Celia R. Berkers

Metabolic dysfunction-associated steatohepatitis (MASH) is characterized by lipid-driven sterile inflammation and fibrosis that leads to liver failure and liver cancer. Metabolites are increasingly recognized as signaling molecules and regulate disease progression through cell-cell crosstalk. To overcome the challenges of studying metabolite crosstalk between different cell types in MASH livers, we developed a novel in vitro MASH model, “MASH-in-a-dish”, a Transwell co-culture system of mouse hepatocyte-derived organoids (HepOrgs), Kupffer cells (KCs), and hepatic stellate cells (HSCs) in a chemically defined liver co-culture medium. MASH-in-a-dish models key clinical features of clinical MASH, including steatosis, hepatocyte ballooning, inflammation and fibrosis. MASH-in-a-dish allowed for rapid and specific separation of co-cultured HepOrgs, KCs and HSCs, enabling cell type-specific metabolomics and stable-isotope fluxomics analysis. Using MASH-in-a-dish, we identified 19 MASH-associated, cell interaction-responsive metabolites and demonstrated that fatty acids, via the carnitine shuttle, drive KC itaconate production, which aggravated oxidative stress, steatosis, and cell damage in lipid-stressed HepOrgs. Inhibiting itaconate production by KC-specific Acod1 knockout in the MASH-in-a-dish system mitigated hepatocyte damage. Our results show that MASH-in-a-dish is a powerful in vitro platform to study cell-cell interactions in MASH pathogenesis and identify itaconate crosstalk between Kupffer cells and hepatocytes as a novel pathogenic mechanism and a potential therapeutic target in MASH.

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer in adults and represents the third most common cause of cancer-related deaths worldwide. With limited treatment options, HCC remains a major global health problem. The majority of HCC develops in a cirrhotic liver, where the tumor microenvironment (TME) is enriched in fibroblasts derived from activated hepatic stellate cells (HSCs). Cell-cell interactions in TME play an important role in metabolic reprogramming, which is increasingly recognized as a crucial driver in HCC proliferation. The tumor-promoting and metabolic effects of HSCs on HCC cells however remain incompletely characterized. Understanding how HSC-HCC interaction regulates HCC proliferation can lead to identification of novel therapeutic targets. Here, we established a Transwell co-culture model of HSCs and HCC cells to investigate how HSC-HCC interactions regulate HCC metabolism and proliferation. Using isotope tracing metabolomics, we show that co-culture with HSCs increases proliferation of HCC cells by rewiring glucose metabolism to increase glycolysis and de novo synthesis of nucleotides. Transcriptomic and immunofluorescence staining analyses revealed that HSCs upregulate hypoxia-inducible factor (HIF) signaling in HCC cells, which contributes to metabolic rewiring and HCC proliferation. We further identified H ₂ O ₂ as a candidate crosstalk signal that could mediate HSC-dependent activation of HIF signaling in HCC cells. Together, these findings reveal a previously overlooked modulatory role of HSC-HCC interaction in metabolism-driven HCC progression. Targeting these pathways may represent a promising strategy to improve HCC treatment.

Microbial communities are dynamic, adaptive ecosystems whose collective behavior emerges from metabolic interactions such as cross-feeding, competition, and cooperation, rather than taxonomic diversity or individual metabolic potential alone. This distinction is clinically significant in the postmenopausal urinary tract, where recurrent urinary tract infections (rUTIs) are associated with complex, persistent infection dynamics including multiple contributing bacterial species. The ability of resident microbial communities to prevent pathogen establishment, known as colonization resistance, is increasingly attributed to the metabolic interactions within the urobiome itself rather than any single resident species. However, current approaches, such as taxonomic profiling and classical differential abundance analysis, can only partially describe the presence or maintenance of such interactions. Consequently, the community-level metabolic architecture determining pathogen resistance remains incompletely understood. To address this gap, we developed PhenoRewire, a network-based framework that quantifies how metabolite co-variation is rewired between biological states using untargeted metabolomics data. We applied this framework to an induced pluripotent stem cell (iPSC) urothelial organoid-derived barrier co-cultured with synthetic urobiome communities as a model of urobiome-pathogen dynamics relevant to rUTIs in two approaches. In an infection model, clinically isolated uropathogens Escherichia coli and Enterococcus faecalis, were co-cultured with a three-member urobiome community consisting of Lactobacillus gasseri , Lactobacillus crispatus , and Gardnerella vaginalis . Here we show how E. coli drove the metabolic reorganization, while E. faecalis amplified it disproportionately. PhenoRewire disentangled the 6-fold metabolic network amplification mediated by E. faecalis as a metabolic facilitator, revealing an emergent urobiome-pathogen co-variation architecture (1,781 vs 227 edges) not recapitulated by either community alone. Moreover, in a six-member urobiome single-strain dropout experiment, we revealed that removal of the sole Actinomycete Winkia anitrata caused significant network collapse (Louvain modularity falls from 0.707 to 0.038), identifying it as the single non-redundant keystone of the community. More broadly, these results demonstrate how untargeted metabolomics co-variation network analysis can be applied to defined synthetic urobiomes in combination with a urothelial host model to elucidate community dynamics. This framework provides a template that can be extended beyond the urobiome to investigate any complex microbial community where ecological behavior remains an open question.

Background: Multiple myeloma is an aggressive plasma cell malignancy that remains incurable despite major therapeutic advances and depends on extensive metabolic remodeling to support malignant growth and drug resistance. Although tumor cell-intrinsic mechanisms of drug resistance are well characterized, the contribution of cells in the tumor microenvironment, particularly bone marrow stromal cells, to metabolic remodeling and therapeutic resistance remains poorly understood. Methods: We combined [U- ¹³ C]glucose tracing metabolomics with a metabolomics-compatible co-culture system of multiple myeloma and bone marrow stromal cells to assess the impact of co-culture on metabolic pathway activity across genetically distinct multiple myeloma cell lines. Cells were additionally subjected to cytokine analysis, gene expression analysis and drug response assays to investigate associations between metabolic changes, treatment responses and cytokine abundances. Results: Bone marrow stromal cell co-culture induced a convergent metabolic rewiring in multiple myeloma cells, enhancing pathways previously linked to bortezomib resistance, including glycolysis, tricarboxylic acid cycle activity, serine synthesis and nucleotide biosynthesis. In parallel, stromal cells acquired a cancer-associated fibroblast-like phenotype characterized by increased interleukin-6 and transforming growth factor-β production. This cytokine milieu promoted bortezomib resistance and stromal cell activation. Interleukin-6 alone was sufficient to rescue multiple myeloma cells from bortezomib-induced cytotoxicity and reproduced a substantial fraction of the co-culture-induced metabolic remodeling. Analysis of patient proteomics revealed a strong positive association between interleukin-6 abundance and expression of enzymes involved in mitochondrial metabolism, glycolysis, serine metabolism, nucleotide metabolism and redox homeostasis. Conclusions: These findings identify bone marrow stromal cell-derived interleukin-6 as a central driver of tumor microenvironment-mediated metabolic reprogramming and bortezomib resistance in multiple myeloma. Targeting interleukin-6-dependent metabolic signaling may represent a promising strategy to overcome stromal-driven therapeutic resistance. Clinical trial number: Not applicable.

Mutations or deletions affecting the TP53 gene predict a dismal outcome in relapsed acute lymphoblastic leukemia (ALL). Loss of p53 function compromises the response to many anti‐leukemic therapies, underscoring the need for agents that are effective in TP53‐ deficient cells. Leukemic blasts depend on de novo pyrimidine synthesis to sustain proliferation, and the mitochondrial enzyme dihydroorotate dehydrogenase (DHODH) is essential for this pathway. While DHODH inhibitors (DHODHis) show preclinical anti‐leukemic activity, the efficacy in TP53 ‐deficient contexts has not been tested. Using both isogenic TP53 ‐wildtype and knock‐out cell line models and patient‐derived xenografts (PDXs), we show that dual inhibition of DHODH and Ataxia Telangiectasia and Rad3‐related (ATR) produces pronounced anti‐leukemic effects, irrespective of the TP53 status. Through integrated transcriptomic and metabolomic analyses, we show that combined inhibition of DHODH and ATR leads to a reduced flux of glucose into the TCA cycle, accompanied by an increase in oxidative stress. This metabolic phenotype triggers cell death through a mechanism converging on Activating Transcription Factor 4 (ATF4), a key integrator of cellular responses to metabolic and oxidative stress, which operates largely independently of p53. Taken together, our findings identify ATF4‐mediated cell death as a previously unrecognized vulnerability in TP53 ‐deficient ALL.

Metabolite detection with mass spectrometry (MS) in untargeted metabolomics is limited by the wide concentration range of metabolites, where high-abundance signals dominate MS1 scans and suppress detection of low-abundance features. This reduces metabolite coverage and obscures biologically relevant signals, particularly in complex cellular systems. Full Scan enhanced Dynamic Range (eDR) MS addresses these limitations by partitioning the MS1 mass range into multiple subscans and mass windows, reducing saturation effects from dominant ions. Here, we systematically evaluate different eDR acquisition strategies for untargeted metabolomics. Across four hepatocellular carcinoma cell lines, Full Scan eDR MS increased detectable features up to ~3.5-fold compared to Full Scan MS. Among equidistant window configurations, 12 windows yielded the highest feature count and broadest dynamic range, while custom window distributions further improved detection in ion-dense regions. In particular, allocating smaller window sizes to the low m/z region selectively increased detection of low-mass features while preserving performance for higher mass ions. Full Scan eDR MS also improved data quality, reducing variation and increasing signal-to-noise ratios, especially for low-abundance metabolites. MS2 coverage and metabolite identifications increased substantially, resulting in unique detection of cancer-relevant metabolites. Importantly, the increased depth of metabolite detection enabled improved discrimination between cancer cell lines, supporting deeper interrogation of metabolic heterogeneity. Overall, these results establish Full Scan eDR MS as a flexible strategy to improve sensitivity and metabolome coverage in untargeted metabolomics. Customization of window size and distribution enable targeted expansion of dynamic range within predefined mass regions, allowing MS acquisition to be tailored to sample complexity and metabolites of interest.

The enteric nervous system (ENS) plays a pivotal role not only in gastrointestinal function but also in neurodegenerative diseases through bidirectional co

Extracranial malignant rhabdoid tumors (ecMRTs) are aggressive pediatric cancers characterized by mutations in genes encoding members of the SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complex, with limited effective treatment options. Metabolic reprogramming, including nucleotide biosynthesis, is a hallmark of cancer that represents a potential therapeutic vulnerability. Previously, we demonstrated that inhibition of the de novo pyrimidine synthesis enzyme dihydroorotate dehydrogenase (DHODH) represents a promising treatment strategy for rhabdoid tumors, including ecMRTs and atypical teratoid/rhabdoid tumors (AT/RTs). Using patient-derived tumoroid models, we now extend these findings by showing that another SWI/SNF-deficient pediatric cancer, small cell carcinoma of the ovary, hypercalcemic type (SCCOHT), is also sensitive to DHODH inhibition. Gene expression analyses further confirmed that SCCOHT and AT/RT, like ecMRT, exhibit elevated expression of de novo nucleotide synthesis genes. To determine whether metabolic features of the tumor microenvironment (TME) influence DHODH inhibitor (DHODHi) response, we profiled plasma and tumor interstitial fluid from orthotopic ecMRT-bearing mice and found that the TME was markedly enriched in nucleosides and nucleobases. Supplementation of standard culture media with these nucleosides demonstrated that pyrimidines, but not purines, can rescue ecMRT cells from DHODHi-induced growth inhibition via activation of the pyrimidine salvage pathway. This resistance mechanism was effectively reversed by co-treatment with human equilibrative nucleoside transporter (hENT) inhibitors, enhancing DHODHi sensitivity in ecMRTs. Collectively, these findings reveal a conserved metabolic vulnerability across SWI/SNF-deficient pediatric cancers and emphasize the critical importance of modeling tumor metabolism under physiologically relevant conditions to accurately identify metabolism-targeted therapeutic strategies for these currently lethal pediatric cancer types.