University Of North Texas

NIH Research Projects · FY 2025 · 2022-07

PROJECT SUMMARY Epilepsy is among the most common serious neurological disorders, and about 40% of epilepsy patients do not respond to existing treatment. Clinically, the prolonged, refractory epilepsy with negative surgical outcomes is often associated with distributed epilepsy onset rather than a local epileptogenic zone. Understanding the epilepsy as a large-scale brain network abnormality enables the development of new treatment options and research directions. At present, the majority of research related to analysis of the epileptic network has been focused on the ictal period, while few have been devoted to the analysis of the earlier stages of epileptogenesis (latent period). Investigating the brain network properties of epileptogenesis is as important and can help develop antiepileptogenic interventions for epilepsy prevention and cure. Early in our experiments, we discovered pathological high-frequency oscillations (pHFOs), which are reliable biomarkers of epileptogenesis. They are generated by clusters of pathologically interconnected neurons (PIN-clusters) and reflect bursts of population spikes. Recent updates in the animal models of chronic epilepsy evidenced the spatially distributed pHFO events, which implies the development of large-scale PIN-cluster networks during epileptogenesis. It is critical to study the network topology and characteristics of PIN-cluster-formed epileptogenic networks in order to further understand the underlying mechanisms of epileptogenesis. To fulfill this gap, the present study plan is to explore pHFO-based networks using the Kainic Acid (KA)- induced status epilepticus (SE) model of epileptogenesis. We hypothesize that epileptogenesis after SE is dependent upon the formation of large-scale PIN-cluster networks that is expressed by the spatial occurrence and temporal coupling of pHFOs. Combining the biocompatible, organic–material based neural interface array (NeuroGrid) with multichannel silicon probes, we aim to identify the spatial and temporal profiles of pHFOs (Aim1). Using the advanced computational algorithms such as graph theory analysis and Shannon Entropy (SE), we propose to investigate the causal relationship and characteristics of the pHFO-based epileptogenic networks (Aim2). The outcome of this study will assess the robustness of novel network-based recording design and algorithm development. It will also determine whether the pHFO-derived network parameters are a reliable biomarker of epileptogenesis. The future plans are to translate the pHFO-network concept and computational tools into the clinical study of epilepsy. This approach may open a new direction to the prevention of epilepsy development and cure epilepsy.

NIH Research Projects · FY 2025 · 2021-09

PROJECT SUMMARY/ABSTRACT Even with recent advances in synthetic biology, it remains a major challenge in developing genetic circuits that involve multiple inputs and outputs. This is because natural genetic systems are only capable of connecting one single chemical input to one specific promoter to control gene expression. This poses a significant barrier in creating engineered organisms with complex signal response behavior for biomedical applications. The long-term goals of this research team are to establish robust strategies for constructing biological parts of genetic circuits, and to use these parts to expand researchers’ ability in engineering new cellular functions for biomedical applications. In their recent progress, the team established a module swapping strategy for building genetic sensors from regulators in the LacI and TetR families and they harnessed these engineered sensors to develop several novel genetic circuits. The two directions in this proposed research represent important steps toward the team’s long-term goals in the next five years. The first direction is to advance the capabilities in engineering transcriptional regulators as modular biosensors. Specifically, the team plans to 1) establish design principles of modifying regulators for enhancing their performance as biosensors and 2) apply module swapping to a wide range of regulator families. The central hypothesis is that each regulator within a family contains a ligand-binding module (LBM) and a DNA-binding module (DBM) for the purpose of detecting an input signal and for interacting with a promoter, respectively; if key module-module interactions are maintained, LBMs and DBMs from different regulators can be mixed and matched to create hybrid regulators with new combinations of input sensing and DNA recognition properties. For their second direction, the team proposes to harness hybrid regulators to explore novel circuit designs in various organisms, aiming to meet emerging needs in biomedical fields. This effort includes developing cellular devices to continuously and simultaneously monitor a range of toxic pollutants, which provides a means to assess the intake of toxicants that are commonly found in contaminated food and water. As an Early Stage Investigator, the PI and his team have already generated significant progress on both proposed directions, showing that they are highly qualified to pursue the proposed projects. The contribution of this project is expected to be the establishment of design principles for creating modular parts from regulators in many families and the advancement in genetic circuit design and implementation. This contribution will be significant because it is expected to release many new possibilities in circuit topologies for biomedical uses, including monitoring devices that will be created in this program. The overall approach is innovative because it represents a new way of using protein engineering and cellular engineering approaches to enhance public health and safety. Therefore, the proposed work is expected to generate positive impacts at both scientific and societal levels.

NIH Research Projects · FY 2025 · 2021-02

Abstract The focus of research in my lab is to determine how physiological functions of Slo2 channels are regulated by other proteins. Slo2 channels are a family of large-conductance potassium channels existing in mammals as well as invertebrates. Human and mouse each have two such channels: Slo2.1/Slick and Slo2.2/Slack, whereas the nematode C. elegans has only one, SLO-2. These channels are widely expressed in the nervous system, and play important roles in shaping neuronal firing properties. Mutations of Slack in humans often cause epilepsies and intellectual disability. Although physiological functions of Slo2 channels are expected to be dependent on many other regulatory proteins, molecular identities and their mechanisms of action are only beginning to be recognized. In the past few years, we identified several proteins required for SLO-2 physiological functions in C. elegans, including two RNA/DNA binding proteins (HRPU-2 and a SAFB-like transcription modulator tentatively named SLTM-1), one RNA editing modulator (ADR-1), one protein tyrosine phosphatase (tentatively named PTP-5), and one pseudokinase (SCYL-1), which all have mammalian homologs. Our results suggest that HRPU-2 and SLTM-1 regulate SLO-2 function through controlling the expression of PTP-5, whereas ADR-1 regulates SLO-2 function through enhancing the expression of SCYL-1. We have demonstrated that SCYL-1 increases SLO-2 activity through direct interactions, and this regulation is conserved between mammalian SCYL1 and Slack. In the next five years, our major goals are to determine how PTP-5 regulates SLO-2 function and whether the regulatory mechanism is conserved with human Slo2 and a human PTP-5 homolog, to determine how HRPU-2 and SLTM-1 regulate PTP-5 expression, and to identify putative proteins required for ADR-1- dependent SCYL-1 expression using a forward genetics approach. We envision that results from the proposed studies will not only provide important new knowledge about the regulation of worm SLO-2, but also have the potential to reveal evolutionarily conserved mechanisms of Slo2 channel regulation.

NIH Research Projects · FY 2025 · 2019-08

Vast amounts of multi-modal biomedical datasets have become available due to advances in high-throughput biomedical technologies. Each of these distinct data modalities such as genomics, epigenomics, and transcriptomics (collectively called as “multi-omics”) offers complementary information about the underlying biology of living systems. Therefore, there is an urgent need for scalable methods capable of integrating multi-modal datasets across millions of individuals. Our research program is devoted to the development of open-source integrative computational tools designed to analyze high-dimensional multi-modal biomedical datasets such as multi-omics and electronic health records (EHR) data. In this MIRA renewal application, we aim to develop generalizable, biologically inspired, and interpretable machine learning solutions to address some of the key challenges present in the biomedical datasets such as data irregularities, dependencies between data modalities, and missing and noisy data. We will develop novel computational methods based on machine learning, deep learning, and graph representation learning to integrate multi-modal biomedical datasets to build generalizable and interpretable models. These models will be used for various prediction tasks such as predicting clinical outcomes, inferring regulatory networks, and identifying drugs that can be repurposed for other diseases. The vision of our research program is to develop open-source computational tools that efficiently integrate multi-modal biomedical datasets, enhancing our understanding of gene regulatory interactions and disease mechanisms. By leveraging machine learning, deep learning, graph representation learning, and foundational models, we aim to advance precision medicine, facilitating more informed treatment decisions.

NIH Research Projects · FY 2025 · 2018-07

Project Summary/Abstract Despite significant progress in understanding factors involved in megakaryocyte development and differentiation, much remains unknown about the mechanisms through which these factors affect megakaryocytes, particularly during maturation. This represents a critical gap in megakaryocyte biology that requires further investigation. Our previous research has shown that zebrafish thrombocytes express approximately 50% of the genes found in megakaryocytes, although they differ in aspects such as polyploidy. Therefore, zebrafish thrombocyte maturation can serve as a valuable model for studying conserved genes and their roles in megakaryocyte development. In this renewal application, building on the progress from our R15 grant, we will investigate the downstream factors of 10 newly identified genes that affect either young or mature thrombocyte production. Additionally, we have identified 453 protein-coding genes expressed in mature thrombocytes that are also found in megakaryocytes. Assuming around 300 genes are expressed downstream of the 10 novel factors, this brings the total to 753 genes to be examined for their involvement in thrombocyte development. The core hypothesis of this proposal is that common downstream factors exist for the novel genes involved in young thrombocyte production, as well as a distinct pathway for the production of mature thrombocytes. To test this hypothesis, we propose the following three specific aims: Aim 1: Generate CRISPR/Cas9-based thrombocyte-specific knockouts for the 10 novel genes and characterize the resulting zebrafish for thrombocyte types. Aim 2: Perform single-cell RNA sequencing on the young and mature thrombocyte populations from the knockout fish generated in aim 1, where these populations are labeled with RFP and GFP, respectively, identify differentially expressed downstream genes for the 10 novel factors, compared to control fish and search for common genes expressed across these mutants to gain further insight into shared regulatory pathways. Aim 3: Conduct a comprehensive knockdown of the downstream genes identified in aim 2, along with mature thrombocyte-specific genes that are also expressed in megakaryocytes, using the high-throughput piggyback knockdown method. The results of this proposal will lead to the identification and confirmation of common downstream factors for the 10 newly identified genes from our previous R15 grant and uncover novel factors involved in thrombocyte maturation. These findings will serve as a foundation for future grant proposals. Additionally, this project will provide training opportunities for two graduate students, who will also mentor two undergraduate students. We will encourage undergraduate students to contribute to the publication of research papers as part of their training.