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Open Access Publications from the University of California

Scholarly Works (8 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2022)

The advent of genomic editing technology offers the tantalizing promise of precisely treating a variety of disease states at the genetic level. CRISPR-Cas has democratized this capability in laboratory settings across the world. However, developing these exciting tools into therapies appropriate for clinical use involves several challenges including the potential recognition of foreign components such as the Cas effector proteins by the adaptive immune system which may limit the effectiveness of gene therapy. Here I approach this problem by first characterizing the immune response to AAV-delivered CRISPR-Cas in a mouse model targeting the PCSK9 protein, known to play a major role in atherosclerotic disease. Next, I propose and test a method to enable redosing by leveraging immune orthogonal components of AAV-CRISPR-Cas therapeutics, systematically defining the capability of this approach to avoid immune-mediated inhibition of therapeutic efficacy. Finally, in an effort towards building a single deimmunized Cas9 protein usable across diverse contexts while circumventing pre- existing immunity, I develop a scalable and portable long-range multiplexed protein engineering platform to progressively de-immunize target proteins by abolishing the most immunogenic MHC-restricted epitopes without disrupting protein function. By applying this technique to Cas9, I identify a Cas9 variant with 7 simultaneously deimmunized epitopes that retains near wild-type functionality for both direct editing and gene activation/repression. Taken together, this work represents a meaningful step towards unlocking the potential to precisely edit genomes and gene expression in the clinical setting.

    Cover page: Engineering Progressively Deimmunized and Redosable CRISPR-Cas Gene Therapies
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2022)
    Adenosine deaminases acting on RNA (ADARs) can be repurposed to enable programmable RNA editing, however their exogenous delivery leads to transcriptome-wide off-targeting, and additionally, enzymatic activity on certain RNA motifs, especially those flanked by a 5' guanosine is very low thus limiting their utility as a transcriptome engineering toolset. Towards addressing these issues, we first performed a novel deep mutational scan of the ADAR2 deaminase domain, directly measuring the impact of every amino acid substitution across 261 residues, on RNA editing. This enabled us to create a domain-wide mutagenesis map while also revealing a novel hyperactive variant with improved enzymatic activity at 5'-GAN-3' motifs. As overexpression of ADAR enzymes, especially hyperactive variants, can lead to significant transcriptome-wide off-targeting, we next engineered a split-ADAR2 deaminase which resulted in >100-fold more specific RNA editing as compared to full-length deaminase overexpression. Taken together, we anticipate this systematic engineering of the ADAR2 deaminase domain will enable broader utility of the ADAR toolset for RNA biotechnology applications.
      Cover page: Comprehensive interrogation of the ADAR2 deaminase domain for engineering enhanced RNA editing activity and specificity
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2021)
      Gene fragments derived from structural domains mediating physical interactions can modulate biological functions. Utilizing this, we developed lentiviral overexpression libraries of peptides comprehensively tiling high-confidence cancer driver genes. Toward inhibiting cancer growth, we assayed ~66,000 peptides, tiling 65 cancer drivers and 579 mutant alleles. Pooled fitness screens in two breast cancer cell lines revealed peptides, which selectively reduced cellular proliferation, implicating oncogenic protein domains important for cell fitness. Coupling of cell-penetrating motifs to these peptides enabled drug-like function, with peptides derived from EGFR and RAF1 inhibiting cell growth at IC50s of 27-63 μM. We anticipate that this peptide-tiling (PepTile) approach will enable rapid de novo mapping of bioactive protein domains and associated interfering peptides.
        Cover page: Peptide-tiling screens of cancer drivers reveal oncogenic protein domains and associated peptide inhibitors
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2019)
        Protein-based therapeutics can activate the adaptive immune system, leading to the production of neutralizing antibodies and the clearance of the treated cells mediated by cytotoxic T cells. Here, we show that the sequential use of immune-orthogonal orthologues of CRISPR-associated protein 9 (Cas9) and adeno-associated viruses (AAVs) evades adaptive immune responses and enables effective gene editing using repeated dosing. We compared total sequence similarities and predicted binding strengths to class-I and class-II major histocompatibility complex (MHC) proteins for 284 DNA-targeting and 84 RNA-targeting CRISPR effectors and 167 AAV VP1-capsid-protein orthologues. We predict the absence of cross-reactive immune responses for 79% of the DNA-targeting Cas orthologues-which we validated for three Cas9 orthologues in mice-yet we anticipate broad immune cross-reactivity among the AAV serotypes. We also show that efficacious in vivo gene editing is uncompromised when using multiple dosing with orthologues of AAVs and Cas9 in mice that were previously immunized against the AAV vector and the Cas9 cargo. Multiple dosing with protein orthologues may allow for sequential regimens of protein therapeutics that circumvent pre-existing immunity or induced immunity.
          Cover page: Immune-orthogonal orthologues of AAV capsids and of Cas9 circumvent the immune response to the administration of gene therapy
          • Article
          • Peer Reviewed
          LBL Publications (2016)

          Background

          Switchgrass (Panicum virgatum L.) is a warm-season perennial grass that can be used as a second generation bioenergy crop. However, foliar fungal pathogens, like switchgrass rust, have the potential to significantly reduce switchgrass biomass yield. Despite its importance as a prominent bioenergy crop, a genome-wide comprehensive analysis of NB-LRR disease resistance genes has yet to be performed in switchgrass.

          Results

          In this study, we used a homology-based computational approach to identify 1011 potential NB-LRR resistance gene homologs (RGHs) in the switchgrass genome (v 1.1). In addition, we identified 40 RGHs that potentially contain unique domains including major sperm protein domain, jacalin-like binding domain, calmodulin-like binding, and thioredoxin. RNA-sequencing analysis of leaf tissue from 'Alamo', a rust-resistant switchgrass cultivar, and 'Dacotah', a rust-susceptible switchgrass cultivar, identified 2634 high quality variants in the RGHs between the two cultivars. RNA-sequencing data from field-grown cultivar 'Summer' plants indicated that the expression of some of these RGHs was developmentally regulated.

          Conclusions

          Our results provide useful insight into the molecular structure, distribution, and expression patterns of members of the NB-LRR gene family in switchgrass. These results also provide a foundation for future work aimed at elucidating the molecular mechanisms underlying disease resistance in this important bioenergy crop.
            Cover page: Identification, characterization, and gene expression analysis of nucleotide binding site (NB)-type resistance gene homologues in switchgrass
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2021)
            Current treatments for chronic pain rely largely on opioids despite their substantial side effects and risk of addiction. Genetic studies have identified in humans key targets pivotal to nociceptive processing. In particular, a hereditary loss-of-function mutation in NaV1.7, a sodium channel protein associated with signaling in nociceptive sensory afferents, leads to insensitivity to pain without other neurodevelopmental alterations. However, the high sequence and structural similarity between NaV subtypes has frustrated efforts to develop selective inhibitors. Here, we investigated targeted epigenetic repression of NaV1.7 in primary afferents via epigenome engineering approaches based on clustered regularly interspaced short palindromic repeats (CRISPR)-dCas9 and zinc finger proteins at the spinal level as a potential treatment for chronic pain. Toward this end, we first optimized the efficiency of NaV1.7 repression in vitro in Neuro2A cells and then, by the lumbar intrathecal route, delivered both epigenome engineering platforms via adeno-associated viruses (AAVs) to assess their effects in three mouse models of pain: carrageenan-induced inflammatory pain, paclitaxel-induced neuropathic pain, and BzATP-induced pain. Our results show effective repression of NaV1.7 in lumbar dorsal root ganglia, reduced thermal hyperalgesia in the inflammatory state, decreased tactile allodynia in the neuropathic state, and no changes in normal motor function in mice. We anticipate that this long-lasting analgesia via targeted in vivo epigenetic repression of NaV1.7 methodology we dub pain LATER, might have therapeutic potential in management of persistent pain states.
              Cover page: Long-lasting analgesia via targeted in situ repression of NaV1.7 in mice.
              • Article
              • Peer Reviewed
              UCLA Previously Published Works (2020)
              We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.
                Cover page: International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium
                • Article
                • Peer Reviewed
                UCLA Previously Published Works (2022)
                Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.
                  Cover page: International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality
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