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Spatial heterogeneity and plasticity of the mammalian liver are critical for systemic metabolic homeostasis in response to fluctuating nutritional conditions. Here, a spatially resolved transcriptomic landscape of mouse livers across fed, fasted and refed states using spatial transcriptomics is generated. This approach elucidated dynamic temporal‐spatial gene cascades and how liver zonation—both expression levels and patterns—adapts to shifts in nutritional status. Importantly, the pericentral nuclear receptor Nr1i3 (CAR) as a pivotal regulator of triglyceride metabolism is pinpointed. It is showed that the activation of CAR in the pericentral region is transcriptionally governed by Pparα. During fasting, CAR activation enhances lipolysis by upregulating carboxylesterase 2a, playing a crucial role in maintaining triglyceride homeostasis. These findings lay the foundation for future mechanistic studies of liver metabolic heterogeneity and plasticity in response to nutritional status changes, offering insights into the zonated pathology that emerge during liver disease progression linked to nutritional imbalances. [ABSTRACT FROM AUTHOR]

Metabolic abnormalities in mammalian joint tissues are closely linked to various diseases, including rheumatoid arthritis and osteoarthritis. The variations of small molecule metabolites within bone joint tissues can affect cell proliferation, drive the secretion of inflammatory mediators, mediate leukocyte infiltration, and consequently induce synovial inflammation and cartilage damage. Therefore, the development of novel mass spectrometry imaging (MSI) methods for visualizing metabolites in joint tissues is crucial for comprehensively understanding the metabolic characteristics of different microregions under both physiological and pathological conditions. In this study, an airflow-assisted desorption electrospray ionization (AFADESI)-MSI was used to establish an ambient MSI method for rat joint tissues. To ensure the effectiveness of the method, dynamic range, sensitivity, and imaging effect were selected as the primary evaluation criteria. The tissue preparation, section thickness, and spray solvent systems were systematically optimized. Compared to the AB glue transfer method, the tape adhesion method can obtain complete bone joint tissue sections and clearer images. A section thickness of 20 μm, as opposed to 15 μm, provides higher ion intensity in the m/z 600-900 range. Among the tested spray solvent systems, compared with methanol-water (8:2, V/V), acetonitrile-water (8:2, V/V), and the acetonitrile-isopropanol-water (6:2:2, V/V/V), the acetonitrile-isopropanol-water (4:4:2, V/V/V) prove to be the most effective, detecting the highest number of metabolites and lipids when employed as the spray solvent. The precision of this method was evaluated on six adjacent rat knee sections under positive and negative ion modes, with the relative standard deviation (RSD) for the ion intensity of representative metabolites being less than 20%. Subsequently, this method was utilized for spatially resolved metabolomics analysis of rat knee tissues. A total of 613 metabolites are annotated in the overall knee tissues. Based on the optical images, the rat knee tissues were segmented into three microregions for bone, marrow, and cartilage. 452, 418, and 451 metabolites in these microregions are identified, respectively. In bone, the pentose phosphate pathway, glyoxylate and dicarboxylate metabolism, and the TCA cycle are more prominent. In bone marrow, ascorbate and purine metabolism pathways are more significant. In cartilage, arginine and proline metabolism, as well as lysine degradation, are more pronounced. These findings highlight significant metabolic heterogeneity across different microregions of rat knee joint tissues. Overall, this study develops an AFADESI-MSI method for in-depth profiling of metabolites within rat joint tissues, providing a powerful tool for in situ visualization of metabolites in metabolic studies of joint tissues. [ABSTRACT FROM AUTHOR]

The small intestine contains a two-front nutrient supply environment created by luminal dietary and microbial metabolites (enteral side) and systemic metabolites from the host (serosal side). Yet, it is unknown how each side contributes differentially to the small intestinal physiology. Here, we generated a comprehensive, high-resolution map of the small intestinal two-front nutrient supply environment. Using in vivo tracing of macronutrients and spatial metabolomics, we visualized the spatiotemporal dynamics and cell-type tropism in nutrient absorption and the region-specific metabolic heterogeneity within the villi. Specifically, glutamine from the enteral side fuels goblet cells to support mucus production, and the serosal side loosens the epithelial barrier by calibrating fungal metabolites. Disorganized feeding patterns, akin to the human lifestyle of skipping breakfast, increase the risk of metabolic diseases by inducing epithelial memory of lipid absorption. This study improves our understanding of how the small intestine is spatiotemporally regulated by its unique nutritional environment. [Display omitted] • A comprehensive map of the small intestinal two-front nutrient supply environment • Characterization of spatiotemporal dynamics and cell-type tropism in nutrient absorption • Two nutrient supplies differentially configure the functional zonation of enterocytes • Breakfast skipping exacerbates metabolic diseases by inducing epithelial lipid absorption Spatiotemporal mapping of the environment of the mouse small intestine enables the examination of how circulating metabolites and dietary and gut microbial metabolites contribute to aspects of host metabolism and intestinal physiology. [ABSTRACT FROM AUTHOR]

Objective: We explored the preliminary value of abnormal spindle-like microcephaly- associated (ASPM) protein in aiding precise risk sub-stratification, prediction of metabolic heterogeneity, and prognosis of neuroblastoma (NB). Methods: This retrospective study enrolled newly diagnosed patients with NB who underwent positron emission tomography/computed tomography (PET/CT) before therapy, and tumor tissue was collected after surgery. Regression analysis was used to evaluate ASPM expression and risk stratification in patients with NB. The expression levels of ASPM, clinical information, and PET/CT text features were analyzed using univariate and multivariate survival analyses. Finally, a correlation analysis was used to explore the relationship between ASPM and tumor metabolic heterogeneity. Results: There were 48 patients with NB in this study (35 boys and 13 girls); 22 patients progressed and 16 died. We found that the level of ASPM was highly associated with risk stratification (OR = 5.295, 95%IC: 1.348–41.722, p = 0.021). Patients with NB and high-risk stratification with high ASPM level had a lower 3-year progression-free survival (PFS) rate (14.28%) and 1-year PFS rate (57.14%) than those with low ASPM level (57.14% and 93.75%, respectively). Using univariate and multivariate survival analyses, this study revealed that ASPM and LDH were independent risk factors for both PFS and overall survival (OS), whales GLZLM_ZLNU was only a risk factor for PFS. Conclusion: ASPM holds promise as a novel biomarker for refining current risk stratification and predicting prognosis in neuroblastoma. Elevated levels of ASPM, LDH, and GLZLM_ZLNU may be associated with poorer survival outcomes in neuroblastoma patients. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BC) exhibits remarkable heterogeneity. However, the transcriptomic heterogeneity of BC at the single-cell level has not been fully elucidated. Methods: We acquired BC samples from 14 patients. Single-cell RNA sequencing (scRNA-seq), bioinformatic analyses, along with immunohistochemistry (IHC) and immunofluorescence (IF) assays were carried out. Results: According to the scRNA-seq results, 10 different cell types were identified. We found that Cancer-Associated Fibroblasts (CAFs) exhibited distinct biological functions and may promote resistance to therapy. Metabolic analysis of tumor cells revealed heterogeneity in glycolysis, gluconeogenesis, and fatty acid synthetase reprogramming, which led to chemotherapy resistance. Furthermore, patients with multiple metastases and progression were predicted to benefit from immunotherapy based on a heterogeneity analysis of T cells and tumor cells. Conclusions: Our findings provide a comprehensive understanding of the heterogeneity of BC, provide comprehensive insight into the correlation between cancer metabolism and chemotherapy resistance, and enable the prediction of immunotherapy responses based on T-cell heterogeneity. [ABSTRACT FROM AUTHOR]

Metabolic abnormalities in mammalian joint tissues are closely linked to various diseases, including rheumatoid arthritis and osteoarthritis. The variations of small molecule metabolites within bone joint tissues can affect cell proliferation, drive the secretion of inflammatory mediators, mediate leukocyte infiltration, and consequently induce synovial inflammation and cartilage damage. Therefore, the development of novel mass spectrometry imaging (MSI) methods for visualizing metabolites in joint tissues is crucial for comprehensively understanding the metabolic characteristics of different microregions under both physiological and pathological conditions. In this study, an airflow-assisted desorption electrospray ionization (AFADESI)-MSI was used to establish an ambient MSI method for rat joint tissues. To ensure the effectiveness of the method, dynamic range, sensitivity, and imaging effect were selected as the primary evaluation criteria. The tissue preparation, section thickness, and spray solvent systems were systematically optimized. Compared to the AB glue transfer method, the tape adhesion method can obtain complete bone joint tissue sections and clearer images. A section thickness of 20 μm, as opposed to 15 μm, provides higher ion intensity in the m/z 600-900 range. Among the tested spray solvent systems, compared with methanol-water (8:2, V/V), acetonitrile-water (8:2, V/V), and the acetonitrile-isopropanol-water (6:2:2, V/V/V), the acetonitrile-isopropanol-water (4:4:2, V/V/V) prove to be the most effective, detecting the highest number of metabolites and lipids when employed as the spray solvent. The precision of this method was evaluated on six adjacent rat knee sections under positive and negative ion modes, with the relative standard deviation (RSD) for the ion intensity of representative metabolites being less than 20%. Subsequently, this method was utilized for spatially resolved metabolomics analysis of rat knee tissues. A total of 613 metabolites are annotated in the overall knee tissues. Based on the optical images, the rat knee tissues were segmented into three microregions for bone, marrow, and cartilage. 452, 418, and 451 metabolites in these microregions are identified, respectively. In bone, the pentose phosphate pathway, glyoxylate and dicarboxylate metabolism, and the TCA cycle are more prominent. In bone marrow, ascorbate and purine metabolism pathways are more significant. In cartilage, arginine and proline metabolism, as well as lysine degradation, are more pronounced. These findings highlight significant metabolic heterogeneity across different microregions of rat knee joint tissues. Overall, this study develops an AFADESI-MSI method for in-depth profiling of metabolites within rat joint tissues, providing a powerful tool for in situ visualization of metabolites in metabolic studies of joint tissues.

Abstract Objective We explored the preliminary value of abnormal spindle-like microcephaly- associated (ASPM) protein in aiding precise risk sub-stratification, prediction of metabolic heterogeneity, and prognosis of neuroblastoma (NB). Methods This retrospective study enrolled newly diagnosed patients with NB who underwent positron emission tomography/computed tomography (PET/CT) before therapy, and tumor tissue was collected after surgery. Regression analysis was used to evaluate ASPM expression and risk stratification in patients with NB. The expression levels of ASPM, clinical information, and PET/CT text features were analyzed using univariate and multivariate survival analyses. Finally, a correlation analysis was used to explore the relationship between ASPM and tumor metabolic heterogeneity. Results There were 48 patients with NB in this study (35 boys and 13 girls); 22 patients progressed and 16 died. We found that the level of ASPM was highly associated with risk stratification (OR = 5.295, 95%IC: 1.348–41.722, p = 0.021). Patients with NB and high-risk stratification with high ASPM level had a lower 3-year progression-free survival (PFS) rate (14.28%) and 1-year PFS rate (57.14%) than those with low ASPM level (57.14% and 93.75%, respectively). Using univariate and multivariate survival analyses, this study revealed that ASPM and LDH were independent risk factors for both PFS and overall survival (OS), whales GLZLM_ZLNU was only a risk factor for PFS. Conclusion ASPM holds promise as a novel biomarker for refining current risk stratification and predicting prognosis in neuroblastoma. Elevated levels of ASPM, LDH, and GLZLM_ZLNU may be associated with poorer survival outcomes in neuroblastoma patients.

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single‐cell omics techniques, flow cytometry‐based methods (Met‐Flow, single‐cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE‐Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease‐relevant contexts.

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single‐cell omics techniques, flow cytometry‐based methods (Met‐Flow, single‐cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE‐Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease‐relevant contexts. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiomics analysis of positron emission tomography (PET) images can provide objective measurements of tumor heterogeneity and spatial patterns. However, the relatively low resolution, high noise, and limited longitudinal data availability make it difficult to systematically investigate the relationship between the microscopic tumor phenotypes and corresponding PET radiomics signatures. To address this challenge, we use a multiscale, stochastic mathematical model of tumor growth to generate cross-sections of tumors in vascularized normal tissue on a microscopic level. By varying the biological parameters of the model, distinct tumor phenotypes are obtained, and their corresponding PET images are generated. The simulated data are then used to find the optimal combination of PET radiomics features that can reliably distinguish visually similar tumor phenotypes. In addition, we study the longitudinal changes in the discriminative power of radiomics features with tumor growth from a single cell to approximately one million cells. Cancers can manifest large variations in tumor phenotypes due to genetic and microenvironmental factors, which has motivated the development of quantitative radiomics-based image analysis with the aim to robustly classify tumor phenotypes in vivo. Positron emission tomography (PET) imaging can be particularly helpful in elucidating the metabolic profiles of tumors. However, the relatively low resolution, high noise, and limited PET data availability make it difficult to study the relationship between the microenvironment properties and metabolic tumor phenotype as seen on the images. Most of previously proposed digital PET phantoms of tumors are static, have an over-simplified morphology, and lack the link to cellular biology that ultimately governs the tumor evolution. In this work, we propose a novel method to investigate the relationship between microscopic tumor parameters and PET image characteristics based on the computational simulation of tumor growth. We use a hybrid, multiscale, stochastic mathematical model of cellular metabolism and proliferation to generate simulated cross-sections of tumors in vascularized normal tissue on a microscopic level. The generated longitudinal tumor growth sequences are converted to PET images with realistic resolution and noise. By changing the biological parameters of the model, such as the blood vessel density and conditions for necrosis, distinct tumor phenotypes can be obtained. The simulated cellular maps were compared to real histology slides of SiHa and WiDr xenografts imaged with Hoechst 33342 and pimonidazole. As an example application of the proposed method, we simulated six tumor phenotypes that contain various amounts of hypoxic and necrotic regions induced by a lack of oxygen and glucose, including phenotypes that are distinct on the microscopic level but visually similar in PET images. We computed 22 standardized Haralick texture features for each phenotype, and identified the features that could best discriminate the phenotypes with varying image noise levels. We demonstrated that "cluster shade" and "difference entropy" are the most effective and noise-resilient features for microscopic phenotype discrimination. Longitudinal analysis of the simulated tumor growth showed that radiomics analysis can be beneficial even in small lesions with a diameter of 3.5–4 resolution units, corresponding to 8.7–10.0 mm in modern PET scanners. Certain radiomics features were shown to change non-monotonically with tumor growth, which has implications for feature selection for tracking disease progression and therapy response. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to validate the 2018 FIGO staging system of cervical cancer (CC) by determining the metabolic and radiomic heterogeneity of primary tumors between stage IIIC1 and IIIC2. Methods: 168 patients with squamous cell CC underwent pre-treatment fluorine-18 fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) and were randomly allocated to training and testing cohorts with a 7:3 ratio. Radiomics features were extracted from the primary tumors based on CT and PET data. Ten metabolic parameters of the primary tumors were also assessed. After feature selection, three logistic regression radiomics models, involving (1) 2 CT features, (2) 3 PET features, and (3) 2 CT features + 3 PET features, respectively, and one random forest model were established. Finally, area under the curve (AUC) values and calibration curves were used to evaluate the 4 models. Results: The IIIC1 and IIIC2 groups did not differ significantly in age, weight, height, or the 10 major metabolic parameters (P > 0.05). The AUCs of the 4 models were 0.577, 0.639, 0.763, and 0.506, respectively, in the training cohort, and 0.789, 0.699, 0.761, and 0.538, respectively, in the testing cohort. The model fit of the logistic regression model based on CT + PET data was good in both the training and testing cohorts. Conclusion: Our study offers additional diagnostic options for PALN metastasis, which could impact treatment decisions. Our results indirectly support the conclusions of previous studies recommending that primary tumors should be considered during IIIC staging. [ABSTRACT FROM AUTHOR]

Glioblastoma is a highly malignant brain tumor consisting of a heterogeneous cellular population. The transformed metabolism of glioblastoma cells supports their growth and division on the background of their milieu. One might hypothesize that the transformed metabolism of a primary glioblastoma could be well adapted to limitations in the variety and number of substrates imported into the brain parenchyma and present it their microenvironment. Additionally, the phenotypic heterogeneity of cancer cells could promote the variations among their metabolic capabilities regarding the utilization of available substrates and release of metabolic intermediates. With the aim to identify the putative metabolic footprint of different types of glioblastoma cells, we exploited the possibility for separation of polar and ionic molecules present in culture media or cell lysates by hydrophilic interaction liquid chromatography (HILIC). The mass spectrometry (MS) was then used to identify and quantify the eluted compounds. The introduced method allows the detection and quantification of more than 150 polar and ionic metabolites in a single run, which may be present either in culture media or cell lysates and provide data for polaromic studies within metabolomics. The method was applied to analyze the culture media and cell lysates derived from two types of glioblastoma cells, T98G and U118. The analysis revealed that even both types of glioblastoma cells share several common metabolic aspects, and they also exhibit differences in their metabolic capability. This finding agrees with the hypothesis about metabolic heterogeneity of glioblastoma cells. Furthermore, the combination of both analytical methods, HILIC-MS, provides a valuable tool for metabolomic studies based on the simultaneous identification and quantification of a wide range of polar and ionic metabolites—polaromics. [ABSTRACT FROM AUTHOR]

Summary: Background: Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited. Methods: We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties. Findings: Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells. Interpretation: This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism. Funding: National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.

Abstract Spatial heterogeneity and plasticity of the mammalian liver are critical for systemic metabolic homeostasis in response to fluctuating nutritional conditions. Here, a spatially resolved transcriptomic landscape of mouse livers across fed, fasted and refed states using spatial transcriptomics is generated. This approach elucidated dynamic temporal‐spatial gene cascades and how liver zonation—both expression levels and patterns—adapts to shifts in nutritional status. Importantly, the pericentral nuclear receptor Nr1i3 (CAR) as a pivotal regulator of triglyceride metabolism is pinpointed. It is showed that the activation of CAR in the pericentral region is transcriptionally governed by Pparα. During fasting, CAR activation enhances lipolysis by upregulating carboxylesterase 2a, playing a crucial role in maintaining triglyceride homeostasis. These findings lay the foundation for future mechanistic studies of liver metabolic heterogeneity and plasticity in response to nutritional status changes, offering insights into the zonated pathology that emerge during liver disease progression linked to nutritional imbalances.

PGC1a, a central player in mitochondrial biology, holds a complex role in the metabolic shifts seen in cancer cells. While its dysregulation is common across major cancers, its impact varies. In some cases, downregulation promotes aerobic glycolysis and progression, whereas in others, overexpression escalates respiration and aggression. PGC1a's interactions with distinct signaling pathways and transcription factors further diversify its roles, often in a tissue-specific manner. Understanding these multifaceted functions could unlock innovative therapeutic strategies. However, challenges exist in managing the metabolic adaptability of cancer cells and refining PGC1a-targeted approaches. This review aims to collate and present the current knowledge on the expression patterns, regulators, binding partners, and roles of PGC1a in diverse cancers. We examined PGC1a's tissue-specific functions and elucidated its dual nature as both a potential tumor suppressor and an oncogenic collaborator. In cancers where PGC1a is tumor-suppressive, reinstating its levels could halt cell proliferation and invasion, and make the cells more receptive to chemotherapy. In cancers where the opposite is true, halting PGC1a's upregulation can be beneficial as it promotes oxidative phosphorylation, allows cancer cells to adapt to stress, and promotes a more aggressive cancer phenotype. Thus, to target PGC1a effectively, understanding its nuanced role in each cancer subtype is indispensable. This can pave the way for significant strides in the field of oncology. [ABSTRACT FROM AUTHOR]

Background and purpose: The pre-surgical estimation of lymph node (LN) metastasis in colorectal cancer (CRC) poses a significant diagnostic predicament. The associations between LN morphology, density, and metabolic heterogeneity and LN metastasis status in CRCs have been seldomly examined through the lens of radiomics. This research aimed to assess 2-[18F]FDG PET-based quantification of intratumoral metabolic heterogeneity for predicting lymph node metastasis in patients with colorectal cancer. Materials and methods: The construction of the model utilized data from 264 CRC patients, all of whom underwent preoperative 2-[18F]FDG PET/CT. Radiomic features were extracted from PET and CT images of LNs. Least absolute shrinkage and selection operator (LASSO) regression was implemented for selecting pertinent imaging features with a tenfold cross-validation. The predictive accuracy for LN metastasis status was juxtaposed against traditional methodologies (comprising CT-reported LN status and PET/CT-reported LN status) by deploying the receiver operating characteristic (ROC) curve analysis. The radiomics signature was evaluated based on discrimination, calibration, and clinical utility parameters. The model was further subjected to validation using an independent cohort of 132 patients from the period of January 2012 to June 2020. Results: The radiomics model was composed of eight significant radiomic features (five from PET and three from CT), encapsulating metabolic and density heterogeneity. The radiomics signature (area under the curve (AUC), 0.908) showcased a significantly superior performance compared to CT-reported LN status (AUC, 0.563, P < 0.001) and PET/CT-reported LN status (AUC, 0.64, P < 0.001) for predicting LN-positive or LN-negative status. The radiomics signature (AUC, 0.885) also showcased a significantly superior performance compared to CT-reported LN status (AUC, 0.587, P < 0.001) and PET/CT-reported LN status (AUC, 0.621, P < 0.001) to identify N1 and N2. This signature maintained its independence from clinical risk factors and exhibited robustness in the validation test set. Decision curve analysis attested to the clinical utility of the radiomics signature. Conclusions: The radiomics signature based on 2-[18F]FDG PET/CT, which derived image features directly from LNs irrespective of clinical risk factors, displayed enhanced diagnostic performance compared to conventional CT or PET/CT-reported LN status. This allows for the identification of pre-surgical LN metastasis status and facilitates a patient-specific prediction of LN metastasis status in CRC patients. [ABSTRACT FROM AUTHOR]

Heterogeneity of tumor metabolism is an important, but still poorly understood aspect of tumor biology. Present work is focused on the visualization and quantification of cellular metabolic heterogeneity of colorectal cancer using fluorescence lifetime imaging (FLIM) of redox cofactor NAD(P)H. FLIM-microscopy of NAD(P)H was performed in vitro in four cancer cell lines (HT29, HCT116, CaCo2 and CT26), in vivo in the four types of colorectal tumors in mice and ex vivo in patients’ tumor samples. The dispersion and bimodality of the decay parameters were evaluated to quantify the intercellular metabolic heterogeneity. Our results demonstrate that patients’ colorectal tumors have significantly higher heterogeneity of energy metabolism compared with cultured cells and tumor xenografts, which was displayed as a wider and frequently bimodal distribution of a contribution of a free (glycolytic) fraction of NAD(P)H within a sample. Among patients’ tumors, the dispersion was larger in the high-grade and early stage ones, without, however, any association with bimodality. These results indicate that cell-level metabolic heterogeneity assessed from NAD(P)H FLIM has a potential to become a clinical prognostic factor.

Abstract Metabolic reprogramming is an emerging hallmark of cancer cells, enabling them to meet increased nutrient and energy demands while withstanding the challenging microenvironment. Cancer cells can switch their metabolic pathways, allowing them to adapt to different microenvironments and therapeutic interventions. This refers to metabolic heterogeneity, in which different cell populations use different metabolic pathways to sustain their survival and proliferation and impact their response to conventional cancer therapies. Thus, targeting cancer metabolic heterogeneity represents an innovative therapeutic avenue with the potential to overcome treatment resistance and improve therapeutic outcomes. This review discusses the metabolic patterns of different cancer cell populations and developmental stages, summarizes the molecular mechanisms involved in the intricate interactions within cancer metabolism, and highlights the clinical potential of targeting metabolic vulnerabilities as a promising therapeutic regimen. We aim to unravel the complex of metabolic characteristics and develop personalized treatment approaches to address distinct metabolic traits, ultimately enhancing patient outcomes.

Purpose: This study aimed to quantitatively assess [68Ga]Ga-PSMA-11 uptake in pathological lesions and normal organs in prostate cancer using the total-body [68Ga]Ga-PSMA-11 PET/CT and to characterize the dynamic metabolic heterogeneity of prostate cancer. Methods: Dynamic total-body [68Ga]Ga-PSMA-11 PET/CT scans were performed on ten prostate cancer patients. Manual delineation of volume-of-interests (VOIs) was performed on multiple normal organs displaying high [68Ga]Ga-PSMA-11 uptake, as well as pathological lesions. Time-to-activity curves (TACs) were generated, and the four compartment models including one-tissue compartmental model (1T1k), reversible one-tissue compartmental model (1T2k), irreversible two-tissue compartment model (2T3k) and reversible two-tissue compartmental model (2T4k) were fitted to each tissue TAC. Various rate constants, including K1 (forward transport rate from plasma to the reversible compartment), k2 (reverse transport rate from the reversible compartment to plasma), k3 (tracer binding on the PSMA-receptor and its internalization), k4 (the externalization rate of the tracer) and Ki (net influx rate), were obtained. The selection of the optimal model for describing the uptake of both lesions and normal organs was determined using the Akaike information criteria (AIC). Receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off values for differentiating physiological and pathological [68Ga]Ga-PSMA-11 uptake. Results: Both 1T1k and 1T2k models showed relatively high AIC values compared to the 2T3k and 2T4k models in both pathological lesions and normal organs. The kinetic behavior of pathological lesions was better described by the 2T3k model compared to the 2T4k model, while the normal organs were better described by the 2T4k model. Significant variations in kinetic metrics, such as K1, k2, and k3, and Ki, were observed among normal organs with high [68Ga]Ga-PSMA-11 uptake and pathological lesions. The high Ki value in normal organs was primarily determined by elevated K1 and low k3, rather than k2. Conversely, the high Ki value in pathological lesions, ranking second to the kidney and similar to salivary glands and spleen, was predominantly determined by the highest k3 value. Notably, k3 exhibited the highest performance in distinguishing between physiological and pathological [68Ga]Ga-PSMA-11 uptake, with an area under the curve (AUC) of 0.844 (95% CI, 0.773–0.915), sensitivity of 82.9%, and specificity of 74.1%. The k3 values showed better performance than SUVmean (AUC, 0.659), SUVmax (AUC, 0.637), and other kinetic parameter including K1 (AUC, 0.604), k2 (AUC, 0.634), and Ki (AUC, 0.651). Conclusions: Significant discrepancies in kinetic metrics were detected between pathological lesions and normal organs, despite their shared high uptake of [68Ga]Ga-PSMA-11. Notably, the k3 value exhibits a noteworthy capability to distinguish between pathological lesions and normal organs with elevated [68Ga]Ga-PSMA-11 uptake. This discovery implies that k3 holds promise as a prospective imaging biomarker for distinguishing between pathologic and non-specific [68Ga]Ga-PSMA-11 uptake in patients with prostate cancer. [ABSTRACT FROM AUTHOR]

Advances in cancer biology over the past decades have revealed that metabolic adaptation of cancer cells is an essential aspect of tumorigenesis. However, recent insights into tumour metabolism in vivo have revealed dissimilarities with results obtained in vitro. This is partly due to the reductionism of in vitro cancer models that struggle to reproduce the complexity of tumour tissues. This review describes some of the discrepancies in cancer cell metabolism between in vitro and in vivo conditions, and presents current methodological approaches and tools used to bridge the gap with the clinically relevant microenvironment. As such, these approaches should generate new knowledge that could be more effectively translated into therapeutic opportunities. [ABSTRACT FROM AUTHOR]

Metabolic reprogramming is an emerging hallmark of cancer cells, enabling them to meet increased nutrient and energy demands while withstanding the challenging microenvironment. Cancer cells can switch their metabolic pathways, allowing them to adapt to different microenvironments and therapeutic interventions. This refers to metabolic heterogeneity, in which different cell populations use different metabolic pathways to sustain their survival and proliferation and impact their response to conventional cancer therapies. Thus, targeting cancer metabolic heterogeneity represents an innovative therapeutic avenue with the potential to overcome treatment resistance and improve therapeutic outcomes. This review discusses the metabolic patterns of different cancer cell populations and developmental stages, summarizes the molecular mechanisms involved in the intricate interactions within cancer metabolism, and highlights the clinical potential of targeting metabolic vulnerabilities as a promising therapeutic regimen. We aim to unravel the complex of metabolic characteristics and develop personalized treatment approaches to address distinct metabolic traits, ultimately enhancing patient outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer stem cell have certain metabolic properties that are distinct from their differentiated counterparts. Our aim is to characterize CSC metabolism in oral cancer. Our study have several impacts: 1. Previous metabolic studies of CSC mainly focused on few energy metabolism pathway. Here we used novel transcription/metabolic joint analysis to reveal comprehensive metabolic alteration of CSC in oral cancer. 2. Assessing CSC metabolic phenotype in vivo is challenging. Here We used single-cell sequencing to explore the metabolic characteristics of CSC in vivo. 3. Our data suggested oral CSCs are metabolically inactive compared with differentiated cancer cells. This state may allow CSCs to resist the metabolic therapeutic strategies currently used for highly proliferative tumors. This knowledge may allow us to better develop metabolic therapy against CSC in oral cancer. Understanding the distinct metabolic characteristics of cancer stem cells (CSC) may allow us to better cope with the clinical challenges associated with them. In this study, OSCC cell lines (CAL27 and HSC3) and multicellular tumor spheroid (MCTS) models were used to generate CSC-like cells. Quasi-targeted metabolomics and RNA sequencing were used to explore altered metabolites and metabolism-related genes. Pathview was used to display the metabolites and transcriptome data in a KEGG pathway. The single-cell RNA sequencing data of six patients with oral cancer were analyzed to characterize in vivo CSC metabolism. The results showed that 19 metabolites (phosphoethanolamine, carbamoylphosphate, etc.) were upregulated and 109 metabolites (2-aminooctanoic acid, 7-ketocholesterol, etc.) were downregulated in both MCTS cells. Integration pathway analysis revealed altered activity in energy production (glycolysis, citric cycle, fatty acid oxidation), macromolecular synthesis (purine/pyrimidine metabolism, glycerophospholipids metabolism) and redox control (glutathione metabolism). Single-cell RNA sequencing analysis confirmed altered glycolysis, glutathione and glycerophospholipid metabolism in in vivo CSC. We concluded that CSCs are metabolically inactive compared with differentiated cancer cells. Thus, oral CSCs may resist current metabolic-related drugs. Our result may be helpful in developing better therapeutic strategies against CSC. [ABSTRACT FROM AUTHOR]

Understanding metabolism is of great significance to decipher various physiological and pathogenic processes. While great progress has been made to profile gene expression, how to capture organ-, tissue-, and cell-type-specific metabolic profile (i.e., metabolic tissue atlas) in complex mammalian systems is lagging behind, largely owing to the lack of metabolic imaging tools with high resolution and high throughput. Here, the authors applied mid-infrared imaging coupled with heavy water (D2 O) metabolic labeling to a scope of mouse organs and tissues. The premise is that, as D2 O participates in the biosynthesis of various macromolecules, the resulting broad C-D vibrational spectrum should interrogate a wide range of metabolic pathways. Applying multivariate analysis to the C-D spectrum, the authors successfully identified both inter-organ and intra-tissue metabolic signatures of mice. A large-scale metabolic atlas map between different organs from the same mice is thus generated. Moreover, leveraging the power of unsupervised clustering methods, spatially-resolved metabolic signatures of brain tissues are discovered, revealing tissue and cell-type specific metabolic profile in situ. As a demonstration of this technique, the authors captured metabolic changes during brain development and characterized intratumoral metabolic heterogeneity of glioblastoma. Altogether, the integrated platform paves a way to map the metabolic tissue atlas for complex mammalian systems.

Abstract Background Bladder cancer (BLCA) represents a highly heterogeneous disease characterized by distinct histological, molecular, and clinical features, whose tumorigenesis and progression require aberrant metabolic reprogramming of tumor cells. However, current studies have not expounded systematically and comprehensively on the metabolic heterogeneity of BLCA. Methods The UCSC XENA portal was searched to obtain the expression profiles and clinical annotations of BLCA patients in the TCGA cohort. A total of 1,640 metabolic-related genes were downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Then, consensus clustering was performed to divide the BLCA patients into two metabolic subtypes according to the expression of metabolic-related genes. Kaplan-Meier analysis was used to measure the prognostic values of the metabolic subtypes. Subsequently, comparing the immune-related characteristics between the two metabolic subtypes to describe the immunological difference. Then, the Scissor algorithm was applied to link the metabolic phenotypes and single-cell transcriptome datasets to determine the biomarkers associated with metabolic subtypes and prognosis. Finally, the clinical cohort included 63 BLCA and 16 para-cancerous samples was used to validate the prognostic value and immunological correlation of the biomarker. Results BLCA patients were classified into two heterogeneous metabolic-related subtypes (MRSs) with distinct features: MRS1, the subtype with no active metabolic characteristics but an immune infiltration microenvironment; and MRS2, the lipogenic subtype with upregulated lipid metabolism. These two subtypes had distinct prognoses, molecular subtypes distributions, and activations of therapy-related pathways. MRS1 BLCAs preferred to be immuno-suppressive and up-regulated immune checkpoints expression, suggesting the well-therapeutic response of MRS1 patients to immunotherapy. Based on the Scissor algorithm, we found that S100A7 both specifically up-regulated in the MRS1 phenotype and MRS1-tumor cells, and positively correlated with immunological characteristics. In addition, in the clinical cohort included 63 BLCA and 16 para-cancerous samples, S100A7 was obviously associated with poor prognosis and enhanced PD-L1 expression. Conclusions The metabolic subtype with S100A7 high expression recognizes the immuno-suppressive tumor microenvironment and predicts well therapeutic response of immunotherapy in BLCA. The study provides new insights into the prognostic and therapeutic value of metabolic heterogeneity in BLCA.

In recent years, dietary interventions have attracted much attention in cancer therapy. Mechanistic studies suggest that dietary interventions can inhibit the progression of cancer through deprivation of essential metabolites, lowering the levels of protumor hormones, activation of anticancer immunity and synergistic effects with conventional anticancer therapies. The feasibility, safety and promising tumor outcomes have also been established in humans. However, the results from both preclinical and clinical studies are inconsistent or even conflicting, the reasons for which have not been extensively considered. In this review, we discuss the various heterogeneity, including dietary protocols, tissue of origin and cancer locations, spatial and temporal metabolic heterogeneity, and divergent combination treatment, that may affect the responses of different cancers to dietary interventions. Understanding this heterogeneity and taking them into consideration when applying dietary interventions to cancer therapy will allow us to deliver the right diet to the right patient at the right time to maximize compliance, safety and efficacy of conventional anticancer therapy and to improve the outcomes of patients with cancer.

Objective: We aimed to investigate the predictive value of pre-treatment 18F-FDG PET/CT multi-metabolic parameters and tumor metabolic heterogeneity for gastric cancer prognosis. Methods: Seventy-one patients with gastric cancer were included. All patients underwent 18F-FDG PET/CT whole-body scans prior to treatment and had pathologically confirmed gastric adenocarcinomas. Each metabolic parameter, including SUVmax, SUVmean, MTV, and TLG, was collected from the primary lesions of gastric cancer in all patients, and the slope of the linear regression between the MTV corresponding to different SUVmax thresholds (40% × SUVmax, 80% × SUVmax) of the primary lesions was calculated. The absolute value of the slope was regarded as the metabolic heterogeneity of the primary lesions, expressed as the heterogeneity index HI-1, and the coefficient of variance of the SUVmean of the primary lesions was regarded as HI-2. Patient prognosis was assessed by PFS and OS, and a nomogram of the prognostic prediction model was constructed, after which the clinical utility of the model was assessed using DCA. Results: A total of 71 patients with gastric cancer, including 57 (80.3%) males and 14 (19.7%) females, had a mean age of 61 ± 10 years; disease progression occurred in 27 (38.0%) patients and death occurred in 24 (33.8%) patients. Multivariate Cox regression analysis showed that HI-1 alone was a common independent risk factor for PFS (HR: 1.183; 95% CI: 1.010–1.387, P < 0.05) and OS (HR: 1.214; 95% CI: 1.016–1.450, P < 0.05) in patients with gastric cancer. A nomogram created based on the results of Cox regression analysis increased the net clinical benefit for patients. Considering disease progression as a positive event, patients were divided into low-, intermediate-, and high-risk groups, and Kaplan–Meier survival analysis showed that there were significant differences in PFS among the three groups. When death was considered a positive event and patients were included in the low- and high-risk groups, there were significant differences in OS between the two groups. Conclusion: The heterogeneity index HI-1 of primary gastric cancer lesions is an independent risk factor for patient prognosis. A nomogram of prognostic prediction models constructed for each independent factor can increase the net clinical benefit and stratify the risk level of patients, providing a reference for guiding individualized patient treatment. [ABSTRACT FROM AUTHOR]

Breast cancer is composed of metabolically coupled cellular compartments with upregulation of TP53 Induced Glycolysis and Apoptosis Regulator (TIGAR) in carcinoma cells and loss of caveolin 1 (CAV1) with upregulation of monocarboxylate transporter 4 (MCT4) in fibroblasts. The mechanisms that drive metabolic coupling are poorly characterized. The effects of TIGAR on fibroblast CAV1 and MCT4 expression and breast cancer aggressiveness was studied using coculture and conditioned media systems and in‐vivo. Also, the role of cytokines in promoting tumor metabolic coupling via MCT4 on cancer aggressiveness was studied. TIGAR downregulation in breast carcinoma cells reduces tumor growth. TIGAR overexpression in carcinoma cells drives MCT4 expression and NFkB activation in fibroblasts. IL6 and TGFB drive TIGAR upregulation in carcinoma cells, reduce CAV1 and increase MCT4 expression in fibroblasts. Tumor growth is abrogated in the presence of MCT4 knockout fibroblasts and environment. We discovered coregulation of c‐MYC and TIGAR in carcinoma cells driven by lactate. Metabolic coupling primes the tumor microenvironment allowing for production, uptake and utilization of lactate. In sum, aggressive breast cancer is dependent on metabolic coupling. [ABSTRACT FROM AUTHOR]

Within biofilms, gradients of electron acceptors such as oxygen stimulate the formation of physiological subpopulations. This heterogeneity can enable cross-feeding and promote drug resilience, features of the multicellular lifestyle that make biofilm-based infections difficult to treat. The pathogenic bacterium Pseudomonas aeruginosa produces pigments called phenazines that can support metabolic activity in hypoxic/anoxic biofilm subzones, but these compounds also include methylated derivatives that are toxic to their producer under some conditions. In this study, we uncover roles for the global regulators RpoS and Hfq/Crc in controlling the beneficial and detrimental effects of methylated phenazines in biofilms. Our results indicate that RpoS controls phenazine methylation by modulating activity of the carbon catabolite repression pathway, in which the Hfq/Crc complex inhibits translation of the phenazine methyltransferase PhzM. We find that RpoS indirectly inhibits expression of CrcZ, a small RNA that binds to and sequesters Hfq/Crc, specifically in the oxic subzone of P. aeruginosa biofilms. Deletion of rpoS or crc therefore leads to overproduction of methylated phenazines, which we show leads to increased metabolic activity—an apparent beneficial effect—in hypoxic/anoxic subpopulations within biofilms. However, we also find that under specific conditions, biofilms lacking RpoS and/or Crc show increased sensitivity to phenazines indicating that the increased metabolic activity in these mutants comes at a cost. Together, these results suggest that complex regulation of PhzM allows P. aeruginosa to simultaneously exploit the benefits and limit the toxic effects of methylated phenazines. [ABSTRACT FROM AUTHOR]

Nutritional and metabolic cues are integral to animal development. Organisms use them both as sustenance and environmental indicators, fueling, informing and influencing developmental decisions. Classical examples, such as the Warburg effect, clearly illustrate how genetic programs control metabolic changes. However, the way that nutrition and metabolism can also modulate or drive genetic programs to instruct developmental trajectories is much more elusive, owing to several difficulties including uncoupling permissive and instructive functions. Here, we discuss recent advancements in the field that highlight the developmental role of nutritional and metabolic cues across multiple levels of organismal complexity. [ABSTRACT FROM AUTHOR]

Although the metabolic phenotype within tumors is known to differ significantly from that of the surrounding normal tissue, the importance of this heterogeneity is just becoming widely recognized. Colorectal cancer (CRC) is often classified as the Warburg phenotype, a metabolic type in which the glycolytic system is predominant over oxidative phosphorylation (OXPHOS) in mitochondria for energy production. However, this dichotomy (glycolysis vs. OXPHOS) may be too simplistic and not accurately represent the metabolic characteristics of CRC. Therefore, in this review, we decompose metabolic phenomena into factors based on their source/origin and reclassify them into two categories: extrinsic and intrinsic. In the CRC context, extrinsic factors include those based on the environment, such as hypoxia, nutrient deprivation, and the tumor microenvironment, whereas intrinsic factors include those based on subpopulations, such as pathological subtypes and cancer stem cells. These factors form multiple layers inside and outside the tumor, affecting them additively, dominantly, or mutually exclusively. Consequently, the metabolic phenotype is a heterogeneous and fluid phenomenon reflecting the spatial distribution and temporal continuity of these factors. This allowed us to redefine the characteristics of specific metabolism-related factors in CRC and summarize and update our accumulated knowledge of their heterogeneity. Furthermore, we positioned tumor budding in CRC as an intrinsic factor and a novel form of metabolic heterogeneity, and predicted its metabolic dynamics, noting its similarity to circulating tumor cells and epithelial-mesenchymal transition. Finally, the possibilities and limitations of using human tumor tissue as research material to investigate and assess metabolic heterogeneity are discussed. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BC) is regarded as one of the most common cancers diagnosed among the female population and has an extremely high mortality rate. It is known that Fibronectin 1 (FN1) drives the occurrence and development of a variety of cancers through metabolic reprogramming. Aspartic acid is considered to be an important substrate for nucleotide synthesis. However, the regulatory mechanism between FN1 and aspartate metabolism is currently unclear. Methods: We used RNA sequencing (RNA seq) and liquid chromatography-mass spectrometry to analyze the tumor tissues and paracancerous tissues of patients. MCF7 and MDA-MB-231 cells were used to explore the effects of FN1-regulated aspartic acid metabolism on cell survival, invasion, migration and tumor growth. We used PCR, Western blot, immunocytochemistry and immunofluorescence techniques to study it. Results: We found that FN1 was highly expressed in tumor tissues, especially in Lumina A and TNBC subtypes, and was associated with poor prognosis. In vivo and in vitro experiments showed that silencing FN1 inhibits the activation of the YAP1/Hippo pathway by enhancing YAP1 phosphorylation, down-regulates SLC1A3-mediated aspartate uptake and utilization by tumor cells, inhibits BC cell proliferation, invasion and migration, and promotes apoptosis. In addition, inhibition of FN1 combined with the YAP1 inhibitor or SLC1A3 inhibitor can effectively inhibit tumor growth, of which inhibition of FN1 combined with the YAP1 inhibitor is more effective. Conclusion: Targeting the "FN1/YAP1/SLC1A3/Aspartate metabolism" regulatory axis provides a new target for BC diagnosis and treatment. This study also revealed that intratumoral metabolic heterogeneity plays an important role in the progression of different subtypes of breast cancer. [ABSTRACT FROM AUTHOR]

Glioblastoma is a highly malignant brain tumor consisting of a heterogeneous cellular population. The transformed metabolism of glioblastoma cells supports their growth and division on the background of their milieu. One might hypothesize that the transformed metabolism of a primary glioblastoma could be well adapted to limitations in the variety and number of substrates imported into the brain parenchyma and present it their microenvironment. Additionally, the phenotypic heterogeneity of cancer cells could promote the variations among their metabolic capabilities regarding the utilization of available substrates and release of metabolic intermediates. With the aim to identify the putative metabolic footprint of different types of glioblastoma cells, we exploited the possibility for separation of polar and ionic molecules present in culture media or cell lysates by hydrophilic interaction liquid chromatography (HILIC). The mass spectrometry (MS) was then used to identify and quantify the eluted compounds. The introduced method allows the detection and quantification of more than 150 polar and ionic metabolites in a single run, which may be present either in culture media or cell lysates and provide data for polaromic studies within metabolomics. The method was applied to analyze the culture media and cell lysates derived from two types of glioblastoma cells, T98G and U118. The analysis revealed that even both types of glioblastoma cells share several common metabolic aspects, and they also exhibit differences in their metabolic capability. This finding agrees with the hypothesis about metabolic heterogeneity of glioblastoma cells. Furthermore, the combination of both analytical methods, HILIC-MS, provides a valuable tool for metabolomic studies based on the simultaneous identification and quantification of a wide range of polar and ionic metabolites—polaromics.

Background: Bladder cancer (BLCA) represents a highly heterogeneous disease characterized by distinct histological, molecular, and clinical features, whose tumorigenesis and progression require aberrant metabolic reprogramming of tumor cells. However, current studies have not expounded systematically and comprehensively on the metabolic heterogeneity of BLCA. Methods: The UCSC XENA portal was searched to obtain the expression profiles and clinical annotations of BLCA patients in the TCGA cohort. A total of 1,640 metabolic-related genes were downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Then, consensus clustering was performed to divide the BLCA patients into two metabolic subtypes according to the expression of metabolic-related genes. Kaplan-Meier analysis was used to measure the prognostic values of the metabolic subtypes. Subsequently, comparing the immune-related characteristics between the two metabolic subtypes to describe the immunological difference. Then, the Scissor algorithm was applied to link the metabolic phenotypes and single-cell transcriptome datasets to determine the biomarkers associated with metabolic subtypes and prognosis. Finally, the clinical cohort included 63 BLCA and 16 para-cancerous samples was used to validate the prognostic value and immunological correlation of the biomarker. Results: BLCA patients were classified into two heterogeneous metabolic-related subtypes (MRSs) with distinct features: MRS1, the subtype with no active metabolic characteristics but an immune infiltration microenvironment; and MRS2, the lipogenic subtype with upregulated lipid metabolism. These two subtypes had distinct prognoses, molecular subtypes distributions, and activations of therapy-related pathways. MRS1 BLCAs preferred to be immuno-suppressive and up-regulated immune checkpoints expression, suggesting the well-therapeutic response of MRS1 patients to immunotherapy. Based on the Scissor algorithm, we found that S100A7 both specifically up-regulated in the MRS1 phenotype and MRS1-tumor cells, and positively correlated with immunological characteristics. In addition, in the clinical cohort included 63 BLCA and 16 para-cancerous samples, S100A7 was obviously associated with poor prognosis and enhanced PD-L1 expression. Conclusions: The metabolic subtype with S100A7 high expression recognizes the immuno-suppressive tumor microenvironment and predicts well therapeutic response of immunotherapy in BLCA. The study provides new insights into the prognostic and therapeutic value of metabolic heterogeneity in BLCA. [ABSTRACT FROM AUTHOR]

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors. [ABSTRACT FROM AUTHOR]

Introduction: Nowadays, it has been recognized that gut microbiome can indirectly modulate cancer susceptibility or progression. However, whether intratumor microbes are parasitic, symbiotic, or merely bystanders in breast cancer is not fully understood. Microbial metabolite plays a pivotal role in the interaction of host and microbe via regulating mitochondrial and other metabolic pathways. And the relationship between tumor-resident microbiota and cancer metabolism remains an open question. Methods: 1085 breast cancer patients with normalized intratumor microbial abundance data and 32 single-cell RNA sequencing samples were retrieved from public datasets. We used the gene set variation analysis to evaluate the various metabolic activities of breast cancer samples. Furthermore, we applied Scissor method to identify microbe-associated cell subpopulations from single-cell data. Then, we conducted comprehensive bioinformatic analyses to explore the association between host and microbe in breast cancer. Results: Here, we found that the metabolic status of breast cancer cells was highly plastic, and some microbial genera were significantly correlated with cancer metabolic activity. We identified two distinct clusters based on microbial abundance and tumor metabolism data. And dysregulation of the metabolic pathway was observed among different cell types. Metabolism-related microbial scores were calculated to predict overall survival in patients with breast cancer. Furthermore, the microbial abundance of the specific genus was associated with gene mutation due to possible microbe-mediated mutagenesis. The infiltrating immune cell compositions, including regulatory T cells and activated NK cells, were significantly associated with the metabolism-related intratumor microbes, as indicated in the Mantel test analysis. Moreover, the mammary metabolism-related microbes were related to T cell exclusion and response to immunotherapy. Conclusions: Overall, the exploratory study shed light on the potential role of the metabolism-related microbiome in breast cancer patients. And the novel treatment will be realized by further investigating the metabolic disturbance in host and intratumor microbial cells. [ABSTRACT FROM AUTHOR]

Simple Summary: The metabolic heterogeneity complicates the clinical treatment of hepatocellular carcinoma. In this study, we classified hepatocellular carcinoma into two clusters based on their energy metabolic pathways' activities. We found this classification system correlated with several clinical characteristics and molecular profiles of liver cancer patients. We also proposed and validated targeted metabolic therapy by exploiting human liver cancer cell lines. Additionally, we revealed that cancer cells might impair the anti-tumor function of cytotoxic T cells through metabolic competition. Metabolic heterogeneity plays a key role in poor outcomes in malignant tumors, but its role in hepatocellular carcinoma (HCC) remains largely unknown. In the present study, we aim to disentangle the metabolic heterogeneity features of HCC by developing a classification system based on metabolism pathway activities in high-throughput sequencing datasets. As a result, HCC samples were classified into two distinct clusters: cluster 1 showed high levels of glycolysis and pentose phosphate pathway activity, while cluster 2 exhibited high fatty acid oxidation and glutaminolysis status. This metabolic reprogramming-based classifier was found to be highly correlated with several clinical variables, including overall survival, prognosis, TNM stage, and -fetoprotein (AFP) expression. Of note, activated oncogenic pathways, a higher TP53 mutation rate, and increased stemness were also observed in cluster 1, indicating a causal relationship between metabolic reprogramming and carcinogenesis. Subsequently, distinct metabolism-targeted therapeutic strategies were proven in human HCC cell lines, which exhibit the same metabolic properties as corresponding patient samples based on this classification system. Furthermore, the metabolic patterns and effects of different types of cells in the tumor immune microenvironment were explored by referring to both bulk and single-cell data. It was found that malignant cells had the highest overall metabolic activities, which may impair the anti-tumor capacity of CD8+ T cells through metabolic competition, and this provided a potential explanation for why immunosuppressive cells had higher overall metabolic activities than those with anti-tumor functions. Collectively, this study established an HCC classification system based on the gene expression of energy metabolism pathways. Its prognostic and therapeutic value may provide novel insights into personalized clinical practice in patients with metabolic heterogeneity. [ABSTRACT FROM AUTHOR]

The most common bone cancer is osteosarcoma (OS), which mostly affects children and teenagers. Early surgical resection combined with chemotherapy significantly improves the prognosis of patients with OS. Existing chemotherapies have poor efficacy in individuals with distant metastases or inoperable resection, and these patients may respond better to novel immunotherapies. Immune escape, which is mediated by immunosuppressive cells in the tumour microenvironment (TME), is a major cause of poor OS prognosis and a primary target of immunotherapy. Myeloid-derived suppressor cells, regulatory T cells, and tumour-associated macrophages are the main immunosuppressor cells, which can regulate tumorigenesis and growth on a variety of levels through the interaction in the TME. The proliferation, migration, invasion, and epithelial-mesenchymal transition of OS cells can all be impacted by the expression of non-coding RNAs (ncRNAs), which can also influence how immunosuppressive cells work and support immune suppression in TME. Interferon, checkpoint inhibitors, cancer vaccines, and engineered chimeric antigen receptor (CAR-T) T cells for OS have all been developed using information from studies on the metabolic properties of immunosuppressive cells in TME and ncRNAs in OS cells. This review summarizes the regulatory effect of ncRNAs on OS cells as well as the metabolic heterogeneity of immunosuppressive cells in the context of OS immunotherapies. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to quantitatively assess [18F]FDG uptake in primary tumor (PT) and metastatic lymph node (mLN) in newly diagnosed non-small cell lung cancer (NSCLC) using the total-body [18F]FDG PET/CT and to characterize the dynamic metabolic heterogeneity of NSCLC. Methods: The 60-min dynamic total-body [18F]FDG PET/CT was performed before treatment. The PTs and mLNs were manually delineated. An unsupervised K-means classification method was used to cluster patients based on the imaging features of PTs. The metabolic features, including Patlak-Ki, Patlak-Intercept, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and textural features, were extracted from PTs and mLNs. The targeted next-generation sequencing of tumor-associated genes was performed. The expression of Ki67, CD3, CD8, CD34, CD68, and CD163 in PTs was determined by immunohistochemistry. Results: A total of 30 patients with stage IIIA–IV NSCLC were enrolled. Patients were divided into fast dynamic FDG metabolic group (F-DFM) and slow dynamic FDG metabolic group (S-DFM) by the unsupervised K-means classification of PTs. The F-DFM group showed significantly higher Patlak-Ki (P < 0.001) and SUVmean (P < 0.001) of PTs compared with the S-DFM group, while no significant difference was observed in Patlak-Ki and SUVmean of mLNs between the two groups. The texture analysis indicated that PTs in the S-DFM group were more heterogeneous in FDG uptake than those in the F-DFM group. Higher T cells (CD3+/CD8+) and macrophages (CD68+/CD163+) infiltration in the PTs were observed in the F-DFM group. No significant difference was observed in tumor mutational burden between the two groups. Conclusion: The dynamic total-body [18F]FDG PET/CT stratified NSCLC patients into the F-DFM and S-DFM groups, based on Patlak-Ki and SUVmean of PTs. PTs in the F-DFM group seemed to be more homogenous in terms of [18F]FDG uptake than those in the S-DFM group. The higher infiltrations of T cells and macrophages were observed in the F-DFM group, which suggested a potential benefit from immunotherapy. [ABSTRACT FROM AUTHOR]

Background: Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited., Methods: We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties., Findings: Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells., Interpretation: This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism., Funding: National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

This study utilizes nanoscale Fourier transform infrared spectroscopy (nanoFTIR) to perform stable isotope probing (SIP) on individual bacteria cells cultured in the presence of 13 C-labelled glucose. SIP-nanoFTIR simultaneously quantifies single-cell metabolism through infrared spectroscopy and acquires cellular morphological information via atomic force microscopy. The redshift of the amide I peak corresponds to the isotopic enrichment of newly synthesized proteins. These observations of single-cell translational activity are comparable to those of conventional methods, examining bulk cell numbers. Observing cells cultured under conditions of limited carbon, SIP- nanoFTIR is used to identify environmentally-induced changes in metabolic heterogeneity and cellular morphology. Individuals outcompeting their neighboring cells will likely play a disproportionately large role in shaping population dynamics during adverse conditions or environmental fluctuations. Additionally, SIP-nanoFTIR enables the spectroscopic differentiation of specific cellular growth phases. During cellular replication, subcellular isotope distribution becomes more homogenous, which is reflected in the spectroscopic features dependent on the extent of 13 C- 13 C mode coupling or to specific isotopic symmetries within protein secondary structures. As SIP-nanoFTIR captures single-cell metabolism, environmentally-induced cellular processes, and subcellular isotope localization, this technique offers widespread applications across a variety of disciplines including microbial ecology, biophysics, biopharmaceuticals, medicinal science, and cancer research., (© 2024 The Authors. Small published by Wiley‐VCH GmbH.)

TP53, a gene with high-frequency mutations, plays an important role in breast cancer (BC) development through metabolic regulation, but the relationship between TP53 mutation and metabolism in BC remains to be explored. Our study included 1,066 BC samples from The Cancer Genome Atlas (TCGA) database, 415 BC cases from the Gene Expression Omnibus (GEO) database, and two immunotherapy cohorts. We identified 92 metabolic genes associated with TP53 mutations by differential expression analysis between TP53 mutant and wild-type groups. Univariate Cox analysis was performed to evaluate the prognostic effects of 24 TP53 mutation-related metabolic genes. By unsupervised clustering and other bioinformatics methods, the survival differences and immunometabolism characteristics of the distinct clusters were illustrated. In a training set from TCGA cohort, we employed the least absolute shrinkage and selection operator (LASSO) regression method to construct a metabolic gene prognostic model associated with TP53 mutations, and the GEO cohort served as an external validation set. Based on bioinformatics, the connections between risk score and survival prognosis, tumor microenvironment (TME), immunotherapy response, metabolic activity, clinical characteristics, and gene characteristics were further analyzed. It is imperative to note that our model is a powerful and robust prognosis factor in comparison to other traditional clinical features and also has high accuracy and clinical usefulness validated by receiver operating characteristic (ROC) and decision curve analysis (DCA). Our findings deepen our understanding of the immune and metabolic characteristics underlying the TP53 mutant metabolic gene profile in BC, laying a foundation for the exploration of potential therapies targeting metabolic pathways. In addition, our model has promising predictive value in the prognosis of BC. [ABSTRACT FROM AUTHOR]

Metabolic reprogramming is one of the main hallmarks of cancer cells. It refers to the metabolic adaptations of tumor cells in response to nutrient deficiency, microenvironmental insults, and anti-cancer therapies. Metabolic transformation during tumor development plays a critical role in the continued tumor growth and progression and is driven by a complex interplay between the tumor mutational landscape, epigenetic modifications, and microenvironmental influences. Understanding the tumor metabolic vulnerabilities might open novel diagnostic and therapeutic approaches with the potential to improve the efficacy of current tumor treatments. Prostate cancer is a highly heterogeneous disease harboring different mutations and tumor cell phenotypes. While the increase of intra-tumor genetic and epigenetic heterogeneity is associated with tumor progression, less is known about metabolic regulation of prostate cancer cell heterogeneity and plasticity. This review summarizes the central metabolic adaptations in prostate tumors, state-of-the-art technologies for metabolic analysis, and the perspectives for metabolic targeting and diagnostic implications. [ABSTRACT FROM AUTHOR]