Diffuse large B cell lymphoma (DLBCL), the most common type of non-Hodgkin lymphoma (NHL), exhibits considerable biological heterogeneity. While its classification has traditionally relied on genetic and transcriptomic features, emerging evidence points to distinct metabolic subtypes that may represent novel therapeutic vulnerabilities. Intriguingly, current chemoimmunotherapy regimens exert profound but non-specific effects on tumour metabolism, inadvertently exploiting metabolic dependencies yet without precision. Novel inhibitors targeting glucose, amino acid, lipid, and mitochondrial metabolism demonstrate selective cytotoxicity in metabolically defined lymphoma subsets. This review investigates how standard therapies exploit DLBCL metabolism and examines heterogeneity across subtypes, and evaluates targeted metabolic therapies. We discuss emerging combination strategies with current therapeutic regimes and immunotherapy. Particular focus is given to the metabolic interactions between tumour cells and immune effectors, including CAR T cells and bispecific antibodies. We highlight the importance of translational research to validate metabolic subtypes through metabolomic profiling, identify predictive biomarkers, and develop rational combinations. Moving beyond empiric therapy towards strategic metabolic targeting offers an opportunity to enhance outcomes for patients with this aggressive and diverse lymphoma.

Immune checkpoint inhibitor therapies induce metabolic dysfunction. A study by Wu et al now pinpoints macrophage programmed cell death protein 1 (PD-1) as a key molecular mediator of the anti-PD-1 treatment-triggered exacerbation of systemic metabolic disorders. Macrophage PD-1 blockade disrupts the moonlighting function of PD-1 in suppressing endoplasmic reticulum stress-mediated inflammatory responses, thereby impairing adipose tissue thermogenesis, reducing energy expenditure, and ultimately leading to systemic metabolic dysfunction.

Metabolic reprogramming is a hallmark of cancer, and the field has predominantly focused on investigating metabolic alterations in tumour cells. However, the relevance, mechanism and consequences of metabolic adaptations in stromal cells remain understudied. Here, we identify aspartoacylase (ASPA) as a metabolic enzyme consistently repressed in tumour stroma and cancer-associated fibroblasts (CAFs). Importantly, we report a reciprocal crosstalk between ASPA and Transforming Growth Factor Beta (TGFβ) signalling that influences fibroblast behaviour. TGFβ suppresses ASPA expression in fibroblasts, whereas ASPA restrains TGFβ-dependent myofibroblast conversion, extracelullar cell matrix (ECM) remodelling, angiogenesis and pro-tumoral macrophage phenotypes. Analyses of human specimens revealed a strong negative prognostic value for ASPA in different tumour types, associated with TGFβ signalling levels and the generation of aggressive pro-tumoral responses. Our findings unveil ASPA expression in fibroblasts as a gatekeeper of TGFβ responses and activation in cancer progression.

Cancer cells utilize many strategies to suppress immune responses. Data now show that these measures include the secretion of branched-chain keto acids into the tumor microenvironment. These metabolites can be taken up by tumor-associated macrophages, in which they can shield NOTCH2 from degradation and thereby favor an immunosuppressive gene expression program.

Melanosomes are lysosome-related organelles that produce and accumulate melanin. Their maturation is regulated through interactions with mitochondria and involves the export and recycling of proteins via tubular transport and fission events whose mechanisms are unknown. Here, we demonstrate that the mitochondrial fission factor protein (MFF) is involved in melanosome fission. MFF is trafficked between mitochondria and melanosomes and locates at melanosome fission events. Upon downregulation of MFF, but not of dynamin-related protein 1 (DRP1), melanosomes enlarge, intracellular melanin accumulates, and melanosomal lumenal catabolism increases, indicating that MFF-dependent melanosome fission is required for their maturation. We show that MFF interacts with regulators of the ARP2/3 complex, which drives F-actin nucleation. Actin filaments accumulate between melanosomes at MFF-enriched membrane constriction sites, and silencing of ARP2/3 subunits mimics the increase in melanosome size. MFF regulates actin-dependent fission of melanosomes via the ARP2/3 complex, indicating an extramitochondrial function for MFF in the regulation of melanosome homeostasis.

ABSTRACT:Metabolic reprogramming is a hallmark of cancer and is essential for sustaining leukemogenesis. In acute myeloid leukemia (AML), a high dependency on oxidative phosphorylation (OXPHOS) is often linked to poor outcomes, and its inhibition has shown to be highly effective. However, most OXPHOS inhibitors are not clinically translatable because of significant side effects. Thus, repurposing safe US Food and Drug Administration-approved drugs that can target OXPHOS is of great interest. Here, we evaluated metformin, an antidiabetic drug that inhibits OXPHOS, in a genetically diverse panel of primary AML samples to identify metabolic profiles that can be used to predict treatment susceptibility. Using label-free quantitative proteome analysis on sorted CD34+/CD117+ AML cells, we performed single-sample gene set enrichment analysis focused on metabolic terms and correlated enrichment scores with metformin sensitivity, followed by functional studies. Ex vivo treatment of AML samples with metformin showed a significant increase in reactive oxygen species levels and ferroptosis induction, especially in samples with disturbed lipid metabolism, such as IDH2- and FLT3-mutant AMLs. In IDH2-mutant cells, cotreatment with palmitate, a saturated fatty acid (FA), increased metformin sensitivity, which could be rescued by CD36 knockdown, rendering these cells more resistant to treatment. Lipidomic analysis revealed profound alterations upon metformin treatment, including increased production of triglycerides and polyunsaturated FAs, further supporting a metabolic shift. We observed upregulation of genes related to lipid droplet formation, including DGAT1, a key enzyme in this process. DGAT1 inhibition was strongly synergistic with metformin, whereas iron chelators acted antagonistically. Our results underscore the potential of leveraging metabolic vulnerabilities in AML to identify more effective and personalized therapeutic strategies.

The lack of standardised workflows and ambiguous metabolite annotations hampers metabolomics integration with prior knowledge, thus limiting the extraction of meaningful biological insights. We present MetaProViz (Metabolomics Processing, functional analysis and Visualization), an open-source Bioconductor R package for metabolomics data analysis that integrates prior knowledge to generate mechanistic hypotheses ( https://saezlab.github.io/MetaProViz/ ). MetaProViz operates on annotated intensity values and offers a flexible framework consisting of five modules: processing, differential analysis, prior knowledge integration, functional analysis and visualisation, applicable to intracellular and exometabolomics experiments. To improve functional analysis, we created the Metabolism Signature Database (MetSigDB), a collection of annotated metabolite sets. MetSigDB includes pathway-metabolite, metabolite-receptor, metabolite-transporter sets, and chemical class-metabolite sets. MetaProViz enables the conversion of gene sets to metabolite sets, metabolite identifier expansion and analyses mapping ambiguities. The MetaProViz functional analysis toolkit includes sample metadata analysis, enrichment analysis and biologically informed clustering. By applying MetaProViz to kidney cancer metabolomics data, we identified increased methionine usage in line with decreased methionine levels in tumour samples. In summary, MetaProViz facilitates and improves the analysis and interpretation of metabolomics data. MetaProViz is an open-source Bioconductor R package that integrates curated prior knowledge and metabolite annotation handling to enable reproducible metabolomics analysis, improve functional interpretation, and generation of mechanistic hypotheses from intracellular and extracellular metabolomics. MetaProViz is an open-source Bioconductor R package that integrates curated prior knowledge and metabolite annotation handling to enable reproducible metabolomics analysis, improve functional interpretation, and generation of mechanistic hypotheses from intracellular and extracellular metabolomics.

An emerging paradox in cancer metabolism is that identical oncogenic mutations produce profoundly different metabolic phenotypes depending on tissue context, with many mutations exhibiting striking tissue-restricted distributions. Here we introduce metabolic permissiveness as the inherent capacity of a tissue to tolerate, adapt to, or exploit metabolic disruptions, providing a unifying framework for explaining this selectivity. We examine tissue-specific metabolic rewiring driven by canonical oncogenes (MYC and KRAS), tumor suppressors (p53, PTEN, and LKB1), and tricarboxylic acid (TCA) cycle enzymes (FH, SDH, and IDH), demonstrating that baseline metabolic architecture, nutrient microenvironment, redox buffering, and compensatory pathways determine whether mutations confer a selective advantage or metabolic crisis. We further discuss how the tumor microenvironment shapes metabolic adaptation and therapeutic vulnerability. This framework reveals shared principles of tissue-specific metabolic vulnerability in cancer and provides a mechanistic basis for precision metabolic therapies.

Here, we present a protocol to assess the lipogenic phenotype of induced neural stem cells (iNSCs) using stable isotopic tracing. We describe steps for the culture and preparation of iNSCs, labeling with [13C6]-glucose and [13C5, 15N2]-glutamine, and the subsequent extraction of metabolites, lipids, and proteins from the same sample. This protocol supports single-specimen, mass spectrometry-based multi-omics workflows and is applicable to steady-state analyses, stable isotope tracing, and characterization of protein post-translational modifications.For complete details on the use and execution of this protocol, please refer to Ionescu et al.1

Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC) is a rare autosomal dominant disorder that is characterized by the development of multiple cutaneous and uterine leiomyomas and predisposes individuals to an aggressive and highly metastatic form of Renal Cell Cancer (RCC).

ABSTRACT Objectives Circulating monocytes from rheumatoid arthritis (RA) patients are pre-primed for inflammatory activation, but their disease-intrinsic features have not been systematically characterized. Given the important role of metabolism in shaping immune cell function, we aimed to determine how this pre-primed state is underpinned metabolically and whether these changes persist across different activation states, using an unbiased multi-omics approach. Methods Peripheral blood CD14⁺ monocytes from RA patients and matched healthy donors were analyzed in an undifferentiated state (M0) and after differentiation into classically activated M(IFNγ+LPS) and alternatively activated M(IL-4) macrophages, followed by acute lipopolysaccharide (LPS) stimulation. Metabolomic (untargeted LC–MS/MS), transcriptomic (RNA-seq), and proteomic (label-free mass LC-MS/MS) profiling were performed. Data was comprehensively analyzed by weighted gene correlation network analysis, differential analysis, gene set enrichment analysis, multi-omics factor analysis and metabolic flux modeling. Results RA monocytes exhibited a stable disease-driven signature across activation states. Integration of metabolomic, transcriptomic and proteomic data revealed an unexpected convergence on metabolic–secretory coupling, with depletion of nucleotide and redox metabolites, downregulation of mitochondrial and translational pathways, and remodeling of the secretory apparatus, including loss of cis-Golgi components. Consistently, metabolic modeling predicted reduced glycosylation fluxes, connecting metabolic changes to altered secretory capacity. Conclusions RA monocytes adopt a stable, disease-intrinsic state that persists across activation conditions. Multi-omics data identify a linked metabolic and secretory defect, with reduced glycosylation capacity as a potential functional consequence. This metabolic-secretory coupling represents a defining feature of RA monocyte dysfunction and a potential therapeutic target.

Loss of host-microbiota balance promotes gut inflammation, colitis and inflammatory bowel disease. Yet, whether host or microbial factors are the critical driver of the pathology remains unclear. Here, we investigate how cardiolipin maintains metabolic fitness of regulatory T (Treg) cells to preserve gut-immune homeostasis. We discover that deleting the cardiolipin-synthesizing enzyme protein tyrosine phosphatase mitochondrial 1 (PTPMT1) in T cells predisposes mice to colitis due to impaired Treg cell function in the absence of dysbiosis. Subsequent pathobiont infections accelerate the progression and severity of gut inflammation. Mechanistically, the absence of cardiolipin impairs Treg cell metabolic fitness and triggers a maladaptive integrated stress response, which can be reversed pharmacologically or genetically, restoring gut homeostasis and extending lifespan in PTPMT1 ΔT mice. Barth syndrome, a genetic disorder marked by severe cardiolipin deficiency, also exhibits gastrointestinal symptoms and inflammation associated with helper T cell imbalance and an active integrated stress response signature. Overall, these results suggest that a cardiolipin-mediated mitonuclear axis in T cells preserves gut-immune homeostasis and dictates outcome in pathobiont infections.

Dihydroorotate dehydrogenase is a rate-limiting enzyme of de novo pyrimidine synthesis. In most eukaryotes, this enzyme is bound to the inner mitochondrial membrane, where it couples orotate synthesis to ubiquinone reduction. As ubiquinone must be regenerated by respiratory complex III, pyrimidine biosynthesis and cellular respiration are tightly coupled. Consequently, inhibition of respiration suppresses DNA synthesis and cell proliferation. Here we show that expression of the Saccharomyces cerevisiae URA1 gene (ScURA) in mammalian cells uncouples pyrimidine biosynthesis from mitochondrial electron transport. ScURA forms a homodimer in the cytosol that uses fumarate as an electron acceptor instead of ubiquinone, enabling respiration-independent pyrimidine biosynthesis. Cells expressing ScURA are resistant to drugs that inhibit complex III and the mitochondrial ribosome. Additionally, ScURA enables growth of mitochondrial-DNA-lacking ρ0 cells in uridine-deficient medium and ameliorates the phenotype of cellular models of mitochondrial diseases. Overall, this genetic tool uncovers the contribution of pyrimidine biosynthesis to the phenotypes arising from electron transport chain defects.

Cell metabolism is a dynamic network of highly interconnected biochemical reactions. In this issue of Nature Metabolism, time-lapse analysis of the amino acid aspartate revealed an unexpected regulation of de novo pyrimidine biosynthesis by the tricarboxylic acid cycle metabolite succinate, with implications for the cell cycle and DNA damage response.

SUMMARY Cellular heterogeneity and plasticity are hallmarks of cancer that contribute to tumor growth and therapy resistance. Here we investigated metabolic heterogeneity in small cell lung cancer (SCLC), an aggressive neuroendocrine (NE) cancer type. Through integrated transcriptomic and metabolomic analyses, we identified a universal dependency on exogenous cysteine/cystine (Cys) across all NE/non-NE SCLC cell states. Notably, NE and non-NE cells with low levels of the ASCL1 transcription factor die from ferroptosis upon Cys depletion. In contrast, ASCL1-high cells die from apoptosis but are ferroptosis resistant. This resistance to ferroptosis is driven by the direct upregulation of the gene coding for the GCH1 enzyme by ASCL1, which results in higher levels of the BH4/BH2 antioxidants. Accordingly, combining cysteine depletion with BH4/BH2 synthesis inhibition effectively reduces tumor growth in patient-derived xenografts. This work elucidates distinct metabolic states in SCLC and suggests new approaches to induce cell death in this lethal form of cancer.

Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome is caused by heterozygous germline variants in the fumarate hydratase (FH) gene. Inheritance follows an autosomal dominant pattern. Loss of FH confers a predisposition for various benign and malignant neoplasms, including cutaneous leiomyomas, uterine fibroids and FH-deficient renal cell carcinoma. While benign, cutaneous and uterine manifestations have a relevant impact on quality of life and risk for complications, the vast majority of FH-deficient RCCs exhibit an aggressive behaviour with invasive growth and the potential for early metastatic spread. Additionally, pathogenic germline FH variants have been associated with other neoplasms, such as adrenal gland and Leydig cell tumours. The aggressive behaviour of FH-deficient RCC challenges nephron-sparing resection strategies, as a wide margin is recommended. Even after early nephrectomy for surgical removal of FH-deficient renal cell carcinomas, there is a relevant risk for distant metastasis as well as the remaining predisposition for de novo primary renal tumours in the other kidney. Active screening is central to HLRCC care since no preventative HLRCC-specific treatment exists. Vascular endothelial growth factor/epidermal growth factor receptor-directed treatment regimes, such as erlotinib/bevacizumab, demonstrate efficacy against HLRCC-associated RCC. This emphasizes the importance of establishing the correct diagnosis in HLRCC early on to guide therapeutic decisions. Morphologic criteria as well as specific immunohistochemical staining and molecular genetics allow the identification of FH-deficient RCC. Changes made in the recent 2022 World Health Organization classification impact the diagnosis of HLRCC in multiple ways. This commentary aims to discuss this impact and raise awareness among pathologists as well as clinicians involved in the care of patients with HLRCC.

With the growing number of metabolomics and lipidomics studies, robust strategies for bioinformatic analyses are increasingly important. However, the absence of standardized and reproducible workflows, coupled with ambiguous metabolite annotations, hampers effective analysis, particularly when integrating prior knowledge with metabolomics data. Moreover, the limited availability of comprehensive, curated prior knowledge further limits functional analyses and reduces the extraction of meaningful biological insights. Here we present MetaProViz (Metabolomics Processing, functional analysis and Visualization), a free open-source R package for metabolomics data analysis that integrates prior knowledge to generate mechanistic hypotheses (). MetaProViz offers a flexible framework consisting of five modules: processing, differential analysis, prior knowledge integration, functional analysis and visualisation, applicable to both intracellular and exometabolomics experiments. To improve functional analysis, we created the Metabolism Signature Database (MetSigDB), a collection of annotated metabolite sets. MetSigDB includes classical pathway-metabolite sets, metabolite-receptor and metabolite-transporter sets, and chemical class-metabolite sets. In addition, MetaProViz enables the conversion of gene sets to metabolite sets by using enzyme-metabolic reaction associations. In addition, MetaProViz translates between metabolite identifiers of commonly used databases, analyzes mapping ambiguities and completes missing annotations. The MetaProViz functional analysis toolkit includes sample metadata analysis, classical enrichment analysis and biologically informed clustering. We showcase MetaProViz functionalities using kidney cancer metabolomics data from cell lines, cell-culture media, and tumour tissue. We found increased methionine usage in clear-cell renal cell carcinoma (ccRCC) cell lines in line with decreased methionine levels in tumour samples. Further, we link this observation to enzymes and transporters crucial for overall survival in ccRCC and suggest that the increased methionine usage reflects the elevated DNA-hypermethylation landscape, a known characteristic in ccRCC. In summary, MetaProViz facilitates and improves the analysis and interpretation of metabolomics data. ![Figure][1] ### Competing Interest Statement C.F. is an adviser for Istesso. J.S.R. reports in the last 3 years funding from GSK and Pfizer and fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Pfizer, Grunenthal, Tempus, Moderna and Owkin. SmartCare by Federal Ministry of Research, Technology and Space (BMFTR) CRUK Programme Foundation award, C51061/A27453 Alexander von Humboldt Foundation Landesinstitut für Bioinformatikinfrastruktur in Baden-Württemberg [1]: pending:yes

Tumor factors and LA induce FRC remodeling. A, PCA plot of RNA-seq data calculated for the 500 genes showing the highest variance in a variance stabilizing transformed matrix with log2-transformed data from in vitro FRCs treated with CCM, B16.F10 TCM, vehicle (Veh – H2O) or 15 mmol/L LA for 4 days. n = 2 biological replicates. B, Volcano plot comparing genes expressed by in vitro cultured FRCs treated for 4 days with TCM or CCM, the x-axis displays the significance (-log10P) and the y-axis displays the log2 FC. Yellow lines are at -log10P = 1.3 and log2 FC 0.68. The top and bottom five are labeled. C, Volcano plot comparing genes expressed by FRCs treated with 15 mmol/L LA or Veh – H2O for 4 days in vitro; plot labels as in (B). D, Overlap of genes significantly deregulated in FRCs treated with TCM versus CCM and 15 mmol/L LA versus Veh – H2O for 4 days in vitro. E, Heatmap displaying the top and bottom 10 most deregulated genes of the 235 genes overlapping in D. F, Summary of key pathways identified in in vitro cultured FRCs treated for 4 days with 15 mmol/L LA versus Veh – H2O using genes with -log10P < 1.3 and log2 FC > 0.68. Detailed analysis in Supplementary Fig. S2. G, Heatmap displaying significant deregulated genes in 15 mmol/L LA versus Veh – H2O (including data for TCM vs. CCM) within the ”response to virus”/IFN signature. H, Heatmap displaying significant deregulated genes in 15 mmol/L LA verus Veh – H2O (including data for TCM vs. CCM) within the ”ECM” signature. I, Confocal images of FRCs treated for 4 days with CCM, B16.F10 TCM, Veh – H2O, or 15 mmol/L LA and stained for collagen I (red) and nuclei (blue; left) and quantification thereof (right). n = 2 independent experiments each with 8 fields of view analyzed. Scale bar: 50 µm. Data are mean with SEM. Significance (*, P < 0.05; **, P < 0.01; ***, P < 0.001; and ****, P < 0.0001) was determined by unpaired two-tailed t test. B16, B16.F10.

Synthetic lethal interactions (SLIs) based on genomic alterations in cancer have been therapeutically explored. We investigated the SLI space as a function of differential RNA expression in cancer and normal tissue. Computational analyses of functional genomic and gene expression resources uncovered a cancer-specific SLI between the paralogs cytidine diphosphate diacylglycerol synthase 1 (CDS1) and CDS2. The essentiality of CDS2 for cell survival is observed for mesenchymal-like cancers, which have low or absent CDS1 expression and account for roughly half of all cancers. Mechanistically, the CDS1-2 SLI is accompanied by disruption of lipid homeostasis, including accumulation of cholesterol esters and triglycerides, and apoptosis. Genome-wide CRISPR-Cas9 knockout screens in CDS1-negative cancer cells identify no common escape mechanism of death caused by CDS2 ablation, indicating the robustness of the SLI. Synthetic lethality is driven by CDS2 dosage and depends on catalytic activity. Thus, CDS2 may serve as a pharmacologically tractable target in mesenchymal-like cancers.