Direction to ELEN building

Abstract

Glioblastoma (GBM) surrounds itself with an immunosuppressive tumor microenvironment (TME) enriched for tumor-supportive microglia (MG), tumor-associated macrophages (TAM) and myeloid-derived suppressor cells (MDSC) but lacking in T cells and functional antigen-presenting cells (APC). Beyond these well-described obstacles, T cells targeting GBM also face unique challenges both in trafficking from the cervical nodes to sites of malignancy and in overcoming immunosuppressive signals within the TME. We refer the dense web of immunosuppressive GBM myeloid stroma composed of TAM, MDSC and MG collectively as glioma-associated myeloid cells (GAM). In patients, GBM has proven refractory to most immunotherapies including T cell checkpoint blockade. Although ultimately a failure of anti-GBM T cell immunity, our findings suggest that the immune suppressive, tumor-supportive phenotypic programming of the GAM compartment acts as the key driver of GBM immune privilege. GAMs suppress anti-tumor immunity through diverse mechanisms including nutrient consumption (e.g., Arg1, IDO), immune checkpoint ligand expression (e.g., PD-L1, VISTA), and secretion of angiogenic, immune suppressive growth factors (e.g., VEGF, PDGF). Equally crippling to tumor immunity is the absence of innate immune activation in this compartment which results in a lack of tumor antigen cross-presentation (Signal 1), co-stimulatory molecule expression (Signal 2), and inflammatory cytokine production (Signal 3). We have now shown that by activating the innate immune sensor pathway known as the Stimulator of Interferon Genes (STING), it is possible to restore pro-inflammatory, immune-supportive programming to GAMs. This approach appears to have remarkable therapeutic benefit against both murine and canine glioblastoma tumors.

Speaker Bio

Dr. Curran received a Ph.D. in Immunology from Stanford University where he was awarded the McDevitt prize for the best graduate thesis in his year. Dr. Curran was the first recipient of the prestigious American Cancer Society Levy Fellowship to fund his post-doctoral studies in the lab of Dr. James P. Allison. While pursuing his postdoctoral studies at Memorial Sloan-Kettering Cancer Center, Dr. Curran published several influential manuscripts describing how T cell co-stimulatory pathways could be modulated in tandem to mediate immunologic rejection of melanomas in mice. Dr. Curran was the first to describe how combination blockade of the T cell co-inhibitory receptors CTLA-4 and PD-1 promoted the rejection of a majority of murine melanomas – a combination that remains the most effective FDA-approved immunotherapy. At the MD Anderson Cancer Center, Dr. Curran is an Associate Professor of Immunology and his Lab seeks to discover the underlying mechanisms of immune resistance in the “coldest” tumors, pancreatic and prostate adenocarcinoma and glioblastoma, so that rational therapeutic interventions can be developed to restore T cell infiltration and sensitivity to T cell checkpoint blockade. This research focuses on normalization of tumor oxygen metabolism to increase T cell metabolic fitness, activation of innate pro-inflammatory immune sensors capable of re-programming tumor myeloid stroma, and on discovery of novel immune checkpoint antibodies capable of depleting stromal elements responsible for T cell exclusion and function suppression.

Abstract

In collaboration with Dr. Seungchan Kim and Sandia National Laboratory’s Bioresource & Environmental Security Team, our research team seeks to identify key microbial and environmental factors for upregulating methanotrophy in typically methanogenic systems in agriculture and waste management, including landfills, waste-water, and rice cultivation, among others. To this end, we employ topological metabolic analysis to synthesize and reconstruct methane pathways in methanotrophic and methanogenic microorganisms. The overarching goal our collaborative effort with Sandia is to provide guidance on how to maintain stable consortia of microorganisms that convert greenhouse gases into usable biomass. In this talk, we will investigate the interplay between methane oxidation, Embden, Meyerhof and Parnas, Entner-Doudoroff, and other biochemical pathways with Serine and TCA cycles, using topological metabolic analysis (TMA), to interrogate conditions for optimal biomass production in methanotrophic bacterial cultures. Specifically, we focus on the development of a TMA framework that enables reconstruction of key biosynthetic pathways in Methylococcus capsulatus, an obligately methanotrophic gram-negative, non-motile coccoid bacterium. The TMA results presented consider specific uptake and growth rates at various conditions for methane substrate and biomass, respectively.

Speaker Bio

Dr. Lealon L. Martin is a faculty member in the Chemical Engineering Department at Prairie View A&M University. Dr. Martin also serves as Associate Dean (interim) of the Roy G. Perry College of Engineering. He received his B.S. degree in Chemical Engineering from Tuskegee University, and a Ph.D. in Chemical Engineering at UCLA in the area of Process Systems Engineering. Prior to joining Prairie View, Lealon served on the faculty at Rensselaer Polytechnic Institute in Troy, NY - with joint appointments in the Iserman Department of Chemical and Biological Engineering and the Department of Decision Science and Engineering Systems – and on the faculty at the University of Texas at Austin in the McKetta Department of Chemical Engineering.

Dr. Martin’s primary research interest lies in the synthesis and reconstruction of metabolic networks using state space optimization-based approaches. Past projects include: probing metabolic networks in Chinese hamster ovary cells to identify energetic limitations in the translational and post-translational processes of monoclonal antibody production, investigating the potential of Eichhornia crassipes (water hyacinth) as an industrial-scale source of biomass, and exploring Pseudomonas putida for microbial fuel cell applications.

Dr. Martin’s current research in the area of systems biology deals with probing metabolic pathways found Brassicae-family microgreens to find ways to increase glucosinolate compound production rates. Glucosinolates (and isothiocyanates) found in microgreens have nutritional and potentially medicinal benefits that far exceed those in their mature counterparts. Thus, the research on cellular metabolic processes in microgreens is to better understand limits on glucosinolate production and the growth conditions that influence those limits. To this end we pose the several fundamental questions: (1) What metabolic pathway networks govern glucosinolate production in microgreens? (2) What are the limits on glucosinolate production over all metabolic pathway networks? and (3) What conditions influence network-controlled limits on glucosinolate production?

As an aside, Dr. Martin is also a licensed attorney and a member of the State Bar of Texas.

Abstract

Cultivation of algae biomass is being pursued as a means for renewable production of various commodities, including fuels, polymers, fertilizers, and feeds, using non-arable land and non-freshwater resources. The especially attractive feature of algae, and the basis of much of the R&D investment, is the especially high productivity of algae biomass, which exceeds terrestrial plants by at least a factor of 3, coupled to favorable biochemical composition. However, DOE’s assessments of scale-up potential for all promising algae biofuel technologies to-date have resulted in Nth-plant model costs that exceed that of petroleum-derived products by factors of ~3-5. In light of this significant technoeconomic hurdle, new approaches for algae production are being pursued for incorporating ecosystem services to offset high costs for utilization of the biomass, including CO2 capture and remediation of compromised surface waters. In this presentation, we will discuss specific algae production technologies, including Open Raceway Ponds, attached ‘Turf Algae’ systems, and off-shore cultivation of macroalgae, and their respective connections to specific bioproducts, CO2 capture, and water resource management. Recent findings from research at Sandia suggest that cost effective, and in some cases, carbon negative solutions exist for algae industry scale-up, especially for generation of multiple products in a biorefinery context coupled to ecosystem services, such as water clean-up.

Speaker Bio

Ryan W Davis, Ph.D., is Principal Member of the BioSciences Staff at Sandia National Laboratories in Livermore, CA. Trained in biophysical chemistry, Ryan’s work focuses on bioenergy systems for coupling industrial and agricultural decarbonization with remediation and reclamation of resources from compromised waters. Since 2014, Dr. Davis has served as co-lead for Sandia’s Algae Testbed Facility, which provides translational research and external engagement for algae system scale-up. Major areas of current research interest include attached algae cultivation and proteinaceous biomass processing, with the overarching goal of understanding the biochemical coupling between the carbon and nutrient cycles from the single-cell to global scale.

Abstract

Within 5 days of its launch, ChatGPT, hailed as a milestone in the field of AI, had already attracted one million users, and just two months later, it had reached an impressive 100 million users from all around the world. In fact, ChatGPT even sparked an AI war. This incredible success presents an exciting opportunity for using this tool to enhance research and education across various fields. This talk aims to initiate a discussion on the opportunities and challenges that arise in research and education using ChatGPT. It begins with an introduction to ChatGPT, including a brief history of its development and its model architecture. The talk then explores the research and education opportunities that this platform presents, as well as the major challenges involved in utilizing it. In particular, specific points will be highlighted for HBCUs. Finally, the talk will showcase the potential works that can be done in research and education using ChatGPT.

Speaker Bio

Dr. Xishuang Dong is a member of CRI Center for Computational Systems Biology and CREDIT, and Assistant Professor at Department of Electrical and Computer Engineering at Prairie View A&M University (PVAMU). He received B.S. degree in computer science and technique at Harbin University of Science and Technology, M.S. degree in computer software and theory at Harbin Engineering University, and Ph.D. in computer application at Harbin Institute of Technology. His research interests include: (1) machine learning based computational systems biology; (2) biomedical information processing; (3) deep learning for big data analysis; (4) natural language processing.

Dr. Xishuang Dong led the team of scientists from Prairie View A&M University and Alabama A&M University for $299,666 research grant from National Science Foundation.

Project Title: Collaborative Research: IUSE-EHR: Improving Programming Skills of Engineering Students at HBCUs Using AI-enhanced Online Personalized Adaptive Learning Tools [Sponsor’s site]

Principal Investigator: Xishuang Dong, Ph.D.

Project Summary: Future engineering education should be able to train students to master traditional engineering knowledge, as well as to provide high-quality training on advanced techniques, such as data analytics and complex simulations, to meet industrial requirements of interdisciplinary talents. Programming skills are imperative for various simulations in the process of engineering education and becoming more and more important to training students on innovative techniques in emerging areas such as artificial intelligence (AI) and data science to be competitive workforce for interdisciplinary technology development like Internet of Things (IoT). A team of faculty with complementary expertise from two HBCUs (Prairie View A&M University (PVAMU) and Alabama A&M University (AAMU)) proposed to build online AI-enhanced personalized adaptive learning (PAL) tools to enhance engineering education on programming skills at HBCUs. To implement these tools, it needs to complete three tasks with advanced AI techniques: 1) basic online tools that implement sharing learning materials and managing assignments, quiz, projects, and examinations; 2) PAL path recommender via deep reinforcement learning that recommends PAL paths to learners for maximizing engagement in learning programming, as well as improving corresponding learning performance by selecting items of appropriate difficulty; 3) smart programming assistant (SPA) via deep learning-based language models (LMs) that can generate reference codes to assist learners for programming activities involved in assignments and projects. These tools would be extended for teaching other engineering courses by transfer reinforcement learning, which will not require substantial efforts on system implementation to improve other skills of engineering students.

Dr. Xishuang Dong is a member of CRI Center for Computational Systems Biology and CREDIT, and Assistant Professor at Department of Electrical and Computer Engineering at Prairie View A&M University (PVAMU). He received B.S. degree in computer science and technique at Harbin University of Science and Technology, M.S. degree in computer software and theory at Harbin Engineering University, and Ph.D. in computer application at Harbin Institute of Technology. His research interests include: (1) machine learning based computational systems biology; (2) biomedical information processing; (3) deep learning for big data analysis; (4) natural language processing.