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Open Access Publications from the University of California

Scholarly Works (4 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2019)

With the advent of high-throughput technologies, large-scale multi-omics data integration approaches have revolutionized our understanding of cancer and its progression. In this dissertation, we focus on deciphering the molecular mechanisms of various targeted cancer treatments and growth factors on chromatin remodeling through integrative analyses of multi-omics data. Chromatin remodeling is involved in the stability of genome structure, gene expression, and DNA repair, as well as, cell growth and progression; therefore, it plays an essential role in tumor suppression. The regulation of chromatin remodeling is carried out by the precise coordination of covalent histone modifications and remodeler proteins through catalytic activities. Disruption of these regulated activities confers a unique ability for healthy cells to reprogram their genome for the maintenance of oncogenic phenotypes. Hence, these histone modifications and remodeler proteins are potential targets for cancer treatments. While many drugs have the potential to target histone modifications and remodeler proteins, their precise mechanisms of action, i.e., alterations in cellular reprogramming, are not well studied. To address this gap, we utilized latent space models to integrate multi-omics data such as proteomic/phosphoproteomic, transcriptomic, and epigenomic data to understand the effects of various drugs and growth factors on specific genes, proteins, and phosphoproteins that are involved in the regulation of a wide range of cellular processes (growth, proliferation, and cell division) and gene activity states in cancer and healthy cell lines. In addition to providing mechanistically-driven targets that can impact chromatin remodeling, the chromatin fingerprints generated from our study can serve as a signature for assessing the efficacy of a given drug in treating cancer. Further, increasing evidence, supported by our study of breast cancer, indicates that this paradigm applies to various cancers, and further analysis can provide insights into more detailed chromatin-based mechanisms. Our study implies that the cancer state is one where chromatin gets remodeled, and effective drugs attempt to restore the chromatin state to that of a healthy cell. Overall, the integrative frameworks we developed reveal the mechanisms of action of specific drugs on chromatin remodeling machinery in breast cancer cells and of growth factors on cellular phenotypes in normal breast cells, which lay the foundation for improved development of chromatin-based cancer therapy.

    Cover page: Integrative Approach to Targeting Chromatin Remodeling in Breast Cancer Therapy
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2021)
    High-quality metadata annotations for data hosted in large public repositories are essential for research reproducibility and for conducting fast, powerful and scalable meta-analyses. Currently, a majority of sequencing samples in the National Center for Biotechnology Information's Sequence Read Archive (SRA) are missing metadata across several categories. In an effort to improve the metadata coverage of these samples, we leveraged almost 44 million attribute-value pairs from SRA BioSample to train a scalable, recurrent neural network that predicts missing metadata via named entity recognition (NER). The network was first trained to classify short text phrases according to 11 metadata categories and achieved an overall accuracy and area under the receiver operating characteristic curve of 85.2% and 0.977, respectively. We then applied our classifier to predict 11 metadata categories from the longer TITLE attribute of samples, evaluating performance on a set of samples withheld from model training. Prediction accuracies were high when extracting sample Genus/Species (94.85%), Condition/Disease (95.65%) and Strain (82.03%) from TITLEs, with lower accuracies and lack of predictions for other categories highlighting multiple issues with the current metadata annotations in BioSample. These results indicate the utility of recurrent neural networks for NER-based metadata prediction and the potential for models such as the one presented here to increase metadata coverage in BioSample while minimizing the need for manual curation. Database URL: https://github.com/cartercompbio/PredictMEE.
      Cover page: Increasing metadata coverage of SRA BioSample entries using deep learning–based named entity recognition
      • Article
      • Peer Reviewed
      UC San Francisco Previously Published Works (2010)
      Human studies, encompassing interventional and observational studies, are the most important source of evidence for advancing our understanding of health, disease, and treatment options. To promote discovery, the design and results of these studies should be made machine-readable for large-scale data mining, synthesis, and re-analysis. The Human Studies Database Project aims to define and implement an informatics infrastructure for institutions to share the design of their human studies. We have developed the Ontology of Clinical Research (OCRe) to model study features such as design type, interventions, and outcomes to support scientific query and analysis. We are using OCRe as the reference semantics for federated data sharing of human studies over caGrid, and are piloting this implementation with several Clinical and Translational Science Award (CTSA) institutions.
        Cover page: The human studies database project: federating human studies design data using the ontology of clinical research.
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2024)
        There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidneys active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.
          Cover page: The chromatin landscape of healthy and injured cell types in the human kidney.
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