The Glucose Metabolism of ADHD: MicroRNA SNPs Impact Glucose Transporter 3 Expression

Overview

ADHD affects millions globally, with symptoms like inattention and hyperactivity significantly impacting quality of life. Despite this, current treatment medications can have substantial side effects, are ineffective long-term, and are inaccessible to many due to shortage. This study aimed to address this by developing a sugar intake-based ADHD symptom management theory. Although most ADHD research focuses on neurotransmitter balance, this study focused on the impact of microRNA (miRNA) variants on Glucose Transporter 3 (GLU T3), a membrane protein that plays a central role in neuronal glucose metabolism. MiRNA single nucleotide polymorphisms (SNPs) that target GLUT3 mRNA and their consequential effects on GLUT3 function were computationally scrutinized with TargetScan and RNA22. SNP rs769854452 in hsa-miR-103a-3p increased miRNA binding function (p<0.05), suggesting a decrease in GLUT3 expression, while SNP rs901180005 in hsa-miR-107 led to complete target loss, suggesting an increase in GLUT3 expression. SH-SY5Y neurons were transfected with the identified miRNA variants: rs769854452 (hsa -miR-103a-3p) and rs901180005 (hsa-miR-107). Analysis via quantitative RT-PCR for mRNA expression and western blot for protein synthesis confirmed that transfection with r s901180005 (hsa -miR-107) led to a significant increase in GLUT3 mRNA expression (p<0.05) and protein synthesis (p<0.05). GLUT3 protein expression (ELISA-analyzed) was increased in the blood of ADHD patients who had micronutrient supplement intervention (and improved symptoms) compared to those that had no intervention. SNPs rs901180005 could inform sugar intake as management for ADHD symptoms. Future research includes further confirming the expression of miRNA SNPs and GLUT3 in ADHD patients. Effects of Bisphenol A and Bisphenol S Exposure on Models for Body Systems Deetya Nagri Nashua High School South, Nashua, NH Teacher Dr. Francine Brown, Nashua High School North; Mentor Dr. Kelly Salmon, New Hampshire Academy of Science Bisphenol A (BPA) is a common plasticizer used in manufacturing that negatively impacts human health. Bisphenol S (BPS) is a common substitute for BPA, but its structural similarity to estrogen sparks concern that it is harmful to the reproductive system, stem cell regeneration, and microbiome health. Past studies in Caenorhabditis elegans found that BPA and BPS exposure reduces fertility and affects DNA double - stranded break repair (DSBR) during meiosis. Mouse studies also showed that BPA exposure decrease s microbiome diversity and hampers sperm cell development from stem cells. In this study, the effect of BPA and BPS exposure on C. elegans fertility and the expression of DSBR genes rad-54 and atl-1 was observed over multiple generations. No consistent significant effects on fertility were observed, but expression of both genes decreased in the exposed generation and their offspring. Modeling stem cell growth, the regeneration of brown planaria exp osed to BPA and BPS was tracked, finding that low BPA concen trations slowed regeneration. Concentrations of BPA above 10 μM caused the planaria to disintegrate, but BPS did not have a significant impact on them. The effect of BPA and BPS exposure on the growth of Escherichia coli and Saccharomyces cerevisiae, both found in the microbiome, was also tracked. Exposure nonlethally affected microbial growth in a disk diffusion test, but BPS slowed growth of E. coli in liquid media. Based on these results, BPA and BPS have distinct effects on these models for reproduction, microbiome health, and stem cell regeneration, supporting further investigation. An Image Segmentation Algorithm Targets Marine Trash for ROV Retrieval Victoria Wahlig Falmouth High School, Falmouth, ME Oceanic debris, predominantly plastic, makes up 88% of the ocean’s surface, inflicting catastrophic damage on marine ecosystems and threatening aquatic life through entanglement, strangulation, and starvation. Tackling this crisis is complex because of the wide-ranging distribution of waste by ocean currents to remote and deep -sea locations. Remotely operated vehicles (ROVs) are a solution, capable of withstanding extreme underwater environments. In this study, I developed a ROV prototype that uses a deep l earning model to discern and identify components of images captured by an ROV. I utilized image segmentation, a process that groups or "masks" all pixels associated with a specific object in the image, to recognize and delineate image components (trash, an imal, plant, ROV). My model, a convolutional neural network employing U-Net architecture, formulated feature maps and class predictions for each object within the images. The ROV prototype featured a Raspberry Pi and VEMONT Sports Camera, testing the model ’s efficacy in a mockup of an underwater environment. To evaluate the model, I compared the overlap of model-produced object masks and reference masks, calculating the Dice Similarity Coefficient (DSC) for each object class (trash = 0.81 ± 0.38, animal = 0.85 ± 0.35, plant = 0.88 ± 0.31, ROV = 0.86 ± 0.29, overall average = 0.84 ± 0.36). My model’s consistent categorization of image components makes it an accurate and effective tool for ROVs to identify previously inaccessible ocean trash for removal. This study lays a robust foundation for future research and additional applications.

Competition history

• JSHS 2024 Category not listed

Resources

• National JSHS abstract book ↗

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