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Open Access Publications from the University of California

Scholarly Works (23 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2019)

Type 1 diabetes (T1D) is a complex disease, involving a genetic predisposition that interacts with environmental triggers, leading to the loss of insulin producing beta cells in the pancreas. However, the molecular cascades underlying T1D are poorly understood and remain to be explored. We hypothesize that genetic risk factors of T1D perturb tissue-specific biological pathways and gene networks, which ultimately leads to the pathogenic end point in beta cells. We sought to identify the gene networks and key regulators for T1D by conducting a comprehensive, data-driven multi-omics analysis that integrates human genome-wide association studies (GWAS) of T1D, tissue-specific genetic regulation of gene expression in the form of expression quantitative trait loci (eQTLs), and tissue-specific gene network models using a computational pipeline Mergeomics. Our integrative genomics approach revealed immune pathways such as adaptive immune system, cytokines and inflammatory response, ZAP70 translocation, primary immunodeficiency and immunoregulatory interactions between a lymphoid and non-lymphoid cell, across various tissues. We also identified tissue-specific signals such as regulation of complement cascade in adipose tissue, macrophages, and monocytes, NOTCH signaling in adipose tissue and macrophages, protein folding, calcium signaling chemotaxis and lysosomal pathways in the pancreas, adipose, and monocytes, and viral infection in macrophages and monocytes. Network modelling of these pathways highlights a number of key regulator genes such as GBP1, USP18, STAT1, RPL17, HLA genes (HLA-A,-B,-C, and –G), and immunomodulatory genes (LCK, VAV1, ZAP-70), each of which has suggestive roles in the pathophysiology of T1D or other autoimmune disorders. Together, our integrative genomics approach offers comprehensive insights into the tissue-specific molecular networks and regulators as well potential between-tissue interactions underlying T1D, with potential for guiding future development of therapeutic strategies targeting the disease.

    Cover page: Integrative Genomics Analysis Reveals Tissue-specific Pathways and Gene Networks for Type 1 Diabetes
    • Thesis
    • Peer Reviewed
    UCLA Electronic Theses and Dissertations (2022)

    Common complex metabolic diseases (MetDs) such as obesity, type 2 diabetes (T2D), coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD), impose an unprecedented burden on public health worldwide and demonstrate sex differences. Our general hypothesis is that genetic risk factors perturb set of genes in the form of functional gene networks, which subsequently induces the initiation and progression of MetDs. Following this hypothesis, our research focuses on dissecting the molecular networks that are perturbed by genetic risk factors of MetDs utilizing multiomics systems biology approaches. To address this challenge, I embarked interdisciplinary systems biology studies encompassing the development of an accessible multi-omics integration webserver, elucidation of genetically perturbed tissue networks in numerous MetDs, and uncovering the relative contribution of three sex factors in gene regulation in tissues relevant to MetDs. First, I contributed to the development of a user-friendly webserver for multiomics integration, network modeling, and network-based drug repositioning for complex diseases such as MetDs. Second, I investigated the genetically perturbed gene networks that underly various MetDs, namely, lipid traits, diabetes, CAD, and NAFLD. Third, I employed systems biology approaches to uncover the individual and interactive contribution of three sex factors (sex chromosomes, gonads, and sex hormones) in gene regulation in tissues relevant to MetDs. Completion of these projects offer a user-friendly bioinformatic tool, molecular insights, and drug candidates for MetDs.

      Cover page: Uncovering the Molecular Networks of Metabolic Diseases Using Systems Biology
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2021)
      Esmaili et al.1 conducted co-expression network analysis to uncover core homeostatic modules critical to a broad spectrum of liver diseases in mouse and human. Perturbation state of core modules may underlie disease stages across species and serve as therapeutic targets.
        Cover page: Found in translation-core network preservation across liver diseases and species.
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2018)
        The success of genome-wide association studies (GWAS) has significantly advanced our understanding of the etiology of coronary artery disease (CAD) and opens new opportunities to reinvigorate the stalling CAD drug development. However, there exists remarkable disconnection between the CAD GWAS findings and commercialized drugs. While this could implicate major untapped translational and therapeutic potentials in CAD GWAS, it also brings forward extensive technical challenges. In this review we summarize the motivation to leverage GWAS for drug discovery, outline the critical bottlenecks in the field, and highlight several promising strategies such as functional genomics and network-based approaches to enhance the translational value of CAD GWAS findings in driving novel therapeutics.
          Cover page: Translating GWAS Findings to Novel Therapeutic Targets for Coronary Artery Disease
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2019)
          Non-alcoholic fatty liver disease (NAFLD) is a progressive condition of the liver encompassing a range of pathologies including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Research into this disease is imperative due to its rapid growth in prevalence, economic burden, and current lack of FDA approved therapies. NAFLD involves a highly complex etiology that calls for multi-tissue multi-omics network approaches to uncover the pathogenic genes and processes, diagnostic biomarkers, and potential therapeutic strategies. In this review, we first present a basic overview of disease pathogenesis, risk factors, and remaining knowledge gaps, followed by discussions of the need and concepts of multi-tissue multi-omics approaches, various network methodologies and application examples in NAFLD research. We highlight the findings that have been uncovered thus far including novel biomarkers, genes, and biological pathways involved in different stages of NAFLD, molecular connections between NAFLD and its comorbidities, mechanisms underpinning sex differences, and druggable targets. Lastly, we outline the future directions of implementing network approaches to further improve our understanding of NAFLD in order to guide diagnosis and therapeutics.
            Cover page: Network Modeling Approaches and Applications to Unravelling Non-Alcoholic Fatty Liver Disease
            • Article
            UCLA Previously Published Works (2020)
            AbstractScopeWe explored the influence of DHA on cardiometabolic and cognitive phenotypes, and multiomic alterations in the brain under two metabolic conditions to understand context-specific nutritional effects.Methods and ResultsRats were randomly assigned to a DHA-rich or a control chow diet while drinking water or high fructose solution, followed by profiling of metabolic and cognitive phenotypes and the transcriptome and DNA methylome of the hypothalamus and hippocampus. DHA reduced serum triglyceride and improved insulin resistance and memory exclusively in the fructose-consuming rats. In hippocampus, DHA affected genes related to synapse functions in the chow group but immune functions in the fructose group; in hypothalamus, DHA altered immune pathways in the chow group but metabolic pathways in the fructose group. Network modeling revealed context-specific regulators of DHA effects, includingKlf4andDusp1for chow condition andLum, Fn1, andCol1a1for fructose condition in hippocampus, as well asCyr61, JunB, Ier2, andPitx2under chow condition andHcar1, Cdh1, andOsr1under fructose condition in hypothalamus.ConclusionDHA exhibits differential influence on epigenetic loci, genes, pathways, and metabolic and cognitive phenotypes under different dietary contexts, supporting population stratification in DHA studies to achieve precision nutrition.
              Cover page: Multi-tissue Multi-omics Nutrigenomics Indicates Context-specific Effects of DHA on Rat Brain
              • Article
              • Peer Reviewed
              UCLA Previously Published Works (2021)
              The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease association studies (GWAS, EWAS, TWAS, PWAS, etc.) as input and features four functions: Marker Dependency Filtering (MDF) to correct for known dependency between omics markers, Marker Set Enrichment Analysis (MSEA) to detect disease relevant biological processes, Meta-MSEA to examine the consistency of biological processes informed by various omics datasets, and Key Driver Analysis (KDA) to identify essential regulators of disease-associated pathways and networks. The web server has been extensively updated and streamlined in version 2.0 including an overhauled user interface, improved tutorials and results interpretation for each analytical step, inclusion of numerous disease GWAS, functional genomics datasets, and molecular networks to allow for comprehensive omics integrations, increased functionality to decrease user workload, and increased flexibility to cater to user-specific needs. Finally, we have incorporated our newly developed drug repositioning pipeline PharmOmics for prediction of potential drugs targeting disease processes that were identified by Mergeomics. Mergeomics is freely accessible at http://mergeomics.research.idre.ucla.edu and does not require login.
                Cover page: Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics
                • Article
                • Peer Reviewed
                UCLA Previously Published Works (2019)
                Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell-cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.
                  Cover page: Network modeling of single-cell omics data: challenges, opportunities, and progresses
                  • Article
                  • Peer Reviewed
                  UCLA Previously Published Works (2020)

                  Scope

                  The influence of docosahexaenoic acid (DHA) on cardiometabolic and cognitive phenotypes, and multi-omic alterations in the brain under two metabolic conditions is explored to understand context-specific nutritional effects.

                  Methods and results

                  Rats are randomly assigned to a DHA-rich or a control chow diet while drinking water or high fructose solution, followed by profiling of metabolic and cognitive phenotypes and the transcriptome and DNA methylome of the hypothalamus and hippocampus. DHA reduces serum triglyceride and improves insulin resistance and memory exclusively in the fructose-consuming rats. In hippocampus, DHA affects genes related to synapse functions in the chow group but immune functions in the fructose group; in hypothalamus, DHA alters immune pathways in the chow group but metabolic pathways in the fructose group. Network modeling reveals context-specific regulators of DHA effects, including Klf4 and Dusp1 for chow condition and Lum, Fn1, and Col1a1 for fructose condition in hippocampus, as well as Cyr61, JunB, Ier2, and Pitx2 under chow condition and Hcar1, Cdh1, and Osr1 under fructose condition in hypothalamus.

                  Conclusion

                  DHA exhibits differential influence on epigenetic loci, genes, pathways, and metabolic and cognitive phenotypes under different dietary contexts, supporting population stratification in DHA studies to achieve precision nutrition.
                    Cover page: Multi‐Tissue Multi‐Omics Nutrigenomics Indicates Context‐Specific Effects of Docosahexaenoic Acid on Rat Brain
                    • Article
                    • Peer Reviewed
                    UCLA Previously Published Works (2023)
                    The gut microbiome modulates seizure susceptibility and the anti-seizure effects of the ketogenic diet (KD) in animal models, but whether these relationships translate to KD therapies for human epilepsy is unclear. We find that the clinical KD alters gut microbial function in children with refractory epilepsy. Colonizing mice with KD-associated microbes promotes seizure resistance relative to matched pre-treatment controls. Select metagenomic and metabolomic features, including those related to anaplerosis, fatty acid β-oxidation, and amino acid metabolism, are seen with human KD therapy and preserved upon microbiome transfer to mice. Mice colonized with KD-associated gut microbes exhibit altered hippocampal transcriptomes, including pathways related to ATP synthesis, glutathione metabolism, and oxidative phosphorylation, and are linked to susceptibility genes identified in human epilepsy. Our findings reveal key microbial functions that are altered by KD therapies for pediatric epilepsy and linked to microbiome-induced alterations in brain gene expression and seizure protection in mice.
                      Cover page: Ketogenic diet therapy for pediatric epilepsy is associated with alterations in the human gut microbiome that confer seizure resistance in mice.
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