Eukaryotic DNA is compacted into cells by wrapping DNA around nucleosomes. Nucleosomes are composed of core or variant histone proteins. Unlike core histones, histone variants enable specialized chromatin functions such as gene transcription or inheritance. Given their crucial and widespread functions, core histones and their variants are typically evolutionarily conserved in sequence and function. Therefore, lineage-specific differences in histone repertoires present unique opportunities to understand their functional consequences on genomic organization and biological processes.

Two such changes, both likely selectively advantageous, have occurred in budding yeast and flies. In budding yeast, the histone variant H2A.X, involved in DNA repair, entirely replaced core histone H2A. A crucial SQ motif on the C-terminal tail of H2A.X is required for its DNA repair mechanism. In flies, the loss of H2A.X and a fusion of the SQ motif from H2A.X with a conserved variant H2A.Z, which is implicated in gene expression, gave rise to a unique H2Av variant. We are recreating the histone H2A compositions of flies in yeast to determine the functional consequences of these evolutionary re-arrangements. Specifically, in S. cerevisiae, we moved the SQ motif or the entire C-terminus of H2A.X to H2A.Z, creating a fusion histone similar to the D. melanogaster H2Av. Therefore, the H2A repertoire in these engineered yeast resembles a fly's. Upon treatment with DNA damage agents, these “Drosophilized” yeast show reduced DNA damage repair efficiencies similar to mutant yeast with no SQ motif. Thus, we hypothesize that the abundance and/or localization of the SQ motif is critical for efficient DNA repair in yeast. Furthermore, we will test if processes like sporulation, meiosis, or resistance to stressful conditions that require DNA damage repair are affected in our “Drosophilized” yeast. These experiments will reveal the biological consequences and potential advantages of the unique yeast H2A repertoire.

Genomic amplification of specific genes is a common mechanism of adaptation that also underpins many human disorders. We use yeast (Saccharomyces cerevisiae) to investigate the mechanism of one such gene amplification. When yeast are grown in sulfate-limited conditions for many generations, the population becomes dominated by cells possessing an inverted triplication of the SUL1 gene, which produces a sulfate transporter. Because of increased transporter levels, these cells have higher fitness in limited sulfate conditions. The Brewer Lab proposed a model — Origin Dependent Inverted Repeat Amplification (ODIRA) — where this gene amplification is initiated via a DNA replication error. In the ODIRA model, DNA replication fork regression at short inverted repeats leads to template switching of the replication machinery and the extrusion of a replication-competent hairpin molecule, which after replication, recombines at the original locus to produce an inverted triplication. An alternative explanation behind the amplification is that the hairpin molecule is generated by double-stranded DNA breaks (DSB). To distinguish between these possibilities, we used an engineered strain in which the selectable marker gene, URA3, is split into overlapping fragments (“ura” and “ra3”) on two different chromosomes. The complete URA3 gene is only present in yeast that undergo rare direct recombination between chromosomes or by recombination of the replicated hairpin formed by ODIRA or DSB. We used CRISPR-Cas9 to induce DSBs upstream of the ura fragment and identify the type of event that restores URA3 function with contour-clamped homogeneous electric field gels (CHEF gels), Southern blots, and polymerase chain reactions (PCR). If DSBs drive hairpin formation, cutting the chromosome upstream of the ura fragment should increase the frequency of URA3 assembly via hairpin intermediate. We demonstrate that double-stranded DNA breaks do not increase frequency of hairpin intermediates, providing further evidence that ODIRA is responsible for the inverted triplications of SUL1 in yeast.

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.

By 2050, the global human population is expected to reach 9.7 billion. Supporting this rapid growth will challenge global food systems, increasing the demand for healthier affordable foods such as poultry-sourced products (meat and eggs). Improving the accuracy of genotype-to-phenotype predictions for farmed animals could enable better breeding strategies and management practices that are crucial to meeting this goal. By characterizing regulatory genomic regions within various tissues, epigenetic factors which dictate specific cellular phenotypes can be pinpointed: improving phenotype prediction accuracy. To this end, we genetically sequenced (whole-genome bisulfite sequencing) samples of reproductive tissues (magnum, shell gland, isthmus, and ovary) from farmed groups of chickens (G. gallus). Through bioinformatics, we functionally annotated regulatory patterns of DNA methylation to identify tissue-specific epigenetic variation across the female chicken reproductive system. To tackle this, we utilized CGmapTools software to comparatively analyze tissues in a pairwise manner. In order to quantify each pairs’ differentially methylated regions (DMRs), all methylated regions were intersected and filtered through a statistical t-test. We found the pairwise comparison analysis between isthmus and ovary tissues to show the highest number of significant DMRs, being 51% hypermethylated in the isthmus. On the other hand, a comparative analysis between isthmus and shell gland tissues showed the lowest number of significant DMRs, being 54% hypermethylated in the isthmus. Upon annotating and conducting enrichment analyses on the DMRs, we learned that genes related to ECM-receptor interactions and focal adhesion were most prominent. Results of this research will aid the FAANG consortium (an international effort to improve farmed animal production) in improving genotype-to-phenotype predictions, hopefully enabling more sustainable genomic selection practices and genome-enabled management in the future of agriculture.

A grand challenge in the field of mass spectrometry proteomics is the problem of peptide sequencing. The dominant approach to this problem uses a database search; however, with the use of machine learning, peptide sequencing can be solved without using a database. Casanovo is a recently-developed, state-of-the-art machine learning model that solves this problem. However, Casanovo is trained on biased data, because most mass spectrometry proteomics experiments digest proteins using trypsin, which preferentially cleaves after lysine and arginine. This can result in incorrect predictions for data that was not generated using trypsin. Hence, we hypothesized that using a non-tryptic dataset to refine an existing Casanovo model would produce more accurate predictions on non-tryptic data. I constructed an unbiased dataset by downsampling from preexisting data, yielding a set of peptides with a uniform distribution of n-terminal amino acids. After splitting the data and fine-tuning Casanovo on an unbiased training dataset, I used the model to predict on an unbiased validation set. I also applied Casanovo to two non-tryptic datasets: antibody sequencing data and immunopeptidomics sequencing data. For the latter dataset, an antibody binding affinity tool, NetMHCpan4.0, was used to determine the binding probability of the predictions to test plausibility. We demonstrate that the fine-tuned model, Casanovo_ne, increases performance when predicting on non-tryptic data. Applied to the uniformly distributed validation set, Casanovo_ne predicts more accurately than the original Casanovo model, and predicts more uniform terminal amino acid distributions. Additionally, Casanovo_ne predicts more peptides that are likely to be MHC binders than a database search strategy. Finally, Casanovo_ne accurately represents the digestion rules for most non-tryptic enzymes. As future work, we will modify Casanovo to take as input the identity of the digestion enzyme, alongside each spectrum. We hypothesize this approach will further improve Casanovo’s performance for samples prepared with alternative enzymes.

In every eukaryotic genome, there is a cluster of tandemly repeated ribosomal DNA (rDNA) that is present in high copy numbers. Besides encoding ribosomal RNAs, rDNA is also involved in many non-ribosomal cellular functions. It is still not fully understood how this cluster of rDNA is maintained and how variation in its copy number impacts cellular function. Kobayashi et al found that Fob1, a protein that binds to replication fork blocking (RFB) sequences, is involved in the expansion and contraction of the rDNA region, however, the underlying mechanism of this copy number control is unknown. To explore the interactions between FOB1 and rDNA I would like to utilize CRISPR/Cas9-mediated editing to edit specific sequences in each rDNA repeat in wild-type and fob1Δ strains of Saccharomyces cerevisiae. Previous studies in our lab utilizing CRISPR/Cas9-mediated editing of the rDNA found that the rDNA copy number was initially significantly reduced, resulting in very slow cellular growth, and after many cell generations, rDNA copy number would expand through a proposed mechanism of reintegration of excised repeats and unequal sister recombination. These observations raise an important question: if FOB1 is needed for rDNA expansion, would it even be possible to perform rDNA editing and recover rDNA copy number in a fob1Δ strain? To address this, I am performing CRISPR/Cas9 editing of rDNA in fob1Δ cells alongside a wild-type control. I am characterizing viable transformants by studying their growth rate, ploidy, and rDNA copy number expansion. I am expecting to see no rDNA expansion occur in strains that do not have the presence of FOB1. By understanding the phenotypic impact of rDNA copy variation in a fob1Δ strain of Saccharomyces cerevisiae, we can come closer to understanding the interactions between RFB, FOB1, and rDNA copy number along with its effects on cellular processes.

Joubert syndrome (JS) is a neurodevelopmental condition diagnosed by the appearance of the “molar tooth sign” on axial brain magnetic imaging (MRI). Patients display hypotonia, abnormal eye movements, and ataxia. Substantial progress has been made on identifying the genetic causes of JS, which typically displays recessive inheritance. Nonetheless, the cause cannot be identified in ~25% of our cohort of JS-affected families. The contribution of variants that impact RNA splicing remains unknown. Our goal is to evaluate the role of noncanonical splice variants in the pathogenesis of JS. Canonical splice variants impact RNA splicing by disrupting the splice site directly, whereas noncanonical splice variants may affect it through alternative mechanisms, which need to be validated by RNA analysis. We previously identified genetic causes in 520 of 679 families with JS. To identify additional causes, we used SpliceAI (SpliceAI score >0.5) to identify candidate variants that impact splicing. We extracted RNA from patient cell lines and converted it into complementary DNA (cDNA). Then we used polymerase chain reaction (PCR) to amplify the affected exons with two sets of primers flanking the relevant splice junction. We evaluated PCR product size and sequence using gel electrophoresis and Sanger sequencing. We found 74 families with ≥1 canonical splice variant. An additional 34 families have ≥1 candidate noncanonical splice variant. We confirmed the pathogenicity of two of the candidate noncanonical splice variants by demonstrating an abnormal splicing event in AHI1 and MKS1 in two patient samples. By extrapolation from our data in JS, noncanonical splice variants may contribute as much as 10% to the genetic causes of recessive conditions. A precise genetic diagnosis informs prognosis, avoids unnecessary work-up, guides monitoring for associated complications, and opens the door to gene-specific treatments.

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.

Deep mutational scanning is a method to analyze the phenotypic effects of thousands to millions of single mutations in parallel. I will use this approach to perform a scan of the yeast GAL4 gene within the fruit fly Drosophila melanogaster. Scoring the functional effects of mutated GAL4 in D. melanogaster will demonstrate the feasibility of using deep mutational scanning in multicellular organisms; so far, it has been used only in single-cell organisms. Gal4 protein is a transcription factor that binds to its Upstream Activating Sequence (UAS), which I am using to drive the expression of Green Fluorescent Protein (GFP). I will measure the functional effects of single missense mutations in GAL4 by crossing flies containing GAL4 variants with a fly reporter line containing five tandem repeats of UAS upstream of the GFP gene. Thus, I can measure the functional effects of mutations in GAL4 by measuring the intensity of green fluorescence of the resulting fruit fly embryos. A previous deep mutational scan of GAL4 in yeast provides a baseline expectation for the effects of different GAL4 variants. I expect the results I obtain in the fly system will be similar to those from the yeast system; obtaining similar results would validate the potential for mutational scanning in flies. Demonstrating the feasibility of deep mutational scanning in fruit flies will allow future studies to use mutational scanning techniques to study complex phenotypes, including behavioral, developmental, and tissue-specific phenotypes in high-throughput.

Azole drug resistance in fungi is a well-established phenomenon. Previous research through the yEvo (yeast Evolution) program used Saccharomyces cerevisiae as a model organism to study how azole resistance arises using experimental evolution. By growing yeast in increasing doses of azole over time, high school students selected for yeast cells that gained favorable mutations for azole resistance. Sequencing this yeast at UW enabled us to identify specific mutations that contribute to azole resistance. We collaborated with Fred Hutch Science Education Partnership to design a lesson kit that can be checked out by local high school instructors for use in their classrooms. We selected twelve strains from our previous azole evolution experiments that contained a variety of mutations. These included missense and synonymous mutations, copy number gains, transposon insertions, and mitochondrial DNA loss. To develop this kit, I tested the experimental conditions by growing individual strains in a range of azole concentrations. From this, I chose an azole concentration that sufficiently introduces environmental pressure but still allows for strain growth. I then performed a series of competitive growth experiments to confirm replicability and test the procedure as it would be used in the kit. Using the results of my tests, I also contributed to creating the accompanying protocol and curriculum for the kit. Students will have the opportunity to make predictions through a bracket-style match-up, learning about each strain through “trading cards” that I am helping design to contain information about the mutations of each strain. In the final step of this project, I will take part in a training session to support high school instructors interested in teaching the kit. From the implementation of this kit, students will learn broadly about the effects of different types of mutations, and specifically how mutations affect anti-fungal drug resistance.

Uridine-5'-triphosphate (UTP) is a precursor for RNA synthesis. Ura3 catalyzes the conversion of orotidine-5'-phosphate (OMP) into uridine monophosphate (UMP) and is commonly used as a selection marker to characterize mutation rates of S. cerevisiae. URA3 can be positively selected for by growing cells in the absence of uracil and can be selected against by growing cells in the presence of the toxic fluorinated UTP precursor, 5-Fluoroorotic acid (5-FOA). While mutations in URA3 make up the majority of 5-FOA-resistant mutants, mutations in a small number of other loci can also cause this phenotype. We whole genome sequenced the 5-FOA-resistant mutants with a wild type URA3 and identified mutations to URA6 in each of these individuals. URA6 is an essential gene that encodes an enzyme that catalyzes the conversion of uridine monophosphate (UMP) into uridine-5'-diphosphate (UDP). Here, we describe 41 non-synonymous mutations to URA6 that permit growth in both the absence of uracil and in the presence of 5-FOA. It remains unclear how the URA6 mutants can maintain a functioning UTP synthesis pathway while remaining resistant to the toxic fluorinated precursors. We hypothesize that these mutations alter the protein structure in a manner that decreases the affinity for fluorinated substrates while maintaining the affinity for UDP. To test this, we will evaluate the structural changes to URA6 resulting from these non-synonymous mutations. Our goal is that our findings can benefit our understanding of the UTP biosynthesis pathway and encourage further investigation of the mechanisms involving fluorinated substrate analogues.

Saccharomyces cerevisiae is a model organism that is essential in the production of products such as wine, bread, beer, and bioethanol. The process of domestication through selection of desired traits for beer brewing has led to genomic changes in these S. cerevisiae strains. Specifically, the brewing process creates conditions that favor asexual reproduction as opposed to sexual reproduction, allowing genomic changes detrimental to meiosis to accumulate. Some genomic changes resulting from domestication include aneuploidy, genome decay, and high copy number variation. Decreased ability to undergo meiosis makes genetic linkage studies like quantitative trait loci (QTL) mapping incredibly difficult compared to lab strains. Meiosis is a key part of QTL mapping, where a parental strain for a phenotype of interest undergoes meiosis to generate progeny with variation in the phenotypic trait and in their genotypes. My work aims to find and develop genetically tractable brewing yeast strains in order to perform QTL mapping on unique brewing traits. The brewing trait of interest to my work is thermotolerance, as higher temperatures around the globe result in harsher selection conditions on brewing yeast. Previous work on Norwegian kveik strains revealed high thermotolerance and the ability to undergo meiosis and produce viable offspring. My project aims to understand the genetic basis of increased thermotolerance in kveik strains. I will conduct heat tolerance assays to determine the effect a select range of temperatures has on growth. I expect to see variation between individuals in a population and the variation will allow me to conduct bulk segregant analysis–the specific type of QTL mapping I aim to do–for the genotypes associated with the increased thermotolerance trait. As global temperatures are rising more rapidly, it is essential to understand how organisms use thermotolerance as an adaptive response.

For Saccharomyces cerevisiae (S. cerevisiae) to survive, they must either synthesize nitrogenous bases or reside in a nitrogenous base-rich environment. If there is a mutated enzyme along the pathway that makes the nitrogenous base uracil, then the organism would be unable to synthesize uracil and die without uracil. When S. cerevisiae is grown in 5-Fluoroortic Acid (5-FOA) media, the URA3 enzyme will catalyze 5-FOA into a toxic intermediate that causes cell death when incorporated into RNA. In URA3 mutations, the intermediate is not produced, and S. cerevisiae can survive. In an experimental setup where we explored mutation patterns in aging S. cerevisiae cells, we used URA3 to select mutants that grow on 5-FOA media when uracil is provided. However, when analyzing the mutant sequences, there were fewer URA3 mutations than expected. We performed Sanger sequencing on the URA3 gene in individual mutants and were surprised to find that some did not have any mutations. We, therefore, sequenced the whole genome of those mutants and found that they had missense mutations in the URA6 gene. We then analyzed the URA6 locus from the original pool. Across the URA6 gene, the mutations appeared randomly spread with the possibility of some mutation hotspots, indicating that they could be loss of function mutations. In contrast with the URA3 mutants, we observed URA6 mutants were able to grow 5-FOA media when uracil was not provided. There may exist an alternative pathway for URA6 S. cerevisiae mutants to synthesize uracil and survive. To further study this observation, we plan on analyzing the function of the URA6 gene in 5-FOA media compared to the URA3 gene and the components of the URA6 pathway. Ultimately, the result of this study could clarify nitrogenous base production pathways in S. cerevisiae and the impacts of 5-FOA on S. cerevisiae.