Parkinson’s disease (PD) is a common neurodegenerative disorder that is caused by the death of dopamine-secreting neurons in the midbrain. The onset of symptoms such as progressively worsening tremors, movement difficulty, and dementia are thought to be caused by protein aggregates called Lewy bodies, mitochondrial defects, and neuroinflammation. Mutations in the GBA gene, one of the genes responsible for PD symptoms, accounts for 5-10% of all PD cases. It codes for the enzyme glucocerebrosidase which breaks down the lipid glucosylceramide. Expression of GBA in muscle cells reduces protein aggregation in the head and GBA is found in extracellular vesicles (EV). Similarly, expression in the midgut partially reduces protein aggregation in the head. Our goal is to determine if GBA expressed in the midgut can travel in EVs. We have developed a GBA mutant fly model that features symptoms similar to human PD – neurodegeneration, shortened lifespan, motor deficits, and increased protein aggregation. Flies were bred and crossed to the correct genotype, collected and frozen, and processed for analysis. To ensure that GBA is not expressed elsewhere to prevent confounding, we use an Npc1b promoter with the GAL4/UAS system, since the Npc1b gene is only expressed in the midgut. Protein is quantified following gel electrophoresis and western blots. We expect to find GBA in EVs which would help confirm that GBA traveling in EVs is responsible for the reduction in protein aggregation. This is potentially an important feature for the development of treatments for PD, as understanding what causes protein aggregation is a key step in eliminating or reducing it to prevent PD.

Although transcriptomes are highly tissue and cell type specific, curated gene set collections are not constructed or analyzed with recognition of this bias despite much of transcriptomic analysis depending on curated gene set collections. Prior work has recognized the potential for gene bias due to the variable nature of manually curating gene set collections, but coverage has yet to be characterized across all tissues and in all commonly used gene set collections. The goal of this study was to perform a comprehensive analysis of curated gene set collections from the data repository Molecular Signatures Database (MSigDB) based upon tissue specific expression. We analyzed KEGG, REACTOME, BIOCARTA, and Gene Ontology (GO) including Biological Processes, Cellular Components and Molecular Function gene set collections available on MSigDB. We curated lists of enriched and elevated genes as defined by Human Protein Atlas for 36 tissues. Analyses, visualization, and statistical analyses were performed using the R statistical programming language. We revealed that the MSigDB gene set collections differ among themselves in the fraction of tissue genes covered. GO Biological Processes has the highest gene coverage. BIOCARTA has the lowest gene coverage. Additionally, each collection differs among tissues in the fraction of genes covered. We also showed differential gene coverage among tissues even when collections are combined. Within elevated tissues, the liver has the highest and the fallopian tube has the lowest gene coverage. Within enriched tissues, the lymphoid has the highest and the testis has the lowest gene coverage. We created a database describing the presence or absence of tissue specific genes for each tissue with which researchers can elect the most appropriate gene set collection to use for analysis of a specific tissue. This increases the utility of our findings and creates a direct resource for researchers in the field.

Unlike humans, zebrafish and salamanders can regenerate entire bony appendages following amputation. This unique regenerative potential may reflect key differences in osteoprogenitor activation and regulation. Here, we used cross-species transcriptomics and single-cell combinatorial indexing RNA sequencing (sci-RNA seq) to 1) identify genes dissimilarly expressed during rat and zebrafish bone regeneration, and 2) determine which cell populations in zebrafish expressed these genes. We modified a previously developed bioinformatics pipeline to include dynamic time warping (DTW) and a robust similarity metric (DTW distance) to quantify gene expression similarity. We analyzed publicly available microarray datasets for zebrafish caudal fin regeneration and rat marrow ablation-induced bone regeneration to determine dissimilarly expressed genes during regeneration. The 30 most similarly expressed genes were enriched with genes of known skeletal function. Of the 30 most dissimilarly expressed genes, 11 were upregulated in zebrafish while downregulated in rat. The most dissimilarly expressed gene during bone regeneration in rat and zebrafish was f13a1b. We performed sci-RNA seq of the regenerating zebrafish caudal fin to investigate these genes at single-cell resolution, identifying what cell types are present in the regenerating fin and how their gene expression varies. f13a1b is expressed within osteoblasts, epidermal, and mesenchymal cells in the regenerating fin. Within the osteoblasts, further clustering identified cycling and non-cycling osteoblasts, pre-osteoblasts (col10a1+), and early-osteoblasts (sp7+). f13a1b expression is enriched in two of the osteoprogenitor populations and less abundant in the later stages of osteoblast differentiation (pre-osteoblasts and early osteoblasts). In conclusion, we have performed comparative transcriptomics to elucidate genes that are upregulated in zebrafish relative to rats and determined the involvement of these genes at single-cell resolution in the regenerating zebrafish caudal fin. Genes upregulated during zebrafish bone regeneration may provide drug targets to improve bone regeneration in humans and a better understanding of the molecular pathways driving appendage regeneration.

CRISPRa is a tool that can be used for metabolic engineering and information processing. However, many of the mechanisms and design rules are unknown making it extremely difficult to engineer metabolic networks without trial-and-error. The aim of this study is to use a data-driven approach to better understand, engineer, and predict the behavior of CRISPRa. Data-driven techniques used were: modeling and machine learning to study kinetics and predict the behavior of CRISPRa, engineering a programmable light for a light-inducible CRISPRa system to make data collection less error prone and to generate more experimental conditions, and data scraping and management to visualize the theoretical best PAM sites to engineer gRNAs for maximum activation. (A PAM is a short DNA sequence that follows the DNA region targeted for cleavage by the CRISPR system). Two notable ODE models resulted from the fitting process: the first has 9 parameters and an R² score of 0.882. The second model has 13 parameters and an R² score of 0.912. A test set of data needs to be generated to evaluate the model performance. The light inducible system is still in development, but once completed could be used to generate more experimental conditions and data to test and train models. Then data visualization was used to choose gene sequences to learn more about PAM design rules. We have preliminary evidence showing moderate activation for 2 of 7 highest scoring genes. Data is still in the process of being analyzed.

Experimental evolution can determine genetic interactions during natural selection in complex systems. Using whole-genome sequencing of 95 parallel populations of haploid Saccharomyces cerevisiae experimentally evolved for 250 generations, we discovered a possible epistatic interaction between two sets of beneficial mutations. The first mutation is a transposable element (TE) insertion into the promoter of FLO1, giving rise to a cellular aggregation phenotype known as flocculation. The second set are putative loss of function mutations in genes encoding members of the SAGA-complex, which is thought to increase expression of genes proximal to TEs. We hypothesize that without the members of the SAGA-complex, the FLO1 gene will be unexpressed, abrogating the flocculation phenotype. We isolated three flocculant clones with TE insertions from different experimental populations and crossed them with three clones with the deletion in the SAGA-complex. Through meiosis, the yeast sporulated into four cells. The ratios of flocculant to wildtype haploid cells are used to determine an epistatic interaction. A 2:2 ratio suggests a non-epistatic interaction while a 1:3 flocculant to wildtype ratio suggests an epistatic interaction. The project is in the early stages but segregation ratios suggest the members of the SAGA-complex with deletion mutations do not hinder the expression of the FLO1 gene. Our alternate hypothesis is members of the SAGA-complex have no effect on the activation of TE insertions that promote the expression of the FLO1 gene. While the initial hypothesis might not hold, this experiment will give us a further understanding of genetic interactions during evolution.

The Davis lab is focused on understanding the pathogenic cellular mechanisms causing Parkinson’s disease and other aging-associated neurodegenerative diseases.We are currently researching how GBA affects extracellular vesicle aggregate protein propagation that increases the advancement of Parkinson’s disease. My own research in the lab is focused around the effects of GBA mutations on ceramide metabolism, a lipid in extracellular membranes, and how ceramide levels affect the propagation of Parkinson’s disease. I will use a GBAdel Drosophila model with Carmofur to increase Ceramide levels and determine if any visible changes in extracellular vesicles can be seen. I will also use a neuronal model created from Parkinson’s disease patients to look at and characterize vesicles when ceramide levels are changed. Through these two models I will be able to analyze and determine if variation in ceramide metabolism levels are due to GBAdel and how ceramide levels affect extracellular vesicles related to Parkinson’s disease. GBA is known to encode for GCase, an enzyme that hydrolyzes glucosylceramide into its parts, glucose and ceramide, since GBA directly allows for the creation of ceramide, it is reasonable to believe a mutation in GBA would affect ceramide levels in a Parkinson’s patient. It is also reasonable to assume that changes in ceramide levels will correlate with the aiding of Parkinson propagation, due to ceramide’s role in extracellular vesicle creationWe are using a fruit fly model based on a genetic risk factor for Parkinson’s disease, and a human neuronal model derived from Parkinson’s disease patients with genetic risk factors for this disease. We are currently investigating how this risk factor may influence faster propagation of pathogenic protein aggregation throughout the brain. We hope to identify new possible therapeutic targets to slow or stop neurodegeneration in Parkinson’s disease, where currently only symptomatic treatment exists.

Despite containing over two meters of DNA, the human nucleus amazingly is still able to fit said quantity of genetic material into a space only ten micrometers in diameter. To explore this phenomenon, we first utilize computational tools to find optimal locale on strands of DNA for the deployment of synthetic oligonucleotide (oligo) hybridization probes that, when hybridized, allow us to visualize the structure of chromosomes in the nucleus using microscopy. To find these optimal regions, we use a set of programs known as OligoMiner to parse through sections of DNA, considering certain parameter restrains like melting temperature and nucleotide content ratios. One area we explore is how altering the acceptable level of the nucleobase guanine in target regions affects the number of optimal sites located on a given strand, as well as whether these findings are relevant to the reverse strand. We hope that pinpointing ratios of only guanine will allow for better hybridization efficiency, which in turn would result in increased probe effectiveness overall. To accomplish this, we first encode the aforementioned checks into OligoMiner since, in its original form, this set of programs only verifies the combined ratio of guanine and cytosine. Following the creation of these additional tools, we run OligoMiner on both random and specific sections of the human X-chromosome. We’ve seen that guanine-rich regions of DNA will see a greater concentration of probe sites on the reverse strand under our new parameter involving solely this nucleobase, compared to that of the combined cytosine ratio, due to the guanine-cytosine pairing ensuring the ratio stays constant between strand compliments. The future of this research lies both in further updating and expanding OligoMiner while also examining probe density in regions beyond just those of the X-chromosome.