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Shedlock, C. J.

Publications and source records attributed to Shedlock, C. J..

2 recordsLinked to original sources

Systemic hypoxia drives glycogen-fueled progression of lung adenocarcinoma

In advanced stages, lung adenocarcinoma obstructs airways and disrupts ventilation-perfusion relationships in the lung, causing systemic hypoxemia and enabling a feed-forward loop that accelerates malignancy. Systemic hypoxemia is also experienced due to common respiratory comorbidities such as chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea (OSA), potentially accelerating malignancy. In a statewide electronic health record network, pre-existing COPD (598 matched pairs) or sleep apnea (235 matched pairs) independently predicted worse survival following incident lung cancer diagnosis. Since the mechanistic basis of the link between malignancy and hypoxia is not well understood, we created systemic hypoxia in KrasLSL-G12D/+;Trp53fl/fl (KP) mice by delivering low inspired oxygen concentrations (8% inspired oxygen; 8 h daily). Hypoxia nearly doubled tumor multiplicity and selectively remodeled cancer central carbon metabolism. Spatially resolved metabolomics revealed marked tumor-compartment glycogen accumulation, elevated tricarboxylic-acid cycle intermediates, and depleted glycolytic pools. Quantitative proteomics across cellular models and autochthonous tumors demonstrated that systemic hypoxia drives glycogen mobilization selectively through the lysosomal enzyme acid -glucosidase (GAA). Tumor-cell-autonomous deletion of GAA eliminated the hypoxia-driven growth advantage and disrupted downstream anabolic biosynthetic pathways. Thus, systemic hypoxia drives lung adenocarcinoma expansion by mobilizing lysosomal glycogen reserves through GAA to sustain proliferative growth.

cancer biology↗

MetaVision3D: Automated framework for the Generation of Spatial Metabolome Atlas in 3D

High-resolution spatial imaging is transforming our understanding of foundational biology. Spatial metabolomics is an emerging field that enables the dissection of the complex metabolic landscape and heterogeneity from a thin tissue section. Currently, spatial metabolism highlights the remarkable complexity in two-dimensional space and is poised to be extended into the three-dimensional world of biology. Here, we introduce MetaVision3D, a novel pipeline driven by computer vision techniques for the transformation of serial 2D MALDI mass spectrometry imaging sections into a high-resolution 3D spatial metabolome. Our framework employs advanced algorithms for image registration, normalization, and interpolation to enable the integration of serial 2D tissue sections, thereby generating a comprehensive 3D model of unique diverse metabolites across host tissues at mesoscale. As a proof of principle, MetaVision3D was utilized to generate the mouse brain 3D metabolome atlas (available at https://metavision3d.rc.ufl.edu/) as an interactive online database and web server to further advance brain metabolism and related research.

neuroscience↗