In vivo CRISPR screening reveals nutrient signaling processes underpinning CD8+ T cell fate decisions.

How early events in effector T cell (TEFF) subsets tune memory T cell (TMEM) responses remains incompletely understood. Here, we systematically investigated metabolic factors in fate determination of TEFF and TMEM cells using in vivo pooled CRISPR screening, focusing on negative regulators of TMEM responses. We found that amino acid transporters Slc7a1 and Slc38a2 dampened the magnitude of TMEM differentiation, in part through modulating mTORC1 signaling. By integrating genetic and systems approaches, we identified cellular and metabolic heterogeneity among TEFF cells, with terminal effector differentiation associated with establishment of metabolic quiescence and exit from the cell cycle. Importantly, Pofut1 (protein-O-fucosyltransferase-1) linked GDP-fucose availability to downstream Notch-Rbpj signaling, and perturbation of this nutrient signaling axis blocked terminal effector differentiation but drove context-dependent TEFF proliferation and TMEM development. Our study establishes that nutrient uptake and signaling are key determinants of T cell fate and shape the quantity and quality of TMEM responses.

Nutritional control of developmental processes.

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.

Spatial heterogeneity in biofilm metabolism elicited by local control of phenazine methylation.

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.

Metabolic Heterogeneity and Potential Immunotherapeutic Responses Revealed by Single-Cell Transcriptomics of Breast Cancer.

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.

Stable Isotope Probing-nanoFTIR for Quantitation of Cellular Metabolism and Observation of Growth-Dependent Spectral Features.

This study utilizes nanoscale Fourier transform infrared spectroscopy (nanoFTIR) to perform stable isotope probing (SIP) on individual bacteria cells cultured in the presence of 13C-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 13C-13C 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.

Assessing dynamic metabolic heterogeneity in prostate cancer patients via total-body [68Ga]Ga-PSMA-11 PET/CT imaging: quantitative analysis of [68Ga]Ga-PSMA-11 uptake in pathological lesions and normal organs.

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.

2-[18F]FDG PET-based quantification of lymph node metabolic heterogeneity for predicting lymph node metastasis in patients with colorectal cancer.

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.

Neuroblastoma with high ASPM reveals pronounced heterogeneity and poor prognosis.

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.

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Metabolic reprogramming plays a critical role in tumorigenesis and tumor progression. The metabolism and the proliferation of tumors are regulated by both intrinsic factors within the tumor and the availability of metabolites in the tumor microenvironment (TME). The metabolic niche within the TME is primarily orchestrated at 3 levels: 1) the regulation of tumor metabolism by factors intrinsic to the tumors, 2) the interaction between tumor cells and T cells, macrophages, and stromal cells, and 3) the metabolic heterogeneity of tumor cells within the tissue space. Herein, we provided a concise overview of the various metabolic regulatory modes observed in tumor cells. Additionally, we extensively analyzed the interaction between tumor cells and other cells within the TME, as well as the metabolic characteristics and functions of different types of cells. Ultimately, this review provides a theoretical basis and novel insights for the precision treatment of tumors.

Metabolic regulation of prostate cancer heterogeneity and plasticity.

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.

Intratumoral metabolic heterogeneity of colorectal cancer.

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.

TP53 Induced Glycolysis and Apoptosis Regulator and Monocarboxylate Transporter 4 drive metabolic reprogramming with c-MYC and NFkB activation in breast cancer.

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.

FN1 mediated activation of aspartate metabolism promotes the progression of triple-negative and luminal a breast cancer.

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.

The landscape of different molecular modules in an immune microenvironment during tuberculosis infection.

Tuberculosis is a chronic inflammatory disease caused by Mycobacterium tuberculosis. When tuberculosis invades the human body, innate immunity is the first line of defense. However, how the innate immune microenvironment responds remains unclear. In this research, we studied the function of each type of cell and explained the principle of an immune microenvironment. Based on the differences in the innate immune microenvironment, we modularized the analysis of the response of five immune cells and two structural cells. The results showed that in the innate immune stress response, the genes CXCL3, PTGS2 and TNFAIP6 regulated by the nuclear factor kappa B(NK-KB) pathway played a crucial role in fighting against tuberculosis. Based on the active pathway algorithm, each immune cell showed metabolic heterogeneity. Besides, after tuberculosis infection, structural cells showed a chemotactic immunity effect based on the co-expression immunoregulatory module.

Evolvability and emergence of tumor heterogeneity as a space-time function.

The loss of control of cell proliferation, apoptosis regulation and contact inhibition leads to tumor development. While benign tumors are restricted to their primary space, i.e. where these tumors first originate, the metastatic tumors not only disseminate- facilitated by hypoxia-driven neovascularization- to distant secondary sites but also show substantial changes in metabolism, tissue architectures, gene expression profiles and immune phenotypes. All these alterations result in radio-, chemo- and immune-resistance rendering these metastatic tumor cells refractory to therapy. Since the beginning of the transformation, these factors- which influence each other- are incorporated to the developing and metastasizing tumor. As a result, the complexities in the heterogeneity of tumor progressively increase. This space-time function in the heterogeneity of tumors is generated by various conditions and factors at the genetic as well as microenvironmental levels, for example, endogenous retroviruses, methylation and epigenetic dysregulation that may be etiology-specific, cancer associated inflammation, remodeling of the extracellular matrix and mesenchymal cell shifted functions. On the one hand, these factors may cause de-differentiation of the tumor cells leading to cancer stem cells that contribute to radio-, chemo- and immune-resistance and recurrence of tumors. On the other hand, they may also enhance the heterogeneity under specific microenvironment-driven proliferation. In this editorial, we intend to underline the importance of heterogeneity in cancer progress, its evaluation and its use in correlation with the tumor evolution in a specific patient as a field of research for achieving precise patient-tailored treatments and amelioration of diagnostic (monitoring) tools and prognostic capacity.

A novel metabolic subtype with S100A7 high expression represents poor prognosis and immuno-suppressive tumor microenvironment in bladder cancer.

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.

The role of metabolic ecosystem in cancer progression - metabolic plasticity and mTOR hyperactivity in tumor tissues.

Despite advancements in cancer management, tumor relapse and metastasis are associated with poor outcomes in many cancers. Over the past decade, oncogene-driven carcinogenesis, dysregulated cellular signaling networks, dynamic changes in the tissue microenvironment, epithelial-mesenchymal transitions, protein expression within regulatory pathways, and their part in tumor progression are described in several studies. However, the complexity of metabolic enzyme expression is considerably under evaluated. Alterations in cellular metabolism determine the individual phenotype and behavior of cells, which is a well-recognized hallmark of cancer progression, especially in the adaptation mechanisms underlying therapy resistance. In metabolic symbiosis, cells compete, communicate, and even feed each other, supervised by tumor cells. Metabolic reprogramming forms a unique fingerprint for each tumor tissue, depending on the cellular content and genetic, epigenetic, and microenvironmental alterations of the developing cancer. Based on its sensing and effector functions, the mechanistic target of rapamycin (mTOR) kinase is considered the master regulator of metabolic adaptation. Moreover, mTOR kinase hyperactivity is associated with poor prognosis in various tumor types. In situ metabolic phenotyping in recent studies highlights the importance of metabolic plasticity, mTOR hyperactivity, and their role in tumor progression. In this review, we update recent developments in metabolic phenotyping of the cancer ecosystem, metabolic symbiosis, and plasticity which could provide new research directions in tumor biology. In addition, we suggest pathomorphological and analytical studies relating to metabolic alterations, mTOR activity, and their associations which are necessary to improve understanding of tumor heterogeneity and expand the therapeutic management of cancer.

Assessing dynamic metabolic heterogeneity in non-small cell lung cancer patients via ultra-high sensitivity total-body [18F]FDG PET/CT imaging: quantitative analysis of [18F]FDG uptake in primary tumors and metastatic lymph nodes.

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.

Metabolic characteristics and markers in viable but nonculturable state of Pseudomonas aeruginosa induced by chlorine stress.

Many Gram-negative pathogens enter the viable but nonculturable (VBNC) state to resist external environmental stress (such as disinfection). However, little is known about the metabolic properties, especially for the metabolic markers, of VBNC bacteria, which impedes the development of efficient disinfection technologies and causes more potential health risks. In this study, we analyzed the metabolic characteristics of chlorine stress-induced VBNC Pseudomonas aeruginosa at the population and single-cell levels. The overall metabolic activity of VBNC bacteria showed a downward trend, but the glyoxylate cycle, fatty acid and glycerophospholipid metabolism pathways were up-regulated. Based on the metabolic profiles of VBNC bacteria, nine metabolic markers (pyruvate, glyoxylate, guanine, glutamate, sn glycero-3-phos-phocholine, fatty acid, D-alanine, glutathione, N-Butanoyl-D-homoserine lactone) were determined. The results of single-cell Raman spectroscopy showed that the metabolic activity of VBNC bacteria was significantly reduced, but showed more significant metabolic heterogeneity. The redshift of the Raman peaks of 15N and 13C labeled VBNC bacteria was significantly weaker than that of the culturable bacteria, suggesting that the VBNC bacteria have a reduced ability to synthesize proteins, nucleotides, phospholipids, and carbohydrates. The result of this study can help to better understand the metabolic mechanisms and energy management strategy of VBNC bacteria, to achieve precise identification and effective control of VBNC bacteria.

Metabolic heterogeneity and techno-functional attributes of fermented foods-associated coagulase-negative staphylococci.

Coagulase-negative staphylococci (CNS) are one of the most pervasive heterogeneous groups of bacteria which are used as starter/adjunct cultures to enhance the aroma and texture of fermented foods. The organoleptic characteristics of fermented foods rely on disparate metabolic attributes of CNS. Nitrate reductase production from CNS improves sensory characteristics of foods by converting nitrate into nitrite. These bacteria utilize arginine via arginine deiminase pathway in the cytosol, and thus, play effective role in the generation of colour of fermented foods. Coagulase-negative Staphylococcus spp. develop flavour in foods by fermenting carbohydrates, converting amino acids, inducing ß-oxidation of lipids, and secreting esterases. Additionally, the characteristic flavour of foods depends on the proteolytic and lipolytic properties of CNS strains too. Coagulase-negative staphylococci strains have revealed exemplary functional or probiotic traits by showing tolerance to acidic pH and bile, depicting adhesion characteristics, producing exopolysaccharide, and secreting therapeutic bacteriocins. Unfortunately, some CNS strains have shown antibiotics resistance, enterotoxins secretions, biogenic amine productions, haemolytic activities, and biofilm formations, thereby indicated the utilization of CNS on strain-by-strain basis. This review sheds light not only on the metabolic heterogeneity and techno-functional traits but also the safety and pathogenic aspects of fermented foods-associated CNS strains.