Cysteine plays critical roles in cellular biosynthesis, enzyme catalysis, and redox metabolism. The intracellular cysteine pool can be sustained by cystine uptake or de novo synthesis from serine and homocysteine. Demand for cysteine is increased during tumorigenesis for generating glutathione to deal with oxidative stress. While cultured cells have been shown to be highly dependent on exogenous cystine for proliferation and survival, how diverse tissues obtain and use cysteine in vivo has not been characterized. We comprehensively interrogated cysteine metabolism in normal murine tissues and cancers that arise from them using stable isotope 13C1-serine and 13C6-cystine tracing. De novo cysteine synthesis was highest in normal liver and pancreas and absent in lung tissue, while cysteine synthesis was either inactive or downregulated during tumorigenesis. In contrast, cystine uptake and metabolism to downstream metabolites was a universal feature of normal tissues and tumors. However, differences in glutathione labeling from cysteine were evident across tumor types. Thus, cystine is a major contributor to the cysteine pool in tumors, and glutathione metabolism is differentially active across tumor types. Significance: Stable isotope 13C1-serine and 13C6-cystine tracing characterizes cysteine metabolism in normal murine tissues and its rewiring in tumors using genetically engineered mouse models of liver, pancreas, and lung cancers.

Preclinical genetically engineered mouse models (GEMMs) of lung adenocarcinoma are invaluable for investigating molecular drivers of tumor formation, progression, and therapeutic resistance. However, histological analysis of these GEMMs requires significant time and training to ensure accuracy and consistency. To achieve a more objective and standardized analysis, we used machine learning to create GLASS-AI, a histological image analysis tool that the broader cancer research community can utilize to grade, segment, and analyze tumors in preclinical models of lung adenocarcinoma. GLASS-AI demonstrates strong agreement with expert human raters while uncovering a significant degree of unreported intratumor heterogeneity. Integrating immunohistochemical staining with high-resolution grade analysis by GLASS-AI identified dysregulation of Mapk/Erk signaling in high-grade lung adenocarcinomas and locally advanced tumor regions. Our work demonstrates the benefit of employing GLASS-AI in preclinical lung adenocarcinoma models and the power of integrating machine learning and molecular biology techniques for studying the molecular pathways that underlie cancer progression.

Increasing evidence supports a neuroprotective role for bile acids in major neurodegenerative disorders. We studied major human bile acids as signaling molecules for their two cellular receptors, farnesoid X receptor (FXR or NR1H4) and G protein-coupled bile acid receptor 1 (GPBAR1 or TGR5), as potential neurotrophic agents. Using quantitative image analysis, we found that 20 μM deoxycholic acid (DCA) could induce neurite outgrowth in NSC-34 cells that was comparable to the neurotrophic effects of the culture control 1 μM retinoic acid (RA), with lesser effects observed for chenodexoycholic acid (CDCA) at 20 μM, and similar though less robust neurite outgrowth in SH-SY5Y cells. Using chemical agonists and antagonists of FXR, LXR, and TGR5, we found that TGR5 agonism was comparable to DCA stimulation and stronger than RA, and that neither FXR nor liver X receptor (LXR) inhibition could block bile acid-induced neurite growth. RNA sequencing identified a core set of genes whose expression was regulated by DCA, CDCA, and RA. Our data suggest that bile acid signaling through TGR5 may be a targetable pathway to stimulate neurite outgrowth.

Summary: Integrative Module Analysis for Multi-omics Data ( iModMix ) is a biology-agnostic framework that enables the discovery of novel associations across any type of quantitative abundance data, including but not limited to transcriptomics, proteomics, and metabolomics. Instead of relying on pathway annotations or prior biological knowledge, iModMix constructs data-driven modules using graphical lasso to estimate sparse networks from omics features. These modules are summarized into eigenfeatures and correlated across datasets for horizontal integration, while preserving the distinct feature sets and interpretability of each omics type. iModMix operates directly on matrices containing expression or abundances for a wide range of features, including but not limited to genes, proteins, and metabolites. Because it does not rely on annotations ( e . g ., KEGG identifiers), it can seamlessly incorporate both identified and unidentified metabolites, addressing a key limitation of many existing metabolomics tools. iModMix is available as a user-friendly R Shiny application requiring no programming expertise ( https://imodmix.moffitt.org ), and as an R package for advanced users ( https://github.com/biodatalab/iModMix ). The tool includes several public and in-house datasets to illustrate its utility in identifying novel multi-omics relationships in diverse biological contexts. Availability and implementation: Shiny application: https://imodmix.moffitt.org . The R package and source code: https://github.com/biodatalab/iModMix .

SUMMARY: Lung cancer is a highly heterogeneous disease and remains the leading cause of cancer-related mortality worldwide. While mouse models and patient-derived organoids have advanced our understanding of lung cancer, key interactions within the tumor microenvironment (TME) remain poorly characterized. We developed microtumor models from lung adenocarcinoma (LUAD) and small cell lung cancer (SCLC) using mouse and patient samples, including surgical resections and rapid autopsy specimens. Microtumors preserve structural, cellular, and molecular features of the native TME, enabling mechanistic studies of tumor progression ex vivo . Multi-omics analyses of LUAD microtumors revealed progression-associated changes, including increased epithelial-to-mesenchymal transition (EMT) and metabolic reprogramming toward fatty acid synthesis. Pharmacologic inhibition of fatty acid synthesis through ACC1/2 reduced proliferation in patient-derived microtumors, identifying a targetable vulnerability. This platform provides a robust system for studying tumor progression, therapeutic response, and resistance mechanisms in lung cancer, including culturing postmortem specimens that are not accessible in current models.