Texas Tech University

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Suicide is the third leading cause of death among youth ages 15-24 in the United States. Accurate assessment of suicidal thoughts and behaviors (STBs) is critical in research and clinical practice. Brief assessments (e.g., single items) are necessary for feasibility in many contexts, but little work has focused on validating brief STB assessments, and extant research has documented wide discrepancies in reports of STBs across measures. Invalid STB assessments have profound implications for development of suicide theories, research on suicide risk and protective factors, and clinical management of STBs, ultimately hampering suicide prevention. Despite the challenges of STB assessment, single-item measures can have adequate psychometric properties, comparable to their multi-item counterparts. Psychometric validation and standardization of STB measures will improve replicability of research findings and facilitate accurate identification of STBs in risk assessment, which is critical to management and prevention of STBs. The proposed study will recruit two samples of youth ages 15-24 with lived experience of STBs in order to refine single-item STB measures, test their psychometric properties, and identify other factors impacting response accuracy. Toward the long-term objective of improving prevention of suicidal thoughts and behaviors in youth, this study has 3 specific aims: (1) develop a pool of clear, interpretable items to assess youths’ experiences with STBs using cognitive interviewing, (2) examine psychometric properties of the resulting set of single-item STB measures, and (3) identify factors that impact responses beyond item interpretation through an implementation science framework. The proposed research and training activities will be conducted at Texas Tech University. This fellowship will provide specialized training necessary for the applicant to become an impactful independent clinical scientist. Training will focus on three goals: (1) enhance knowledge of youth STBs and research best practices with youth through regular mentor meetings, lab meetings, guided readings, and attendance at youth advisory board meetings and relevant conferences, (2) gain expertise in cognitive interviewing and other qualitative research methods through formal courses and workshops, regular mentor meetings, guided readings, and lab meetings, guided applied practice, and collaborating on projects using existing cognitive interview data, and (3) gain expertise in stakeholder-centered approaches to designing and implementing suicide-focused research with youth using dissemination and implementation science principles through guided readings, lab meetings, regular mentor meetings, attending workshops, and guided applied practice. Results of this study will provide actionable recommendations for brief assessment of STBs, improving management of risk and data collection accuracy for STBs assessed in research and clinical contexts.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Cell signaling is initiated at the plasma membrane (PM), but our understanding of the chemical interactions that regulate membrane protein function is still incomplete. The overall theme of my research group is to resolve functional, molecular interactions in the complex environment of the PM. These efforts produce fundamental insights into structure-function relationships of cell surface receptors and provide a crucial link between structural biology and cell signaling. My work is aimed at the two largest families of membrane proteins: receptor tyrosine kinases (RTKs) and G protein-coupled receptors (GPCRs). Both of these protein families are heavily targeted in disease. GPCRs are the targets of over 30% of all FDA approved drugs. RTK inhibitors are the most successful targeted therapies in cancer treatment. RTK function is coupled to dimerization, but the degree of heterodimerization between receptors has not been investigated in a systematic way with a quantitative, live cell methodology. Our objective is to quantify these interactions in live cells and determine the effects of ligands and the plasma membrane environment. Chemokine receptors are class A GPCRs that regulate cell movement like migration and infiltration in a broad range of cell types. Consequently, they are important in diseases ranging from asthma and arthritis to psoriasis and cancer. CXCR4, for example, is the target of an FDA approved compound for mobilizing hematopoietic stem cells in cancer. Many studies have demonstrated that chemokine receptors can assemble into homodimers and heterodimers that regulate cell signaling and can be targeted in drug development. These dimerization interactions are non-covalent and thus dynamic in nature, although the precise thermodynamic and kinetic principles governing the interactions are not yet resolved. Furthermore, the prevalence and stability of putative dimeric complexes has only been well characterized for a small number of receptors, leaving open many questions about the importance of dimerization for other chemokine receptors. My lab will use an innovative approach called pulsed interleaved excitation fluorescence cross-correlation spectroscopy (PIE-FCCS) to resolve and quantify membrane protein interactions in live cells. Our future research plans are to: (1) Investigate how ligand binding and cellular environment affect the local network of RTK interactions, (2) Resolve the role of heteromerization on chemokine receptor GPCRs, and (3) Develop a 3-color- PIE-FCCS instrument to resolve competition in the local interaction network of membrane proteins in live cells. The outcomes of this work will be a quantitative description of membrane protein interaction networks, which will provide fundamental insight into cell signaling pathways involving these receptors. This insight will in turn create new opportunities for drug development and translational research.

NSF Awards · FY 2024 · 2024-09

Electric charge transport physics is at the core of several technologies driving our economic and national security interests. For instance, the design of novel semiconductor devices requires a proper understanding of electron transport in the high-frequency regime. Similarly, the operation of directed energy systems hinges on the development of novel microwave sources and high-voltage high-current pulsed-power infrastructure. The project aims to provide innovative and robust numerical methods that will greatly enhance our predictive capabilities in the context of high-frequency electric charge transport simulation. This project will contribute to developing a new educational curriculum targeting the interdisciplinary training of graduate students at the intersection of mathematical modeling, numerical analysis, scientific computation, and physics. The project will develop numerical methods to solve electrostatic and electrodynamic fluid models of electric charge transport. The Euler-Maxwell and Euler-Poisson systems are some of the simplest electrodynamic and electrostatic (respectively) fluid models of electric charge transport. These models describe electrically non-neutral plasmas, electron inertia effects, high-frequency electrostatic plasma oscillation, and collective cyclotron motions such as the Diocotron instability. This project comprises numerical analysis, scientific computing, and graduate-level education. The research program will advance space and time discretizations for hydrodynamic models of electric charge transport that are mathematically guaranteed to be robust and preserve key mathematical properties of interest. Among such properties, we have preservation of pointwise stability properties (e.g. positivity of density and minimum principle of the specific entropy), discrete energy-stability, and well-posedness of linear algebra systems. This project comprises three research tasks involving the development of: (I) Semi-implicit schemes for Euler-Maxwell and Euler-Poisson systems, (II) Maxwell's equations formulations and solvers, and (III) Graph-based solvers for nonlinear hyperbolic systems (mathematical theory and high-performance implementation). The resulting methods will be implemented using the library deal.II. It will extend the investigator and collaborators' high-performance software developments. This project will also lead to a new graduate-level class to train a new generation of students on the nature of these models and their technological applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Brain tumors are among the most fatal cancers, affecting millions of individuals worldwide. In the United States alone, each year thousands of adults and children receive a primary brain tumor diagnosis. A key focus for oncologists, neurologists, and other scientists involved in the field is in determining the specific anatomical origin site of the tumor. Knowing this site might help in gaining insights in how the tumor behaves, in predicting the symptoms it is likely to cause, and in identifying genetic syndromes that have a high association with brain tumors. Therefore, this source information can be an important aid in the early detection of brain cancer. This project studies a reliable and efficient inversion algorithm called Deep Quasi-Reversibility Method (DQRM) that can quickly reconstruct the primary tumor’s location. By actively involving undergraduate students in research activities and fostering collaborations between institutions, the project will contribute to the well-being of individuals affected by brain tumors as well as help to cultivate a skilled STEM workforce. The project is based at a long-established HBCU, thus providing an opportunity to broadening research participating in STEM. The project involves numerical and theoretical studies of the proposed DQRM for solving the source localization oncological question with different levels of complexity. The DQRM is a combination of a variational quasi-reversibility (QR) method and a deep learning mesh-free-based algorithm. The design brings together techniques of computational mathematics, partial differential equations, and machine learning to fast deliver a reliable and accurate quasi-solution. On one hand, the variational QR approach can overcome localized features, highly dynamic nonlinearities, and the inherent exponential instability of the reconstruction process. On the other, the deep learning approach handles the curse of dimensionality and the costs associated with data measurement. The first objective of the project is to study the effectiveness of the inverse solver in tackling the quasi-linear parabolic models associated with the evolutionary dynamics of tumor cells. The second is to investigate the applicability of the algorithm by incorporating an advanced tumor growth model that considers factors such as age, size, and spatial structure. The theoretical theme is centered around the convergence of a neural network approximator towards the quasi-solution. This project is funded in part by the Historically Black Colleges and Universities - Excellence in Research (HBCU-EiR) program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This EArly-concept Grants for Exploratory Research (EAGER) award is made in response to Dear Colleague Letter 23-109, as part of the NSF-wide Clean Energy Technology initiative. Hydrogen (H2) is one of the cleanest potential energy sources as only water is generated when it is consumed. As of 2022, the global hydrogen demand is 95 Mt, while it will increase to be more than 500 Mt by 2050 to reach net zero according to the International Energy Agency. However, the current hydrogen production technologies, such as water electrolysis and steam methane reforming (SMR), are either too costly or emits too much greenhouse gases (i.e., methane and carbon dioxide, CO2). For example, the SMR process generates about 9-10 kg CO2e/kg H2. Therefore, carbon-negative hydrogen production technology is needed to simultaneously provide low-cost hydrogen energy and sequestrate CO2. Through this interdisciplinary project scientists from Texas Tech University advance clean energy technologies by developing a new, high-risk carbon-negative hydrogen production approach from the reactions of iron-rich rocks and water in the Earth's subsurface. The ‘cost-free’ geothermal energy and heat generated by exothermic reactions will be leveraged to sustain the hydrogen production for extraction; in particular, this innovative technology may enable a new clean hydrogen source with a cost lower than blue/green H2. Simultaneous CO2 injection will also tackle climate change by permanently locking CO2 as carbonate minerals. This interdisciplinary research will improve the fundamental understanding of the involved geochemistry, geophysics, heat transfer, and fluid flows from pore to reservoir scales. Additionally, a planned one-day ‘Clean Energy and Hydrogen’ workshop aims to enhance middle school girls’ interests in clean energy, while the integration of the research outcomes into an undergraduate course will transform students’ skills from petroleum engineering to clean geo-energy. The principal investigators investigate a potentially transformative approach to initiate and stimulate the in-situ, carbon-negative H2 production via serpentinization reactions of olivine-rich rocks and water. CO2 is co-injected with water to achieve carbon-negative H2 production. Assessing and deciphering the self-sustainability of this carbon-negative hydrogen production process over the human time scale is at the center of the project. A synergistic combination of rigorous core-scale reactive percolation experiments, advanced characterization, and numerical modeling are performed. More specifically, the principal investigators characterize the physicochemical and geomechanical coupling between reactions, fluid transport, and evolution of rock properties. The new data, knowledge, and insights obtained at the core scale are integrated into the reservoir-scale assessment of the self-sustained H2 production process by both analytical approaches and thermal-hydro-mechanical-chemical modeling. This work is expected to yield unprecedented understanding of the fundamental mechanisms and build a necessary framework for the game-changing innovation in large-scale, low-cost, and in-situ carbon-negative H2 production. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

NON-TECHNICAL SUMMARY Solid-solution metal chalcogenides are emerging semiconductors consisting of metals and chalcogens (e.g., sulfur, selenium, tellurium). These materials offer considerable potential for energy conversion and data storage applications due to their tunable electronic properties; however, their thermal stability remains limited by elemental sublimation and phase segregation after thermal exposure. Although past studies have shown that alloying and doping can mitigate thermal degradation, thermal stability varies with chemical compositions, highlighting the need to understand degradation pathways under complex compositions. This LEAPS-MPS project will use a reactive printing technique to accelerate the design, understanding, and discovery of new metal chalcogenides with improved thermal stability. To achieve this goal, the PI will develop a printing-based high-throughput synthesis technique for solid-solution chalcogenides, facilitating rapid screening and detailed study of their structure-property relationships. This method allows for the synthesis and manipulation of complex chalcogenide systems without the high-energy processes that limit traditional synthesis techniques, facilitating the understanding of mesoscale and interface-related phenomena. With an emphasis on the local and surrounding rural schools in West Texas, this project will involve actively recruiting undergraduate researchers from these groups and incorporating research results into educational outreach programs. Through career-focused outreach programs, the project will provide rural K-12 minority students with opportunities for hands-on research experience and prepare them for STEM careers. TECHNICAL SUMMARY While solid-solution metal chalcogenides have attracted significant attention in energy, sensing, and computing, the poor thermal stability of chalcogenide materials limits their ability to operate in extreme environments or dynamic climates. To overcome this limitation, this LEAPS-MPS project seeks to understand and control the critical factors in the thermal degradation pathways of printed chalcogenides to develop effective strategies for improving the thermal stability of metal chalcogenide materials. To achieve this goal, the PI will develop a high-throughput combinatorial reactive printing technique that synthesizes a gradient of chalcogenides from the chemical reaction of starting precursors (e.g., nanoparticles, dopants, and ligands), which will form compositionally varying alloys during the post-print atmosphere-controlled sintering stage. These compositionally complex samples will undergo extreme thermal flux, where the compositional and structural changes at different film locations will be studied to understand the role of elemental modulation in thermal stability as a function of temperature and time. A complementary machine-learning study of the stability data will be performed to identify the key factors limiting the chemical stability of printed chalcogenides under extreme conditions. By studying the fundamental relationship between the composition, processing conditions, and stability of the samples, the project will develop a deep understanding of what, and more importantly, why the optimal composition and doping factors will advance the stability of the metal chalcogenides. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

Project Summary/Abstract Frailty-related medical expenses cost approximately $18 billion annually in the USA. Progressive losses in skeletal muscle (SKM) mass and function, often observed with aging and type 2 diabetes (T2D), contribute to phenotypic frailty characterized by slow gait speed, weakness, weight loss, fatigue, and low physical activity. T2D increases frailty risk by nearly 50% and risk for both conditions increases with age; therefore, preventing progression to T2D in older adults with prediabetes is critical. Additionally, SKM is the largest glucose disposal site in the body and insulin responsiveness, a component of glycemic control, is essential to maintain functional SKM mass. Thus, impaired glycemic control, a pathophysiological change underlying the development of T2D and observed in prediabetes, increases frailty risk. Conversely, improving capillarization and mitochondrial function support increased SKM mass and glycemic control, thereby decreasing T2D and frailty risk. Together, these SKM architectural variables (e.g., cross-sectional area [CSA], capillarization, mitochondria) are attractive targets for interventions such as exercise in prediabetic older people. High-intensity interval training (HIIT) has been used effectively in older adults with SKM benefits similar to those from aerobic and resistance training, and is more time-efficient. However, prediabetic older people may be exercise-intolerant or -resistant, underscoring the need for alternative therapies in place of (or in addition to) exercise. Emerging evidence supports repeated heat therapy as an alternative method to improve glycemic control and SKM architecture, and such adaptations may also improve muscle growth responses to subsequent exercise. Heat-sensitive transient receptor potential vanilloid receptor 1 (TRPV1), a highly Ca2+-permeable ion channel, is a promising mechanistic candidate underlying SKM adaptations to repeated heat stress. While whole-body heat therapy is widely studied, local heat therapy (e.g., heat pad) is more practical and likely provides similar health benefits. Whether local heat therapy would have similar benefits to HIIT or whether heat pre-conditioning would improve adaptations to subsequent HIIT in prediabetic older adults is unknown. Therefore, the overall hypothesis of this ESI R01 application is that local heat therapy improves SKM architecture, glycemic control, and subsequent exercise adaptations, and decreases frailty risk in prediabetic older adults, with TRPV1 as an underlying mechanism. The project’s specific aims will test whether: 1) local heat therapy improves SKM architecture, glucose tolerance, and frailty indicators similar to HIIT in older prediabetic people; 2) local heat therapy pre-conditioning improves the SKM response to HIIT in older prediabetic people; and 3) TRPV1 mechanistically underlies SKM adaptations to heat therapy. The findings of this study will provide evidence supporting a directly translatable local heat therapy intervention for older adults at risk for T2D. In line with the Katz mechanism, the proposed work leverages the expertise of the PIs and represents a shift in research focus. We propose to test a highly practical and promising intervention to combat metabolic complications in aging, a crucial area of study with the current aging population.

NSF Awards · FY 2024 · 2024-08

Optical hybrid materials (OHM) are emerging systems combining organic (carbon-based) and inorganic (non-carbon-based) components at the nanoscale. These materials can potentially lead to applications in efficient light-emitting diodes, advanced medical imaging, and enhanced solar cells for cleaner energy. However, the manufacturing of OHM remains challenging due to the undesired scattering from the poor design of the matrix-nanofiller interfaces. This NSF project will use a combinatorial printing technique to understand how compositional doping influences OHM hybrid structures and develop a spatially resolved optical analysis system to identify the key factors affecting these materials under extreme material gradients. The successful execution of this research will enable new manufacturing capacities of high-performance optical materials for advanced lenses, lasers, and optoelectronics. The developed system will offer an ideal model for manufacturing and characterizing soft optical hybrid systems that are challenging for the existing fabrication techniques. In addition, the collaboration of two Hispanic-serving institutions (Texas Tech and Texas A&M University) could increase the participation rate of underrepresented groups. The team will actively recruit undergraduate researchers for the project and provide K-12 students with opportunities for hands-on research experience in the multidisciplinary fields of manufacturing science, chemical engineering, and advanced materials. While optical hybrid materials (OHM) hold great promise due to unique optical structures combining the benefits of soft polymers and functional nanofillers, a lack of understanding of polymer-nanofiller interactions at the interface level and subsequent difficulty controlling undesired scattering pose considerable challenges. As a result, it is crucial to develop knowledge connecting nanoscale OHM compositions with their detailed optical characteristics (refractive index, birefringence, etc.). This grant supports fundamental research to understand the effect of dopant compositions and compositional gradients on OHM. The team will leverage the combinatorial printing of structure-programmable nanofillers to understand defects and doping of combinatorial optical materials and their hybrid structures. In addition, a spatially resolved optical characterization system will be developed to identify the key factors controlling the interface-related phenomena and properties of the OHM under extreme material gradients. In this process, the project will quantify the effect of graded optical dopants in polymers and examine the refractive index gradients, birefringence gradients, and photoluminescent gradients (both compositionally and optically). If successful, this method will yield rich knowledge in advanced optical manufacturing and potentially challenge the conventional energy-intensive melting-based or clean-room thin-film deposition approaches for OHM and related devices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Valley Fever, also known by its medical name - Coccidioidomycosis, is an emerging infectious disease caused by the inhalation of Coccidioides fungal spores. This causes respiratory infections that can range from mild symptoms to severe pulmonary or disseminated disease. The Coccidioides fungus preferentially grows in warm, water-limited environments of the Southwestern USA; and approximately 20,000 cases are reported there each year. With the onset of climate change and increasing incidence of both drought and extreme rainfall events, the endemic range of the disease is expected to expand in the near future. This research investigates the distribution of Coccidioides in West Texas soils and correlates this and the soil environmental conditions conducive to fungal growth. These data will be combined with data on current and recent Valley Fever incidents in the general population and predict where affliction incidence and transmission rates in humans could be high. Broader impacts of the project include support education and cross-training of a graduate student in both geoscience and the health implications of climate-driven environmental changes. It will also increase collaboration and knowledge/skill exchange between academic institutions, USDA-ARS, city and state public health departments, and healthcare professionals in the West Texas region. Outreach and engagement activities through Texas Tech Climate Center’s social media pages and public seminars will allow for the research team to communicate the research findings to the public and communities in West Texas to increase awareness and causes of Valley Fever. Coccidioidomycosis, or Valley Fever, is presently a reportable disease in a number of US states. There is potential, however, and the strong liklihood of its expansion to new regions of the US because of climate chnge. This project studies the soil/geological environmenta and ecological niche of the Coccidioides fungus that is responsible, in West Texas, for Valley Fever. It will also examine details on how West Texas communities and residents are exposed to this disease and the conditions that could spread the disease to other regions. Due to its climatic suitabilty for the growth of the infecting fungus and the diversity and abundance of its small mammal population and frequent dust storms, West Texas is the ideal location to study the environmental conditions ripe for human contraction of Valley Fever. In addition, in West Texas many people work outdoors in hot dry weather due to the large presence of the oil and gas and renewable energy industries; and this can expose them to inhalation of air borne Valley Fever fungal spores. To examine both the environmental and human components of the disease, the research uses a combination of field testing of soil moisture and erodibility with a portable wind erosion simulator, laboratory tests to determine the distribution of Coccidioides in soil and dust samples collected from selected study sites, and meteorological data analytical work to generate quantitative metrics of oscillating wet and dry periods. The lengths, severity, impact on local meteorological conditions of dust emission and transport will also be studied; and population surveys, in which participants will be recruited via pop-up booths at local businesses, will be used to study the current and recent incedences of coccidioidomycosis infection. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-07

Project Summary/Abstract Cancer is a metabolically heterogeneous disease. At the core of most metabolic pathways, mitochondria play central roles in cancer cell proliferation, migration, invasion, metastasis and therapeutic resistance. Due to significant amount of somatic and germline mutations, cancer-relevant modifications in mitochondria are found. While some mitochondrial modifications provide aggressive advantages to cancer cells, others are detrimental. More understandings of how cancer cells manipulate mitochondrial modifications, replenish mitochondrial repertoire, and regulate mitochondria related metabolisms would therefore hold great promises to tackle cancer down. Supported by our preliminary work, we hypothesize that a small population of cancer cells, the cancer stem cells (also known as cancer initiating cells), can horizontally transfer their mitochondria to macrophages. Due to the predisposed modifications in mitochondria of cancer stem cells, we further hypothesize that these cancer stem cells derived mitochondria could rewire metabolic pathways in macrophages thus leading to alternative activation of macrophages (M2). The hypotheses will be tested by two specific aims. We will initially test the reproducibility of the horizontal transfer of mitochondria initiated by different types of cancers (Aim 1). We will also determine if such mitochondrial transfer is actively conducted by cancer stem cells or passively achieved via phagocytosis by macrophages (Aim1). Once the mitochondria of cancer stem cells are present in macrophages, we will examine how these mitochondria mask arginine metabolism, oxidative phosphorylation and reactive oxygen species production in macrophages (Aim2). Additionally, we will determine the polarization spectrum of macrophages in response to the acquisition of exogenous mitochondria from cancer stem cells (Aim 2). With successful completion of this proposal, the information collected from this R03 will not only provide new insights of new immune evasion mediated through the horizontally mitochondrial transfer from cancer stem cells but also present an enticing target for more effective therapeutic interventions specifically targeting the cancer stem cells or the tumor associated macrophages. In addition, it may also serve as a proof-of-principle to apply to other types of stromal cells that may account for other cancer related diseases.

NSF Awards · FY 2024 · 2024-07

The broader impact of this I-Corps project is the development of a non-contact health monitoring method with broad applications. The solution has the potential to seamlessly monitor vital signs without requiring users to wear any devices. Unlike wired systems, this technology offers convenience, reliability, and long-term monitoring, benefiting both healthcare providers and individuals seeking advanced care. In addition to targeting diseases like sleep apnea and sudden infant death syndrome, the technology also promises innovation in motion-adaptive cancer radiotherapy. The technology can potentially revolutionize patient care, reducing costs, and improving outcomes, especially for vulnerable populations. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an advanced beamforming that supports concurrent multiple target sensing and tracking of human subjects. This technology includes the use of advanced signal processing algorithms that enable high sensitivity and a wide dynamic range for the detection of micro-motions. The technology considers a coherent detection architecture that supports multiple operation modes for high dynamic range. Finally, the solution includes an antenna-in-package for the integration of the system with compact size, low cost, and high performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

Driven by a vision for reducing the carbon footprint of large-scale scientific computing loads, the Renewable Energy Powered Advanced Computing Systems and Services (REPACSS) project will carry out a five-year plan to develop new methods and tools to build, operate, and manage a high-performance computing infrastructure at sufficient scale to demonstrate the use of renewable energy in powering advanced computing systems. The project includes plans to deploy a modern computing facility of significant size and to tackle current R&D issues that relate to managing scientific workflows in renewable energy settings. The project will develop and apply a combination of new tools for remote data center control, automation, and scientific workflow management to explore the use of renewable energy sources for large-scale computing. Barriers and tradeoffs between designs and the ability to predict and schedule scientific workloads based on varied energy production will be studied in a facility large enough to deliver significant amounts of computing and to attract use by other segments of the broad research community. Workloads are targeted to include small- to mid-scale jobs from one to a few thousand cores per job across broad areas of science and engineering, including long-tail science applications, deep learning, artificial intelligence, and machine learning applications by providing significant amounts of modern accelerator-based computing. As part of the research program, REPACSS will also study user workflows and behavior in responding to distinct types of power availability, including cost versus quality-of-service choices. These factors are important when considering commercial sources of computing such as commercial clouds for various categories of machine types and availability. In the latter stages, this project will expand and explore the use of these innovations to promote adoption by other facilities and throughout the data center industry. The REPACSS project will study in detail many factors related to the efficiency and practicality of delivering computing and optimizing the use of renewable energy to power science and engineering data centers and innovate significantly beyond their current limitations. Tools and methods will be developed throughout the project aimed towards lowering costs for electrical power and achieving dramatic reductions in climate impact for supplying large quantities of capacity computing. The REPACSS project will also carry out a series of outreach activities to engage undergraduate and high-school students in research activities, support and promote underrepresented minority students in computing, and train a broadly inclusive and globally competitive science workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-04

Project Summary/Abstract Mirtrons are a new class of small RNAs. Previous findings show mirtrons are generated by splicing and are less stable than canonical miRNAs. Thus, the expression of mirtrons is often negligible. Additionally, most mirtrons exhibit distinct evolutionary properties relative to solo and mammals have way more mirtrons than lower organisms suggesting some unknown importance of mirtrons in mammals. Recently, elevation of mirtrons has been linked to human cancers and immune disorders. However, the biological significance of mirtrons in humans is not well understood due to very limited efforts of mirtron research in humans. It is therefore important to investigate how biogenesis of mirtrons is stabilized during disease development and how increased mirtron expression promotes tumor/disease progression. In ongoing experiments, we show most mirtrons elevated in human cancers contain guanine rich sequence that forms G-quadruplex, a secondary structure that has been implicated as a strategy adopted by some RNA viruses to evade the host exoribonucleases, XRN1/2 mediated digestion. We also show XRN/1/2 are crucial enzymes for mirtron stability that mirtrons in cancer cells significantly increase when XRN1/2 are knocked down. Additionally, all cancer mirtrons are featured with many (long) terminal nucleotide additions which result in abundance of mirtrons present in the cancer derived exosomes. Furthermore, we also show mirtrons from the cancer derived exosomes bind to the endosomal Toll-like receptor 7 (TLR7) of macrophages suggesting mirtrons are novel ligands of TLR7 and potentially modulate immune responses. Given these findings, we hypothesize that cancer cells selectively express mirtrons exhibiting high guanine contents that could form G-quadruplex structure to bypass XRN1/2 mediated degradation. The nucleotide addition to the 3’ tail provides preferential sorting of mirtrons into exosomes. Cancer exosomes can be taken by immune cells through endocytosis providing the opportunity for mirtrons to bind to TLR7. The binding of mirtron to TLR7 serves as an allosteric inhibitor to block TLR7 signaling and the associated downstream immunological responses. These hypotheses will be addressed in the experiments of the following Specific Aims: (1) to determine the impact of G-quadruplex structure of mirtrons on the resistance to exoribonucleases, XRN1/2; (2) to determine the consequence of altered mirtron precursor tailing on exosomal sorting; and (3) to evaluate the immune modulatory role of cancer mirtrons through binding to TLR7. Should this exploratory study reveal novel mechanisms of cancer mirtron stabilization and sorting into exosomes, and should these cancer mirtrons negatively regulate the TLR7 signaling and the related immune functions, novel therapeutics aimed to destabilize mirtron, to prevent their sorting into exosomes, and/or to compete mirtron binding to TLR7 may provide benefits to cancer treatment in addition to advance current knowledge of mirtrons in humans.

NIH Research Projects · FY 2025 · 2023-09

The Liang laboratory uses molecular simulations to fundamentally understand how electronically non- adiabatic reactions couple with protein’s structure, dynamics, and function. Electronically non-adiabatic reactions, such as photochemical and electron transfer reactions, switch electronic states during the chemical transformation. A fundamental understanding of how they interact with proteins is essential for advances in biomedical sciences. However, two central and fundamental questions remain elusive: (1) how does the protein environment modulate the pathway, dynamics, and quantum yields of the non-adiabatic reactions? (2) how do the non-adiabatic reactions induce structural changes in the protein? Molecular simulation is indispensable to answering these questions because it can resolve the energetics and kinetics of chemical reactions at atomic- level detail, which is often beyond the limit of current experimental techniques. Also, simulation incurs minimal cost and has no risk for human subjects. However, the multiscale nature of these processes poses significant challenges for traditional computational methods. Specifically, standard molecular mechanics (MM) simulations cannot describe the quantum-mechanical (QM) nature of the non-adiabatic reactions. Meanwhile, typical QM simulations are too expensive to characterize the slow biomolecular motions in response to these reactions. To overcome these challenges, in the next five years, our research program will expand our current efforts to develop and employ multiscale simulation methods to understand (1) the light-regulated signaling activities of transient receptor potential channels and metabotropic glutamate receptors by synthetic molecular switches, which are of top interest in optogenetics and photopharmacology, and (2) the long-range electron transfer events in cryptochromes and electron bifurcating enzymes, which are fundamental to understanding the circadian clocks, magnetic field sensing and energy metabolism in living organisms. The unique advantages of our approaches include (1) accurate and efficient non-adiabatic dynamics simulations with “on-the-fly” ab initio calculations of nuclear gradients and electronic couplings; (2) effective integration of the high-quality non-adiabatic dynamics simulations with high-efficiency MM sampling of protein conformational change. These key methodological advantages will enable the comprehensive characterization of non-adiabatic chemical reactivity in complex biomolecular systems and answer the above-mentioned fundamental questions with unprecedented accuracy. Explicitly simulating the photodynamics of biomolecules of this size and complexity is not routine, especially with the proposed multiscale simulation framework that incorporates ab initio non-adiabatic dynamics simulations. Therefore, five years into the future, our research will provide new insights into the design principles of next- generation photochemotherapy with minimal side effects, create powerful computational tools for simulating electron transfer in biomolecules, and deepen our fundamental understanding of the roles of quantum mechanics in biology in general.

NIH Research Projects · FY 2025 · 2023-07

Project Summary/Abstract The spread of antibiotic resistance is a growing concern as the emergence of resistance mechanisms among human pathogens is occurring more rapidly than the development of new antimicrobial agents. This issue contributes to the inability to fully clear persistent infections such as chronic wound and lung infections, which represent a major source of human morbidity and mortality. In turn, the inability to eradicate these persistent infections creates more opportunities for the evolution of novel microbial mechanisms to circumvent therapeutic treatment, exacerbating the problem of antibiotic resistance. There are multiple aspects of the chronic infection environment that contribute to therapeutic failure and the emergence of antibiotic resistance. First, several stressors encountered at the host-pathogen interface are mutagenic, which helps drive evolutionary adaptation in these sites. Second, the polymicrobial nature of many chronic infections can contribute to the spread of resistance mechanisms via horizontal gene transfer. The presence of polymicrobial communities can also further compound the issue of therapeutic clearance of infection since interspecies microbial interactions are known to alter bacterial physiological and lead to antimicrobial tolerance. In this proposal, we seek to target both the microbial evolutionary trajectory at the host-pathogen interface and the polymicrobial nature of chronic infections to design improved therapeutic strategies for eradication of pathogens contributing to otherwise persistent infections. In Aim 1, we propose to target antibiotic resistant isolates through the identification of vulnerability tradeoffs that can occur as the cell shifts its fundamental physiology to cope with antibiotic exposure. In addition to published examples of this phenomenon, we demonstrate our ability to uncover novel examples of tradeoffs that can be exploited to eradicate otherwise recalcitrant microorganisms. We seek to uncover more examples of vulnerability tradeoffs and determine the effectiveness of targeting these tradeoffs in a murine model of chronic wound infection. In Aim 2, we establish polymicrobial community wound pathogen models and use a methodology that we propose can be adapted for use in the clinical laboratory to demonstrate shifts in antibiotic efficacy driven by polymicrobial interactions. We demonstrate that both polymicrobial synergism (a reduction in antibiotic efficacy in complex bacterial communities) and polymicrobial antagonism (an increase in antibiotic efficacy in the context of a polymicrobial consortium) can be readily observed. Preliminary data suggest that combinatorial treatment strategies can be developed to exploit polymicrobial antagonism to overcome synergistic interactions. We propose to validate this strategy in a murine model of chronic wound infection. Together, these Aims will be used to identify antibiotic treatment strategies that will extend the efficacy of the currently available repertoire of antibiotics.

NIH Research Projects · FY 2026 · 2022-02

Project Summary Our research aims to understand the role of serotonin modulated mitochondrial biogenesis in diabetes, depression, and dementia (Alzheimer's disease, AD). Diabetes and depression are independent risk factors for dementia and worsen the dementia pathology and therapeutic response. Depression comorbidity impaired metabolic function such as hyperglycemia, insulin resistance, inflammation, and oxidative stress results in physical depression and cognitive impairment. Serotonin is an essential neurotransmitter that performs synaptic transmission, plasticity, energy homeostasis in aging and dementia. For a long time, it is known that the brainstem harbors unique neurons to synthesize and project serotonin to the entire central nervous system. However, molecular links between serotonin levels and mitochondrial biogenesis, mitochondrial dynamics, mitophagy/autophagy in dementia, diabetes, and depression are not entirely understood. It is established that serotonin synthesis in dorsal raphe is protective and essential for cell homeostasis and energy metabolism. It has been hypothesized that low serotonin levels induce defective mitochondrial biogenesis, impaired mitochondrial dynamics, mitochondrial dysfunction and defective mitophagy/autophagy in depression, diabetes, and dementia, and selective serotonin reuptake inhibitors, such as citalopram treatment reverses defective mitochondrial biogenesis and all mitochondrial defective aspects. We have conceptualized the study in rodents focusing on the transgenic models of 3Ds focusing hippocampus (APP, Tau, HT22 cells), hypothalamus (DbDb, mHypo cells), and raphe (TPH2/ko, RN46A-B14 cells). Therefore, the current study proposes to understand the pathologies and the protective effects of citalopram (SSRI) against defective mitochondrial biogenesis, impaired mitochondrial dynamics, and defective mitophagy/autophagy. The outcome of our proposed experiments will provide new insights into the role of serotonin in depression, diabetes, and dementia concerning mitochondrial biogenesis, mitochondrial dynamics, mitochondrial function, and mitophagy/autophagy. The outcome will also provide beneficial effects of citalopram against common serotonin-induced defects of mitochondrial dynamics, mitochondrial function, and mitophagy/autophagy.

NIH Research Projects · FY 2024 · 2020-09

Antibiotic Resistance Surveillance in Retail Food Specimens in Texas and Oklahoma Project Summary The National Antimicrobial Resistance Monitoring System (NARMS), a surveillance system established in 1996, tracks antibiotic resistance in enteric bacteria from humans, retail meats, and food animals, and its different programs on emerging bacterial resistance promote and protect public health. The goal of this study is to determine the prevalence of antimicrobial resistance (AMR) among Salmonella, Campylobacter, E. coli and Enterococcus isolated from retail samples of chicken, turkey, beef and pork and from Enterococcus, Vibrio, Aeromonas and lactose fermenting bacteria from seafood purchased from grocery stores in Texas and Oklahoma using standardized methods so comparisons can be made to other national locations participating in the NARMS food surveillance program. Achieving this goal will contribute to our understanding of the burden and magnitude of antibiotic resistant bacteria in retail meat, poultry and seafood products distributed and sold to consumers in this region that comprises approximately 10% of the population of the United States. Specifically, the results of this project will provide the FDA-CVM with isolates and sequencing data (whole genome sequencing) from important public health enteric bacteria, helping to conduct informed risk assessments on antimicrobial resistance (AMR) in animal products. During each year of the Cooperative Agreement, the Texas Tech University food microbiology laboratory will obtain food samples as follows: 120 bone-in, skin-on chicken, 120 ground turkey, 120 ground beef, 120 ground pork or pork chops, 24 shrimp, 36 salmon, and 36 tilapia, all produced under either conventional, organic or natural production systems. Samples will be collected monthly within 400 miles (6-hour drive) of Lubbock, Texas, including small metro, urban, suburban and rural cities and towns as well as large, medium and small grocery stores, supermarkets, and independent grocers throughout the region that will add representativeness to the diverse sampling region. Collected samples will be processed using approved FDA-NARMS protocols for bacterial detection, and isolates will be shipped monthly to FDA-CVM for AMR testing, while sequencing data will be used for further analysis and molecular determination of antimicrobial resistance. The results of this project will improve and increase educational activities and knowledge on antibiotic resistance as well as improve laboratory capacity on testing methodologies and protocols used by government laboratories.

NIH Research Projects · FY 2026 · 2019-02

Because of the pressing needs to comprehensively understand the biological attributes of glycosylation in many critical biological functions such as the immune response, cell development, cellular differentiation/adhesion and host-pathogen interactions, glycoproteomics continues to be a highly dynamic research area. Aberrant glycosylation for decades has been recognized as the attribute of many mammalian diseases, including osteoarthritis, cystic fibrosis, and cancer. Moreover, isomeric alterations of glycoproteins have been observed in diseases such as Alzheimer’s Disease and cancers. Recently, glycans and their isomers have been reported to be vital to the SARS-CoV-2 viral infection, making them a crucial target for the drug development of COVID-19. Therefore, reliable, and efficient characterization of glycopeptides and their isomers is necessary to better understand the attributes of glycosylation in biological and biomedical processes. We are proposing here four specific aims: Aim 1. To enhance the separation and identification of glycopeptide isomers using mesoporous graphitized carbon (MGC)-LC-MS and hydrophobicity index of peptides (HIP); Aim 2. To enhance the quantification of glycopeptide isomers using a 15N metabolic-TMT multiplexing approach and a parallel reaction monitoring (PRM) method; Aim 3. To enhance the glycopeptide isomeric characterization using novel derivatization methods; and Aim 4. To enhance automated isomeric glycopeptide data processing by the development of improved software. The outcome of these aims will provide reliable and efficient glycoproteomic platforms and algorithms for a better isomeric characterization of glycopeptides which can be employed to address biomedical issue, thus contributing to the glycoscience community. The innovations of this proposal originate from the uniqueness of the proposed analytical methods and software. The isomeric separation of glycopeptides using MGC-LC-MS/MS is a highly innovative method, developed in our lab, permitting efficient separation of glycopeptide isomers on a 1 cm short column. The retention time normalization of glycans intra and inter-laboratories has been introduced and demonstrated to be necessary previously through a glucose unit index (GUI), but not in glycoproteomic analysis. For the first time, we will investigate the retention time normalization of glycopeptides and glycopeptide isomers using a set of peptides which have known hydrophobicity factors on different instruments and different laboratories. The combination of 15N stable isotope labeling of glycopeptides and TMT will double the multiplexing capacity of TMT to 36-plex when studying in vitro cell line glycoproteomics. Although PRM has been utilized for glycoproteomic profiling, the analysis of isomeric glycoproteomics is lacking. Thus, this will be the first comprehensive investigation of glycopeptide isomers using PRM. Moreover, the derivatization methods we proposed are of great innovation. It will be the first time to achieve 2-aminobenzamide (2-AB) labeling on sialic acids of glycopeptides, and the first time to achieve efficient isomeric separation of sialoglycopeptides on a 15 cm C18 column via a two-step oxidation-reductive amination reaction. In addition, we have achieved a derivatization of sialylated glycopeptides (DOSG) method that will introduce mass difference to distinguish α2,3 and α2,6 linked sialoglycopeptides. Consequently, the combination oxidation- reductive amination and DOSG methods leads to an innovative enrichment method for sialoglycopeptides. The derivatization of carboxyl groups of sialic acids on glycans and amino acids on peptide backbone neutralizes additional charges and the addition of quaternary ammonium functionalized molecules provides controllable positive charges which make the enrichment using strong cation exchange (SCX) possible. This is a novel method for the efficient enrichment of sialoglycopeptides where sialic acid linkage isomers can be distinguished at the same time. The deliverables of this proposal are reliable, adaptable, and affordable strategies and improved software to enhance the isomeric glycopeptide studies by any laboratory interested in defining comprehensive protein glycosylation using LC-MS/MS. The proposed technologies are expected to enable a better understanding of the biological attributes of glycoprotein isomers in the development and progression of diseases.

NIH Research Projects · FY 2024 · 2014-09

Glycosylation is one of the most complex protein modifications; more than 50% of mammalian proteins are glycosylated. The fact that there are 100,000 proteoforms coded by only ~20,300 genes identified in the human genome emphasizes the importance of posttranslational modifications (PTMs) like glycosylation. Aberrant protein glycosylation has been implicated in many diseases, such as Alzheimer’s disease, congenital/metabolic disorders, diabetes, inflammation, Parkinson’s disease, bacterial/viral infectious diseases, and various cancers. More recently, glycans have been associated with coronavirus spike glycoproteins, including the SARS- CoV-2 virion. The diverse biological roles of glycans and their implications in diseases have created a demand for reliable qualitative and quantitative glycomic approaches, which facilitates sensitive investigation of glycan changes in different biological and biomedical samples. Mass spectrometry (MS) is the most efficient technique in glycomics due to its high sensitivity and capacity for acquiring structural information. However, glycomic research remains a challenge because of the microheterogeneity of glycan compositions in complex biological samples; the relatively low abundance in nature and low ionization efficiency in MS analysis; and the existence of variant positional and linkage isomers caused by the biosynthesis process. To overcome these challenges, several separation methods have been coupled to MS. Despite the development of these separation techniques, isomeric separation of glycans remains insufficient. There is an increasing demand for more efficient isomeric separation approaches since glycan isomers have been related to different diseases. The main aim of this proposal is to provide easily accessible, adaptable, and affordable strategies for better separation and characterization of glycans and glycan isomers derived from different glycoconjugates. Aim 1 is focused on finding a replacement for porous craphitic carbon columns that sufferes from low reprodcubility and loss of resolution and efficiency with time. The in-house mesoporous graphitic carbon (MGC)-LC-MS will be investigated for both permethylated (Aim 1a) and native (Aim 2b) isomeric separation of N- and O-glycans, glycolipid glycans, and free oligosaccharides. Other alternatives (also part of Aim 1a), such as 50 cm and 200 cm micro pillar array columns (μPAC)-C18-LC-MS, and a 50 cm long capillary nanoC18-LC-MS, will also be evaluated to achieve an improved isomeric separation of glycan isomers. Subsequently, GUI will be utilized to improve the identification of glycan isomers. A GUI libraries for the separation strategies developed in Aim 1 will be established to normalize the possible retention time shift among different runs (Aim 2). LC-M based glycomics quantitative strategies will be developed and assessed in Aim 3. The combination of permethylation and TMT will capitalize on the advantages of both techniques, providing enhanced sensitivity and accuracy in glycan quantitation (Aim 3a). Additionally, the combination of 8-plex isotopic permethylation and 6-plex TMT will facilitate a reliable 48-plex, thus significantly increasing the throughput of analysis (Aim 3b). Furthermore, with the improved separation and identification (Aim 1 and 2), PRM will be employed to assist the better quantitation of glycans and the differentiation of glycan isomers. A library containing the fragments fingerprint of each glycan isomer will be created to aid the glycan isomeric identification (Aim 3c). The development of automated software for easy, fast, and accurate glycomic data processing is the main focus of Aim 4. Finally, the aforementioned strategies and tools will be applied to clinical samples to address a variety of biological and biomedical issues such as COVID-19, breast cancer brain metastasis, liver cancer vs. cirrhosis, kidney diseases, algae development, and others (Aim 5). The development of the proposed methods and algorithms will help us and our collaborators to better understand the attributes and biomedical significance of glycan isomers in the development and progression of esophageal, breast, and liver cancer. Furthermore, we expect the analytical tools and algorithms proposed here to benefit researchers and scientists who are interested in understanding the biological attributes of glycan isomers in other systems.