Cannabis use has dramatically increased in response to legalization in the U.S., with U.S. sales jumping 46% from 2019 to 2020. ᐃ9-tetrahydrocannabinol (THC) is the primary psychoactive compound in Cannabis, and it has been shown to modify learning and motivation amongst regular users. Learning and motivation are key central processes primarily organized by the prefrontal cortex (PFC) brain region. I sought to test effects of THC on PFC activity during appetitive Pavlovian conditioning in mice- a behavior in which a subject learns to pair two stimuli together over time. Doing so provided much needed insight into learning and motivation under the effect of THC. THC acts on the endocannabinoid CB1 receptor, a presynaptic signaling protein responsible for modulating neural activity throughout the brain, with robust expression in the PFC. To monitor neural activity during behavioral trials, we implanted optic fibers into the PFC and virally expressed biological sensors. We used VGLUT1-Cre mice with a Cre-dependent GCaMP6f sensor to selectively target pyramidal glutamatergic activity during conditioning. We also utilized machine learning tracking software, SLEAP, to analyze behavior through video recordings. In our conditioning paradigm, animals were presented with a houselight and a sucrose reward, which they consolidated an association between after many trials. The mice experienced 5 days of Pavlovian conditioning, and I injected a moderate i.p. dose of THC (5 mg/kg) to one cohort, while another was given a vehicle before undergoing further trials. Our preliminary results showed that glutamatergic activity correlated with learning and association to the cue over time. We expected and observed that THC decreased the signals across the animals and reduced motivation. We categorized THC-induced behavior using SLEAP, a program tracking the mouse’s body parts to capture real-time movement, and found that locomotion decreased and resting behaviors increased in the THC cohort.

 âˆ†9-Tetrahydrocannabinol (THC), the primary psychoactive compound in Cannabis, is responsible for the experience known colloquially as “being high.” Considering its alarmingly high rates of usage, THC’s effects on movement behavior are insufficiently studied. My project addresses this crucial gap in our knowledge by investigating the dose-dependent effects of THC on movement behavior using mouse models in tandem with novel behavioral neuroscience techniques. My research aims to establish a preclinical model for THC-induced impairment, focusing on studying its impact on locomotor control. My main experimental tool is a behavioral linear track, which is a clear glass corridor with a 45 degree-angled mirror placed beneath it. The linear track allows us to create a standardized multi-dimensional environment in which mice are recorded after they are treated with either a control or variable doses of THC. The videos taken of the mice are then analyzed using SLEAP. SLEAP is a machine-learning, pose-estimation algorithm that I helped train to track individual points of interest on the mice, such as the nose, paws, and tail. Behaviors of interest, such as walking, rearing, and grooming, are classified by a random forest algorithm that analyzes SLEAP label data to output identified behaviors. This data is then tabulated and graphed to reflect the dose-dependent changes in behavior elicited by THC. These classifications are also used to further analyze metrics during a represented behavior. For instance, for a walk event, we can utilize positional data from SLEAP to calculate and measure kinematic features such as stride length and limb speed, allowing us to distinguish between an unimpaired and an impaired walk. This computerized analysis approach minimizes human bias, reduces error, and produces exhaustive data that can characterize subtle differences in behavior, like when comparing mice exposed to low THC doses of 0.1mg/kg and 0.3 mg/kg.

Various anti-cancer therapeutics, known as microtubule targeting agents (MTAs), target the microtubule, a tube-like structure that is a major component of cell mechanisms including mitosis and maintenance of the cell shape. MTAs selectively bind to tubulin — the building block of microtubule —, disrupting microtubule dynamics and inducing cell death. Despite their known impact on antitumor activity, the precise mechanism by which MTAs promote cell death remains unclear. To understand the efficacy of ST-401 (an MTA drug) as a tumor suppressor, I conduct various assays including drug treatment and Western blot to compare the expression of specific proteins in response to the drug treatments. These assays contribute to understanding cellular processes, molecular interactions, and the effects of various treatments or conditions on cells. Currently, I am being trained to conduct an experiment called XFe Seahorse analyzer. I’m carrying out this experiment to assess how the laboratory-discovered drug affects the mitochondrial function in various cancer cell lines. This experiment aims to determine whether the compound down-regulates mitochondrial function, leading to cell death. I’m leading a project to test a specific fission (cell splitting) protein, DRP1, and how its protein level responds to treatment with ST-401 in two GBM cell lines (resistant and sensitive) and one Colon cancer cell line. DRP1 regulates mitochondrial fission to maintain healthy mitochondrial function. Recently, I found an increase in mitochondrial fission 24 hours after ST-401 treatment in the sensitive GBM cell line, so I’ll further examine DRP1 expression by drug treatment and Western blot to understand these results and see whether ST-401 recruits DRP1, resulting in promotion of mitochondrial division. After DRP1 project, I will expand the project to asses the combination drugtreatment where we combine FDA-approved cancer drug and ST-401 to reflect real-world scenarios, aiming to ensure clinical relevance and safety by studying potential drug interactions.