Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (35 results)

  • Article
  • Peer Reviewed
UCLA Previously Published Works (2016)
A systems approach deconvolutes genes specific to and enriched in endothelium from whole-organ transcriptome data, with applications to other cell types and tissues.
    Cover page: Deconvolution of the Human Endothelial Transcriptome
    • Thesis
    • Peer Reviewed
    UCLA Electronic Theses and Dissertations (2013)

    Heart failure (HF) accounts for 1 in 9 deaths in the United States and is the leading cause of hospitalization for people over the age of 65 and the incidence of HF is predicted to rise over the coming years. The complexity which underlies common forms of HF has hindered the study of the disease in humans, and approaches, such as genome-wide association studies (GWAS), have had only modest success in identifying genes which are related to this disease. Here we describe the use of a panel of mice to facilitate the study of this complex disorder, reducing heterogeneity and facilitating systems-level approaches.

    We used the beta-adrenergic agonist isoproterenol to induce HF in 105 unique strains drawn from the Hybrid Mouse Diversity Panel, a novel mouse resource population for the analysis of complex traits. Our first study reports the results of a GWAS on heart weights, cardiac fibrosis and other surrogate traits relevant to HF. Among the 32 significant loci, we identified several strong candidates which had previously been shown to contribute to mendelian forms of cardiomyopathy. We were also able to validate two novel candidate genes, the orphan transporter Abcc6, and the long noncoding RNA Miat, using gene targeting, transgenic and in vitro approaches.

    As part of our systems genetics approach, we developed a novel gene network construction algorithm, which improves on prior methods by allowing non-linear interactions and the ability for genes to operate in multiple modules at once. We were able to demonstrate using previously published data that our results either matched or exceeded another well-known network construction algorithm. In a subsequent study, we applied this method to transcriptomes taken from the HF study. We identified a module of 41 genes which significantly regulates the response of the heart to isoproterenol and HF and which contains several genes of interest such as Lgals3, a diagnostic marker for human HF.

    Our results provide a valuable resource toward a better understanding of the pathways and gene-by-environment interactions influencing heart failure.

      Cover page: A Systems Genetics Approach to the Identification of Causal Genes in Heart Failure Using a Large Mouse Panel
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2017)
      In this chapter we address the recent explosion in large multilevel population studies such as the METSIM study in humans as well as large panels of animal models such as the Hybrid Mouse Diversity Panel or the BXD set of recombinant inbred strains. These studies have harnessed the increasing affordability of large-scale high-throughput profiling to gather massive quantities of data. These datasets, spread across different -omics levels (genome, transcriptome, etc.), different tissues (e.g. heart, plasma, bone) and different environmental factors (e.g. diet, drugs) each individually have led to a number of novel findings relevant to a variety of complex diseases and other phenotypes. The analysis of these results, however, is often limited to individuals with a comprehensive understanding of database languages such as SQL. In this chapter, we describe the development of a GUI-based database analysis suite, using the Hybrid Mouse Diversity Panel as an example to lay out a series of methods for visualization and integration of large systems genetics datasets. The database is based on the Shiny suite of tools in R, and is transferrable to other SQL-based datasets.
        Cover page: A Suite of Tools for Biologists That Improve Accessibility and Visualization of Large Systems Genetics Datasets: Applications to the Hybrid Mouse Diversity Panel
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2015)

        Purpose of review

        In contrast to many other human diseases, the use of genome-wide association studies (GWAS) to identify genes for heart failure (HF) has had limited success. We will discuss the underlying challenges as well as potential new approaches to understanding the genetics of common forms of HF.

        Recent findings

        Recent research using intermediate phenotypes, more detailed and quantitative stratification of HF symptoms, founder populations and novel animal models has begun to allow researchers to make headway toward explaining the genetics underlying HF using GWAS techniques.

        Summary

        By expanding analyses of HF to improved clinical traits, additional HF classifications and innovative model systems, the intractability of human HF GWAS should be ameliorated significantly.
          Cover page: Genetics of common forms of heart failure
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2020)
          Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.
            Cover page: Systems Genetics for Mechanistic Discovery in Heart Diseases
            • Article
            • Peer Reviewed
            UCLA Previously Published Works (2017)

            Background

            The implantation of mechanical circulatory support devices in heart failure patients is associated with a systemic inflammatory response, potentially leading to death from multiple organ dysfunction syndrome. Previous studies point to the involvement of many mechanisms, but an integrative hypothesis does not yet exist. Using time-dependent whole-genome mRNA expression in circulating leukocytes, we constructed a systems-model to improve mechanistic understanding and prediction of adverse outcomes.

            Methods

            We sampled peripheral blood mononuclear cells from 22 consecutive patients undergoing mechanical circulatory support device (MCS) surgery, at 5 timepoints: day -1 preoperative, and postoperative days 1, 3, 5, and 8. Clinical phenotyping was performed using 12 clinical parameters, 2 organ dysfunction scoring systems, and survival outcomes. We constructed a strictly phenotype-driven time-dependent non-supervised systems-representation using weighted gene co-expression network analysis, and annotated eigengenes using gene ontology, pathway, and transcription factor binding site enrichment analyses. Genes and eigengenes were mapped to the clinical phenotype using a linear mixed-effect model, with Cox models also fit at each timepoint to survival outcomes.

            Results

            We inferred a 19-module network, in which most module eigengenes correlated with at least one aspect of the clinical phenotype. We observed a response of advanced heart failure patients to surgery orchestrated into stages: first, activation of the innate immune response, followed by anti-inflammation, and finally reparative processes such as mitosis, coagulation, and apoptosis. Eigengenes related to red blood cell production and extracellular matrix degradation became predictors of survival late in the timecourse corresponding to multiorgan dysfunction and disseminated intravascular coagulation.

            Conclusions

            Our model provides an integrative representation of leukocyte biology during the systemic inflammatory response following MCS device implantation. It demonstrates consistency with previous hypotheses, identifying a number of known mechanisms. At the same time, it suggests novel hypotheses about time-specific targets.
              Cover page: Integrative model of leukocyte genomics and organ dysfunction in heart failure patients requiring mechanical circulatory support: a prospective observational study
              • Article
              • Peer Reviewed
              UCLA Previously Published Works (2015)
              Human genome-wide association studies have identified thousands of loci associated with disease phenotypes. Genome-wide association studies also have become feasible using rodent models and these have some important advantages over human studies, including controlled environment, access to tissues for molecular profiling, reproducible genotypes, and a wide array of techniques for experimental validation. Association mapping with common mouse inbred strains generally requires 100 or more strains to achieve sufficient power and mapping resolution; in contrast, sample sizes for human studies typically are one or more orders of magnitude greater than this. To enable well-powered studies in mice, we have generated high-density genotypes for ∼175 inbred strains of mice using the Mouse Diversity Array. These new data increase marker density by 1.9-fold, have reduced missing data rates, and provide more accurate identification of heterozygous regions compared with previous genotype data. We report the discovery of new loci from previously reported association mapping studies using the new genotype data. The data are freely available for download, and Web-based tools provide easy access for association mapping and viewing of the underlying intensity data for individual loci.
                Cover page: High-Density Genotypes of Inbred Mouse Strains: Improved Power and Precision of Association Mapping.
                • Article
                • Peer Reviewed
                UCLA Previously Published Works (2013)

                Background

                Network construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems.

                Results

                We have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case.

                Conclusions

                In making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions.
                  Cover page: Maximal information component analysis: a novel non-linear network analysis method
                  • Article
                  • Peer Reviewed
                  UCLA Previously Published Works (2017)

                  Background

                  Chronic myocardial infarction (MI) triggers pathological remodeling in the heart and cardiac nervous system. Abnormal function of the autonomic nervous system (ANS), including stellate ganglia (SG) and dorsal root ganglia (DRG) contribute to increased sympathoexcitation, cardiac dysfunction and arrythmogenesis. ANS modulation is a therapeutic target for arrhythmia associated with cardiac injury. However, the molecular mechanism involved in the pathological remodeling in ANS following cardiac injury remains to be established.

                  Methods and results

                  In this study, we performed transcriptome analysis by RNA-sequencing in thoracic SG and (T1-T4) DRG obtained from Yorkshire pigs following either acute (3 to 5 hours) or chronic (8 weeks) myocardial infarction. By differential expression and weighted gene co-expression network analysis (WGCNA), we identified significant transcriptome changes and specific gene modules in the ANS tissues in response to myocardial infarction at either acute or chronic phases. Both differential expressed genes and the member genes of the WGCNA gene module associated with post-infarct condition were significantly enriched for inflammatory signaling and apoptotic cell death. Targeted validation analysis supported a significant induction of inflammatory and apoptotic signal in both SG and DRG following myocardial infarction, along with cellular evidence of apoptosis induction based on TUNEL analysis. Importantly, these molecular changes were observed specifically in the thoracic segments but not in their counterparts obtained from lumbar sections.

                  Conclusion

                  Myocardial injury leads to time-dependent global changes in gene expression in the innervating ANS. Induction of inflammatory gene expression and loss of neuron cell viability in SG and DRG are potential novel mechanisms contributing to abnormal ANS function which can promote cardiac arrhythmia and pathological remodeling in myocardium.
                    Cover page: Inflammatory and apoptotic remodeling in autonomic nervous system following myocardial infarction
                    • Article
                    • Peer Reviewed
                    UCLA Previously Published Works (2015)

                    Background

                    Chronic stress-induced cardiac pathology exhibits both a wide range in severity and a high degree of heterogeneity in clinical manifestation in human patients. This variability is contributed to by complex genetic and environmental etiologies within the human population. Genetic approaches to elucidate the genetics underlying the acquired forms of cardiomyopathies, including genome-wide association studies, have been largely unsuccessful, resulting in limited knowledge as to the contribution of genetic variations for this important disease.

                    Methods and results

                    Using the β-adrenergic agonist isoproterenol as a specific pathological stressor to circumvent the problem of etiologic heterogeneity, we performed a genome-wide association study for genes influencing cardiac hypertrophy and fibrosis in a large panel of inbred mice. Our analyses revealed 7 significant loci and 17 suggestive loci, containing an average of 14 genes, affecting cardiac hypertrophy, fibrosis, and surrogate traits relevant to heart failure. Several loci contained candidate genes which are known to contribute to Mendelian cardiomyopathies in humans or have established roles in cardiac pathology based on molecular or genetic studies in mouse models. In particular, we identify Abcc6 as a novel gene underlying a fibrosis locus by validating that an allele with a splice mutation of Abcc6 dramatically and rapidly promotes isoproterenol-induced cardiac fibrosis.

                    Conclusions

                    Genetic variants significantly contribute to the phenotypic heterogeneity of stress-induced cardiomyopathy. Systems genetics is an effective approach to identify genes and pathways underlying the specific pathological features of cardiomyopathies. Abcc6 is a previously unrecognized player in the development of stress-induced cardiac fibrosis.
                      Cover page: Mapping genetic contributions to cardiac pathology induced by Beta-adrenergic stimulation in mice.
                      Top