Si Wu - US grants

With this award from the Major Research Instrumentation Program (MRI) and support from the Chemistry Research Instrumentation Program (CRIF) as well as the Experimental Program to Stimulate Competitive Research (EPSCoR), Professor Robert Cichewicz from the University of Oklahoma Norman Campus and colleagues Laura Bartley, Marc Libault, Zhibo Yang and Si Wu have acquired a high resolution exact mass liquid chromatograph mass spectrometer (LCMS) with ultra performance liquid chromatograhic (UPLC) capabilities. In general, mass spectrometry (MS) is one of the key analytical methods used to identify and characterize small quantities of chemical species embedded in complex matrices. In a typical experiment, the components flow into a mass spectrometer where they are ionized into the parent ion and its fragment ions and their masses are measured. This highly sensitive technique allows detection and determination of the structure of molecules in a complex mixture. An instrument with a liquid chromatograph provides additional structural identification power by separating mixtures of compounds before they reach the mass spectrometer. The acquisition strengthens the research infrastructure at the University and regional area. The instrument broadens participation by involving diverse students in research and research training using this modern analytical technique. It also provides training opportunities to a large number of undergraduate, graduate and postdoctoral students as well as high school students through a variety of internship programs, such as the STEM-to-Store Academy and Sooner Upward Bound.

The proposal is aimed at enhancing research and education at all levels, especially in areas such as (a) analyzing chemical and biochemical species in single cells; (b) studying changes to the biomolecules inside of single plant root hair cells in response to environmental stresses; (c) mapping the spatial distribution of molecules in biological tissues; (d) studying the spatial distribution of metabolites of grass stems to investigate cell wall development; (e) characterizing the proteomics of regulatory networks controlling legume nodulation such as detecting the transcription factor binding partner networks involved in nodulation; (f) analyzing the metabolites of plant stem cell wall synthesis and regulation; (g) analyzing the proteomics of fungal secretomes for biomass degrading enzymes and (h) quantifying the diversity of the fungal natural product chemosphere.

SUMMARY Systemic Lupus Erythematosus (SLE) is a multi-organ, systemic autoimmune disorder, estimated to affect at least 1.5 million Americans. The hallmark of SLE is the production of serum autoantibodies, a unifying feature present in over 99% of untreated patients. Such autoantibodies are directly pathogenic, eventually causing the symptoms of SLE including debilitating joint pain and rashes, followed by organ damage and early mortality. Previous work has shown that autoantibodies begin to accrue months to years before the symptoms of SLE appear which may allow a window for detecting them and starting medications to prevent or at least delay the onset of SLE. These serum autoantibodies, however, consist of a complex mixture in the blood including pathogenic, non-pathogenic, and beneficial antibodies which may number in the millions. While current diagnostic platforms can screen for total autoantibodies during autoimmune disease, finding specific monoclonal autoantibodies linked to the development of SLE is currently impossible. Thus, the lack of capability to directly detect monoclonal, pathogenic, autoantibodies presents a significant barrier in understanding how autoantibodies arise, and there is a critical need to develop advanced analytical tools to characterize these antibodies. Our long-term goal is to understand SLE autoantibody development at the monoclonal level and to develop high diagnostic value autoantibody biomarkers. The overall objective of this proposal to establish a novel integrated proteomics platform that employs two complementary scientific approaches, a quantitative top-down MS approach for autoantibody biomarker discovery, and a top-down proteogenomics sequencing approach for autoantibody biomarker validation and functional characterization. Our proposed top-down autoantibody proteomics platform will be applied to identify intact autoantibody Fab signatures in longitudinal SLE serum samples. As a result, we will provide a first top-down proteomics platform for characterizing SLE autoantibodies at the monoclonal level. Applying it to the analysis of SLE autoantibodies will provide foundations for new strategies in SLE prognosis, intervention, and prevention, and may lead to novel high diagnostic value biomarkers. After development, our top-down autoantibody characterization platform can be easily adapted to other autoimmune diseases such as Sjogren?s Syndrome.