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Enhancing cardiomyocyte reprogramming efficiency by targeting cellular senescence is mediated via Rb1 gene
Get PDFIntroduction Direct reprogramming of fibroblasts into cardiomyocytes by overexpressing cardiac transcription factors Gata4, Mef2c, and Tbx5 (GMT) is a promising way for cardiac repair, however, the low reprogramming efficiency remains a significant challenge. Cellular senescence, an irreversible cell-cycle arrest occurring in mitotic cells, has been reported to influence the efficiency of induced pluripotent stem cell (iPSC) reprogramming. Methods We established an inducible GMT expression system in mouse embryonic fibroblasts (MEFs) and human fetal cardiac fibroblasts (hFCFs) using the PiggyBac transposon system. RNA sequencing was performed to identify genes associated with cellular senescence during reprogramming. Selected senescence-related genes were knocked down using shRNA, and their impact on reprogramming efficiency was assessed via flow cytometry, gene expression analysis, and staining for senescence and apoptosis markers. Results Direct cardiac reprogramming induced cellular senescence and apoptosis, evidenced by enhanced β-Gal staining, elevated expression of senescence markers P16 and GLB1, and increased apoptosis rates. RNA sequencing and gene set enrichment analysis (GSEA) revealed significant upregulation of senescence-related genes (RB1, RBBP4, RBBP7, CBX8, and CDKN1B). Knockdown of these genes, particularly RB1, significantly enhanced reprogramming efficiency, increasing the proportion of GFP + cells in MEFs and α-actinin + cells in hFCFs. RB1 inhibition also reduced senescence marker levels and upregulated endogenous cardiac transcription factors GATA4 and MEF2C. Conclusions Our findings demonstrate that cellular senescence might serves as a barrier to direct cardiac reprogramming and offer novel insights into the regulatory mechanisms involved in this process. Supplementary Information The online version contains supplementary material available at 10.1186/s13287-025-04776-7
Translational Insights for the Precision Treatment of Peripheral T-cell Lymphomas
Get PDFThis work addresses a persistent challenge presented by the heterogeneous drug sensitivities of peripheral T-cell lymphomas (PTCL), where there are many options for treatment, but therapies are only effective for small subsets of PTCL patients, and the molecular drivers of drug sensitivity shared among these small patient subsets are not currently well-defined. The influence of molecular heterogeneity on drug sensitivity is multi-faceted, and therefore this thesis employs functional genomics, preclinical drug screening, proteomic profiling, and computational modeling to identify and demonstrate the impact of biomarkers selection in PTCL treatment. Chapter 1 outlines the development of PTCL classification and treatment, framing the need for subtype-specific treatment strategies to improve upon the current underperforming landscape of PTCL treatments. Chapter 2 makes use of preclinical drug sensitivity measurements with two histone deacetylase (HDAC) inhibitors, romidepsin and belinostat, that are used to treat PTCL to determine if the clinically-observed, subtype-specific efficacy of romidepsin can be applied to the design for an upcoming trial of belinostat. Chapter 3 addresses a challenge in the identification of biomarkers of drug sensitivity. Where previous attempts at correlating drug sensitivity with mutations or expression profiles for various cancers have elected many possible candidate biomarkers, it is often difficult to pinpoint true drivers of drug sensitivity due to the covariance of many of these features and their altered significance upon adjustments for multiple hypothesis testing. We integrate functional genomic screens with proteomic profiling across multiple human cell culture models to uncover biomarkers that are predictive of drug sensitivity in PTCL. Mechanistically distinct, predictive features are identified for therapies including the antifolate therapy, pralatrexate, for which the reduced folate carrier, SLC19A1, is shown to both positively correlate with and confer drug sensitivity upon knockdown to pralatrexate. Together, these chapters provide experimental foundation and rationale for refining PTCL treatment through molecular stratification, and this work more generally supports the precise administration of small molecule therapies in oncology.Doctor of Philosoph
COMING OF AGE IN THE AGE OF MIGRATION: MIGRATION IN THE TRANSITION TO ADULTHOOD IN A LOWER INCOME SETTING
Get PDFThis dissertation examines the intersection of migration and the transition to adulthood (TTA) in Nepal using a life course framework. Drawing on life course theory’s concepts of linked lives, institutional embeddedness, and cumulative pathways, I analyze how migration is rooted in youth TTA trajectories amid rising temporary labor migration, strong intergenerational ties, and persistent gender norms. Paper One uses yearly data on marriage, childbearing, migration, and education from the Chitwan Valley Family Study (CVFS) to detail the pathways to adulthood in Nepal, considering how migration is embedded within these trajectories and how membership into these trajectories is shaped by socio-demographic characteristics. Paper Two also uses CVFS to test the association between membership into these clusters with exposure to parental migration. Paper 3 uses 60 semi-structured interviews with young adults and their mothers to understand how they conceptualize migration within the broader process of becoming an adult. Across all three papers, findings reveal that migration has become increasingly embedded within TTA pathways. Paper 1 reveals that the integration of migration into TTA pathways varies by age cohort and gender. Paper 2 test the association between parental separation and membership into these clusters and reveals that paternal separation, largely from migration, encourages sons’ pre-marital migration, while maternal separation, primarily through migration, is linked to more novel and education-intensive trajectories. And the interviews in Paper 3 underscore migration’s dual role as both a personal strategy and a moral obligation to family and nation. Collectively, this dissertation reveals how migration functions not just as a life event but as a structuring force in the transition to adulthood.Doctor of Philosoph
ENHANCING MATERNAL AND CHILD HEALTH SERVICE COVERAGE ESTIMATES AND PROGRAM EVALUATION AT THE SUB-NATIONAL LEVEL THROUGH ROUTINE HEALTH INFORMATION SYSTEMS DATA ADJUSTMENT IN NIGERIA
Get PDFNigeria continues to face diverse challenges in maternal and child health (MCH). There is growing interest in health system strengthening programs in low- and middle-income countries to address the MCH challenges, but few evaluations have quantified their effectiveness due to lack of rigorous study designs and challenges in accessing timely and reliable data. The objectives of this dissertation were to assess the feasibility of using underexplored routine health information systems (RHIS) data for coverage estimates and program evaluation in three states in Nigeria (Ebonyi, Kebbi, and Zamfara). In Aim 1, RHIS data was used to estimate four or more antenatal care (ANC4) coverage and infant fever prevalence. I comprehensively assessed data quality of RHIS, evaluated the effectiveness of various adjustment methods for coverage and prevalence estimates, conducted sensitivity analysis, and triangulated different coverage and prevalence estimates with various population-based surveys in Nigeria. The adjusted ANC4 coverage using service-based denominators aligned more closely with population-based surveys estimates. Quarterly infant fever prevalence estimates showed strong seasonality and good alignments, with the adjusted infant fever prevalence using population-based denominators aligned most closely with survey estimates. In aim 2, I used quasi-experimental design with an interrupted time series analysis with 6- or 12- month seasonality to evaluate the impact of integrated (IHP) and vertical (PMI-S) programming on three child malaria cascade ratios: rapid diagnostic test (RDT) Testing Prevalence; RDT Positivity Prevalence; and Confirmed Case Treatment Prevalence. IHP in Kebbi and PMI-S in Zamfara were associated with significantly increased RDT testing and treatment prevalence, while results were more complex in Ebonyi due to the higher values of all outcomes at baseline in intervention facilities. State-level differences in program impact highlight that both integrated IHP program and disease-specific PMI-S program can be effective for malaria interventions, but their relative impact varies by context. The study filled research gaps by providing a replicable methodological framework that used RHIS data for sub-national coverage/prevalence estimates and program evaluation. Our findings with further validation can inform timely and evidence-based decision-making, reduce the malaria burden, inform resource allocation, strengthen health systems, increase service accessibility, inform health initiatives, and improve MCH outcomes.Doctor of Philosoph
Controlled Release of a Mast Cell Agonist for Glioblastoma Immunotherapy
Get PDFGlioblastoma (GBM) is a devastating illness that claims the lives of nearly a quarter of a million people annually. The aggressive and immunologically complex nature of GBM, as well as its location behind the blood-brain barrier, restricts the efficacy of many cutting-edge therapies. While new immunotherapeutic strategies have made significant advances for certain patient subsets, there is a critical and unmet need for better treatments. One emerging player in the tumor microenvironment (TME) is the mast cell. A granulocyte of the innate immune system, the mast cell has established pro- and anti-tumor functions across many cancers, with emerging evidence for a role in GBM. Herein we establish MCs as an overrepresented population within two immunocompetent murine GBM models, when compared to healthy contols. We further identify the effects a GBM standard-of-care treatment (SOC), resection, has on MC populations. We identify and characterize a 2nd generation MC agonist, MP12W, for its in vitro safety profile, comparing MP12W to known MC activators, including another well-studied 2nd generation agonist and a standard degranulating control. Next, we produced two scaffolds for the interstitial delivery of a MC-agonist in GBM using the acetalated-dextran (Ace-DEX) controlled release platform. These scaffolds were then investigated for their effects on survival and MC phenotypes in two models of murine GBM. We observed successful in vivo MC activation but no tumor control in either model. To understand the mechanism of treatment failure, we completed multi-organ immunophenotyping of the T-cell and myeloid compartments. Our results show that MC agonism in GBM elicits broad immunosuppression. Further, when this agonism is sustained, as is achieved with Ace-DEX scaffolds, we observed increased exhausted CD4 T-cells. Altogether this work establishes MCs as an elevated immune population in GBM impacted by the SOC. Local delivery of a MC agonist impacts regional MC populations, but encapsulation of the agonist within an Ace-DEX scaffold results in phenotypic changes in systemic MC populations. However, we identify that activation of MCs in GBM dampens local and systemic immune populations. Our work further suggests that sustained MC agonism may push key populations within the TME to adopt classic pro-tumor phenotypes.Doctor of Philosoph
Nutrient Uptake and Growth Kinetics of Novel Environmental Phytoplankton Isolates from the Galápagos Archipelago
Get PDFPhytoplankton are the foundation of the marine food web, sustaining countless endemic species in the unique ecosystem of the Galápagos Archipelago. Their functional traits—such as cell size, nutrient quotas, growth and uptake rates—shape resource competition and resilience to environmental pressures. Yet, little is known about their kinetic characteristics in this region. Present research explores functional traits of diverse Galápagos eukaryotic phytoplankton spanning two orders of magnitude in size. We evaluated various allometric relationships between metabolic parameters and cell size, identifying deviations. In particular, diatoms exhibited faster growth than similarly sized taxa, suggesting distinct power law-based scaling for diatoms versus non-diatoms. The carbon quotas of larger diatoms were lower than predicted by general allometry due to presence of large vacuoles. Additionally, we explored how diurnal cycles affect physiology of Galápagos diatom Chaetoceros curvisetus. These findings enhance understanding of the effects of environmental changes on primary productivity and biodiversity.Master of Scienc
MODELING AND INFERENCE FOR REAL-WORLD HEALTH DATA: ADDRESSING COMPLEX TRAJECTORIES, MISSINGNESS, AND UNSTRUCTURED TEXT
Get PDFThe proliferation of complex, high-dimensional, and longitudinal data from sources like clinical trials and Electronic Health Records (EHRs) presents significant analytical challenges. Standard analytical methods are often insufficient, as their underlying assumptions fail to capture the nuanced dynamics of non-linear disease progression, complex microbial ecosystems, and unstructured clinical text. This dissertation addresses these limitations by developing and validating three novel computational frameworks, each tailored to the unique complexities of oncology clinical trials, longitudinal microbiome studies, and unstructured EHR analysis, respectively. In the first project, we address the limitations of conventional models in oncology, which often fail to capture the complex, non-linear nature of treatment response. We introduce a cure rate joint model for the simultaneous analysis of longitudinal tumor burden and time-to-event data. This model integrates a random change-point structure to capture tumor shrinkage followed by potential regrowth, and a cure-rate component for patients achieving long-term disease control. The primary innovation is leveraging survival data to impose a biologically plausible constraint on the change point's timing, ensuring tumor regrowth precedes the clinically observed progression event. Applied to a non-small cell lung cancer trial, the framework yields more accurate estimations of treatment effects and offers a deeper understanding of disease dynamics. The second project confronts the pervasive challenge of irregularly-sampled data in longitudinal microbiome studies. We propose the Bidirectional GRU-ODE-Bayes (BGOB) model, a novel deep-learning-based interpolation framework. BGOB leverages a Neural Ordinary Differential Equation (ODE) architecture to learn the underlying continuous-time trajectories of microbial dynamics directly from sparse observations. Its innovative bidirectional structure processes each time-series both forwards and backwards, enhancing accuracy, particularly for early time points. By interpolating both missing time points (``unavailable'' data) and unobserved biological zeros (``undetectable'' data), BGOB transforms fragmented datasets into complete, regularly-spaced data matrices. As demonstrated through applications to early childhood caries and inflammatory bowel disease cohorts, these imputed datasets significantly improve the statistical power of downstream analyses like differential abundance testing and clustering. The third project introduces the precLLM framework to address critical barriers of patient privacy and computational cost, enabling smaller, locally-deployed Large Language Models (LLMs) for unstructured Electronic Health Record (EHR) analysis. The core innovation is a smart preprocessing step—using techniques like regular expressions and Retrieval-Augmented Generation—that filters long clinical notes to isolate the most relevant text before LLM inference. Evaluations on private and public EHR datasets demonstrate that this preprocessing step dramatically enhances the accuracy of smaller models for clinical phenotyping, often outperforming much larger models and proving more efficient than fine-tuning with limited data. We also discuss its potential for enabling more complex longitudinal analyses of patient trajectories within EHR data.Doctor of Philosoph
INTELLIGENT WEARABLE OPTICAL SENSING: MULTIMODAL, MULTICHANNEL, AND MULTI-WAVELENGTH APPROACHES FOR ADVANCED PHYSIOLOGICAL MONITORING
Get PDFWearable optical sensing holds significant promise for personalized medicine, yet its translation to robust, real-world applications is hindered by fundamental limitations. This dissertation, "Intelligent Wearable Optical Sensing: Multimodal, Multichannel, and Multi-Wavelength Approaches for Advanced Physiological Monitoring," confronts critical barriers in non-invasive sensing, including superficial light penetration, signal corruption from motion, low spatial resolution, and biochemical non-specificity. This work presents a series of advanced, skin-interfaced optical systems that integrate novel hardware design with intelligent computational methods to achieve new sensing capabilities.To access deeper physiological information, a novel interface utilizing biocompatible microneedle waveguides is introduced, creating a photonic pathway that bypasses superficial tissue to enable reliable deep-tissue oximetry monitoring. To address the challenges of ambulatory use and complex signal interpretation, multimodal and multichannel systems are developed. One such system fuses optical data with inertial measurements, employing a computational architecture to effectively isolate true laryngeal muscle activity from motion artifacts. Another platform, a high-resolution optical myography array, generates detailed spatiotemporal maps of muscle dynamics. This system leverages advanced data-processing techniques to interpret complex gestures, enabling robust human-machine interaction. Finally, to move beyond conventional oximetry, a multi-wavelength spectroscopic sensor performs real-time, non-invasive quantification of a specific blood analyte. By optically deconvolving the unique spectral signature of ethanol from capillary blood, this wrist-worn device demonstrates a viable pathway toward direct, continuous monitoring of blood biochemistry.Overall, the investigations in this dissertation demonstrate effective solutions to long-standing barriers in wearable optical sensing. By thoughtfully combining advancements in optical interfacing, sensor dimensionality, and signal processing, this work delivers a validated toolkit of new sensing strategies. These contributions lay the groundwork for a new generation of reliable, non-invasive devices for personalized diagnostics, advanced assistive technologies, and more intuitive human-machine interfacing.Doctor of Philosoph
Burning “ExposHome”: Deriving a Mixture of Combustible Materials in American Homes at the Wildland‐Urban Interface for Health Studies
Get PDFApproximately 39% of U.S. homes are now located in the Wildland-Urban Interface (WUI) and are at elevated risk of burning during wildfires. WUI fires emit a cocktail of chemicals from the combustion of anthropogenic materials, including compounds that may differ from the burning of biogenic-only materials. There is currently limited knowledge on the mixture composition of combustible materials in WUI homes, representing a data gap and need to further characterize exposure chemistries and toxicological impacts of WUI-relevant smoke exposures. To address this issue, this study integrated combustible materials in an average American WUI home to derive what we are referring to as the "Burning ExposHome." Items such as structural materials, plumbing, furnishings, and appliances were included in the Burning ExposHome. Calculations were based on an average American household, a 2,016 sq. ft. single family home of four bedrooms, using materials typical to California due to the high incidence of WUI fires in that geographic region. All materials were sorted and summed by type of base material such as wood materials, plastics, textiles, and metals. This list is notably modular and detailed per item, allowing for the addition/subtraction of components to address future study designs. In summary, the total combustible mass of an average American home was around 46,500 kg, including 81% wood materials, 6% plastics, and 2% metals. This list of materials serves as a foundational mixture of home materials to integrate into exposure characterization, mechanistic toxicology, and ecological/human health research addressing wildfires occurring at the growing WUI
The effective connectome over a century of human life
Get PDFBrain functional connectomes describe the coordination principles of neural systems. Understanding normative developmental connectomes is crucial for standardized growth assessment and early disease detection. However, functional connectome has been mostly studied using undirected functional connectivity estimated based on statistical correlation of functional MRI (fMRI) signals. Effective connectivity (EC), in comparison, builds on a generative model of neural interactions and provides directed connectivity strengths among neural populations. To understand how EC evolves with age, we charted the lifespan effective connectome of human brain networks based on high-quality fMRI data from the Lifespan Human Connectome Projects. We found that global and network EC follows an overall inverted U-shape development with an average maturation time at around 9 years of age, significantly earlier than functional connectivity. Regional EC strength exhibits diverse evolution patterns and is more variable during early development than later life, underscoring a critical early window of plasticity. Also, maturation of excitatory and inhibitory nodal EC follows opposite hierarchical sequences. Moreover, the development of nodal EC is constrained by the sensorimotor-association (S-A) gradient, primarily governed by inhibitory EC. Our work reveals fundamental development principles of directed causal interactions between functional networks, offering a foundation for more precise and individualized brain assessments