To test questions on the evolutionary history of a species, it is important to consider the drivers of genetic diversification that lead to speciation. Species diversification is often driven by the formation of geographical boundaries, ecological diversity, sexual preference, or a combination of these factors. However, for the Western Banded Gecko (Coleonyx variegatus), a species native to the southwestern region of the United States, few of these factors exist. Despite the lack of clear barriers to gene flow, prior research identified several distinct populations of C. variegatus in this region, though there is some uncertainty with these distinctions as they relied solely on the signal from one mitochondrial DNA (mtDNA) locus. The aim of this project is to instead use genomic data to assess the validity of the C. variegatus populations defined by mtDNA, and to investigate how these populations are distributed across the geographic region they inhabit. Using genomic data allows us to more confidently define population boundaries and assess how they have evolved through time. To explore the aim of this project, we sequenced reduced representation genomic data for 224 individuals across the range of the species to determine how populations of C. variegatus are structured. We then built species trees to assess the relatedness of these populations with respect to each other and the overall evolutionary history of the species. Our findings show that there are consistencies between both the genomic and mitochondrial data population definitions, but distinct differences are also present, with mtDNA overestimating the number of populations. Thus, relying on mtDNA data alone may be insufficient for confidently ascribing population boundaries for C. variegatus. Accurately defining populations can have great implications for the conservation of native biodiversity, but as shown by our study, relying on a single mtDNA locus may mislead this crucial process.

BRCA1-associated RING domain protein 1 (BARD1) is a key interactor with tumor suppressor BRCA1. Due to this interaction, deleterious variants of BARD1 have been associated with breast and ovarian cancer. In recent years, the use of clinical sequencing technologies to inform and personalize patient care, precision medicine, has skyrocketed. Despite the increased prevalence of clinical sequencing, in many clinically relevant genes, like BARD1, most single-nucleotide variants (SNVs) are cataloged as variants of uncertain significance (VUS). These VUS effectively prevent clinicians from using this data to help patients as it is unknown if the observed variant is pathogenic or benign. Consequently, a strong need to functionally assess BARD1 SNVs exists. To help resolve BARD1 VUS, we are applying saturation genome editing (SGE). SGE is a multiplex assay for variant effect that functionally assesses all SNVs for genes, like BARD1, that are essential in the HAP1 cell line. SGE uses CRISPR-Cas9 gene editing to integrate a plasmid library containing all possible BARD1 SNVs into a HAP1 population. Due to BARD1’s essentiality, cells with deleterious variants become depleted from the population. These changes in cell viability are quantified through next-generation sequencing and bioinformatic analysis comparing the abundance of a variant in the original SNV library versus its abundance in the cell population at the end of the experiment. Functional scores are then calculated for each variant. To date, I have designed targeted SNV libraries for 34 regions that span the entire coding region of BARD1. These libraries are preparing to enter tissue culture as we complete final quality checks. Ultimately, we expect the functional scores for BARD1 SNVs to be bimodally distributed, showing strong separation between deleterious and benign variants. These scores will be directly used to reclassify current BARD1 VUS – allowing clinicians to better guide patient care with respect to BARD1 SNVs.

The Seattle Coronavirus Assessment Network (SCAN) study is a voluntary SARS-CoV-2 (COVID-19) testing program that enrolled participants across Seattle and King County. We collected self-reported demographic data, vaccination status, SARS-CoV-2 test results, and viral genomes from study participants. The reason visualizing this biological and logistics data is so important is so that we can analyze the Covid 19 pandemic and learn how to put measures in place to prevent future pandemics. In our dashboard, we visualized demographic and molecular data on study participants and circulating pathogens using a mix of data analysis with Python, Amazon Web Services tools, and dynamic Tableau dashboards. With data from ~69,000 swab samples collected from May 1st, 2020, to July 31st, 2022, the result was a robust map of COVID-19 trends across King County. Moving forward, our project seeks to explore what it takes to run a community surveillance program for respiratory disease, looking to answer questions such as: Who the people were who used SCAN? Were there any power users vs one-time participants? How effectively did the study reach low-income participants? How many requests from high-income regions did we have to deny every day to get representative samples? Can we identify any opportunities in kit fulfillment? Additionally, how can we gauge the costs of couriering samples, and can we find a less costly alternative? The results of this analysis looking at the SCAN community surveillance program will influence the design of future public health measures to reduce barriers to healthcare; curb community pathogen spread; better allocate resources to support community health. Our goal is to create a future where we can adequately identify and treat diseases before they become pandemics.

 Although clinical next-generation sequencing is accepted as the gold standard for the accurate and reproducible discovery of genetic variants, using sequencing to guide clinical management is severely limited by these variants of uncertain significance (VUS). In recent years, Saturation Genome Editing technology (SGE) has emerged as a high-throughput solution to reclassify VUS. SGE has strict inclusion criteria, the main one being that only essential genes in the HAP1 cell line (~2,000 genes) are compatible with the assay. Unfortunately, this leaves 18,000+ nonessential genes incompatible with SGE. This begs the question: how do we assess variants in these nonessential genes? This project aims to develop a next-generation SGE method using the principle of synthetic lethality, the genetic interaction where perturbing two co-dependent genes leads to cell death. For this pilot project, I have developed a proof-of-concept assay by searching for synthetic lethal partners of the nonessential gene STK11, a tumor suppressor implicated in cancer with thousands of VUS. I have designed a genome-wide dual combinatorial CRISPR screen, where genes are perturbed in pairs to report if they induce cell death when disrupted together. The results of this experiment will be a comprehensive landscape of STK11’s synthetic lethal interactors. Identifying co-dependent lethal partners of STK11 will further allow STK11 to mimic an essential gene through engineering knockout cell lines of its lethal partners, therefore making it amenable as an SGE target for multiplexed functional reclassification of STK11 VUS. If successful, this method can be generalized to any nonessential gene with synthetic lethal interactors in HAP1 cells. This will expand the potential gene targets for SGE and eventual VUS reclassification in order to prevent, diagnose, and manage clinical care for individuals with genetic diseases.

Adoptive T cell therapy presents a promising possibility of a safe and effective therapeutic for a wide range of cancers through utilizing CD8+ T cell-mediated killing of targets expressing tumor-associated antigens (TAA). However, developing these therapies require knowledge of a T cell receptor’s (TCR) antigenic specificity. My project aims to find the antigen specificity of CD8+ T cells from a patient with an exceptional response to hematopoietic cell transplantation (HCT). We hypothesize that leukemia-specific T cells may have helped prevent early relapse and can be identified in samples from this patient. Bone marrow T cells from the patient post-HCT were previously identified and their TCRs were sequenced. By using genetic engineering to knock out (KO) endogenous TCRs on a healthy donor’s T cells and then transducing the patient’s TCRs into the T cell via lentivirus, TCR-transduced healthy donor T cells can be prepared for functional testing. These TCR KO, TCR transduced lines are expected to provide more consistent responses in functional assays due to the presence of only one TCR on the cell surface. . The Long Killing Assay, a flow cytometry-based cytotoxicity assay requiring the targets to be in co-culture with T cells long enough to ensure T cell-mediated killing, will be utilized for functional testing. This will determine whether target cells presenting a variety of antigens are killed by the TCR-expressing T cells. After the assay, the target cells will be further studied and deconvoluted to determine the resulting TCR antigen specificity. This project will be important in refining our lab’s method of determining antigen specificity of reconstructed TCRs and potentially provide insight on the antigens responsible for antileukemic immune responses. 

Inherited retinal diseases (IRDs) are a diverse family of disorders which cause vision loss and retinal degeneration. With only 1-2% of the genome being protein-encoding, genetic variation within the expansive noncoding genome is critical to the development of disease phenotypes in the retina. Macular Telangiectasia Type II (MacTel) is an IRD resulting in disruption of central vision and greatly impacting vision-related quality of life. MacTel has an estimated prevalence of 1 in 1000 individuals, affecting approximately two million people globally. Though MacTel etiology largely remains unknown, accumulation of improperly degraded lipids within the retina is a leading hypothesis in its pathogenesis. Additionally, genome-wide association studies have implicated numerous loci in the development of MacTel, including the novel gene locus ceramide synthase 4 (CERS4). As CERS4 plays a critical role in the synthesis of lipid precursors and is highly expressed in the retina, it stands as a promising candidate for influencing MacTel development. We hypothesize that cis-regulatory element (CRE) mutations are central to the genetic frameworks underlying MacTel. We aim to characterize the sufficiency of putative enhancer regions to drive gene expression. We have identified potential CERS4 enhancer regions through a machine learning approach using adult human retina ATAC sequencing datasets. Sufficiency of candidate enhancer regions will be evaluated by insertion to a barcoded reporter library and electroporation into mouse retinas. Following proof of sufficiency, we will perform saturation mutagenesis on identified enhancers to investigate the impact of all possible single nucleotide variants (SNVs) within these regions. The results of our investigation will aid in identifying SNVs of interest within the CERS4 locus, potentially implicating specific mutations towards the development of MacTel. Greater understanding of CRE mutations will improve early clinical diagnosis and inform future therapies for patients with MacTel.