Cystic fibrosis (CF) is a genetic disease characterized by defects in lung host defenses against infection. Chronic lung infections by the bacterium Pseudomonas aeruginosa (Pa) are a major cause of death in CF, and recent work shows that infecting Pa strains can evolve extensive genetic diversity in vivo during infection. A new class of drugs called modulators markedly improves the CF host defense defect and improves symptoms but does not frequently produce infection eradication. We hypothesize that the extent of phenotypic diversity within Pa populations infecting individual subjects will negatively affect Pa clearance after modulator treatment. This hypothesis stems from observations in environmental systems showing that diversity generally increases the stress resistance of populations. To test this hypothesis, we sampled the lungs of 9 subjects with bronchoscopy before and 18 months after they started the most effective modulator currently available and collected ~ 100 Pa isolates from each of the five different lung regions. The collected isolates are being tested for variation in resistance to different classes of antibiotics and their nutritional requirements, motility, capacity to secret virulence factors, and attach to and aggregate on surfaces. These measurements will enable us to compare the phenotypic diversity of Pa populations infecting subjects that do and do not eradicate infection. In my preliminary work examining Pa populations from two subjects, I found much lower levels of diversity in resistance to three antibiotic classes in the subject that cleared infection as compared to the subject that did not (mean variance in resistance of 0.22 vs 75.3, respectively), which is consistent with my hypothesis. Understanding the consequences of Pa diversity in CF infections could shed light on factors causing different infection outcomes and suggest new approaches to treatment.

Chronic sleep disruption, present in 25-60% of patients suffering from Alzheimer's Disease (AD), often precedes cardinal disease symptoms. While little is known about the mechanisms underlying chronic sleep disruption and the development of clinical pathology, both acute and chronic sleep deprivation have been found to increase biomarkers of AD including neuroinflammation and amyloid-beta accumulation. Additionally, in people without AD, sleep deprivation can result in a deterioration of working memory and attention. In this study, we examine the impact of chronic sleep disruption on cognition both at baseline and in the 5xFAD mouse model of AD. The 5xFAD mouse model is a transgenic mouse model of familial amyloidosis which expresses neurocognitive impairment as early as 2 months. To define the effect of chronic sleep disruption on cognition in the absence of AD pathology, 60 wild type mice were exposed to chronic sleep disruption or sham procedure for 8 weeks between 10 and 18 weeks of age. At 18 weeks of age, I evaluated the animals for changes in spatial memory (Barnes maze), short-term memory (Y-maze), locomotion and anxiety (open field test), and activities of daily living (burrowing trials). To test whether chronic sleep disruption specifically exacerbates AD-related neurocognitive decline, the same cognitive tests were performed on 60 5xFAD+ animals exposed to 8 weeks of sleep disruption or sham treatment. I then analyzed the collected data to isolate any trends of cognitive performance, finding that chronic sleep disruption impaired cognitive performance in 5xFAD+ and littermate controls, with a more significant impact on 5xFAD+ animals. These findings highlight the critical association between dysfunctional sleep and the development of cognitive impairment with AD disease progression which then guides us toward better preventative care and treatments.

Alzheimer’s Disease (AD) is a neurodegenerative disease that affects more than 5 million Americans. The glymphatic system (a network of perivascular spaces that facilitate fluid movement and solute clearance from the brain) and its dysfunction associated with aging has been implicated in the development of AD. The water channel aquaporin 4 (AQP4), located in astrocytic endfeet bordering the perivascular spaces, is crucial for the proper functioning of the glymphatic system. Data suggests that loss of AQP4 localization results in amyloid-ß deposition, a hallmark of AD pathology, and loss of AQP4 localization accompanies aging in rodents as well as AD in humans. In this study, we quantitatively analyze the expression of aquaporin- 4ex (AQP4ex)—a translational readthrough variant of AQP4 believed to play a role in its localization—to identify any correlation with aging and AD pathology. Selective deletion of AQP4ex results in the mislocalization of AQP4 all over the astrocytic membrane, indicating that AQP4ex is a crucial element in the localization of AQP4. We analyze young, old and AD groups in the murine (mouse) brain as well as AD versus control in a human case series. Currently, we see a trend towards decline in cortical perivascular AQP4ex in the AD group, with more analysis ongoing. This is the first characterization of AQP4ex expression in the murine brain and in a human case series, and these data will contribute to the small but growing body of research on AQP4ex and its relationship with AQP4 localization, creating opportunities to identify a new novel mechanism and novel target in AD pathology.

The glymphatic system, which is primarily active during sleep, is a network of astroglial perivascular channels within the brain that allow for Cerebrospinal Fluid (CSF) influx and exchange. Glymphatics play a crucial role in the waste clearance of amyloid beta, a hallmark in the development of Alzheimer’s Disease and neurodegeneration. Recently, a bidirectional relationship between Alzheimer's Disease and sleep has also been suggested with the aggregation of amyloid beta associated with mid-life sleep disruption. However, the mechanistic link between sleep disruption, particularly over chronic time scales, and the development of Alzheimer’s pathology remains unclear. This study investigates whether chronic sleep disruption, similar to that experienced in humans, will impact downstream neuropathology. We hypothesize chronic sleep disruption will result in decreased glymphatic function and subsequently increased amyloid plaque burden. This experiment utilizes a chronic sleep fragmentation model in 120 5xFAD mice from 8 weeks to 16 weeks of age. In the Lafayette Sleep Fragmentation chambers, 60 animals are disturbed every two minutes during normal sleeping periods (daylight hours). 60 mice were placed in normal sleeping conditions. After eight weeks of sleep fragmentation or sham exposure, glymphatic function is assessed by in vivo near infrared imaging following stereotactic CSF tracer injection. Animals are perfusion fixed, cryosectioned, and glymphatic function is assessed by measurement of fluorescent cerebrospinal fluid tracers in brain tissue. Aquaporin-4 localization, amyloid plaque deposition, and markers of astroglial and microglial activation are assessed by immunofluorescence. In this project, I specifically work on cryosectioning the tissue, and understanding glymphatic function through the processes of immunofluorescence imaging and analysis. The collected data demonstrated that sleep disruption did increase neuropathological outcomes.The measured impact of glymphatic function was also correlated with these downstream pathological effects. These findings could be an indicator of interactions between neurological disease progression and an inflammatory expression after sleep disruption. They can also shed more light on the complex relationship between Alzheimer’s disease progression, the glymphatic system, and chronic sleep disruption.

Studying mesocarnivore interactions is vital to understanding ecosystem function, particularly in the absence of apex predators. Competition and niche partitioning between bobcats (Lynx rufus) and coyotes (Canis latrans), two abundant mesocarnivores, is poorly understood but may have significant impacts on prey dynamics and ecosystem structure. Understanding how bobcats and coyotes coexist will provide insights into the context and occurrence of intraguild exploitative competition. This study aims to determine if exploitative competition between bobcats and coyotes is occurring in the Eastern Cascades of Washington state by analyzing the diet and habitat use of sympatric and allopatric bobcat and coyote populations. I hypothesize that sympatric bobcat and coyote populations will occupy smaller niche spaces than allopatric bobcat and coyote populations due to the niche partitioning of shared resources. To test this hypothesis, I will compare the frequency of occurrence of food items in bobcat and coyote scats using DNA metabarcoding and Next-Generation sequencing. Using microsatellite analysis, I will genotype a subset of coyote and bobcat scat samples to determine the number of individuals and their home range size. I will also determine the habitat type of the georeferenced scat using geographic information systems. This will be the first study comparing allopatric and sympatric bobcat and coyote populations and will clarify the conflicting literature on the occurrence of exploitative competition between bobcats and coyotes.

Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) that have been engineered into three-dimensional heart tissues (EHTs) are valuable research tools for investigating debilitating genetic diseases that afflict the heart, such as Duchenne muscular dystrophy (DMD). Ensuring iPSC-CMs can be sufficiently matured to model such diseases remains a hurdle in current research, and maturational analysis techniques for iPSC-CMs are either qualitative, manual, or primarily based in two dimensions, leaving much to be desired. This poster details the creation of a suite of MATLAB image-processing scripts that can quantify the effect of three-dimensional culture and disease-causing DMD mutations on cardiomyocyte structure and maturation state. The iPSC-CMs were differentiated from stem cells, cast into EHTs, stained using immunofluorescence, and imaged using confocal microscopy. Using the scripts to analyze these 3D images of iPSC-CM stains, key maturational features of the cells can be quantified such as nuclei count; cardiomyocyte area; and sarcomere length, orientation, and z-disk width. Analyzing cardiomyocyte area can give key information on cardiomyocyte hypertrophy while examining sarcomere length, orientation, and Z-disk width can provide information on myofibril structure and organization. The suite allows analysis of these maturational features in both 2D and 3D cultures and offers a method for quantitatively assessing maturation in an automated manner. Measuring iPSC-CM maturation will also allow better comparison of existing maturational methods, such as mechanical loading, electrical stimulation, and small molecule treatment. The suite can also create graphical outputs to elegantly display data. Recent progress also includes a script that can count cell nuclei and quantify cell area. Overall, the suite will help improve maturational analysis of EHTs, and hopefully contribute to the discovery of new treatments for diseases that affect the heart. 

In the United States, 1.5 million individuals suffer a fracture due to bone disease each year. It is well documented that mechanical load affects bone development, but our understanding of the cellular mechanisms behind bone development under load is limited. Current human induced pluripotent stem cell (hiPSC) derived bone tissue models have more relevant human physiology compared to traditional animal models. However, there is a lack of dynamically loaded hiPSC bone tissue and diseased hiPSC bone tissue models in vitro. We propose a novel, three-dimensional bone tissue model as a platform for musculoskeletal disease modeling that allows for compressive loading that will enhance maturity as well as induce diseased bone phenotypes. We improved upon existing poly-L-lactide solvent cast scaffold techniques by incorporating a polyvinyl alcohol mold and an annealing step that increases the uniformity of the scaffolds and allows for higher throughput fabrication. Osteoblasts were derived from hiPSCs using established differentiation protocols and seeded into the 3D, porous, poly-L-lactide scaffold to generate in vitro bone tissue that generates significant extracellular calcium. We propose an arduino-powered, 3D-printed loading device that can apply physiologically relevant dynamic loads to the scaffold and hypothesize improved bone tissue maturity in comparison to 2D cultures and unloaded 3D scaffolds. By screening for markers of early bone development such as type I collagen, markers of later development such as osteocalcin, and assays for extracellular calcium, we can track the maturity and development of bone tissue. We expect that 3D bone growth with static loading will reveal diseased bone phenotypes such as decreased calcium deposition and immature bone, whereas dynamic loading will promote bone growth and lead to mature bone. Ultimately, this model will improve our ability to investigate the effects of mechanical loading in developing and diseased bone.

Chronic pain is characterized by persistent pain following the healing process or even in the absence of injury. The neural basis of chronic pain has been linked to several structures and pathways in the brain. However, the underlying neural electrophysiology behind chronic pain is not yet well understood. Here, we present a novel pain intensity decoder that inputs segments of neural electrophysiology and outputs predicted pain intensity values. We work with epilepsy patients who have week-long electrode implants for clinical monitoring. Using historical patient-reported pain intensities, scaled from 0 to 10, we identified segments of neural electrophysiology time-synched to when patients reported their pain levels. From these time segments, we extracted neural features, such as power-in-band values from the delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (13-30Hz), gamma (30-55Hz), and high gamma (65-115Hz) frequency bands. We then used these neural features as inputs to train supervised machine learning models to predict pain intensity. We expect that prediction accuracy will depend on electrode location, where increased accuracy would occur in pain-related regions such as the anterior cingulate and insular cortex. Our classification model demonstrates that pain can be successfully decoded from neural electrophysiology and indicates potential for informing pain interventions and treatment methods for chronic pain.

Spinal cord injuries (SCIs) produce motor impairments that have devastating consequences for the independence and quality of life of affected individuals. These impairments result from the weakening of connections between the cerebral cortex and the spinal cord. Therefore, there is an ongoing need to develop interventions that strengthen corticospinal connections post SCI. Our laboratory focuses on a hybrid intervention that combines intraspinal neuromodulator delivery with use-dependent physical rehabilitation, which increases motor performance after SCI. However, the mechanisms behind this recovery remain relatively unexplored. Our project aims to address this knowledge gap by using evoked potentials (EPs) as biomarkers to quantify the strength of neuronal connections. EPs represent electrical responses in the brain to stimuli. Following a stimulus event, measuring the EP amplitude allows us to assess the strength of neuronal connections. For our experiment, we will implant chronic cortical and spinal microwire arrays in adult rats with chronic cervical SCI and conduct weekly recording sessions before, during, and after a 6-week therapy period. We will then compare changes in the size of EPs recorded during these sessions. We will also assess motor recovery through behavioral scores on a forelimb reach-and-grasp task, which the cervical cord injury directly impairs. We hypothesize that our interventions will strengthen corticospinal connections damaged by the injury, as manifested in a correlation between an increase in EP amplitudes and changes in motor performance. Ultimately, results from our experiments will help us understand how physical rehabilitation and targeted delivery of neuromodulators mediate recovery of the damaged central nervous system. We also hope our project will inform future rehabilitation strategies targeting SCI.

Exploring the neural mechanisms modulating complex social behavior requires a holistic understanding of both central and peripheral body states. In freely interacting mice, social behaviors are often registered by changes in autonomic nervous function, including altered blood pressure, heart and breathing rates, and core body temperature. Unfortunately, these physiological metrics are difficult to obtain during complex social behavior due to substantial hardware requirements, like collars and tethers, restricting full movement and interaction. In collaboration with an industry partner, we are developing a fully implantable, battery-free device for wireless data acquisition of physiological data, including heart and respiratory rate, temperature, and other behavioral information such as locomotion and orientation of mice using biomechano-acoustic (MA) methods. Here, we validate the use of MA devices in both anesthetized and freely moving mice. First, we tested MA devices during emergence from anesthesia and compared anesthetized recordings using MA devices to a widely used and commercially available rodent pulse oximetry device. Second, we obtained MA recordings in freely interacting mice during complex social behaviors. This technology represents a crucial advanced tool for experimental behavioral research that enables non-invasive operations in cages with simple or complex environments in an individual or groups of animals.

The pairing of high-resolution volumetric imaging methods with cellular markers of neural activity holds network-level explanatory power over behavior. However, common statistical analysis approaches fall short in capturing the functional relationships between brain regions across multiple spatial dimensions. Here, we propose combining unsupervised machine learning clustering methods with network graph theory visualization to reveal intricacies from these data beyond conventional standards. We demonstrate the feasibility of this approach on a recent single-cell dataset describing the longitudinal changes of brain-wide activation during relapse to palatable food in mice. We applied a new analytical framework combining the functionality of two open-source programs: (i) Histo-Cytometric Multidimensional Analysis Pipeline (CytoMAP), packaged with unsupervised k-means clustering and t-distributed stochastic neighbor embedding (t-SNE), and (ii) Cytoscape, a network analysis program. Hierarchical radial network diagrams were applied to the dataset in which we visualized the anatomical organization of regions that underwent statistically significant changes in activation. Across abstinence duration, we found an initial suppression in activation followed by widespread increases in activation. This increase correlated with observed behavioral changes and appeared to be triggered by activation hotspots. We next interrogated the co-activational relationships amongst the >900 brain regions by running unsupervised t-SNE dimensionality reductions on the data from each experimental group. These results were validated using k-means clustering and Davies-Bouldin indexing. We observed an intuitive segregation of regions dependent on activation status. Cluster number decreased in a time-dependent manner, suggesting increases in modular processing are associated with increased activation due to abstinence length. This trend indicated that cluster membership of regions likely also changed in a time-dependent fashion, indicating a dynamic recruitment effect at a regional level underlies abstinence-related relapse vulnerability. By analyzing cellular whole-brain data in this novel manner, we gained new insight into a previously unexplored dimension of brain activation dynamics underlying complex behavior.

 Tuberculosis (TB) is the second leading cause of death due to infectious disease worldwide, accounting for 10 million infections and 1.5 million deaths in 2020. The ubiquitin-binding protein, Toll Interacting Protein (TOLLIP), is associated with TB susceptibility in genetic studies and small animal models. In mice lacking TOLLIP (Tollip-/-), lungs develop profuse lipid-laden foamy macrophages, which induces the integrated stress response (ISR), a cellular adaptation to harsh environmental conditions. However, how Mycobacterium Tuberculosis (Mtb) induces foam cells and the relationship between foam cells and the ISR is uncertain. We treated mice with the Mtb cell wall lipid mycolic acid (MA), a model of lipid accumulation, to understand how TOLLIP influences lipid accumulation and ISR activation in macrophages. MA was administered to Tollip-/- mice intraperitoneally and Mtb replication, lipid accumulation, and the ISR were evaluated. MA treatment was associated with increased mycobacterial replication. ISRIB treatment, a drug inhibitor of the ISR, restored immune control in Tollip-/- macrophages. Three and five days after MA administration, Tollip-/-  peritoneal macrophages (PEM) developed increased intracellular lipid accumulation measured by flow cytometry, demonstrating that TOLLIP is essential to prevent MA-induced foam cell formation and intracellular Mtb replication. Future studies will characterize the impact of MA treatment on host cytokine responses and the impact of the ISR on lipid accumulation feedback loops. These findings can contribute to novel therapeutics for the treatment of TB infection.

Magnetoencephalography (MEG) is a powerful noninvasive technique for measuring the location of brain activity in real time. MEG is used today in research and clinical settings around the world, and is especially advantageous for measurement on children. An array of highly sensitive sensors outside the skull measures the magnetic field produced by current in the brain. Algorithms then localize this synaptic current via a process called the magnetic inverse problem. Critical to this challenge are assumptions intrinsic to the equations that describe magnetic fields. This project explores the assumptions and fundamental structure of major equations used in MEG today. By performing simulations and mathematical modeling we shed light on the interpretations of MEG signal as well as scrutinize what types of synaptic current MEG is capable of seeing. The ultimate goal of our exploration is to help researchers, neuroscientists, and clinicians understand the power and limitations of MEG and make accurate and meaningful claims.

In the United States, 296,000 people live with spinal cord injury (SCI)1. SCI is one of the most debilitating neurological conditions due to the crucial role played by the spinal cord in everyday life. The current treatments for SCI involve invasive surgeries, such as spinal laminectomies and decompressions, which are often paired with non-invasive therapies, such as medications and physical rehabilitation. Despite major innovations in medicine, regeneration of central nervous system (CNS) neurons remains challenging. As a result, reorganization and adaptation, promoted by current therapies, play key roles in recovery after CNS damage with physical rehabilitation being the gold standard of treatment after SCI. Recent SCI research in our laboratory has focused on development of neural interfaces for targeted, intraspinal delivery of plasticity-enhancing neuromodulators, such as brain-derived neurotrophic factor and serotonin. In these experiments, neuromodulator delivery was paired with use-dependent physical rehabilitation on a forelimb reach-and-grasp behavior that is directly impaired by the cervical SCI. We found that our combined intervention promoted greater forelimb-motor recovery compared to physical training alone. However, there were variabilities in the recovery profiles across animals with similar injuries within the same intervention group. Low levels of motivation can lead to less engagement in physical therapy and may be contributing to the observed variabilities. The purpose of this study is to assess motivation levels in spinal-cord injured rats to understand how it affects forelimb-motor recovery given our interventions. Motivation levels will be assessed before, during, and after therapy to examine its influence on motor recovery. We will also assess how SCI and motor recovery impact animal motivation. Preliminary analysis in 6 adult female rats, whose motivation levels were compared before and after injury, shows that SCI lowers motivation (t(5) = 3.052, p = .03, paired samples t-test). Experiments in additional rats are currently ongoing.

Rapid and smooth emergence from the anesthetized state to the awake state is important for patient safety and perioperative efficiency, yet is currently a passive process and the underlying mechanism is not well understood. In mice, emergence from anesthesia is modeled by the return of righting reflex (RORR) signaled by righting from the supine to prone position as the mouse emerges to an awake state. Using this model, it is possible to investigate the neuropharmacological mechanisms of emergence. While commonly studied in concert with neuronal recordings and optogenetic manipulation, these approaches can be combined with high-throughput automated behavior analysis using deep and machine learning approaches. Here, my goal is to create an automated behavioral classification pipeline for annotating the RORR in combination with experimental manipulations and recordings. I aim to characterize the transition between unconscious and awake states to define a binary output. This is accomplished by using DeepLabCut pose-estimation software to track subject mouse body parts, followed by the generation of supervised behavioral classifiers for RORR-related behaviors using the SimBA (Simple Behavioral Analysis) machine learning pipeline. My ongoing directions focus on performing unsupervised classification with this model to cluster additional behaviors. This use of advanced behavioral analysis will enable a better understanding of behaviorally-relevant neural activity in emergence and help bridge the gap between preclinical animal models and clinical intervention.

This abstract is no longer available. Please contact urp@uw.edu if you have any questions.

Neuroligins (NLGNs) are a family of postsynaptic cell adhesion proteins that are essential to the formation and proper functioning of synapses and play a critical role in maintaining neural excitation/ inhibition balance. Neuroligin mutations are linked to several neuropsychiatric disorders like autism, although their role in maladaptive social behavior remains unclear. Inappropriate aggression and agitation are often comorbid with neuropsychiatric disease and understanding the neural pathways underlying aggressive behavior may help to identify potential therapeutic targets. Neuroligin-2 (NLGN-2) specifically supports inhibitory synapse function and plays a key role in regulating social stress behaviors. Here, we examine the role of NLGN-2 in mediating adaptive and maladaptive aggressive behavior in adult male outbred CD-1 mice. In Experiment 1, we use immunohistochemistry to localize and quantify NLGN-2 in Fos-positive cells in nucleus accumbens of mice following resident-intruder reactive aggression. In Experiment 2, we train mice in an operant aggression self-administration procedure and examine changes in NLGN-2 in nucleus accumbens Fos-positive neurons following appetitive, or rewarding, aggression. In Experiment 3, we selectively knockdown NLGN-2 in nucleus accumbens in a neural circuit-specific manner to determine the functional effects of NLGN-2 manipulation on adaptive and maladaptive aggressive behavior. Together, these data demonstrate an important role for nucleus accumbens NLGN-2 in mediating the spectrum of aggressive behavior.

Asian Americans are underrepresented in public health research in the U.S. My research aims to explore the historical, social, and institutional factors that contribute to this, both in Seattle and the broader U.S. I will identify these factors through an extended literature review and demonstrate their role in constraining the scope of Asian public health research using a case-study on industrial pollution in Seattle’s Duwamish Waterway. The case-study will focus on Seattle’s Industrial District, Beacon Hill, and surrounding neighborhoods because of their proximity to the Duwamish Waterway and high exposure to industrial pollutants, such as benzene. This region also has a large Asian population. Genome-wide studies have shown that Asians are genetically predisposed to pancreatic dysfunction when chronically exposed to benzene and other volatile-organic-compounds. Pancreatic dysfunction can lead to diabetes mellitus, a health condition in which the body is unable to process glucose naturally. The spatial overlap of Asian residents and industrial pollution around the Duwamish Waterway is concerning and merits investigation. My methodology includes an extended literature review and a quantitative geospatial analysis of a Seattle case-study. The literature review explores systemic limitations of public health research on Asian Americans, examining factors like systemic racism and data collection issues through a critical Science and Technology Studies (STS) lens. The geospatial analysis will be used during the Duwamish Waterway case-study. I will use publicly available data to map and compare Asian population sizes, diabetes incidence, and industrial pollution in Seattle. The gaps in existing data for these factors will make it difficult to draw conclusive generalizations from the case-study, but paired with STS analysis, highlighting these gaps can contribute to improving public health research by demonstrating the limitations that currently hinder Asian public health research in Seattle and pointing the way to future research to fill in what is missing.

Adolescents with type 1 diabetes (T1D) are at risk for poor physical and psychosocial outcomes. Diabetes-related family conflict has previously been associated with youths' glycemic control (HbA1c). However, less is known about how family conflict associates with other health outcomes. This project aimed to explore correlations between adolescent and parent reported family conflict with diabetes-distress, depressive symptoms, resilience, and health-related quality of life (HRQOL) for both adolescent and parent. Eligible patients were enrolled in a two-site randomized controlled psychosocial intervention study. Participants were ages 13-18 with T1D for over a year and elevated levels of diabetes distress. At baseline, patients and their parent completed measures of: diabetes-specific family conflict (DFCS), HRQOL (T1DAL), diabetes distress (PAID-T), depressive symptoms (PHQ-8), and resilience (CD-RISC). HbA1c was pulled from participants medical charts. Descriptive statistics were used to summarize demographic variables, and bivariate correlation analyses were used to investigate the association between DFCS and the other psychosocial variables. Adolescents (N= 131; 53.4% female, 6.1% identified as another gender, 78.6% White, 9.9% Black, 2.3% Asian, 3.1% American Indian/Alaskan Native, and 80.9% Non-Hispanic, average age 15.38  1.5) DFCS scores correlated with more diabetes-distress (r=0.386, p<0.001), depressive symptoms (r=0.334, p<0.001), and less HQOL (r= -0.303, p<0.001). Parents’ (N=131; 79.4% White, 9.2% Latino/Mexican 6.1% Black, 2.3% Asian, 0.8% other, 79.4% private insurance) DFCS scores correlated with higher youth A1C (r=0.280, p<0.001), higher parent diabetes distress (r= 0.479, p <0.001), and lower parent resilience (r= - 0.200, p = 0.022) and HQOL (r = -0.369, p < 0.001). Both parent and patient reports appear to be an important area of further investigation for determining correlates of poor physical and psychosocial wellbeing in this high-risk group. While further investigation is needed, screening for family conflict may be important in clinical procedure, as it may become a future target for intervention.