Decoupling reproductive and somatic aging using novel C. elegans technology - ABSTRACT As women age, the risk of infertility and recurrent pregnancy loss rises; however, the molecular mechanisms driving the onset and progression of reproductive senescence remain largely unexplored. A key challenge is that reproductive aging is largely coupled with the decline of somatic health, complicating efforts to interrogate genes and pathways that solely govern reproductive cessation. We propose that uncovering how the nutrient-sensing mammalian target of rapamycin complex 2 (mTORC2) regulates reproduction could be crucial for decoupling reproductive longevity from somatic aging. However, the role of mTORC2 in reproductive aging is poorly understood. To pursue this research, we could study reproductive health decline in humans, primates, and mice; however, such studies are often complex, expensive, and subject to high variability. Instead, the nematode C. elegans is a powerful model system to study reproduction declines, as its reproductive cessation, like in humans, is driven by a decline in oocyte quality during mid-adulthood. Another challenge is that, although worms are by far the most accessible model for gaining mechanistic insights into reproductive senescence, manually tracking the reproduction of hundreds of individual worms each day is highly labor-intensive. To address this, we aim to design and develop an automated device to assay reproductive traits in C. elegans, allowing us to disentangle the molecular underpinnings of reproductive aging from those of somatic aging. Our proposed research will focus on SGK-1 kinase, a critical component of mTORC2, whose dysregulation in humans has been linked to reproductive failure. We will investigate how autophagy, fat metabolism, and mitochondrial dynamics regulate reproductive senescence downstream of SGK-1—independently of somatic aging. To accomplish these objectives, we will utilize tools and techniques across diverse fields of microfluidics, machine learning, genetics, and genomics. This interdisciplinary R15 project is ideally designed for undergraduate and junior graduate students, providing them with hands-on experience in engineering, computer science, and biology while emphasizing the importance of cross-disciplinary collaboration. Through this project, students will gain experience in developing machine learning algorithms, building devices, and performing biological assays. In doing so, they will be equipped to establish new paradigms for exploring the interplay of multiple molecular pathways governing reproductive senescence.
