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Matzuk, M. M.

Publications and source records attributed to Matzuk, M. M..

4 recordsLinked to original sources

Reversible in vivo regulation of drug metabolizing enzyme CYP1A2 activity through a dTAG knock-in strategy

Drug-metabolizing enzymes determine therapeutic exposure, efficacy and toxicity, but defining their isoform-specific functions in vivo remains challenging. Cytochrome P450 enzymes (P450s) are central to drug metabolism and pharmacokinetics (DMPK) and mediate the phase I metabolism of [~]75% of all marketed drugs. However, conventional knockout models can induce develop-mental and compensatory adaptations, and selective inhibitors are unavailable for many P450 isoforms. Here, we report the use of an inducible chemical-genetic platform for acute and specific degradation of the endogenous P450 enzyme Cyp1a2 in mice. Using CRISPR-Cas9-mediated knock-in editing, we introduced an FKBP12F36V degron into the endogenous Cyp1a2 locus to generate Cyp1a2dTAG mice. Treatment with the dTAG degrader dTAG-13 recruited an E3 ubiquitin ligase to CYP1A2dTAG, resulting in rapid and reversible proteasomal depletion of CYP1A2dTAG in vivo. Temporally controlled CYP1A2dTAG loss altered caffeine pharmacokinetics as expected, validating this model as a functional tool for DMPK studies. By enabling reversible suppression of drug-metabolizing enzymes without permanent deletion or chronic inhibitor exposure, this work establishes targeted protein degradation as a broadly adaptable strategy for studying drug metabolism in vivo and provides a foundation for extending inducible DMPK control to other P450s, conjugating enzymes and transporters.

pharmacology and toxicology↗

Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models

No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights on how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (n = 4) and a triple mutant mouse models (n = 4) of high-grade serous ovarian cancer (HGSC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes compared to those in healthy mouse reproductive tissue (n = 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide a direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis.

cancer biology↗

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages, and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer. TeaserTime-resolved lipidome remodeling in an ovarian cancer model is studied through lipidomics and machine learning.

cancer biology↗

Cooperation-based sperm clusters mediate sperm oviduct entry and fertilization

Sperm cooperation has been observed in multiple species, yet its existence and benefit for reproductive success in mammals remains underexplored. Here, combining tissue-clearing with deep three-dimensional imaging, we demonstrate that postcopulatory mouse sperm congregate into unidirectional sperm cooperative clusters at the utero-tubal junction (UTJ), a key physical barrier for passage into the oviduct. Reducing sperm number in male mice by unilateral vasoligation or busulfan-treatment impairs sperm cluster formation and oviduct entry. Interestingly, sperm derived from Tex101-/- mouse has normal number, motility and morphology, yet they cannot form sperm cluster and fail to pass through the UTJ, which is at least in part due to the altered tail beating pattern of the Tex101-/- sperm. Moreover, Tex101-/- sperms defect in oviduct entry cannot be rescued by the presence of wild-type (WT) sperm in the same uteri by sequential mating, suggesting sperm cooperative cluster as an essential behavior contributing to male fertility, which could be related to human infertility or subfertility.

developmental biology↗