Schizosaccharomyces pombe

We utilize Schizosaccharomyces pombe (fission yeast) as a model organism to explore the intricate connections between genomic instability, lipid dysregulation, and aging, particularly in the context of metabolic and genotoxic stress. Our research investigates how these disruptions impact cellular homeostasis and contribute to the accelerated decline observed during aging.

Central to our work is the application of various microscopy techniques, including bright field, phase-contrast, epifluorescence, live-cell imaging, and machine learning-assisted cell segmentation using tools like PhotoPhenosizer. These approaches enable precise, high-throughput quantification of morphological changes and cell cycle dynamics across S. pombe’s chronological (CLS) and replicative (RLS) lifespans. By integrating these methods with genetic and molecular analyses, we assess how genetic perturbations, environmental challenges, and replication stress influence cell size, metabolic homeostasis, and DNA damage responses over time.

Our studies focus on how metabolic stress, such as excess glucose conditions, exacerbates replication stress-induced genomic instability, resulting in significant morphological and functional alterations. Furthermore, we investigate the roles of checkpoint activation and DNA repair pathways in mediating cellular responses to these stressors, shedding light on the conserved mechanisms that drive aging.
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By leveraging S. pombe’s tractable genetics and well-defined aging phenotypes, our lab seeks to uncover fundamental insights into the cellular drivers of aging and identify pathways with potential for therapeutic intervention in age-associated diseases.
Caenorhabditis elegans
We use Caenorhabditis elegans as a model organism to investigate the intersection of fat metabolism and DNA damage, exploring how these processes converge to influence reproductive lifespan and accelerate physiological decline during aging. With its genetic tractability, short lifespan, and well-characterized biology, C. elegans provides an unparalleled platform for high-throughput, detailed studies of molecular and cellular aging.

Our research focuses on uncovering how metabolic and genotoxic stresses disrupt cellular homeostasis, driving age-related dysfunction. By leveraging genetic and environmental perturbations, we dissect the roles of lipid metabolism, DNA repair pathways, and stress response mechanisms in modulating the aging process. These studies have revealed conserved pathways that link metabolic instability to genomic damage, offering insights into the broader context of human aging and disease.

The simplicity and genetic flexibility of C. elegans allow us to apply innovative methodologies, including RNAi screens and fluorescent live imaging, to study aging at cellular and organismal levels. Through these approaches, we aim to identify key regulatory nodes that govern healthspan and lifespan, providing a foundation for developing therapeutic strategies to combat age-associated diseases.

Our lab leverages Caenorhabditis elegans to study the long-term impacts of environmental exposures on aging and physiology. By examining the effects of agents such as caffeine and other metabolic disruptors during critical developmental stages, we uncover how early-life exposures influence morphological changes, metabolic adaptations, and behavioral phenotypes across the lifespan. These studies have demonstrated that developmental stressors can induce lasting alterations in fat metabolism, cellular signaling, and overall healthspan.

​Using high-throughput genetic tools and microscopy-based analyses, we aim to identify the molecular pathways that mediate these responses, offering insights into how early environmental factors contribute to age-related decline and disease susceptibility in more complex organisms, including humans.

Bioinformatics & Genomics Analysis

Our lab integrates computational biology to investigate how genomic and lipid instability contribute to aging and disease, with a particular focus on mutational and structural analyses. Using publicly available genomic databases such as COSMIC and open-source bioinformatics tools, we analyze patterns of recurrent mutations in genes that regulate genomic stability, lipid metabolism, and cellular homeostasis. These analyses allow us to assess whether specific mutations have significant structural consequences, such as impairing protein folding, stability, or interactions with critical ligands, which are pivotal for cellular function.

A key area of our computational research involves structural modeling, where we employ predictive machine-learning algorithms to evaluate the functional impact of mutations. By simulating protein-ligand interactions and calculating energy minimization effects, we pinpoint mutations that are likely to disrupt protein stability or alter enzymatic activity. These insights have profound implications for understanding the molecular basis of diseases such as cancer, where dysfunctional regulatory pathways can drive tumorigenesis. For instance, we study mutations that affect DNA repair mechanisms and lipid regulation, revealing how these disruptions might accelerate aging or promote early-onset cancer.

​Our work also emphasizes the translational potential of computational biology. By combining mutational analysis with predictive tools, we aim to inform precision medicine strategies, tailoring therapeutic approaches to an individual’s genetic profile. This approach enables us to identify druggable targets and predict patient-specific responses to therapies, improving outcomes for diseases linked to genomic instability. Through this computational framework, we not only deepen our understanding of the molecular underpinnings of aging and disease but also contribute to developing actionable insights for personalized healthcare interventions.

Our Vision

To investigate the fundamental mechanisms underlying aging and disease using model organisms, bridging basic biological insights with their relevance to human health. We strive to equip students with the skills and curiosity to contribute to scientific knowledge and address pressing biomedical challenges.

Our Mission

Our mission is to produce clear, meaningful discoveries that advance our understanding of aging and cancer, while cultivating an inclusive research environment where students from diverse backgrounds can build the skills and confidence to achieve their scientific pursuits.
Our Motto:


​"Discovery through curiosity,

growth through perseverance"

Recent Work by the Lab

Kuo-Esser, L., Lawson, K., King, N., Chen, R., Slattery, C., Barhorst, A., Huseman, A., Escorcia, W., Bange, A., Wetzel, H. Creatine Exposure in Caenorhabditis elegans Induces Physiological and Morphological Changes Without Metabolic Conversion to Creatinine: A Novel HPLC Analysis. Accepted for publication (May 2025) in ACS Omega. 
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Lawson, K., Skrtic, S., Vo, M., Escorcia, W. Measuring Cell Dimensions in Fission Yeast Using Machine Learning. In: Methods in Molecular Biology. 2025;2862:33-46. doi: 10.1007/978-1-0716-4168-2_3. 

Kuchinski, K., King, N., Driggers, J., Lawson, K., Vo, M., Skrtic, S., Slattery, C., Lane, R., Simone, E., Mills, S.A., Escorcia, W., & Wetzel, H. (2024). Catalogue of Somatic Mutations in Cancer Database and Structural Modeling Analysis of CYP2D6 Mutations in Human Cancers. The Journal of Pharmacology and Experimental Therapeutics, 391(3), 441-449. doi: 10.1124/jpet.124.002136. 

Stan A, Bosart K, Kaur M, Vo M, Escorcia W, Yoder RJ, Bouley RA, Petreaca RC. Detection of driver mutations and genomic signatures in endometrial cancers using artificial intelligence algorithms. PLoS One. 2024 Feb 26;19(2):e0299114. doi: 10.1371/journal.pone.0299114. PMID: 38408048; PMCID: PMC10896512.

Vo M, Kuo-Esser L, Dominguez M, Driggers J, Kuchinski K, Perez S, Luck T, Tran T, Tran T, Gerberry D*, Wetzel H*, Escorcia W*. Quality Control Method and Device for Producing Agarose Micropads. MicroPubl Biol. 2024 Jan 26;2024:10.17912/micropub.biology.001081. doi: 10.17912/micropub.biology.001081. PMID: 38344063; PMCID: PMC10853819. (*Co-corresponding authorship)

Kuo-Esser L, Chen R, Lawson K, Kuchinski K, Simmons N, Dominguez M, Scandura T, Vo M, Dasenbrock-Gammon E, Hagan N, Esposito H, Thompson M, Le S, Escorcia W*, Wetzel HN*. Early-life caffeine exposure induces morphological changes and altered physiology in Caenorhabditis elegans. Biochem Biophys Res Commun. 2024 Jan 1;690:149240. doi: 10.1016/j.bbrc.2023.149240. Epub 2023 Nov 14. PMID: 37988878. (*Co-corresponding authorship)

Vo M, Kuo-Esser L, Dominguez M, Barta H, Graber M, Rausenberger A, Miller R*, Sommer N*, Escorcia W*. Photo Phenosizer, a rapid machine learning-based method to measure cell dimensions in fission yeast. MicroPubl Biol. 2022 Aug 4;2022:10.17912/micropub.biology.000620. doi: 10.17912/micropub.biology.000620. PMID: 35996688; PMCID: PMC9391947. (*Co-corresponding authorship)

Contact Us

The Escorcia Laboratory
​Department of Biology
Citrus Hall 3220
18111 Nordhoff Street
Northridge, CA 91330
​California State University, Northridge
(818) 677-5603
[email protected]