Tumor cell survival and recurrence remain a leading cause of death among cancer patients, and it is likely that the residual tumor cells that form the secondary tumor have distinct phenotypes from the primary tumor. The transcription factor NRF2 is thought to play a role in tumorigenesis, metabolic reprogramming, and recurrence in breast cancer. Emerging evidence suggests that NRF2 also intersects with the circadian rhythm, the 24-hour oscillatory clock present in all cells. My project investigates how NRF2 interacts with circadian rhythm genes, and how this interaction affects cancer cell growth. Mouse cell lines NMuMG, EMT6, 66Cl4, and 4T1 were cultured, treated with dexamethasone for synchronization of cellular clock, and harvested over three days. Cell pellets were collected every eight hours after synchronization, for a total of seven timepoints across 48 hours. I performed RNA extraction, cDNA synthesis, and RT-qPCR to analyze gene expression of  NRF2 (Nfe2l2), NRF2 target genes (Nqo1, Slc7a11, G6pdx, Gpx2, Txn1), and circadian rhythm genes (Bmal1, Clock, Per2, Cry1, Per1, Nr1d1) at each timepoint. 66Cl4 cells were further used to perform a CRISPR knockout screen for NRF2 target genes, to investigate which genes are essential for tumor cell viability. I cultured and infected cells with Cas-9 enzyme and sgRNA corresponding to 30 NRF2 targets using lentivirus, then allowed them to proliferate to 14 population doublings over the course of the screen. After the screen had completed, cells were sent for genomic sequencing to identify hit genes. Though these experiments are ongoing, we aim to identify 4-5 hit genes through the screen to direct future research on how NRF2 promotes tumor cell survival and proliferation. My data on NRF2 and circadian clock will also shed light on the intricate role of NRF2 in the cell, and open the door for new therapeutic targets.

Adoptive cell therapy with CAR-T cells has shown promise in hematological malignancies, but efficacy in solid tumors remains a challenge in part due to CAR-T cell exhaustion and antigen heterogeneity. However, the vast majority of preclinical models do not recapitulate the tumor-immune interactions that produce these barriers. To study CAR-T therapy in a rigorous model that recapitulates tumor-immune barriers, we adapted a Kras^LSL-G12D/+;P53^f/f (KP) genetically engineered mouse model (GEMM) of lung adenocarcinoma. However, adapting the KP-GEMM model for various target antigens, genetic drivers of disease, or interfacing with the vast array of powerful genetic mouse models is resource intensive which prohibits widespread utility. Here, we propose a defined, modular system for generating GEMM for CAR-T preclinical studies using the Sleeping Beauty (SB) transposon system. The proposed system uses polyethylenimine (PEI) to deliver SB transposon encoding oncogenic Kras^G12D and P53^R175H dominant alleles as well as our target antigen hROR1, in vivo to wild-type mice. We demonstrate that in vitro PEI successfully introduces genetic cargo into lung epithelial cell lines, while SB transposons mediate stable integration and expression. Next, we will test this in vivo. This system affords the induction of tumors with specific oncogenic driver mutations and specific tumor antigens on any genetic background. Ultimately, we expect that this approach will streamline preclinical use of GEMM in preclinical research. 

Most of our chemicals come from petroleum, a nonrenewable resource and a significant source of pollution. Purple non-sulfur bacteria (PNSB) also produce some of these chemicals from one-carbon (C1) feedstocks, however, genetic engineering toolkits are underdeveloped for these organisms. The ability to integrate heterologous genes is a crucial component of genetic engineering toolkits, enabling stable and precise gene expression. Despite their metabolic versatility, PNSB lack well-characterized genomic integration sites, limiting advanced strain engineering efforts. Here, we identify and characterize genomic integration sites in Rhodobacter sphaeroides 2.4.1 that can serve as stable integration loci for heterologous gene expression. Using RNA-Seq transcriptomic data, we identified intergenic regions with minimal transcriptional activity, ensuring that insertions into these regions would not disrupt native gene function. Seven candidate integration sites were selected across the genome, spanning both chromosomes and plasmids. Two-step allelic exchange was used to integrate “landing pads” for Serine Recombinase-Assisted Genome Engineering (SAGE), a site-specific recombination system, into candidate sites. Our next step is to use the SAGE system to integrate fluorescent reporters into these sites to assess positional effects on gene expression. These seven integration sites serve as a testbed, allowing us to validate the workflow for integration into a broader range of genomic locations. Our findings will provide a resource for engineering R. sphaeroides and expand the genetic toolkit for PNSB, facilitating their use in synthetic biology and bioproduction applications.

The Pacific Northwest (PNW) saw an unprecedented heatwave between June 25 to July 3 of 2021, with temperatures reaching up to 15℃ above the climatological mean. Previous studies have focused on this event’s impacts on plants in Western Washington and Oregon through direct observations, or have focused on the economic implications from poor crop turnout. We used remote sensing data to take a holistic approach and examined how all plants throughout the PNW fared during and after this historical heatwave. We found that solar induced fluorescence (SIF) and near-Infrared reflectance of vegetation (NIRv), two remotely sensed vegetation health markers, had regionally dependent plant responses to the extreme heat. In particular, anomalously high SIF regions coincided with anomalously high photosynthetically active radiation (PAR) regions due to low cloud cover. As SIF has been used as a proxy for gross primary productivity (GPP), our findings begs the question: was the elevated SIF during the heatwave indicative of higher GPP, or was the SIF response an artifact of the higher radiation? Our study aims to further our understanding of how extreme events impact plant health, which is increasingly important as heatwaves become more intense and frequent in the future.

Bacterial metabolic engineering shows great promise for sustainable chemical production. Non-model microbes such as Pseudomonas putida, Rhodobacter sphaeroides, and Rhodopseudomonas palustris offer unique opportunities for metabolic engineering, given their tolerance to environmental stressors, their ability to grow on waste substrates, and their natural production of industrially relevant compounds. However, tools for engineering these bacteria are underdeveloped. Here we present genome engineering and gene regulation tools that are generalizable to multiple non-model microbes, offering improved versatility for metabolic engineering. Firstly, we employed a high-efficiency genome engineering tool using serine recombinases (SAGE) in R. sphaeroides and R. palustris. We evaluated integration efficiency for 10 different recombinases using a fluorescent reporter screen, revealing variation in recombinase performance across microbial hosts. We used BxbI, the top-performing recombinase, to integrate a heterologous metabolic pathway into the genome of R. palustris for the bioproduction of a biofuel precursor. In addition to genome engineering tools, we developed gene regulation tools using dCas13, a protein which regulates genes at the translational level. Genome-wide functional screens were conducted in P. putida using an inducible guide RNA system to study levels of gene regulation in native aromatic biosynthesis pathways. Overall, this work advances tools for genomic integrations and gene regulation in non-model microbes, offering new strategies for metabolic engineering and expanding the host range for synthetic biology applications. 

Microbial bioproduction supports the manufacturing of sustainable chemicals but requires accurate and easy-to-use tools for monitoring cell growth. A simple and effective tool for estimating cell concentration in aqueous systems is optical density (OD). However, commercially available OD measurement systems are expensive and require manual sampling, which is time-consuming and disrupts culture growth, particularly in anaerobic microbes. To address this, I developed a low-cost OD sensor for continuously monitoring anaerobic bacteria in culture tubes. The sensor design, based on Deutzmann et al. (2022), consists of a 3D-printed sample holder with an LED and a photosensor positioned on opposite sides. The photosensor generates a voltage, which a Python script processes to calculate optical density values for each bacterial species. Plotting these OD values provides researchers with insights into bacterial growth behavior and enables optimization of culture conditions. This device's advantage over commercial spectrophotometers is that it can measure optical density directly from sealed culture tubes, eliminating the need for manual sampling into cuvettes and saving researchers valuable time. It can be configured to run autonomously, further minimizing measurement time and disruptions to bacterial growth. Additionally, the design is fully open-source and customizable while costing less than $100 to reproduce, making it accessible for a wide variety of lab setups. Overall, this low-cost, open-source OD sensor offers a practical, efficient, and customizable solution for continuous monitoring of anaerobic bacterial growth, making it a valuable tool for research laboratories.

HCoV-OC43 is a member of the viral family Coronaviridae, commonly known as coronavirus, and is known to cause respiratory infections in humans. HCoV-OC43 is therefore categorized as a human coronavirus, which includes the virus SARS-CoV-2, known to cause COVID-19. Previous studies showed that human coronavirus infections, specifically HCoV-OC43 and SARS-CoV-2, activate the IRE1α component of the unfolded protein response, leading to a splicing of XBP1 mRNA, which then encodes for a transcription factor. Additionally, the IRE1α and XBP1 host factors were found to be necessary for ideal viral replication. However, the specific genes upregulated by XBP1 that contribute to viral replication remain unknown. Given data suggesting XBP1 regulates genes involved in lipid metabolism, our research aims to explore whether Acetyl-CoA Carboxylase (ACC), an enzyme involved in fatty acid synthesis, is upregulated by IRE1α and involved in human coronavirus replication. We used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to measure relative gene expression of ACC after HCoV-OC43 infection, and in the presence of the IRE1α inhibitor, 4μ8c. We found that activation of IRE1α during HCoV-OC43 infection caused increased expression of the gene encoding ACC, which was blocked by 4μ8c. We then tested the hypothesis that ACC supports viral infection using small molecule inhibitors and found that viral RNA was decreased after inhibition of ACC. Next, we bypassed ACC by adding the downstream product, palmitate, and found restoration of viral RNA. Our results indicate that IRE1α induced splicing of XBP1 mRNA increases ACC transcription, which then promotes optimal viral replication. A greater understanding of the mechanisms behind human coronavirus replication allows for the development of potential therapies targeting these viruses. In our continuation of this research, we plan to expand our knowledge of human coronaviruses by investigating the role of IREα and ACC expression in SARS-CoV-2 infections.