Christina Chan - US grants

The overall goal of this project is to integrate quantitative analysis with biology to provide a systems approach to understanding hepatocyte (liver) metabolism in order to gain insights into Type II diabetes. The specific goals of the project are to: (1) characterize a comprehensive set of metabolic alterations in the hepatocytes induced by environmental factors with the aid of metabolic flux analysis, (2) incorporate physiochemical information; namely, kinetic binding data to provide a mechanistic insight into hepatic metabolism beyond that obtained with metabolic flux analysis alone, and (3) develop a phenomenological model using multivariate analysis to identify candidate pathways or factors that contribute most to the diabetic phenotype. The objective is the development of a model capable of identifying the potential for diabetes.

The overall goal of this project is to develop a quantitative systems approach to elucidate the role of elevated free fatty acids and tumor necrosis factor-alpha on hepatic metabolism. Mounting evidence suggests that elevated levels of free fatty acids along with tumor necrosis factor-alpha in the plasma play an important role in regulating hepatic metabolism. Fatty acids are involved in the generation of secondary messengers for signal transduction, regulation of hepatic enzyme activity, mediators of gene expression, and storage of metabolic energy. Similarly, evidence suggests tumor necrosis factor-alpha plays a role in mediating lipid metabolism and triggers a complicated array of intracellular signals.

Current mechanistic understanding of lipid metabolism is limited due to the lack of comprehensive information on the interplay of genes and proteins that may act in concert, or in opposition, in their regulation of metabolic enzymes. This short-coming is primarily due to traditional approaches, that measure a few physiological, biochemical or genetic markers, which provide limited insight into the overall molecular mechanisms involved. A "systems biology" approach reconstructs the associations that lead to a system's behavior by integrating the information derived from RNA, protein, and metabolite expression profiles as a function of its surrounding environment. The ability to quantitate expression levels of multiple genes and proteins, corresponding to a cellular metabolic state and/or environment, provides a more comprehensive and integrative approach to understanding hepatic lipid metabolism and elucidating relevant intracellular information.

In summary, this project seeks to develop a quantitative framework to obtain fundamental knowledge of hepatocyte metabolism that will be subsequently applied to study the more specific aspects of Type 2 diabetes and other metabolic diseases. The quantitative framework developed in this proposal is applicable to other cellular systems.

0425821
Chan
This research aims to test a hypothesis that plasma levels of fatty acids palmitate and oleate can have cytotoxic or cytoprotective effects in hepatocytes, respectively, and that by interfering with their respective biosynthetic pathways, one may modulate cell death or protection. In lipid metabolism, it has been shown that saturated fatty acids (e.g., palmitate) initiate apoptosis while unsaturated fatty acids (e.g., oleate) do not. In both cases, oxidation is the primary means of breakdown for these molecules. The presence of both saturated and unsaturated fatty acids appears to confer protection from apoptosis. The signaling cascades that result from metabolism of fatty acids have not, to date, been fully elucidated due to network complexity and interconnectivity that could not be deconvoluted by traditional biochemical methods. This research will leverage new high throughput capacity for information generation to help elucidate the mechanism by which the cellular fate is decided upon exposure to the palmitate and/or oleate, and thereby improve our understanding of how a cell chooses among the multiple possible pathways available to the cell. Two types of mathematical approaches will be used to analyze the data, feature extraction and Bayesian network analysis.

[unreadable] DESCRIPTION (provided by applicant): Improved understanding of disease mechanisms and drug target identification requires better understanding of how diseases alter cellular processes from the healthy state. Our group has developed an integrative pathway search algorithm that reconstructs networks of active pathways from gene expression and phenotypic profiles. Preliminary studies illustrate that this framework is able to reconstruct networks that include those pathways that should be altered, and how they should be altered, to obtain a desired phenotype. Nevertheless, the current framework may not fully capture transients, such as cycles and feedback loops. Furthermore, cells continuously reprogram gene regulatory networks as they sense changes in their environment. To understand how cells are regulated in response to environmental alterations, time series (i.e., dynamic) data are required. Correspondingly, a dynamic model is required to uncover the mechanisms from time series data. We hypothesize that incorporating domain knowledge and metabolic data into a dynamic model would enhance the accuracy of the genes chosen and in turn improve the prediction of the reconstructed networks. Unlike previous studies that have focused on using the gene ontology information, we propose to incorporate domain knowledge retrieved from the free text. This is significant because a large portion of the genes do not have gene ontological keywords. Additionally, it is often difficult to assess the accuracy of the network structures that have been inferred from experimental data because the underlying "true" regulatory network is unknown or unavailable a priori. Therefore, one needs to have a known network structure that can be used to optimize and evaluate the modeling frameworks. Once so optimized, the static and dynamic models will be applied to an experimental cell culture system, which has a perturbed (transfected or silenced) gene, and assessed as to how well each model predicts the resulting, measured phenotypic responses. The cost-effectiveness of the cell culture, in contrast to in vivo animal studies, allows us to establish, with experimental data, which model produces predictions of greater confidence. Having established which model is more predictive, we will apply that model to rats that are maintained on high fat diets, so as to identify the pathways that could be altered to reduce triglyceride storage (steatosis) and inflammation in the livers of these rats. The findings could have implications for identifying potential therapies for steatosis and, perhaps, even non-alcoholic steatohepatitis (NASH). The objectives will be achieved through the following aims: 1) Develop a novel approach that incorporates domain knowledge retrieved from the free text as well as gene expression data to predict cellular or phenotypic responses. 2) Develop an optimized dynamic Bayesian Network to infer gene regulatory networks from time series data. 3) Experimentally validate the model predictions for the cell culture system. 4) Characterize the livers from rats fed high fat vs. normal diets. [unreadable] [unreadable] [unreadable]

PI: M. Thomashow (Michigan State University)
CoPIs: C. Chan (Michigan State University) and T. Chen (Oregon State University; subawardee)
Collaborator: S.-H. Shiu (Michigan State University)

The long range goals of the project are to gain a systems level understanding of plant responses to abiotic stress and to use the first principles gained to develop novel strategies to improve the stress tolerance of agriculturally important crops. These goals are important as abiotic stresses limit the geographical locations where crops can be grown and account for the majority of losses in yield on an annual basis. The overall objective is to identify the low temperature transcriptional networks that plants have evolved to survive freezing. As there is a direct link between freezing and dehydration injury, the results should also provide further insights into the nature of gene modules that impart tolerance to drought and other dehydration stresses. The specific aims of the project are two-fold. The first is to develop a detailed understanding of the low temperature transcriptional network of Arabidopsis and determine which components contribute to freezing tolerance. This will be accomplished by identifying transcription factors and other regulatory proteins that have key roles in configuring the low temperature transcriptome; identifying the cis-acting DNA regulatory elements through which these regulatory proteins function; and identifying the regulatory programs and gene modules that contribute to freezing tolerance (and potentially tolerance to drought and other abiotic stresses). The second aim of this project is to determine whether the low temperature transcriptional networks and gene modules that impart freezing tolerance in Arabidopsis are conserved in plants that cold acclimate (i.e., increase in freezing tolerance in response to low non-freezing temperatures) and whether ""deficiencies"" in these networks and modules contribute to the freezing sensitivity of those plants that do not cold acclimate. This aim will be accomplished through comparative genomic analysis of three closely related Solanum species which differ in cold tolerance: S. commersonii, potato and tomato. Together, the proposed studies incorporate the determination of gene expression at a genome level, a comparative genomic analysis of model and crop species, and the integration of computational analysis and empirical testing to reconstruct and model transcriptional regulatory networks that are fundamental to plant life and have importance in agriculture.


Broader Impacts

Improving the abiotic stress tolerance of crops is crucial to meeting future demands for food and fiber. In addition, it is a key component of an emerging national vision to produce sufficient biomass per year in the U.S. to replace significant percentages of petroleum-based transportation fuels with biofuels produced from renewable resources. The studies proposed here directly relate to these important areas as they will provide a deeper understanding of the genomic mechanisms that plants have evolved to cope with abiotic stress and have the potential to provide genetic tools to improve the abiotic stress tolerance of plants. Microarray data generated in the course of these studies will be available through a project website (http://aztec.stanford.edu/cold/index.html) and through GEO and ArrayExpress.

The project will also contribute to the training of a next generation of scientists who are experienced in bringing genomic, bioinformatic and computational approaches to bear on fundamental questions in biology. Finally, the project includes a summer undergraduate education and research training program which will target inclusion of underrepresented groups of our society. The goal is to provide the students with opportunities to learn more about different areas of genomic research and to discuss broadly and informally issues that relate to pursuing careers in science.

PI: Christina Chan, Michael Feig and Amadeu Sum
Institution: Michigan State University
Proposal Number: 0941055


Intellectual Merit. This project aims to integrate molecular biology, biophysics, and cellular studies with molecular modeling to provide a transformative understanding of complex biological systems comprised of multiple interacting processes. Specifically for this project the PIs plan to study the endoplasmic reticulum transmembrane protein kinase/endoribonuclease (IRE1), which is activated in response to the Unfolded Protein Response (UPR). This has broad implications on a number of diseases, since UPR is known to be activated in cancers, viral infection and many other diseases. This study is a first at developing a multi-scale model to integrate the various domains of the transmembrane protein to understand how palmitate activates the protein, representing an innovative, multi-scale approach to gather, process, and interpret data from the molecular to the cellular level. Among the fatty acids, saturated fatty acids, i.e., palmi-tate, are typically the most cytotoxic. Saturated fatty acids have been shown to cause cell death in many types of cells and be risk factors associated with a variety of diseases.

Transformation/Innovation. This project will provide much needed insight into the general understanding of transmembrane protein kinases. The findings of will rely on computational analysis of biophysical events as well as integrating tools from biology, chemistry, physics, and engineering. This approach will provide insight that will complement and aid in the interpretation and analysis of macroscopic measurements of cellular function, signaling and toxicity, and eventual design of strategies to control or prevent cell damage and modulate cell signaling induced by fatty acids. The computational and experimental approaches rely on the findings at the molecular levels, through a combination of molecular modeling and molecular biology, to drive the bio-chemical and signaling transduction studies. While the biochemical and signaling studies of the cellular responses are necessary, the complexity of cellular processes hinders a clear interpretation and understanding of the experimental results. As such, design and engineering of these studies will be driven by computational findings of the molecular processes. This approach represents a cyber enabled methodology for a rational investigation of the biophysical effects of fatty acids on cells. Therefore, this study has implications on how saturated fatty acids may be involved in multiple diseases as well as on the efficacy of current drug therapies that are targeted to inhibit transmembrane kinases.

Broader Impact. The studies pursued here are centered on biophysical interactions with membranes and proteins as one of the underlying mechanisms behind palmitate-induced alterations in cellular function leading to cytotoxicity or death of cells. The application of bio-physical concepts to this problem is a novel approach that has been largely overlooked in favor of biochemical and signaling processes. From an educational standpoint, this project will provide a broad exposure to the students with an opportunity to integrate the knowledge base from vastly differing fields and to apply quantitative tools to investigate non-traditional engineering problems. The PIs have supported both (female and minority) high school and undergraduate students in the laboratory. The PIs will continue to enlist the help of high school and undergraduate students to obtain preliminary results during the summer months. The undergraduate and high school students provide an opportunity for the graduate students in the lab to gain supervisory experience along with their traditional research experience. For the high school and undergraduate students, the goal is to encourage them to pursue careers in engineering and science by exposing them to hands-on laboratory research experiences. The PIs have and will continue to develop new courses that incorporate the research development and findings. The methodology developed will be disseminated through the web, in addition to traditional modes, such as oral presentations at scientific meetings and publications in scholarly journals.

0922999
Drzal

This proposal seeks financial support from the NSF towards the purchase of an Environmental Scanning Electron Microscope (ESEM) integrated with a Dual Beam FocusedIon Beam (FIB). This instrument will serve two purposes. First, the instrument will replace an early generation ESEM (purchased in 1994) widely used to support research of 30+ faculty members in 9 academic departments in 3 colleges, as well off-campus collaborators and second, to bring the nanoscale materials processing flexibility offered by a dual beam FIB to a wide range of research programs at MSU in the areas of nanoscale electronics and devices, bioengineering, metals and alloys, composites, ceramics, and polymers. The acquisition of this instrument will not only greatly enhance the portfolio of users by expanding the nanoscale research infrastructure and other government agencies and industry, but will also be an integral part of the educational infrastructure at the graduate, undergraduate, K-12 and lifelong learning levels.

Of the adult stem cells, mesenchymal stem cells (MSCs) appear to have the best potential for regenerative medicine. MSCs contain precursors that are able to differentiate into numerous types of cells but with a much lower risk of tumorigenicity. The heterogeneity of the bone marrow stem cell population makes it difficult to resolve the conflicting data on MSC plasticity. The specificity of current markers available to identify MSCs fails to separate the populations of structurally distinct cells. Hence, strategies that isolate and maintain the subpopulations of MSCs would transform the field of stem cell biology. In this work, a novel hypothesis for separating and isolating pure populations of stem cells will be tested. To test this hypothesis, investigators will use an integrative approach that couples mechanics modeling with experiments to predict the surface properties required to maintain subpopulations of MSCs. This will eventually facilitate a more uniform cell population to be established and studied for their capacity to differentiate into specific lineages, thereby enhancing their differentiation efficiency. The PI will integrate research into curriculum at Michigan State University and engage graduate, undergraduate and high school students in research in this multidisciplinary area.

DESCRIPTION (provided by applicant): This project aims to develop a novel platform that would help accelerate the development of novel antibody- based therapeutics. Numerous antibody therapies undergoing clinical trials are concerned with immunological and oncological targets. The proposed project is innovative and differs from previous studies by using deep sequencing to yeast display to systematically screen antibodies binding different epitopes on therapeutically relevant target proteins. Our technology generates unprecedented sequence-function maps that cover the entire sequence of protein binders [1]. We hypothesize that this same method can be used to develop rapid fine epitope maps of antibody-antigen interactions. Thus, the overall goal of the project is to develop the deep scanning methodology on proteins target proteins whose structures or active domains are currently unknown. To demonstrate that our novel platform can identify functional antibodies that target such proteins, we will evaluate their ability to inhibit the function of TROP2 (a.k.a tumor-associated calcium signal transducer 2, TACSTD2), our model protein, from promoting growth and metastasis. The project involves two aims. The first aim is to develop a novel epitope mapping strategy that couples yeast display with deep sequencing to generate unprecedented sequence-function maps of protein binders. The second aim will develop and screen potential novel antibodies to TROP2 for their ability to inhibit cell proliferation and metastasis through in vitro and in vivo experiments. Completion of this project will provide a platform that can identify functional antibodies that inhibit the activty of target proteins for which structures are not available. The proposed methodology will aid in the discovery and development of new therapeutics not only for cancer but other diseases, including autoimmune diseases that are amendable to antibody therapeutics.

1510895
Walton, Stephen P.

RNA interference (RNAi) is a natural cellular pathway that can be used to reduce the expression of a targeted protein. This pathway can be activated by double-stranded RNAs called short, interfering RNAs (siRNAs). The specific aims of the proposed work are to: i) define the sequence and structural features of siRNAs that result in the proper processing of siRNAs by the RNAi pathway proteins; ii) characterize the siRNA features that affect binding of the siRNAs by RNAi pathway proteins; and iii) determine the cellular uptake pathways for siRNA-containing complexes that maximize function of the delivered siRNAs. Due to the potential specificity of RNAi and its applicability against nearly any protein target, siRNAs have garnered considerable attention as the next generation of biomolecular therapeutics.

RNA interference holds promise as a tool for manipulation of biological processes and for the development of a new class of therapeutics. RNAi is initiated by short, interfering RNAs (siRNAs) and results in the knockdown of a targeted protein. To achieve knockdown, exogenous siRNAs must be delivered to the cytoplasms of targeted cells and interact productively with the RNAi pathway proteins. However, siRNA delivery remains a significant challenge. To achieve silencing, siRNAs must first enter the targeted cells by a mechanism that allows them to be active. Currently, the uptake pathways that result in siRNA function are poorly understood. The proposed work seeks to identify the rules that govern the design of siRNAs and the vehicles used to deliver them to cells. Using in vitro and cellular systems, siRNAs and delivery vehicles with unique characteristics will be studied to determine which combination of features results in the highest siRNA activity. The specific aims of the proposed work are to i) define the sequence and structural features of siRNAs that are associated with functional asymmetry using an in vitro system; ii) characterize the sequence features that influence in vitro binding interactions of siRNAs with RNAi pathway proteins using degenerate siRNAs and parallel RNA sequencing; and iii) determine the cellular uptake pathways for siRNA-containing complexes that maximize silencing efficiency of the delivered siRNAs.

This award by the Biotechnology and Biochemical Engineering Program of the CBET Division is co-funded by the Systems and Synthetic Biology Program of the Division of Molecular and Cellular Biology.

PI: Chan, Christina
Proposal Number: 1547518

Stem cells are promising for the regeneration of aged, injured and diseased tissues and organs, but to realize their therapeutic potential growth of unwanted cell types should be arrested, and improvement in efficiency of converting them to a specific cell type is necessary. The proposed work will examine a genome editing approach for eliminating growth of unwanted cell lineages.

The overall goal of this project is to increase the efficiency and specificity of stem cell differentiation by reducing off-target cell types through CRISPR. CRISPR/Cas9 mediated knockout of transcription factors involved in the maturation of serotonergic, glutamatergic and GABAergic phenotypes. Clones will be DNA sequenced to confirm deletions in the regions involved in serotonin, glutamate and GABA synthesis pathways, and those lacking these genes will be treated with FI (forskolin and IBMX) to induce a higher percentage of cells to express a dopaminergic phenotype. This method will also be applied to increase the efficiency and specificity of neural stem cell differentiation. The results are anticipated to easily transfer to induced pluripotent, embryonic, and adult stem cells. From an educational standpoint, this project will provide interdisciplinary training to students with an opportunity to integrate knowledge from different fields to investigate non-traditional engineering problems. The investigators will recruit and retain female and minority engineering students into the research project.

PROJECT SUMMARY It is also becoming clear that many diseases have an epigenetic component. However, in neurological diseases, such as neuropathic pain, it has been shown that certain genes for ion channels and pain receptors are downregulated and maintained through epigenetic silencing. Current treatments for neuropathic pain use pain killers which provide temporary relief for patients, but fail to cure damaged nerves. Further, the long-term use of pain killers carry a high risk of addiction. Thus, better approaches to treat neuropathic pain are urgently needed. Studies have linked changes in genes involve in neuropathic pain to the master transcriptional repressor, neuron- restrictive silencing factor (NRSF). NRSF silences gene expression by recruiting chromatin modifiers to leave repressive epigenetic marks including histone deacetylases, histone methytransferases, and DNA methytransferases, on neural genes involved in neuropathic pain. NRSF has been investigated as a therapeutic target and efforts to modulate its activity through small molecules, siRNA, and mimetics have been attempted. Since the transcriptionally repressive function of NRSF is mediated through epigenetic mechanisms, we propose that a Cas9-based approach to actively reverse repressive epigenetic marks left by NRSF on ion channel and pain receptor genes could provide a novel therapeutic strategy for treating neuropathic pain. This provides advantages over approaches using pan-acting small molecules and mimetics because it (1) would enzymatically remove and replace epigenetic marks, and (2) can be targeted to genes of interest in a sequence specific manner. To achieve these goals, first, we propose to develop an in vitro cell-based model of neuropathic pain dependent on NRSF-mediated repression, and secondly, we will explore a Cas9-based approach to reverse the repressive epigenetic marks brought about by NRSF on genes involved in neuropathic pain. This project will highlight epigenetic marks themselves as possible therapeutic targets and show that a Cas9-based system could be used therapeutically with a high degree of specificity.

Many drugs are being tested for efficacy against COVID-19. The side effects of these drugs are poorly understood. The issue is complicated because a number of organ systems (lungs, heart, liver) can be affected by the infection. In addition, underlying conditions such as hypertension, diabetes, or cardiovascular disease increase the likelihood of serious complications or death. This project is designed to identify the effects of these drugs on the organ systems of vulnerable populations. This information would inform the selection and application of effective drugs that also cause minimal negative consequences. This project will also advance the education and research experience of under-represented groups in the STEM disciplines.

This project combines ?horizontal? and ?vertical? analyses of global genomic datasets. The ?horizontal? perspective will map the landscape of gene expression under various conditions that will enable broader consideration of potential changes that drug treatments could have on Covid-19. The ?vertical? perspective will identify regulatory mechanisms that suggest possible treatments to target specific responses (e.g., increases in the different types and levels of cytokines or decreases in the ACE2 levels) for the different phenotypes. The integrative approach of this proposal will capitalize on the timely results from the latest studies and incorporate these results into the gene regulatory network analysis to provide phenotypic-specific guidance on potential anti-inflammatory treatments and insight into the host response as a function of the phenotype. The scientific and engineering contribution of this project is the development and application of an integrative, multi-scale, and multi-faceted approach that models cellular interactions (signaling and regulatory) to enable prediction of the phenotypic responses to external stimuli, including drugs and pathogens. This integrative modeling framework will be applicable to other pathogens and patient populations.

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.