Background Human brain organoids (BOs) are important models for studying early brain development and neurological disorders. While techniques for creating BOs are advancing, they remain developmental structures. Therefore, when human BOs are used to studying glioma-host interactions, the tumor behavior may be influenced by the BO-developmental microenvironment. Here, we describe the maturation of rat brain organoids (rBOs) into fully differentiated BOs and demonstrate their value as a model for studying glioblastoma (GB)-host interactions and their use in testing therapeutic interventions.Materials and Methods rBOs were obtained from fetal cortical brains on the 18th day of gestation. Transcriptomic, proteomic, and metabolomic analyses determined their differentiation into maturity. Their developmental trajectory was compared to human BOs derived from induced pluripotent stem cells as well as to rat brain development. Tumor-rBO interactions, including invasion parameters and therapeutic interactions, were studied using 5 human GB models.Results The rBOs develop into organized structures with myelinated neurons, oligodendrocytes, synapses, and glial cells, mirroring the rat brain development. GB invasion in rBOs matched those observed in orthotopic xenografts, enabling real-time assessment of invasion metrics: cellular heterogeneity, single-cell invasion speed, and tumor progression. The BOs had a strong impact on GB transcriptional activity and can be used to study therapeutic interventions. The rBO differentiation status influenced GB invasion capacity.Conclusions The rBOs serve as an effective target brain structure for studying GB invasion parameters and for evaluating therapeutic interventions. Their rapid development into mature brain tissue makes rBOs a valuable brain avatar system for studying tumor-host interactions.

Supplementary Figure 1 Surfactant lipids are enriched in the vicinity of lung metastases from patients with breast cancer; Supplementary Figure 2 AT2 cells and surfactant lipids co-localize in the vicinity of metastases from mice; Supplementary Figure 3 Metastases progression increases the enrichment of AT2 cells; Supplementary Figure 4 The metastasis secretome reprograms AT2 cell lipid metabolism by activating SREBP-1; Supplementary Figure 5 Gpam knockdown in cancer cells does not affect 3D spheroid growth in vitro; Supplementary Figure 6 GPAM and FASN inhibition in cancer cells does not affect metastasis formation in vitro and in vivo

Abstract Despite extensive genetic heterogeneity, lung tumors frequently converge on shared signaling dependencies that remain therapeutically underexploited. Here, we identify mTORC2 signaling as a convergent dependency across genetically distinct lung cancer subtypes and uncover HIF-1β as a selective metabolic effector downstream of mTORC2 that promotes lung tumor progression. Elevated mTORC2 signaling in lung adenocarcinoma was associated with poor overall survival, metastatic dissemination and metabolic rewiring. Using complementary genetically engineered mouse models of Rictor deletion or overexpression in Kras -driven lung tumors, we show that mTORC2 activity is dispensable for normal lung homeostasis but required for tumor progression and metabolic adaptation in vivo . Mechanistically, mTORC2 stabilized HIF-1β by preventing its ubiquitin-independent proteasomal degradation through a non-canonical PKCα-CK2 signaling axis, independently of AKT. Integrated multi-omics analyses identified extensive metabolic rewiring downstream of the mTORC2-HIF-1β axis, with sphingolipid metabolism emerging as a prominent and therapeutically exploitable vulnerability. Accordingly, pharmacological targeting of sphingolipid metabolism markedly impaired the growth of mTORC2-driven lung tumors in vivo . Together, our findings establish a non-canonical mTORC2-HIF-1β signaling axis that couples oncogenic signaling to metabolic adaptation and defines therapeutically actionable metabolic vulnerabilities in lung cancer.

Short hairpin RNAs (shRNAs) used to generate knockdown cell lines

Cancer cells that seed in the lung require lipids often produced by alveolar type II (AT2) cells. However, whether overt metastases depend on AT2 cell-derived lipids and whether AT2 cells can be targeted to reduce metastasis growth remains unknown. We discovered that breast cancer-derived lung metastases stimulate the proliferation of AT2 cells in their vicinity and reprogram them into lipid feeder cells in mice and patients using spatial analysis. Mechanistically, the metastasis secretome activates the transcription factor sterol regulatory element-binding transcription factor 1 (SREBP-1) in AT2 cells, enhancing the expression of key de novo lipid synthesis genes, including fatty acid synthase (FASN) and glycerol-3-phosphate acyltransferase 1 (GPAM). Deleting Fasn selectively in AT2 cells or targeting FASN and GPAM systemically significantly impairs lung metastasis growth in mice. In summary, we discovered that overt metastases reprogram AT2 cells and that targeting the lipid metabolism of AT2 cells impairs metastasis growth. SIGNIFICANCE:Current therapies in oncology targeting the cancer or immune cell compartment of tumors show limited efficacy against breast cancer-derived metastases. We discovered that decreasing the lipid metabolism of lung resident AT2 cells is sufficient to impair lung metastasis growth in mice without apparent adverse effects.

Birth is a profound physiological and immunological transition, during which the newborn must rapidly adapt to a microbe-rich environment. In contrast to adults, neonates allocate substantial energy toward body growth, which in turn influences their antimicrobial defenses. Using systematic analysis of immunometabolism, RNA expression and plasma kinase activity in human cord blood, peripheral neonatal blood, and adult blood, we uncovered rapid postnatal metabolic changes, which were confirmed in mice. Immune cells from neonates exhibit a marked increase in metabolic activity immediately after birth, characterized by elevated glycolytic flux and enhanced mitochondrial function. We further demonstrate an activation of the CCL2-CCR2 signaling axis as a critical mechanism mobilizing metabolically active monocytes from the bone marrow to the circulation. This specific monocytic immune-metabolic phenotype is related to mode of delivery and not to early microbial exposure as demonstrated in gnotobiotic mice. Collectively, our study demonstrates rapid immune-metabolic adaptations, comparable to emergency hematopoiesis, that drive postnatal immune development during the first days of life.

Expression of sEV-related genes correlates with disease progression and poor survival in CLL patients. Gene-expression analysis was performed by RT-qPCR for 7 genes involved in sEV biogenesis and secretion in a cohort of 144 CLL patients. The correlation between gene expression and survival was evaluated by Cox univariate regression analysis. Gene expression in clinical groups was evaluated by differential expression analysis for single genes or by logistic regression (LR) analysis for multiple genes. A, Calculated hazard ratios >1 (red dots, P < 0.05) indicate an increased risk for patients with high single-gene expression in terms of OS. B, Correlation between high or low gene expression and OS. Low and high groups are of identical size (n = 72) Median OS is indicated in months (mo). C, Correlation between high or low combined 7-gene expression and OS. D, Calculated hazard ratios >1 (red dots, P < 0.05) indicate an increased risk for patients with high multiple gene expression in terms of OS. E, Calculated hazard ratios >1 (red dots, P < 0.05) indicate an increased risk for patients with high single-gene expression in terms of TFS. F, Correlation between high or low gene expression and TFS. G and H, Standardized expression of single genes (G) or LR scores for multiple genes (H) in groups of patients according to prognostic markers (CytoG, cytogenetics, group size indicated in each panel). *, P < 0.05; **, P < 0.01. Data are mean with 95% confidence intervals.

Gut microbial species contribute to colorectal cancer (CRC) by interacting with tumor or immune cells, however if CRC-associated bacteria engage with stromal components of the tumor microenvironment remains unclear. Here, we report interaction between the CRC-associated bacterium Fusobacterium nucleatum and cancer-associated fibroblasts (CAFs), and show that F. nucleatum is present in the stromal compartment in murine CRC models in vivo and can attach to and invade CAFs. F. nucleatum-exposed CAFs exhibit a pronounced inflammatory-CAF (iCAF) phenotype, marked by elevated expression of established iCAF markers, secretion of pro-inflammatory cytokines such as CXCL1, IL-6 and IL-8, generation of reactive oxygen species (ROS), and an increased metabolic activity. In co-culture experiments, the interaction of cancer cells with F. nucleatum-stimulated CAFs enhances invasion, a finding further validated in vivo. Altogether, our results point to a role for the tumor microbiome in CRC progression by remodeling the tumor microenvironment through its influence on cancer-associated fibroblasts, suggesting novel therapeutic strategies for targeting CRC.

LME-sEVs decrease CD8+ T-cell functions. A and B, Percentages (A) and numbers (B) of CD62L−KLRG1+ CD8+ T cells after 48 hours of treatment with LME- and HCME-sEVs assessed by FC. C, Expression of ICP on CD62L−KLRG1+ CD8+ T cells from B. HSNE clustering depicting treatments, cluster identity, and marker expression. D, Hierarchical clustering based on ICP expression. E, Percentages of PD1+TIM3+ICOS+ CD8+ T cells from cluster C1 (top) and of PD1+LAG3+TIM3+TIGIT+ICOS+ CD8+ T cells from cluster C8 (bottom). F–I, CD8+ T cells were isolated from C57BL/6 and CLL cells from TCL1 mice. F, Percentage of T cell–mediated killing of TCL1 cells (cytotoxic assay) in the presence of HCME-sEVs or LME-sEVs (N = 6). G, Quantification of CD8+ T-cell:TCL1 cell conjugates upon treatment with LME- or HCME-sEVs (N = 3–4) and representative images (scale bar, 10 μm). H, Quantification of immune synapse formation (F-actin area in μm2, HCME-, n = 31 and LME-sEVs, n = 39, dashed line representing median) and representative medial optical sections (scale bar, 5 μm) with arrows indicating the synapse. I, Mean Fluorescence intensity (MFI) of GzmB at the synapse between CD8+ T and CLL cells (HCME-, n = 31 and LME-sEVs, n = 51) and representative 3D volume-rendered images. J, miRNA levels quantified by RT-qPCR in CD8+ T cells treated with HCME- or LME-sEVs for 24 hours. K, Protein levels of miRNA targets determined by FC in CD8+ T cells treated for 48 hours with HCME-sEVs or LME-sEVs transfected with scramble or antagomiRs (miR-150, -155, and -378a). Preincubation of LME-sEVs with heparin was used as an inhibitor of sEV internalization. L, ICP levels determined by FC in CD8+ T cells treated for 48 hours with HCME-sEVs or LME-sEVs preincubated with blocking Abs (PD-L1, GAL9, VISTA, and MHC-II) or corresponding isotypes. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 (unpaired Student t test). Data are mean.

LME-sEVs enter different lymphocyte subsets and modify CD8+ T cells in the microenvironment. A, Percentage of splenocytes internalizing MB488+ sEVs. Splenocytes from C57BL/6 mice were incubated for increasing periods of time with MB488+ LME-sEVs and analyzed by FC. sEV preincubation with heparin sulfate (Hep) was performed for 4 hours. B, Representative confocal microscopy pictures of total splenocytes after 4 hours of treatment with LME-sEVs, in the absence or presence of heparin (scale bar, 5 μm). C, Splenocytes from C57BL/6 mice were incubated for 24 hours with MB488+ LME-sEVs and then analyzed by FC with lymphocyte-lineage markers. D, FACS-sorted CD4+ and CD8+ T cells were incubated for increasing periods of time with MB488+ LME-sEVs and analyzed by FC. E, Representative confocal microscopy pictures of FACS-sorted CD4+ Tconv cells, CD8+ T cells, and Tregs after treatment with LME-sEVs (24 hours; scale bar, 5 μm). F–H, MB570+-LME-sEVs were i.v. injected in C57BL/6 mice. Total splenocytes were harvested 24 hours later and analyzed by FC directly (F) or after staining for specific immune subsets (CD19+ B cells, CD4+ and CD8+ T cells, G–H). I, Volcano plot showing differential expression of genes (DEG) with FDR <0.05 and log2FC >1 in CD8+ T cells isolated from spleens of mice treated with LME- or HCME-sEVs for 1 week. J, Hierarchical clustering of DEG from I. K, t-distributed stochastic neighbor embedding (t-SNE) of samples from I. L, Hierarchical clustering of selected genes from J, grouped by enriched gene ontologies. M and N, mRNA (M) or protein levels (N) of 3 selected DEG from I, quantified by RT-qPCR or FC, in CD8+ T cells treated in vitro for 48 hours with HCME- or LME-sEVs. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 (unpaired Student t test). Data are mean.

LME-sEVs impact CD8+ T-cell transcriptome, proteome, and metabolome. A, Volcano plot showing DEG identified by RNA-seq from CD8+ T cells treated for 48 hours with LME- (n = 4) or HCME-sEVs (n = 3) with FDR <0.05 and log2FC >1. B and C, Hierarchical clustering of all DEG (B) and of selected genes from relevant cell functions (C). D, Volcano plot showing differentially expressed proteins (DEP) identified by mass spectrometry from CD8+ T cells treated for 96 hours with LME- (n = 3) and HCME-sEVs (n = 3) with FDR <0.05 and log2FC >1. E and F, GzmB mRNA expression and GzmB and perforin levels in CD8+ T cells treated for 48 hours with HCME- or LME-sEVs. G and H, Ontology analysis of enriched (G) or diminished (H) DEP in CD8+ T cells treated with LME- or HCME-sEVs (from D). I, Levels of glucose measured by mass spectrometry in culture medium in CD8+ T-cell treated with LME- or HCME-sEVs for 96 hours. Negative value represents consumption. J, Immunoblot analysis of glycolysis-related proteins from CD8+ T cells treated for 96 hours with LME- or HCME-sEVs. K, Levels of ADP and ATP generated from 13C-glucose measured by mass spectrometry in CD8+ T cells treated with LME- (n = 5) or HCME-sEVs (n = 6) for 96 hours. L, Oxygen consumption measured by SeaHorse assay from CD8+ T cells treated with LME- or HCME-sEVs for 96h. *, P < 0.05; **, P < 0.01; ****, P < 0.0001 (unpaired Student t test). Data are mean and SEM.

Supplemental Table S3 - Patient clinical information

LME-sEVs present a specific proteome and miRNA fingerprint. A, Hierarchical clustering of sEVs differentially expressed proteins (DEP with q < 0.05) identified by mass spectrometry between HCME-sEVs (n = 3, isolated from independent pools of 5 C57BL/6 spleens) and LME-sEVs (n = 14). B, Volcano plot showing DEP between LME- and HCME-sEVs with FDR <0.05 and log2FC >1. C, PCA based on DEP. D and E, Ontology analysis of DEP between LME-sEVs and HCME-sEVs. F, Expression of ICP ligands on HCME- or LME-sEVs quantified by bead-based FC. G, Representative pictures of ICP ligand expression on single MB488+CD20+ LME-sEVs visualized by imaging FC. H and I, HSNE clustering of MB488+ LME-sEVs based on CD20, PD-L1, GAL9, and MHC-II expression analyzed by FC and related combinations of markers on CD20+ LME-sEVs. J, miRNA levels measured using RT-qPCR from HCME- (isolated from a pool of 5 C57BL/6 mice spleens) and LME-sEVs (n = 8). Data are mean.

Colorectal cancer (CRC) patients have been shown to possess an altered gut microbiome. Diet is a well-established modulator of the microbiome, and thus, dietary interventions might have a beneficial effect on CRC. An attenuating effect of the ketogenic diet (KD) on CRC cell growth has been previously observed, however the role of the gut microbiome in driving this effect remains unknown. Here, we describe a reduced colonic tumor burden upon KD consumption in a CRC mouse model with a humanized microbiome. Importantly, we demonstrate a causal relationship through microbiome transplantation into germ-free mice, whereby alterations in the gut microbiota were maintained in the absence of continued selective pressure from the KD. Specifically, we identify a shift toward bacterial species that produce stearic acid in ketogenic conditions, whereas consumers were depleted, resulting in elevated levels of free stearate in the gut lumen. This microbial product demonstrates tumor-suppressing properties by inducing apoptosis in cancer cells and decreasing colonic Th17 immune cell populations. Taken together, the beneficial effects of the KD are mediated through alterations in the gut microbiome, including, among others, increased stearic acid production, which in turn significantly reduces intestinal tumor growth.

NAD kinases have a crucial role in the de novo synthesis of the cofactor NADP+ by phosphorylating NAD+ to yield NADP+. A study by Flickinger et al. uses physiologically relevant cell culture media with low folic acid levels and identifies the cytosolic NAD kinase NADK as essential factor for supporting dihydrofolate reductase (DHFR) activity and folate-dependent nucleotide synthesis in cancer cells.