of ethanol, as evidenced by the established clinical efficacy of the opioid receptor antagonists naltrexone and nalmefene in reducing ethanol intake, relapse propensity, and craving. Animal studies have demonstrated that ethanol activates various opioid peptide-containing circuits within the brain, including regions of the mesocorticolimbic reward circuitry and amygdala. Recently we have generated multiple lines of evidence indicating that ethanol activates a subset of neurons within the arcuate nucleus (ArcN) of the hypothalamus expressing pro-opiomelanocortin (POMC), which gives rise to numerous bioactive neuropeptides including β-endorphin. Using patch clamp electrophysiology, we observed that bath application of ethanol (5-40 mM) increases the firing frequency of ~35% of recorded ArcN POMC neurons. Similarly, using FosB immunohistochemistry, we demonstrated that binge-like ethanol intake activates approximately a subset of ArcN POMC neurons, the majority of which synthesize β-endorphin vs. α-MSH. Retrograde tracing revealed binge- like ethanol intake primarily activates ArcN POMC neurons projecting to the amygdala, with fewer activated neurons projecting to the ventral tegmental area (VTA) or nucleus accumbens (NAc). Surprisingly, chemogenetic modulation of ArcN POMC neurons without subpopulation delineation had no effect on ethanol intake. However, we speculate that these lack of effects were due to the non-specific nature of activation of a number of ArcN POMC neuron containing subcircuits. Baseline sex differences in binge-like ethanol intake were observed, where female mice consumed significantly more ethanol than their male counterparts, and we observed that ERα is the primary female sex hormone receptor located on ArcN POMC neurons as compared to ERα or progesterone receptors. Together, these observations have led to our overarching hypothesis that ArcN POMC neuron projections to regions of the mesolimbic reward system regulate binge-like ethanol intake in a sex-dependent manner primarily via ERα dependent mechanisms. To test this hypothesis, we have formulated the following inter-related yet independent Specific Aims. In Aim 1, we will determine the effects of ethanol on the neurophysiological properties of ArcN POMC projection neurons. In Aim 2, we will determine the effects of chemogenetic modulation of specific ArcN POMC efferent projection neurons on binge-like ethanol intake. Finally, in Aim 3, we will determine the role of ERα on POMC neurons in the regulation of binge-like ethanol intake and potential interactions with midbrain dopamine neurons. Together, these studies will elucidate specific opioid circuits and mechanisms regulating binge-like ethanol intake, which will guide the improvement of neuromodulatory and/or pharmacological approaches for the treatment of alcohol use disorders.

NIH Research Projects · FY 2025 · 2023-05

Project Summary/Abstract Cardiometabolic (CM) diseases including cardiovascular (CV) and metabolic diseases are the leading cause of preventable death in the United States and Worldwide. CM diseases are interconnected and positively associated with multi-domain Cardiometabolic Risk Factors (CMRFs) such as metabolic dysregulation, obesity, physical inactivity, poor nutrition, and other emerging factors (including and especially sleep disorders). CMRFs are highly and increasingly prevalent in adolescents and young adults, which foreshadows a future epidemic of incident CM diseases as they age. However, existing studies have primarily focused on the adult and senior population with little to no knowledge on the young population. CM data hold great promise to facilitate CM subgroup discovery for early risk stratification and precise prognosis. However, significant gaps exist in fully leveraging CM data. Gap I: Lack of inclusion of multi- domain CMRFs (especially sleep health). Gap II: Lack of “outcome-predictive” CM subgroups in early risk stratification. Gap III: Lack of “subgroup-specific” precise prognosis of “multi-dimensional” CM outcomes. Gap IV: Under-utilization of the rich but “incomplete” multi-domain CM data in NHANES and NSRR. We propose a multi-study multi-domain secondary analysis for CM subgroup discovery and risk prediction in U.S. adolescents (11-18) and young adults (19-39). The objective is to create 2 combined NHANES and NSRR datasets and examine multi-domain CMRFs including metabolic dysregulation, physical inactivity, poor nutrition, and multi- dimensional sleep measures for CM subgroup discovery and risk prediction in the large and diverse U.S. adolescent and young adult population of Hispanics/Latinos, African Americans, Caucasians, and Asian Americans. Aim 1. CM risk subgroup discovery at baseline for U.S. adolescents & young adults. (1.1) Develop a novel sparse Incomplete Multi-domain Mixed-typed Factor Mixture Model (IM2-FMM) for subgroup discovery from incomplete multi-domain mixed-typed CMRFs. (1.2) Apply IM2-FMM to identify, characterize, and evaluate CM subgroups in adolescents and young adults from incomplete multi-domain mixed-typed CMRFs at baseline including: (a) self-reported sleep measures in NHANES; (b) self-reported and objective sleep measures in NSRR. Aim 2. Subgroup-specific prediction of multi-dimensional longitudinal CM outcomes for young adults. (2.1) Develop a novel sparse Transfer Learning-based Generalized Multi-level Model (TL-GMM) to predict multi- dimensional longitudinal CM outcomes from clustered CMRFs at baseline. (2.2) Apply TL-GMM to young adults in NSRR to: (1) examine fixed effects and random effects of baseline CMRFs on CM outcomes; (2) provide subgroup-specific multi-dimensional prognosis of CM health from clustered CMRFs at baseline. Impact: Our study will generate novel insights into CM subgroup discovery to facilitate early and targeted interventions and help establish health promoting behaviors in adolescents and young adults, eventually improving CM health care in their transition to adulthood and reducing CM health disparities and costs as the young population ages.

NIH Research Projects · FY 2026 · 2023-04

ABSTRACT Aerobic exercise is a promising treatment for Alzheimer’s disease (AD) and AD-related dementia (ADRD), but exercise trials have shown mixed effects on cognition, physical function, behavioral and psychological symptoms of dementia (BPSD), quality of life (QoL), and caregiver burden. These findings are likely due to Individual differences in aerobic-fitness responses, long established in adults using peak oxygen consumption (VO2peak) and first reported in older adults with ADRD by our team. AD/ADRD exercise trials report large variance in VO2peak changes from moderate-intensity continuous training (MICT). Mechanistically, animal studies support aerobic exercise modifying AD’s AT(N) biomarkers (Amyloid-beta [Aβ], Tau, and Neurodegeneration), but similar human studies are rare. Hence, precision exercise is critical to identify MICT non-response early to initiate alternative interventions (High Intensity Interval Training [HIIT] or Combined Aerobic and Resistance Exercise [CARE]). Because VO2peak can improve and peak from 3-month MICT, it is logical to use VO2peak at 3 months to identify non-response and initiate HIIT or CARE. This Phase II, pilot trial will be a Sequential, Multiple Assignment, Randomized Trial. Its purpose is to test the effects of 6-month aerobic exercise on aerobic fitness and its mechanisms of action in community-dwelling older adults with mild AD dementia. Our central hypothesis is that MICT augmented with HIIT or CARE will improve aerobic fitness, white matter hyperintensity (WMH), and plasma biomarkers, which underlie exercise’s cognitive effects. This trial builds on our previous work showing: successful recruitment, retention, adherence, and safety; 6-month MICT maintained memory and reduced WMH; individual differences in VO2peak and cognitive responses to MICT; MICT improved physical function, QoL, and caregiver distress; plasma neurofilament light chain (NfL) predicted cognition; and MICT affected plasma p-tau181. It will randomize 108 participants 2:1 to 3-month MICT or 6-month stretching control after baseline. VO2peak will be assessed after 3-month MICT to identify non- responders (<5% increase) and re-randomize them 1:1 to HIIT or CARE for 3 months. Responders will continue MICT for 3 months. Participants will be followed for another 6 months. Primary outcomes are aerobic fitness measured at 0, 3, 6, 9, and 12 months and WMH volume at 0, 6, and 12 months. Secondary outcomes (memory, physical function, BPSD, QoL, caregiver burden) and plasma Aβ42/40, p-tau181, t-tau, and NfL will be assessed at 0, 3, 6, 9, and 12 months. This trial has 80% power for all primary hypotheses, assuming 18% and 25% attrition at 6 and 12 months, respectively. The specific aims are to: I) test the effects of aerobic exercise on aerobic fitness, WMH volume, and patient-centered outcomes in older adults with mild AD dementia; II) the best exercise to improve aerobic fitness and reduce non-responses over 6 months in older adults with mild AD dementia; and III) examine the mechanisms of aerobic exercise’s action on memory in mild AD dementia. This trial is the first precision-exercise trial in AD, and will utilize MRI/blood biomarkers, which are scalable.

NIH Research Projects · FY 2026 · 2023-04

Abstract/Project Summary African American women experience a high burden of cardiometabolic disease conditions. Fifty-seven percent are obese, 57% have cardiovascular disease, and 12% have diagnosed diabetes. Engaging in regular aerobic physical activity is an established mechanism to prevent and manage these cardiometabolic diseases. The purpose of this study is test the efficacy of a culturally tailored, theory-based smartphone-delivered physical activity intervention to increase physical activity, promote adherence to national aerobic physical activity guidelines, and improve cardiometabolic disease risk factors among African American women. In a 12-month, two arm randomized trial, 240 sedentary (i.e., < 60 minutes/week of moderate-to-vigorous intensity physical activity) African American women with obesity (i.e., BMI >30 kg/m2) will be assigned to receive either Smart Walk, a culturally tailored, Social Cognitive Theory-based physical activity promotion intervention (n=120), or a Fitbit-only comparison arm (n=120). The Smart Walk intervention group will receive a culturally tailored physical activity intervention delivered via the Smart Walk smartphone app, virtual physical activity coaching, and text messages. Features available on the Smart Walk app include: 1) personal profile pages, 2) culturally relevant multi-media (i.e., text and video) physical activity promotion modules, 3) message/discussion boards, and 4) physical activity self-monitoring/tracking feature that integrates with Fitbit activity monitors for participants to track their daily, weekly, and monthly activity. Virtual physical activity coaches will actively engage and facilitate group-based dialogue among participants on the discussion boards and provide individualized, one-on-one physical activity coaching via telephone or commercially available app-based video teleconferencing software, based on participant preference. Smart Walk participants also receive three physical activity promotion text messages each week for the duration of the active 4-month intervention. The Fitbit-only arm comparison arm will receive a Fitbit activity monitor and be encouraged to use the commercially available device to increase physical activity. We hypothesize participants in the Smart Walk intervention group will demonstrate significantly greater improvements in physical activity and cardiometabolic risk factors when compared to the Fitbit-only comparison group. Primary outcomes include self-reported and accelerometer- measured changes in physical activity. Secondary outcomes include traditional risk factors for cardiometabolic disease (i.e., BMI, blood pressure, serum lipid profiles, glucose intolerance, insulin resistance) and more novel and prognostic risk factors, including cardiorespiratory fitness, aortic pulse wave velocity, pro-inflammatory biomarkers of TNF-α, IL-6, and anti-inflammatory biomarkers of IL-10 and IL-15. We will also explore mediators and moderators of intervention effectiveness and determine the total societal cost per participant of delivering the Smart Walk intervention and the cost-effectiveness of the two study groups to increase minutes/week of moderate-to-vigorous physical activity.

NIH Research Projects · FY 2025 · 2023-03

Youth-onset type 2 diabetes (YO-T2D) is increasingly prevalent in parallel with the obesity epidemic, yet effective treatment and prevention strategies are limited. The physiologic increase in insulin resistance occurring during puberty, in combination with obesity-related insulin resistance, enhances the risk of T2D. Yet, it remains unclear why some youth progress through puberty with intact β-cell function, while others do not, despite similar phenotypic and metabolic characteristics. More information is needed regarding the unique events during puberty to better understand 1) the basic pathophysiology of glucose control, insulin sensitivity, β-cell function, and T2D risk in youth, 2) differences among girls and boys, populations at highest risk, and urban and rural geographies, and 3) the potential contribution of other risk factors including psychological, behavioral, and social and external contexts. Importantly, this research needs to address the timeline of pathophysiology and progression from normoglycemia or prediabetes to YO-T2D. The DISCOVERY of Risk Factors for Type 2 Diabetes in Youth (DISCOVERY) study provides a unique opportunity to characterize the risk progression profile and mechanisms underlying the development of YO-T2D, and evaluate the effects of modifiable and non-modifiable risk factors. Ultimately, the results of this study will establish a basic pathophysiology to inform future studies aimed at achieving target glycemia, improving insulin sensitivity, preserving β-cell function, and/or preventing YO-T2D. To address this goal, DISCOVERY will recruit, enroll, and follow a nationally-representative cohort of 3,600 at-risk obese youth in early puberty; extensively phenotype them as they transition through puberty; and characterize the course of decline and dysfunction in pathophysiological indicators that lead to YO-T2D. The expected duration of the DISCOVERY is 5 years, including planning, recruitment, follow-up, analysis, and reporting. In addition, DISCOVERY will store longitudinal biospecimens and genetic material with the intention of acquiring additional ancillary funding to pursue analysis of emerging indicators. The Phoenix site which, includes Arizona State University and Phoenix Children’s has experience in multicenter and diabetes-related investigations and will contribute to DISCOVERY through the recruitment of approximately 240 at-risk youth, implementation of the IRB-approved consensus protocol, participation on DISCOVERY committees, and collaboration on the analyses and dissemination of the findings from DISCOVERY.

NIH Research Projects · FY 2026 · 2023-03

Project Summary Background and Knowledge Gap: Unraveling life's intracellular processes at single molecule (SM) spatiotem- poral scales is critical toward monitoring therapeutic agents and developing disease diagnostics. Yet drawing insight on biomolecular events at such scales presents profound challenges to existing fluorescence imaging. Fundamentally, this arises due to the model selection problem: unavoidable (quantum, thermal, detector) noise at the SM scale means that the data cannot easily be used to resolve “models" such as the number of molecules located within a small region of space. An experimental solution toward resolving this problem earned the 2014 Chemistry Nobel prize though such solutions necessarily come at a cost. Either spatial or temporal resolution is compromised while samples are often irradiated over extended durations inducing sample photodamage. Recent Progress: Thanks to having reached the funding midpoint of both our NIGMS R01s, we developed mathematical tools allowing us to mitigate, sometimes dramatically, spatial (R01GM130745) and temporal (R01 GM134426) compromises of existing experimental solutions to model selection. Our work has resulted in 10 publications, 15 collaborations, and 18 ongoing projects. Here are just 3 projects: 1) in recent publications, we derived SM properties using 2-3 orders of magnitude fewer photons than would normally be used to obtain bulk properties from fluorescence correlation spectroscopy (FCS); 2) in accepted work, we provide a means to determine protein cluster stoichiometry (up to hundreds of subunits) eliminating the requirement to control fluorescent label properties; 3) in work about to be submitted, we track with equal accuracy and precision about an order of magnitude more labeled molecules as winners of the Nature Methods tracking competition. Overview of Future Work: We've organized our future work as extensions of both R01's, projects merging both R01's and directions beyond both. Briefly, to extend existing R01's, we will: 1) provide the first direct single- photon analysis of single molecule fluorescence resonant energy transfer (smFRET) data that simultaneously learns the number of states of biomolecules even lifting the assumption of discrete states. We will apply this, for example, to the unresolved rotational and translational dynamics of a transcription factor to DNA; 2) seek computational solutions to aberration and illumination artifacts that can dramatically deteriorate our ability to reliably track molecules intracellularly. In doing so, we will provide a computational alternative to adaptive optics and apply our tools to the trafficking and silencing activity of microRNAs often located deep within the cellular nucleus. As we merge both R01's: we hope to track reaction-diffusion events of many molecules, resolved at the SM level, and apply them toward understanding heterogeneous interactions of intrinsically disordered proteins. Beyond both R01s: we will borrow Mathematics from SM to resolve the dynamics of a bacterial predator, a candidate living antibiotic, as it hunts for its prey (E. coli) within the gut of c. elegans. Finally, we propose to generalize refractive index (RI) mapping and structured illumination analyses currently limited to slow dynamics.

NIH Research Projects · FY 2026 · 2023-03

The selectivity of orthosteric drugs is often limited by the structural similarity of their binding sites in homologous proteins, while allosteric binding sites are far less conserved. This allows allosteric drugs to bind a target protein with higher selectivity, which reduces the potential for side-effects and lowers drug toxicity. However, allosteric drug discoveries have been limited to serendipitous observations because rational allosteric drug design strategies face several inherent challenges. These challenges are directly related to current limits in the predictability of protein conformational fluctuations and collective dynamics that are central to the mechanisms of allosteric drugs. The objective of this project is the development of computational methods to facilitate the rational design of allosteric drugs via predictions of protein conformational fluctuations and collective dynamics from all-atom simulations. In principle, all-atom molecular dynamics simulations can directly explore protein conformational dynamics but require sampling on timescales of milliseconds to seconds for systems of pharmacological interest. Even with state-of-the-art enhanced sampling techniques, the associated computational costs and hardware requirements (special purpose computers, national supercomputers, large distributed computing networks) limit such applications to a small number of systems. This project aims to make the computational discovery of allosteric mechanisms in proteins more efficient and achievable with computer hardware available in most research laboratories and universities. To this end, the methods developed for this project maximize the information on inherent protein dynamics that can be extracted from molecular dynamics simulations. These methods will initially be applied to identify allosteric mechanisms in matrix metallo-proteinases (MMPs), which represent a family of structurally homologous proteins involved in the degradation of the extracellular matrix. Several members of the MMP family have been identified as drug targets in the context of chronic inflammatory disease and cancer metastasis. MMPs feature a highly conserved catalytic center, which results in a low selectivity of orthosteric small molecule inhibitors for individual MMPs and limits their therapeutic use. This project aims to identify allosteric mechanisms in MMPs that enable the targeted development of highly selective allosteric MMP inhibitors as potential drug candidates. The methods developed for this project are general and not specific to MMPs. They will thus be applicable for the identification of allosteric mechanisms in other drug target proteins and will be made available as open- source software.

NIH Research Projects · FY 2026 · 2023-02

Patients with Barrett's Esophagus are at increased risk of developing esophageal adenocarcinoma, but there is currently no reliable way to determine who is at high versus low risk. We propose to quantify the evolutionary dynamics in Barrett's and determine if those measures predict cancer risk. Neoplastic progression is fundamentally a stochastic evolutionary process at the cell level. However, there are often many different genetic and epigenetic alterations that can contribute to the development of a cancer, and the ones that evolve in any given cancer are random. This makes it difficult to assess the risk that a pre-cancerous tissue will evolve into cancer based simply on what mutations have occurred previously. We propose a different approach: Measure the dynamics of cell level evolution rather than the products of that evolution. We hypothesize that the rate of cellular evolution in a tissue should predict how likely and how quickly that tissue will evolve into a cancer. We will test this hypothesis in Barrett’s esophagus. We propose to fit both population genetic and phylogenetic models to data from Barrett’s esophagus biopsies spread over space and time in order to estimate parameters of the rate of cellular evolution and test if they are predictive of progression to esophageal adenocarcinoma. Five parameters determine the rate of evolution: 1) stem cell population size, 2) stem cell generation time, 3) mutation rate, 4) the strength of selection, and 5) heritability of the alterations. We have recently shown that we can measure all those parameters in clinical biopsies. We will measure all five parameters in Barrett’s esophagus based on CpG methylation and copy number alterations using methylation arrays, as well as single nucleotide variants identified in whole genome sequencing, in a prospective cohort of Barrett’s patients having either stable, benign disease or validated cancer outcomes. We will use those measures of the rate of evolution in Barrett’s esophagus to derive multivariable risk models of progression to esophageal adenocarcinoma as well as a classification system for the types of evolution present, that are clinically useful for the management of Barrett’s esophagus. If we are successful, we will reduce morbidity in patients likely to progress through earlier intervention, reduce suffering and stress in low-risk patients, and focus resources and interventions on those high-risk patients who will benefit from them. Our proof of principle would also justify using our tools to measure evolution in other pre-cancers to predict progression. In full-blown cancers, our measures may also predict their response to therapy and to select therapies that are matched to the evolutionary dynamics of a patient’s cancer.

NIH Research Projects · FY 2026 · 2023-01

Project Summary Aflatoxin B1 (AFB1) is a carcinogen produced by Aspergillus flavus and A. parasiticus which grow on maize. Given the high prevalence of stunting and other nutritional disorders in low- and middle-income countries where maize is highly consumed, the role of aflatoxin exposure is worth investigating. Observational reports have shown associations between aflatoxin exposure and child growth. However, most have been cross-sectional and have not assessed seasonal variations in aflatoxin, food preparation, and dynamic changes in growth. In addition, biological mechanistic data on how aflatoxin may exert impact on growth is missing. We will advance the science of aflatoxin and child growth by assessing temporal changes in diet, aflatoxin exposure, and growth faltering in a prospective cohort of children from rural Guatemala, a country that has one of the highest rates of child stunting and aflatoxin exposure in the world. In addition, we will use bioreactors to investigate possible biological mechanisms, specifically direct aflatoxin-gut microbiome interactions. We hypothesize that aflatoxin exposure negatively impacts child growth by inducing inflammation and disrupting the gut microbiome. In Aim 1, we will prospectively evaluate aflatoxin exposure and height-for-age difference trajectories among 185 children between 6-9 and 24-27 months of age. We will assess aflatoxin exposure using serum AFB1-albumin adduct levels, and we will measure biomarkers of systemic and gut inflammation. In Aim 2, we will evaluate the association between aflatoxin exposure, stunting status, and gut microbiome composition and function. We will evaluate the fecal microbiome of each child using 16S rRNA gene amplicon and whole genome sequencing to identify key species and metabolic pathways for differing AFB1 exposure levels and stunting status. In parallel, we will use bioreactors inoculated with fecal samples to evaluate the response of the gut microbiome composition to varying levels of AFB1 exposure. In Aim 3, we will evaluate the impact of aflatoxin exposure on microbial nutrient metabolism and the impact of gut microbiota on aflatoxin detoxification/metabolism by monitoring key nutrient metabolites (e.g., short-chain fatty acids) and AFB1 biotransformation products in bioreactors. We will also evaluate the effect of inflammation, aflatoxin, and microbiome composition on child stunting based on the results from the three aims using path analysis. Significance and Innovation: Through a multidisciplinary approach that leverages access to an exposed population and expertise on epidemiology, toxicology, environmental engineering and human microbiome, we will be able to evaluate the impact of aflatoxin exposure on growth and its possible mediation by the gut microbiome. Impact: Completion of this R01 proposal will advance understanding of the physiologic mechanism of aflatoxin- mediated growth restriction, pointing the way toward new public health strategies for mitigation of aflatoxin exposure and for microbiome-directed treatments.

NIH Research Projects · FY 2026 · 2023-01

PROJECT SUMMARY / ABSTRACT Stuttering is a speech fluency disorder that negatively impacts the communicative abilities of 5–8% of children and 1% of adults. Stuttering also limits the individual’s academic-occupational achievement and social- psychological wellbeing. Unfortunately, existing stuttering treatments are associated with considerable individual variation in their outcomes, and in some cases, are entirely ineffective. Additionally, existing treatments require many sessions, and some are susceptible to as much as 70% relapse, leading to costly solutions with limited accessibility, especially for individuals from lower socioeconomic backgrounds. A major barrier to developing effective stuttering treatments is our incomplete understanding of the specific neural processes underlying the behavioral aspects of stuttering. There is a critical need to (1) identify the deficient neural processes underlying stuttering, (2) determine their functional contributions to breakdowns in speech fluency, and (3) develop neural and behavioral interventions that specifically target the deficient processes, and thus, promote fluency in individuals who stutter. Current speech theories posit that as the brain prepares speech movements, it uses its predictions to prepare the sensory systems for more efficient and accurate speech monitoring. These predictive sensorimotor processes and their interplay with error-detection processes are critical for fluent speech production. This project’s overall objective is to elucidate the role of predictive sensorimotor processes in the breakdowns of speech fluency in children and adults who stutter. We will address this question using behavioral and neurophysiological recordings combined with neurostimulation techniques. Our central hypothesis is that stuttering is associated with deficits in predictive sensorimotor processes, leading to inaccurate predictions. Aim 1 will evaluate the effects of exposure to auditory errors on predictive sensorimotor processes of individuals who stutter across the lifespan. Aim 2 will characterize the temporal alignment of prediction and auditory feedback by delaying auditory feedback or speech initiation. Aim 3 will determine the functional contributions of the speech premotor cortex in predictive sensorimotor processes. Overall, the expected outcome of this mechanistic research program is a detailed neuro-developmental account of deficits in predictive processes of stuttering individuals across the lifespan. This project’s results will have a critical positive impact because (1) they will form a robust scientific foundation for developing neural and behavioral interventions for stuttering, and (2) they will have significant implications for theories of stuttering and speech production.

NIH Research Projects · FY 2025 · 2022-09

Component A – Abstract: Annual influenza vaccination is the primary prevention strategy for infection and severe disease. A constantly evolving influenza virus through antigenic drift dictates that vaccines are re-evaluated every year. COVID-19 has overlapping symptoms with influenza and has significantly complicated the healthcare burden associated with viral infections, morbidity, and mortality. While COVID-19 vaccines received Emergency Use Authorization (EUA) from the Food and Drug Administration (FDA), additional COVID-19 vaccines are under development due to emerging variants, some of which are known to evade currently authorized vaccines. As such, boosters are recommended to thwart spikes and new waves of variant infections which complicates assessment of the effectiveness of both COVID-19 and seasonal influenza vaccines simultaneously. Phoenix, Arizona is the fifth largest and fastest growing city in the nation, and, importantly, is home to an ethnically and socioeconomically diverse population. Twice during the COVID-19 pandemic, Arizona was #1 worldwide in per capita COVID-19 cases. Arizona has seen a mixed adoption of vaccine use for both COVID-19 and influenza, allowing for excellent local comparisons. In this project, leveraging Arizona State University’s (ASU) core capabilities, we propose to study vaccine effectiveness (VE) in a diverse demographic and clinical population (including immunocompromised HIV patients) seen at outpatient clinics managed by ValleyWise Community Hospital, Phoenix Children’s Hospital and ASU Student Health Services. Given identified health disparities in infection and vaccination, we propose to examine social determinants of health to identify the most vulnerable groups. We will collect specimens (nasopharyngeal and/or anterior nasal swabs) and relevant demographic and clinical data from laboratory-confirmed cases of influenza and COVID-19 in children and adults with acute respiratory infection, seeking care in ambulatory clinics, to calculate vaccine effectiveness for both influenza and COVID- 19 vaccines. We will also sequence viral genomes to identify subtype/variants using our deep expertise and incomparable resources in next-generation sequencing and viral genomic bioinformatics. We will use this genomic sequencing data to further investigate VE analyses and understand virus evolution. Importantly, to examine health disparities in vaccination and vaccine effectiveness, we will implement longitudinal surveys and geographical information systems mapping to measure and model social determinants of health. Overall, our multidisciplinary program provides a comprehensive approach to study VE and to understand social determinates that drives health disparities. We believe the findings will have important, long lasting policy implications towards vaccination and examination of VE.

NIH Research Projects · FY 2025 · 2022-09

PROJECT SUMMARY Over 2 million people are now estimated to have an opioid use disorder (OUD). The U.S. health care system is struggling to meet this challenge, as the need for OUD treatment far exceeds the supply of qualified clinicians who can prescribe and monitor medications for opioid use disorder (MOUD). MOUD are effective at reducing overdose deaths and improving outcomes, but are vastly underused, with only 25-40% of adults with OUD receiving MOUD. Rates of OUD are particularly high among Medicaid enrollees, and Medicaid is the largest payor of OUD treatment services. Studies have demonstrated racial, ethnic, geographic, and age differences in overdose death rates, patterns of opioid drug use and OUD, and patterns of treatment. Yet, few studies have undertaken more nuanced approaches to examining the quality of OUD care that take into account differences that may compound across dimensions such as race, gender, and age groups, referred to as intersectionality. Observed disparities may reflect regional limitations in available treatment providers as well as differences in upstream pathways to diagnosis and treatment (e.g., care after an overdose, access to primary care). Using real-time Medicaid claims data from North Carolina, supplemented by claims from Medicare and private insurance (with these sources together accounting for over three-quarters of all North Carolina residents) will allow us to characterize the quality of OUD care delivered by each provider across payers. We will examine differences in OUD care quality by intersectional status and examine key drivers of those differences. Our first aim is to assess differences in the quality of OUD care by intersectional status. We will also examine whether the presence of other chronic behavioral health and medical conditions moderate these disparities in OUD care quality by intersectional status. Our second aim is to assess the extent to which differences in the receipt of high-quality OUD care by intersectional status are due to differences in the availability of providers who provide high-quality OUD treatment, by geographic areas. Finally, our third aim is to compare differences by intersectional status in the receipt of high-quality OUD care before, during, and after North Carolina transitions its Medicaid program from fee-for-service to capitation in July 2021. We will use national Medicaid data to compare patterns of OUD care quality in North Carolina to the country as a whole. We will also explore the use of electronic health record data to provide an alternative assessment of OUD quality of care by examining clinician notes, orders, and lab tests. Understanding the differences in OUD care quality by intersectional status and the factors underlying and driving those differences is critical to attaining an equitable, high-quality health care system. Our results will guide policy and practice aimed at improving OUD care engagement and quality for all individuals.

NIH Research Projects · FY 2025 · 2022-09

Project Summary/Abstract Intrinsically disordered proteins (IDPs), which lack of a well-defined folded structure alone, play important roles in a variety of intracellular activities. This is usually made possible via a disorder-to-order transition when interacting with other biomolecules. However, there has been growing evidence towards the indispensable roles of conformational flexibility and dynamics on regulating biological activities. Zheng's lab focuses on investigating IDP interactions through developing multiscale computational modeling methods. Dr. Zheng has a track record of developing all-atom, coarse-grained and polymer models for IDPs with publications directly relevant to the research focuses. The lab has already contributed to modeling methods for interpreting experimental data of IDPs through collaborating with multiple experimental groups. The research program will be a good addition to the biophysics community within Arizona State University and provide research opportunities to students on this timely topic. The long-term goal of the group is to gain a comprehensive understanding of the driving force of disordered protein assemblies. Two interrelated research topics are proposed including (1) deciphering the role of flexible regions when an IDP interacts with its folded partner; and (2) investigating the mechanism of IDP-driven liquid-liquid phase separation (LLPS). The project combines computational methods in multiple resolutions with a variety of experimental techniques through three collaborations. Such unique combination of computational and experimental methods will provide an unprecedented level of insights on the molecular mechanism of IDP interactions. The designing of novel methodology framework for studying IDP assembly will benefit a broad range of audience interested in IDP relevant biological process.

NIH Research Projects · FY 2025 · 2022-09

Project Summary Collagen, the most abundant human protein, provides a scaffold for cells to maintain tissue and organ integrity. Fibrillar collagen is highly resistant to proteolysis and is degraded by specific matrix metalloproteases (MMPs). The degradation of fibrillar collagen is an essential part of tissue remodeling and is involved in many normal and pathological processes. While the degradation of triple-helical collagen monomers is well-studied, degradation of native collagen fibrils remains poorly understood. Fibrils are insoluble in physiological buffers, heterogeneous, and extended substrates, making them challenging to study using standard biochemical assays. We have overcome these limitations by developing a single-molecule method to track and analyze the motion of labeled MMPs on fibrils using a home-built total internal reflection fluorescence microscope (TIRFM). We propose to study the roles of MMP1 and MMP9 in degrading type-1 collagen fibrils. MMP1 can degrade triple-helical collagen, whereas MMP9 cannot degrade triple-helical collagen significantly. However, MMP9 is upregulated in numerous skin diseases, cancer metastasis, wound, inflammation, pneumonia, and atherosclerosis. We propose to study the mechanism of fibril degradation by two important MMPs by a combination of single-molecule tracking, innovative analysis, simulations, ensemble assays, and animal studies.

NIH Research Projects · FY 2025 · 2022-09

Summary Advanced sequencing technologies provide ever-increasing quantities of data about human genetic variation and viral evolution. However, predicting the outcomes of missense mutations in protein coding regions remains a challenge, creating a bottleneck in discriminating biomedically-relevant variants from neutral ones (with little or no effect on phenotype). In particular, outcome predictions are very poor when a missense mutation alters amino acids that are located far from a protein’s functional/binding sites. These shortcomings also impair protein design. We propose to ameliorate these needs by developing quantitative, computational models that predict the effects of long-distance substitutions on binding interactions. To that end, we have developed an approach in which (1) a protein’s collective motions are first revealed by molecular dynamics simulations and then (2) force perturbation is used to disrupt the protein’s equilibrium, thereby approximating the effects of ligand binding. We have used this approach in published studies and preliminary data to illuminate the propagation of dynamical changes through a protein’s anisotropic network of interactions. Results suggest that changes in these dynamic networks have crucial effects on protein function, thereby leading to our central hypothesis: The effects of long- distance substitutions on ligand binding are emergent properties of changes in the protein’s dynamically-coupled, anisotropic network. The goal of the current proposal is to extend this computational approach to develop models that predict: (Aim 1) the magnitudes of binding affinity changes arising from long-distance, modulating substitutions; (Aim 2) which pairs of non-contact substitutions have non-additive effects on binding affinities (“epistasis”); and (Aim 3) which long-distance positions contribute to ligand specificity. To that end, we have a well-established collaboration that allows us to iterate between computational predictions and experimental testing, enabling development of quantitative models with computed accuracies. Our preliminary studies used the well-characterized E. coli lactose repressor protein (LacI), for which experimental results validate our preliminary computational models and provide specific hypotheses for Aims 1-3. Additional model proteins will be used to show the generality of our approach and will include the LacI homolog PurR, the cAMP receptor protein, and a viral protease SARS-Cov2-Mpro. Results will be used to provide novel computational tools for predicting functional outcomes of long-distance substitutions. The success of this project will catalyze research at the interface of protein structural biology, molecular genetics, evolution and medicine by advancing the mechanistic understanding of how substitutions distal from functional sites alter ligand binding.

NIH Research Projects · FY 2024 · 2022-09

PROJECT SUMMARY / ABSTRACT In Uganda, adolescent girls and young women (AGYW) are disproportionately affected by HIV and have poor viral suppression rates, increasing their risk of onward transmission. Intimate partner violence (IPV) is a major barrier to mitigating the impact of HIV among AGYW. AGYW living with HIV (AGYWLHIV) in sub- Saharan Africa (SSA) who have experienced IPV have worse medication adherence, viral suppression, and care engagement than those without IPV. Further, male partner alcohol use directly and indirectly increases IPV risk among AGYW in SSA. Thus, an intervention with components that address heavy alcohol use among male partners could decrease AGYW’s IPV risk, especially in Uganda, which has the highest alcohol use per capita in SSA. Couples- based interventions have effectively reduced male partner alcohol use, relationship conflict IPV, and improved viral suppression and HIV care engagement; yet, none have been tailored to AGYWLHIV in SSA. We propose to develop and pilot a couples-based intervention that focuses on improving HIV care engagement and ART adherence among AGYWLHIV by reducing heavy alcohol use among male partners and couple IPV risk. Additionally, we will explore the intervention’s effects on AGYW viral load for the additional key benefit of treatment as prevention. Our aims are to: 1) Adapt the behavioral components of a brief MI-based alcohol intervention to create the proposed Kisoboka Mukwano (“It is possible, my love!”) intervention. The intervention will promote strategies for reductions in male partner alcohol use, coping with relationship conflict and stress, changing norms that reduce IPV and support engagement in HIV care and ART adherence among AGYWLHIV, and, thereby, enhance future sustained viral suppression and benefits of treatment as prevention. The intervention will be adapted and tailored to be delivered with heterosexual couples, involve peer navigators, address IPV, and be developmentally appropriate for AGYWLHIV in Uganda. We will develop and refine the intervention in collaboration with an intervention steering committee through: qualitative research with married/cohabiting AGYWLHIV, married/cohabiting men, and key informants and an initial pilot test with 6 couples. 2) We will assess safety, acceptability, feasibility, and preliminary estimates of the potential for the intervention, as compared to the control group, to improve HIV, alcohol, and IPV outcomes. We will examine preliminary effects on AGYW HIV care engagement, AGYW ART adherence, heavy alcohol use among male partners, and couple IPV risk and explore effects on AGYW viral load as well as intermediate outcomes related to intervention components. We will assess these outcomes at baseline and then at 3- and 6-month follow-up. Study findings will be used to guide a subsequent R01 proposal to test the intervention in a larger clinical trial.

NIH Research Projects · FY 2025 · 2022-09

Project Summary Developing new approaches for obtaining accurate estimates of dietary intake is crucial for revealing true associations between dietary intakes and disease risk, with the ultimate aim of establishing evidence-based guidelines. While the evidence on dietary sugars and increased risks of dental caries and obesity have been stronger, the association between sugars and increased risk of cardiovascular disease (CVD), type 2 diabetes (T2D), and cancer remains inconclusive. The findings on total and animal protein (AP) intake in relation to T2D risk and CVD or all-cause mortality have also been inconsistent. Dietary biomarkers alleviate the problem of measurement errors (ME) associated with self-reporting dietary instruments commonly used in nutritional epidemiology, and have been used to validate and calibrate self-reported diet or to adjust for ME in self- reports, and reveal important associations. In the parent project of this renewal application, (SugarsBio study, U01-CA197902), we confirmed 24-h urinary sucrose and fructose (24uSF) as a predictive biomarker of total sugars (TS) intake, and demonstrated the transportability of the biomarker equation for estimating biomarker- based TS intake and its use across different populations. Furthermore, we have identified serum carbon isotope ratio (CIR) as a candidate predictive biomarker of the AP to total protein intake ratio (APR), a novel biomarker of protein quality, and developed a biomarker equation that can be used to generate biomarker- based APR in studies with available biological samples. The aim of this proposal is to investigate the utility and application of 24uSF and serum CIR biomarkers in diverse populations in two prospective cohorts. For this purpose, we will leverage data from two large dietary validation studies with comprehensive validation protocols (IDATA and SOLNAS) nested within cohorts, the NIH-AARP Diet and Health (AARP) Study and the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). First, we will study the ME in self-reported TS, and APR, AP and plant protein (PP) intake, using 24uSF and serum CIR biomarkers. Second, we will develop regression calibration equations for self-reported intake, based on biomarkers, in IDATA and SOLNAS that we will apply in their respective cohorts. Third, we will investigate uncalibrated and calibrated (i.e., ME- corrected) self-reported intakes of TS, AP, PP and APR in relation to CVD mortality in the AARP, and T2D risk in the HCHS/SOL cohort. Our study will be the first study that applies the newly developed US population- based sugars and protein intake biomarkers and their calibration equations to race/ethnically diverse US population-based studies and evaluates ME-corrected dietary intakes in relation to chronic diseases. By informing the best practices for applying these biomarkers in future diet validation studies and studies of diet-disease associations, this proposal will significantly contribute to improving dietary assessments and enhancing scientific rigor of nutritional epidemiologic studies.