Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism

A micro-metabolic rewiring assay for assessing hypoxia-associated cancer metabolic heterogeneity

Cancer metabolism plays an essential role in therapeutic resistance, where significant inter- and intra-tumoral heterogeneity exists. Hypoxia is a prominent driver of metabolic rewiring behaviors and drug responses. Recapitulating the hypoxic landscape in the tumor microenvironment thus offers unique insights into heterogeneity in metabolic rewiring and therapeutic responses, to inform better treatment strategies. There remains a lack of scalable tools that can readily interface with imaging platforms and resolve the heterogeneous behaviors in hypoxia-associated metabolic rewiring. Here we present a micro-metabolic rewiring (μMeRe) assay that provides the scalability and resolution needed to characterize the metabolic rewiring behaviors of different cancer cells in the context of hypoxic solid tumors. Our assay generates hypoxia through cellular metabolism without external gas controls, enabling the characterization of cell-specific intrinsic ability to drive hypoxia and undergo metabolic rewiring. We further developed quantitative metrics that measure the metabolic plasticity through phenotypes and gene expression. As a proof-of-concept, we evaluated the efficacy of a metabolism-targeting strategy in mitigating hypoxia- and metabolic rewiring-induced chemotherapeutic resistance. Our study and the scalable platform thus lay the foundation for designing more effective cancer treatments tailored toward specific metabolic rewiring behaviors.

iMetAct: An integrated systematic inference of metabolic activity for dissecting tumor metabolic preference and tumor-immune microenvironment

Metabolic enzymes play a central role in cancer metabolic reprogramming, and their dysregulation creates vulnerabilities that can be exploited for therapy. However, accurately measuring metabolic enzyme activity in a high-throughput manner remains challenging due to the complex, multi-layered regulatory mechanisms involved. Here, we present iMetAct, a framework that integrates metabolic-transcription networks with an information propagation strategy to infer enzyme activity from gene expression data. iMetAct outperforms expression-based methods in predicting metabolite conversion rates by accounting for the effects of post-translational modifications. With iMetAct, we identify clinically significant subtypes of hepatocellular carcinoma with distinct metabolic preferences driven by dysregulated enzymes and metabolic regulators acting at both the transcriptional and non-transcriptional levels. Moreover, applying iMetAct to single-cell RNA sequencing data allows for the exploration of cancer cell metabolism and its interplay with immune regulation in the tumor microenvironment. An accompanying online platform further facilitates tumor metabolic analysis, patient stratification, and immune microenvironment characterization.

Simultaneous quantitative analysis of multiple metabolites using label-free surface-enhanced Raman spectroscopy and explainable deep learning

Metabolites serve as vital biomarkers, reflecting physiological and pathological states and offering insights into disease progression and early detection. This study introduces an advanced analytical technique integrating label-free Surface-Enhanced Raman Spectroscopy (SERS) with deep learning, and leverages SHAP (SHapley Additive exPlanations) to provide a visual interpretative analysis of the predictive rationale of the deep learning model, facilitating simultaneous detection and quantitative analysis of multiple metabolites. Monolayer silver nanoparticle SERS substrates were fabricated via a triple-phase interfacial self-assembly method, which captured complex spectral information of target metabolites in mixed solutions. A custom-built deep neural network model with multi-channel feature extraction was employed to predict the concentrations of uric acid (R2 = 0.976), xanthine (R2 = 0.971), hypoxanthine (R2 = 0.977), and creatinine (R2 = 0.940). The method's scalability was validated as the performance remained consistent with an increasing number of simultaneous targets. This approach offers a sensitive, cost-effective, and rapid alternative for metabolite analysis, with significant implications for clinical diagnostics and personalized medicine.

Towards an interpretable deep learning model of cancer

Cancer is a manifestation of dysfunctional cell states. It emerges from an interplay of intrinsic and extrinsic factors that disrupt cellular dynamics, including genetic and epigenetic alterations, as well as the tumor microenvironment. This complexity can make it challenging to infer molecular causes for treating the disease. This may be addressed by system-wide computer models of cells, as they allow rapid generation and testing of hypotheses that would be too slow or impossible to perform in the laboratory and clinic. However, so far, such models have been impeded by both experimental and computational limitations. In this perspective, we argue that they can now be achieved using deep learning algorithms to integrate omics data and prior knowledge of molecular networks. Such models would have many applications in precision oncology, e.g., for identifying drug targets and biomarkers, predicting resistance mechanisms and toxicity effects of drugs, or simulating cell-cell interactions in the microenvironment.

A human metabolic map of pharmacological perturbations reveals drug modes of action

Understanding a small molecule's mode of action (MoA) is essential to guide the selection, optimization and clinical development of lead compounds. In this study, we used high-throughput non-targeted metabolomics to profile changes in 2,269 putative metabolites induced by 1,520 drugs in A549 lung cancer cells. Although only 26% of the drugs inhibited cell growth, 86% caused intracellular metabolic changes, which were largely conserved in two additional cancer cell lines. By testing more than 3.4 million drug-metabolite dependencies, we generated a lookup table of drug interference with metabolism, enabling high-throughput characterization of compounds across drug therapeutic classes in a single-pass screen. The identified metabolic changes revealed previously unknown effects of drugs, expanding their MoA annotations and potential therapeutic applications. We confirmed metabolome-based predictions for four new glucocorticoid receptor agonists, two unconventional 3-hydroxy-3-methylglutaryl-CoA (HMGCR) inhibitors and two dihydroorotate dehydrogenase (DHODH) inhibitors. Furthermore, we demonstrated that metabolome profiling complements other phenotypic and molecular profiling technologies, opening opportunities to increase the efficiency, scale and accuracy of preclinical drug discovery.

Advancing Anticancer Drug Discovery: Leveraging Metabolomics and Machine Learning for Mode of Action Prediction by Pattern Recognition

A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). Metabolomics combined with machine learning allowed to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate cancer cells (PC-3). As proof of concept, 38 drugs are studied with known effects on 16 key processes of cancer metabolism, profiling low molecular weight intermediates of the central carbon and cellular energy metabolism (CCEM) by LC-MS/MS. These metabolic patterns unveiled distinct MoAs, enabling accurate MoA predictions for novel agents by machine learning. The transferability of MoA predictions based on PC-3 cell treatments is validated with two other cancer cell models, i.e., breast cancer and Ewing's sarcoma, and show that correct MoA predictions for alternative cancer cells are possible, but still at some expense of prediction quality. Furthermore, metabolic profiles of treated cells yield insights into intracellular processes, exemplified for drugs inducing different types of mitochondrial dysfunction. Specifically, it is predicted that pentacyclic triterpenes inhibit oxidative phosphorylation and affect phospholipid biosynthesis, as confirmed by respiration parameters, lipidomics, and molecular docking. Using biochemical insights from individual drug treatments, this approach offers new opportunities, including the optimization of combinatorial drug applications.

Multiwavelength Surface-Enhanced Raman Scattering Fingerprints of Human Urine for Cancer Diagnosis

Label-free surface-enhanced Raman spectroscopy (SERS) is capable of capturing rich compositional information from complex biosamples by providing vibrational spectra that are crucial for biosample identification. However, increasing complexity and subtle variations in biological media can diminish the discrimination accuracy of traditional SERS excited by a single laser wavelength. Herein, we introduce a multiwavelength SERS approach combined with machine learning (ML)-based classification to improve the discrimination accuracy of human urine specimens for bladder cancer (BCa) diagnosis. This strategy leverages the excitation-wavelength-dependent SERS spectral profiles of complex matrices, which are mainly attributed to wavelength-related vibrational changes in individual analytes and differences in the variation ratios of SERS intensity across different wavelengths among various analytes. By capturing SERS fingerprints under multiple excitation wavelengths, we can acquire more comprehensive and unique chemical information on complex samples. Further experimental examinations with clinical urine specimens, supported by ML algorithms, demonstrate the effectiveness of this multiwavelength strategy and improve the diagnostic accuracy of BCa and staging of its invasion with SERS spectra from increasing numbers of wavelengths. The multiwavelength SERS holds promise as a convenient, cost-effective, and broadly applicable technique for the precise identification of complex matrices and diagnosis of diseases based on body fluids.

Plasma metabolomics and lipidomics reveal potential novel biomarkers in early gastric cancer: An explorative study

BACKGROUND

Early identification and therapy can significantly improve the outcome for gastric cancer. However, there is still no perfect biomarker available for the detection of early gastric cancer. This study aimed to investigate the alterations in the plasma metabolites of early gastric cancer using metabolomics and lipidomics based on high-performance liquid chromatography-mass spectrometry (HPLC-MS), which detected potential biomarkers that could be used for clinical diagnosis.

METHODS

To investigate the changes in metabolomics and lipidomics, a total of 30 plasma samples were collected, consisting of 15 patients with early gastric cancer and 15 healthy controls. Extensive HPLC-MS-based untargeted metabolomic and lipidomic investigations were conducted. Differential metabolites and metabolic pathways were uncovered through the utilization of statistical analysis and bioinformatics analysis. Candidate biomarker screening was performed using support vector machine-based multivariate receiver operating characteristic analysis.

RESULTS

A disturbance was observed in a combined total of 19 metabolites and 67 lipids of the early gastric cancer patients. The analysis of KEGG pathways showed that the early gastric cancer patients experienced disruptions in the arginine biosynthesis pathway, the pathway for alanine, aspartate, and glutamate metabolism, as well as the pathway for glyoxylate and dicarboxylate metabolism. Plasma metabolomics and lipidomics have identified multiple biomarker panels that can effectively differentiate early gastric cancer patients from healthy controls, exhibiting an area under the curve exceeding 0.9.

CONCLUSION

These metabolites and lipids could potentially serve as biomarkers for the screening of early gastric cancer, thereby optimizing the strategy for the detection of early gastric cancer. The disrupted pathways implicated in early gastric cancer provide new clues for additional understanding of gastric cancer's pathogenesis. Nonetheless, large-scale clinical data are required to prove our findings.

Metabolomics and cellular altered pathways in cancer biology: A review

Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other "-omics" technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

Machine learning-based screening and validation of liver metastasis-specific genes in colorectal cancer

Colorectal liver metastasis (CRLM) is challenging in the clinical treatment of colorectal cancer. Limited research has been conducted on how CRLM develops. RNA sequencing data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Four machine learning algorithms were used to screen the hub CRLM-specific genes, including Least Absolute Shrinkage and Selection Operator (Lasso), Random forest, SVM-RFE, and XGboost. The model for identifying CRLM was developed using stepwise logistic regression and was validated using internal and independent datasets. The prognostic value of hub CRLM-specific genes was assessed using the Lasso-Cox method. The in vitro experiments were performed using SW620 cells. The CRLM identification model was developed based on four CRLM-specific genes (SPP1, ZG16, P2RY14, and PRKAR2B), and the model efficacy was validated using GSE41258 and three external cohorts. Five CRLM-specific prognostic hub genes, SPP1, ZG16, P2RY14, CYP2E1, and C5, were identified using the Lasso-Cox algorithm, and a risk score was constructed. The risk score was validated using the GSE39582 cohort. Three genes have both efficacy in identifying CRLM and prognostic value: ZG16, P2RY14, and SPP1. Immune infiltration and enrichment analyses demonstrated that SPP1 was associated with M2 macrophage polarization and extracellular matrix remodeling. In vitro experiments indicated that SPP1 may act as a cancer-promoting factor. The hub CRLM-specific gene SPP1 can help determine the diagnosis, prognosis, and immune infiltration of patients with CRLM.

Including glutamine in a resource allocation model of energy metabolism in cancer and yeast cells

Energy metabolism is crucial for all living cells, especially during fast growth or stress scenarios. Many cancer and activated immune cells (Warburg effect) or yeasts (Crabtree effect) mostly rely on aerobic glucose fermentation leading to lactate or ethanol, respectively, to generate ATP. In recent years, several mathematical models have been proposed to explain the Warburg effect on theoretical grounds. Besides glucose, glutamine is a very important substrate for eukaryotic cells-not only for biosynthesis, but also for energy metabolism. Here, we present a minimal constraint-based stoichiometric model for explaining both the classical Warburg effect and the experimentally observed respirofermentation of glutamine (WarburQ effect). We consider glucose and glutamine respiration as well as the respective fermentation pathways. Our resource allocation model calculates the ATP production rate, taking into account enzyme masses and, therefore, pathway costs. While our calculation predicts glucose fermentation to be a superior energy-generating pathway in human cells, different enzyme characteristics in yeasts reduce this advantage, in some cases to such an extent that glucose respiration is preferred. The latter is observed for the fungal pathogen Candida albicans, which is a known Crabtree-negative yeast. Further, optimization results show that glutamine is a valuable energy source and important substrate under glucose limitation, in addition to its role as a carbon and nitrogen source of biomass in eukaryotic cells. In conclusion, our model provides insights that glutamine is an underestimated fuel for eukaryotic cells during fast growth and infection scenarios and explains well the observed parallel respirofermentation of glucose and glutamine in several cell types.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Profiling of serum metabolome of breast cancer: multi-cancer features discriminate between healthy women and patients with breast cancer

Introduction

The progression of solid cancers is manifested at the systemic level as molecular changes in the metabolome of body fluids, an emerging source of cancer biomarkers.

Methods

We analyzed quantitatively the serum metabolite profile using high-resolution mass spectrometry. Metabolic profiles were compared between breast cancer patients (n=112) and two groups of healthy women (from Poland and Norway; n=95 and n=112, respectively) with similar age distributions.

Results

Despite differences between both cohorts of controls, a set of 43 metabolites and lipids uniformly discriminated against breast cancer patients and healthy women. Moreover, smaller groups of female patients with other types of solid cancers (colorectal, head and neck, and lung cancers) were analyzed, which revealed a set of 42 metabolites and lipids that uniformly differentiated all three cancer types from both cohorts of healthy women. A common part of both sets, which could be called a multi-cancer signature, contained 23 compounds, which included reduced levels of a few amino acids (alanine, aspartate, glutamine, histidine, phenylalanine, and leucine/isoleucine), lysophosphatidylcholines (exemplified by LPC(18:0)), and diglycerides. Interestingly, a reduced concentration of the most abundant cholesteryl ester (CE(18:2)) typical for other cancers was the least significant in the serum of breast cancer patients. Components present in a multi-cancer signature enabled the establishment of a well-performing breast cancer classifier, which predicted cancer with a very high precision in independent groups of women (AUC>0.95).

Discussion

In conclusion, metabolites critical for discriminating breast cancer patients from controls included components of hypothetical multi-cancer signature, which indicated wider potential applicability of a general serum metabolome cancer biomarker.

Integrated Metabolomic and transcriptomic analyses reveal deoxycholic acid promotes transmissible gastroenteritis virus infection by inhibiting phosphorylation of NF-κB and STAT3

BACKGROUND

Acute diarrhea, dehydration and death in piglets are all symptoms of transmissible gastroenteritis virus (TGEV), which results in significant financial losses in the pig industry. It is important to understand the pathogenesis and identify new antiviral targets by revealing the metabolic interactions between TGEV and host cells.

RESULTS

We performed metabolomic and transcriptomic analyses of swine testicular cells infected with TGEV. A total of 1339 differential metabolites and 206 differentially expressed genes were detected post TEGV infection. The differentially expressed genes were significantly enriched in the HIF-1 signaling pathway and PI3K-Akt signaling. Integrated analysis of differentially expressed genes and differential metabolites indicated that they were significantly enriched in the metabolic processes such as nucleotide metabolism, biosynthesis of cofactors and purine metabolism. In addition, the results showed that most of the detected metabolites involved in the bile secretion was downregulated during TGEV infection. Furthermore, exogenous addition of key metabolite deoxycholic acid (DCA) significantly enhanced TGEV replication by NF-κB and STAT3 signal pathways.

CONCLUSIONS

We identified a significant metabolite, DCA, related to TGEV replication. It added TGEV replication in host cells by inhibiting phosphorylation of NF-κB and STAT3. This study provided novel insights into the metabolomic and transcriptomic alterations related to TGEV infection and revealed potential molecular and metabolic targets for the regulation of TGEV infection.

Predictive biomarkers for the early detection and management of heart failure

Cardiovascular disease (CVD) is a serious public health concern whose incidence has been on a rise and is projected by the World Health Organization to be the leading global cause of mortality by 2030. Heart failure (HF) is a complicated syndrome resulting from various CVDs of heterogeneous etiologies and exhibits varying pathophysiology, including activation of inflammatory signaling cascade, apoptosis, fibrotic pathway, and neuro-humoral system, thereby leading to compromised cardiac function. During this process, several biomolecules involved in the onset and progression of HF are released into circulation. These circulating biomolecules could serve as unique biomarkers for the detection of subclinical changes and can be utilized for monitoring disease severity. Hence, it is imperative to identify these biomarkers to devise an early predictive strategy to stop the deterioration of cardiac function caused by these complex cellular events. Furthermore, measurement of multiple biomarkers allows clinicians to divide HF patients into sub-groups for treatment and management based on early health outcomes. The present article provides a comprehensive overview of current omics platform available for discovering biomarkers for HF management. Some of the existing and novel biomarkers for the early detection of HF with special reference to endothelial biology are also discussed.

Nanotechnology Reprogramming Metabolism for Enhanced Tumor Immunotherapy

Mutation burden, hypoxia, and immunoediting contribute to altered metabolic profiles in tumor cells, resulting in a tumor microenvironment (TME) characterized by accumulation of toxic metabolites and depletion of various nutrients, which significantly hinder the antitumor immunity via multiple mechanisms, hindering the efficacy of tumor immunotherapies. In-depth investigation of the mechanisms underlying these phenomena are vital for developing effective antitumor drugs and therapies, while the therapeutic effects of metabolism-targeting drugs are restricted by off-target toxicity toward effector immune cells and high dosage-mediated side effects. Nanotechnologies, which exhibit versatility and plasticity in targeted delivery and metabolism modulation, have been widely applied to boost tumor immunometabolic therapies via multiple strategies, including targeting of metabolic pathways. In this review, recent advances in understanding the roles of tumor cell metabolism in both immunoevasion and immunosuppression are reviewed, and nanotechnology-based metabolic reprogramming strategies for enhanced tumor immunotherapies are discussed.

Aldolase A promotes cervical cancer cell radioresistance by regulating the glycolysis and DNA damage after irradiation

Radioresistance is the major obstacle that affects the efficacy of radiotherapy which is an important treatment for cervical cancer. By analyzing the databases, we found that aldolase A (ALDOA), which is a key enzyme in metabolic reprogramming, has a higher expression in cervical cancer patients and is associated with poor prognosis. We detected the expression of ALDOA in the constructed cervical cancer radioresistance (RR) cells by repetitive irradiation and found that it was upregulated compared to the control cells. Functional assays were conducted and the results showed that the knockdown of ALDOA in cervical cancer RR cells inhibited the proliferation, migration, and clonogenic abilities by regulating the cell glycolysis. In addition, downregulation of ALDOA enhanced radiation-induced apoptosis and DNA damage by causing G2/M phase arrest and further promoted radiosensitivity of cervical cancer cells. The functions of ALDOA in regulating tumor radiosensitivity were also verified by the mouse tumor transplantation model in vivo. Therefore, our study provides new insights into the functions of ALDOA in regulating the efficacy of radiotherapy and indicates that ALDOA might be a promising target for enhancing radiosensitivity in treating cervical cancer patients.

O-GlcNAc transferase promotes glioblastoma by modulating genes responsible for cell survival, invasion, and inflammation

Metabolic reprogramming has emerged as one of the key hallmarks of cancer cells. Various metabolic pathways are dysregulated in cancers, including the hexosamine biosynthesis pathway. Protein O-GlcNAcylation is catalyzed by the enzyme O-GlcNAc transferase (OGT), an effector of hexosamine biosynthesis pathway that is found to be upregulated in most cancers. Posttranslational O-GlcNAcylation of various signaling and transcriptional regulators could promote cancer cell maintenance and progression by regulating gene expression, as gene-specific transcription factors and chromatin regulators are among the most highly O-GlcNAcylated proteins. Here, we investigated the role of OGT in glioblastoma. We demonstrate that OGT knockdown and chemical inhibition led to reduced glioblastoma cell proliferation and downregulation of many genes known to play key roles in glioblastoma cell proliferation, migration, and invasion. We show that genes downregulated due to OGT reduction are also known to be transcriptionally regulated by transcriptional initiation/elongation cofactor BRD4. We found BRD4 to be O-GlcNAcylated in glioblastoma cells; however, OGT knockdown/inhibition neither changed its expression nor its chromatin association on promoters. Intriguingly, we observed OGT knockdown led to reduced Pol II-Ser2P chromatin association on target genes without affecting other transcription initiation/elongation factors. Finally, we found that chemical inhibition of BRD4 potentiated the effects of OGT inhibition in reducing glioblastoma cell proliferation, invasion, and migration. We propose BRD4 and OGT act independently in the transcriptional regulation of a common set of genes and that combined inhibition of OGT and BRD4 could be utilized therapeutically for more efficient glioblastoma cell targeting than targeting of either protein alone.

Host metabolome predicts the severity and onset of acute toxicities induced by CAR T-cell therapy

Anti-CD19 chimeric antigen receptor (CAR) T-cell therapy is a highly effective treatment option for patients with relapsed/refractory large B-cell lymphoma. However, widespread use is deterred by the development of clinically significant acute inflammatory toxicities, including cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), that induce significant morbidity and require close monitoring. Identification of host biochemical signatures that predict the severity and time-to-onset of CRS and ICANS may assist patient stratification to enable timely mitigation strategies. Here, we report pretreatment host metabolites that are associated with CRS and ICANS induced by axicabtagene ciloleucel or tisagenlecleucel therapy. Both untargeted metabolomics analysis and validation using targeted assays revealed a significant association between the abundance of specific pretreatment biochemical entities and an increased risk and/or onset of clinically significant CRS (q < .1) and ICANS (q < .25). Higher pretreatment levels of plasma glucose and lower levels of cholesterol and glutamate were associated with a faster onset of CRS. In contrast, low baseline levels of the amino acids proline and glycine and the secondary bile acid isoursodeoxycholate were significantly correlated with clinically significant CRS. Lower concentration of the amino acid hydroxyproline was associated with higher grade and faster onset of ICANS, whereas low glutamine was negatively correlated with faster development of ICANS. Overall, our data indicate that the pretreatment host metabolome has biomarker potential in determining the risk of clinically significant CRS and ICANS, and may be useful in risk stratification of patients before anti-CD19 CAR T-cell therapy.

Breast cancers as ecosystems: a metabolic perspective

Breast cancer (BC) is the most frequently diagnosed cancer and one of the major causes of cancer death. Despite enormous progress in its management, both from the therapeutic and early diagnosis viewpoints, still around 700,000 patients succumb to the disease each year, worldwide. Late recurrency is the major problem in BC, with many patients developing distant metastases several years after the successful eradication of the primary tumor. This is linked to the phenomenon of metastatic dormancy, a still mysterious trait of the natural history of BC, and of several other types of cancer, by which metastatic cells remain dormant for long periods of time before becoming reactivated to initiate the clinical metastatic disease. In recent years, it has become clear that cancers are best understood if studied as ecosystems in which the impact of non-cancer-cell-autonomous events-dependent on complex interaction between the cancer and its environment, both local and systemic-plays a paramount role, probably as significant as the cell-autonomous alterations occurring in the cancer cell. In adopting this perspective, a metabolic vision of the cancer ecosystem is bound to improve our understanding of the natural history of cancer, across space and time. In BC, many metabolic pathways are coopted into the cancer ecosystem, to serve the anabolic and energy demands of the cancer. Their study is shedding new light on the most critical aspect of BC management, of metastatic dissemination, and that of the related phenomenon of dormancy and fostering the application of the knowledge to the development of metabolic therapies.

FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data

Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.

Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer

Introduction

Glucose and glutamine are major carbon and energy sources that promote the rapid proliferation of cancer cells. Metabolic shifts observed on cell lines or mouse models may not reflect the general metabolic shifts in real human cancer tissue.

Method

In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including the glycolytic pathway, production of lactate, tricarboxylic acid (TCA) cycle, nucleic acid synthesis, glutaminolysis, glutamate, glutamine, and glutathione metabolism, and amino acid synthesis, in 11 cancer subtypes and nine matched adjacent normal tissue types using TCGA transcriptomics data.

Result

Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of the TCA cycle, i.e., the Warburg effect, in almost all the analyzed cancer. However, increased lactate production and the second half of the TCA cycle were only seen in certain cancer types. More interestingly, we failed to detect significantly altered glutaminolysis in cancer tissues compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes; (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls; and (3) the different shifts in tissue-specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression.

Discussion

This study strongly suggests the possibility of having a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.

Metabolic Footprinting-Based DNA-AuNP Encoders for Extracellular Metabolic Response Profiling

Metabolic footprinting as a convenient and non-invasive cell metabolomics strategy relies on monitoring the whole extracellular metabolic process. It covers nutrient consumption and metabolite secretion of in vitro cell culture, which is hindered by low universality owing to pre-treatment of the cell medium and special equipment. Here, we report the design and a variety of applicability, for quantifying extracellular metabolism, of fluorescently labeled single-stranded DNA (ssDNA)-AuNP encoders, whose multi-modal signal response is triggered by extracellular metabolites. We constructed metabolic response profiling of cells by detecting extracellular metabolites in different tumor cells and drug-induced extracellular metabolites. We further assessed the extracellular metabolism differences using a machine learning algorithm. This metabolic response profiling based on the DNA-AuNP encoder strategy is a powerful complement to metabolic footprinting, which significantly applies potential non-invasive identification of tumor cell heterogeneity.

Parallelized multidimensional analytic framework applied to mammary epithelial cells uncovers regulatory principles in EMT

A proper understanding of disease etiology will require longitudinal systems-scale reconstruction of the multitiered architecture of eukaryotic signaling. Here we combine state-of-the-art data acquisition platforms and bioinformatics tools to devise PAMAF, a workflow that simultaneously examines twelve omics modalities, i.e., protein abundance from whole-cells, nucleus, exosomes, secretome and membrane; N-glycosylation, phosphorylation; metabolites; mRNA, miRNA; and, in parallel, single-cell transcriptomes. We apply PAMAF in an established in vitro model of TGFβ-induced epithelial to mesenchymal transition (EMT) to quantify >61,000 molecules from 12 omics and 10 timepoints over 12 days. Bioinformatics analysis of this EMT-ExMap resource allowed us to identify; -topological coupling between omics, -four distinct cell states during EMT, -omics-specific kinetic paths, -stage-specific multi-omics characteristics, -distinct regulatory classes of genes, -ligand-receptor mediated intercellular crosstalk by integrating scRNAseq and subcellular proteomics, and -combinatorial drug targets (e.g., Hedgehog signaling and CAMK-II) to inhibit EMT, which we validate using a 3D mammary duct-on-a-chip platform. Overall, this study provides a resource on TGFβ signaling and EMT.

Gene length is a pivotal feature to explain disparities in transcript capture between single transcriptome techniques

The scale and capability of single-cell and single-nucleus RNA-sequencing technologies are rapidly growing, enabling key discoveries and large-scale cell mapping operations. However, studies directly comparing technical differences between single-cell and single-nucleus RNA sequencing are still lacking. Here, we compared three paired single-cell and single-nucleus transcriptomes from three different organs (Heart, Lung and Kidney). Differently from previous studies that focused on cell classification, we explored disparities in the transcriptome output of whole cells relative to the nucleus. We found that the major cell clusters could be recovered by either technique from matched samples, but at different proportions. In 2/3 datasets (kidney and lung) we detected clusters exclusively present with single-nucleus RNA sequencing. In all three organ groups, we found that genomic and gene structural characteristics such as gene length and exon content significantly differed between the two techniques. Genes recovered with the single-nucleus RNA sequencing technique had longer sequence lengths and larger exon counts, whereas single-cell RNA sequencing captured short genes at higher rates. Furthermore, we found that when compared to the whole host genome (mouse for kidney and lung datasets and human for the heart dataset), single transcriptomes obtained with either technique skewed from the expected proportions in several points: a) coding sequence length, b) transcript length and c) genomic span; and d) distribution of genes based on exons counts. Interestingly, the top-100 DEG between the two techniques returned distinctive GO terms. Hence, the type of single transcriptome technique used affected the outcome of downstream analysis. In summary, our data revealed both techniques present disparities in RNA capture. Moreover, the biased RNA capture affected the calculations of basic cellular parameters, raising pivotal points about the limitations and advantages of either single transcriptome techniques.

Distinct T helper cell-mediated antitumor immunity: T helper 2 cells in focus

The adaptive arm of the immune system is crucial for appropriate antitumor immune responses. It is generally accepted that clusters of differentiation 4+ (CD4+) T cells, which mediate T helper (Th) 1 immunity (type 1 immunity), are the primary Th cell subtype associated with tumor elimination. In this review, we discuss evidence showing that antitumor immunity and better prognosis can be associated with distinct Th cell subtypes in experimental mouse models and humans, with a focus on Th2 cells. The aim of this review is to provide an overview and understanding of the mechanisms associated with different tumor outcomes in the face of immune responses by focusing on the (1) site of tumor development, (2) tumor properties (i. e., tumor metabolism and cytokine receptor expression), and (3) type of immune response that the tumor initially escaped. Therefore, we discuss how low-tolerance organs, such as lungs and brains, might benefit from a less tissue-destructive immune response mediated by Th2 cells. In addition, Th2 cells antitumor effects can be independent of CD8+ T cells, which would circumvent some of the immune escape mechanisms that tumor cells possess, like low expression of major histocompatibility-I (MHC-I). Finally, this review aims to stimulate further studies on the role of Th2 cells in antitumor immunity and briefly discusses emerging treatment options.

Metabolomics in Cell Biology

Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.

Probing Folate-Responsive and Stage-Sensitive Metabolomics and Transcriptional Co-Expression Network Markers to Predict Prognosis of Non-Small Cell Lung Cancer Patients

Tumour metabolomics and transcriptomics co-expression network as related to biological folate alteration and cancer malignancy remains unexplored in human non-small cell lung cancers (NSCLC). To probe the diagnostic biomarkers, tumour and pair lung tissue samples (n = 56) from 97 NSCLC patients were profiled for ultra-performance liquid chromatography tandem mass spectrometry (UPLC/MS/MS)-analysed metabolomics, targeted transcriptionomics, and clinical folate traits. Weighted Gene Co-expression Network Analysis (WGCNA) was performed. Tumour lactate was identified as the top VIP marker to predict advance NSCLC (AUC = 0.765, Sig = 0.017, CI 0.58-0.95). Low folate (LF)-tumours vs. adjacent lungs displayed higher glycolytic index of lactate and glutamine-associated amino acids in enriched biological pathways of amino sugar and glutathione metabolism specific to advance NSCLCs. WGCNA classified the green module for hub serine-navigated glutamine metabolites inversely associated with tumour and RBC folate, which module metabolites co-expressed with a predominant up-regulation of LF-responsive metabolic genes in glucose transport (GLUT1), de no serine synthesis (PHGDH, PSPH, and PSAT1), folate cycle (SHMT1/2 and PCFR), and down-regulation in glutaminolysis (SLC1A5, SLC7A5, GLS, and GLUD1). The LF-responsive WGCNA markers predicted poor survival rates in lung cancer patients, which could aid in optimizing folate intervention for better prognosis of NSCLCs susceptible to folate malnutrition.

Integrated Analysis of Gene Expression and Metabolite Data Reveals Candidate Molecular Markers in Colorectal Carcinoma

Background: This study investigated potential gene targets and metabolite markers associated with colorectal carcinoma (CRC). Materials & Methods: Gene expression data (GSE110224) related with CRC were obtained from Gene Expression Omnibus, including 17 tumor tissues and 17 normal colon ones. The gene differential analysis, functional analysis, protein-protein interaction (PPI) analysis, and metabolite network construction were performed to identify key genes related to CRC. Moreover, an external dataset was used to validate genes of interest in CRC, and corresponding survival analysis was also conducted. Results: The authors extracted 197 differentially expressed genes (75 upregulated and 122 downregulated genes). Moreover, upregulated genes were closely associated with rheumatoid arthritis and amoebiasis pathways. The downregulated genes were mainly related to bile secretion and proximal tubule bicarbonate reclamation pathway. Combined with PPI network and metabolite prediction, the overlapped nine genes (CXCL1, CXCL8, CXCL10, HDS1782, IL18, PCK1, PTGS2, SERPINB2, TMP1) were found to be critical in CRC. Similar gene expression profiles of nine critical genes were validated by an external dataset, except for SERPINB2. In addition, the expressions of TIMP1, IL1B, and PTGS2 were closely related with prognosis. Finally, the metabolite network analysis revealed that there were close associations between prostaglandin E2 and three pathways (rheumatoid arthritis, amoebiasis, and leishmaniasis). Conclusion: CXCL1/CXCL8/IL1B/PTGS2-prostaglandin E2 axes were the potential signatures involved in CRC progression, which could provide new insights to understand the molecular mechanisms of CRC.

Heterogeneity of the cancer cell line metabolic landscape

The unravelling of the complexity of cellular metabolism is in its infancy. Cancer-associated genetic alterations may result in changes to cellular metabolism that aid in understanding phenotypic changes, reveal detectable metabolic signatures, or elucidate vulnerabilities to particular drugs. To understand cancer-associated metabolic transformation, we performed untargeted metabolite analysis of 173 different cancer cell lines from 11 different tissues under constant conditions for 1,099 different species using mass spectrometry (MS). We correlate known cancer-associated mutations and gene expression programs with metabolic signatures, generating novel associations of known metabolic pathways with known cancer drivers. We show that metabolic activity correlates with drug sensitivity and use metabolic activity to predict drug response and synergy. Finally, we study the metabolic heterogeneity of cancer mutations across tissues, and find that genes exhibit a range of context specific, and more general metabolic control.

A functional analysis of 180 cancer cell lines reveals conserved intrinsic metabolic programs

Cancer cells reprogram their metabolism to support growth and invasion. While previous work has highlighted how single altered reactions and pathways can drive tumorigenesis, it remains unclear how individual changes propagate at the network level and eventually determine global metabolic activity. To characterize the metabolic lifestyle of cancer cells across pathways and genotypes, we profiled the intracellular metabolome of 180 pan-cancer cell lines grown in identical conditions. For each cell line, we estimated activity for 49 pathways spanning the entirety of the metabolic network. Upon clustering, we discovered a convergence into only two major metabolic types. These were functionally confirmed by 13 C-flux analysis, lipidomics, and analysis of sensitivity to perturbations. They revealed that the major differences in cancers are associated with lipid, TCA cycle, and carbohydrate metabolism. Thorough integration of these types with multiomics highlighted little association with genetic alterations but a strong association with markers of epithelial-mesenchymal transition. Our analysis indicates that in absence of variations imposed by the microenvironment, cancer cells adopt distinct metabolic programs which serve as vulnerabilities for therapy.

DrDimont: explainable drug response prediction from differential analysis of multi-omics networks

MOTIVATION

While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem.

RESULTS

We present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e. molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response.

AVAILABILITY AND IMPLEMENTATION

DrDimont is available on CRAN: https://cran.r-project.org/package=DrDimont.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sorting-free metabolic profiling uncovers the vulnerability of fatty acid β-oxidation in in vitro quiescence models

Quiescent cancer cells are rare nondiving cells with the unique ability to evade chemotherapies and resume cell division after treatment. Despite the associated risk of cancer recurrence, how cells can reversibly switch between rapid proliferation and quiescence remains a long-standing open question. By developing a unique methodology for the cell sorting-free separation of metabolic profiles in cell subpopulations in vitro, we unraveled metabolic characteristics of quiescent cells that are largely invariant to basal differences in cell types and quiescence-inducing stimuli. Consistent with our metabolome-based analysis, we show that impairing mitochondrial fatty acid β-oxidation (FAO) can induce apoptosis in quiescence-induced cells and hamper their return to proliferation. Our findings suggest that in addition to mediating energy and redox balance, FAO can play a role in preventing the buildup of toxic intermediates during transitioning to quiescence. Uncovering metabolic strategies to enter, maintain, and exit quiescence can reveal fundamental principles in cell plasticity and new potential therapeutic targets beyond cancer.

Blockade of the amino acid transporter SLC6A14 suppresses tumor growth in colorectal Cancer

Background

The amino acid transporter SLC6A14, which transports 18 of the 20 proteinogenic amino acids, is too low to be detected in healthy normal tissues but is significantly increased in some solid cancers. However, little is known about the roles of SLC6A14 in colorectal cancer (CRC).

Methods

The mRNA and protein levels of SLC6A14 were detected using TCGA database, real-time polymerase chain reaction, western blot, and tissue microarrays, respectively. Amino acids concentration was determined by LC-MS/MS. Cell proliferation and apoptosis were determined using MTT assay and flow cytometry. Transwell invasion assay and wound healing assay were employed to analyze cell migration and invasion. The protein levels of Akt-mTOR signaling pathway and MMPs proteins were detected by western blot.

Results

Both of the mRNA and protein levels of SLC6A14 were upregulated in CRC tissues, and the protein levels of SLC6A14 were closely related to the tumor cells differentiation: the higher the expression of SLC6A14 was, the poorer the differentiation of the tumor cells was. Further knockdown SLC6A14 with siRNA or treatment with α-MT in CRC cell lines reduced cell proliferation and migration in vitro and inhibited xenograft tumor growth in vivo. Mechanistically, SLC6A14 was demonstrated to regulate the expression and phosphorylation of Akt-mTOR, which mediates the promoting tumor growth function of SLC6A14. Blockade of SLC6A14 with α-MT inhibited the activation of mTOR signaling.

Conclusion

SLC6A14 was upregulated in CRC and could promote tumor progression by activating the Akt-mTOR signaling pathway, which may serve as an effective molecular target for the treatment of CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09935-0.

Multiomics reveal the central role of pentose phosphate pathway in resident thymic macrophages to cope with efferocytosis-associated stress

Tissue-resident macrophages (TRMs) are heterogeneous cell populations found throughout the body. Depending on their location, they perform diverse functions maintaining tissue homeostasis and providing immune surveillance. To survive and function within, TRMs adapt metabolically to the distinct microenvironments. However, little is known about the metabolic signatures of TRMs. The thymus provides a nurturing milieu for developing thymocytes yet efficiently removes those that fail the selection, relying on the resident thymic macrophages (TMφs). This study harnesses multiomics analyses to characterize TMφs and unveils their metabolic features. We find that the pentose phosphate pathway (PPP) is preferentially activated in TMφs, responding to the reduction-oxidation demands associated with the efferocytosis of dying thymocytes. The blockade of PPP in Mφs leads to decreased efferocytosis, which can be rescued by reactive oxygen species (ROS) scavengers. Our study reveals the key role of the PPP in TMφs and underscores the importance of metabolic adaptation in supporting Mφ efferocytosis.

Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer

The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.

Precise Metabolomics Reveals a Diversity of Aging-Associated Metabolic Features

Mass spectrometry-based metabolomics has emerged as a powerful technique for biomedical research, although technical issues with its analytical precision and structural characterization remain. Herein, a robust non-targeted strategy for accurate quantitation and precise profiling of metabolomes is developed and applied to investigate plasma metabolic features associated with human aging. A comprehensive set of isotope-labeled standards (ISs) covering major metabolic pathways is incorporated to quantify polar metabolites. Matching rules to select ISs for calibration follow a primary criterion of minimal coefficients of variations (COVs). If minimal COVs between specific ISs for a particular metabolite fall within 5% window, a further selection of ISs is conducted based on structural similarities and proximity in retention time. The introduction and refined selection of appropriate ISs for quantitation reduces the COVs of 480 identified metabolites in quality control samples from 14.3% to 9.8% and facilitates identification of additional metabolite. Finally, the precise metabolomics approach reveals perturbations in a diverse array of metabolic pathways across aging that principally implicate steroid metabolism, amino acid metabolism, lipid metabolism, and purine metabolism, which allows the authors to draw correlates to the pathology of various age-related diseases. These findings provide clues for the prevention and treatment of these age-related diseases.

Krüppel-like factor 15 integrated autophagy and gluconeogenesis to maintain glucose homeostasis under 20-hydroxyecdysone regulation

The regulation of glycometabolism homeostasis is vital to maintain health and development of animal and humans; however, the molecular mechanisms by which organisms regulate the glucose metabolism homeostasis from a feeding state switching to a non-feeding state are not fully understood. Using the holometabolous lepidopteran insect Helicoverpa armigera, cotton bollworm, as a model, we revealed that the steroid hormone 20-hydroxyecdysone (20E) upregulated the expression of transcription factor Krüppel-like factor (identified as Klf15) to promote macroautophagy/autophagy, apoptosis and gluconeogenesis during metamorphosis. 20E via its nuclear receptor EcR upregulated Klf15 transcription in the fat body during metamorphosis. Knockdown of Klf15 using RNA interference delayed pupation and repressed autophagy and apoptosis of larval fat body during metamorphosis. KLF15 promoted autophagic flux and transiting to apoptosis. KLF15 bound to the KLF binding site (KLF bs) in the promoter of Atg8 (autophagy-related gene 8/LC3) to upregulate Atg8 expression. Knockdown Atg8 reduced free fatty acids (FFAs), glycerol, free amino acids (FAAs) and glucose levels. However, knockdown of Klf15 accumulated FFAs, glycerol, and FAAs. Glycolysis was switched to gluconeogenesis, trehalose and glycogen synthesis were changed to degradation during metamorphosis, which were accompanied by the variation of the related genes expression. KLF15 upregulated phosphoenolpyruvate carboxykinase (Pepck) expression by binding to KLF bs in the Pepck promoter for gluconeogenesis, which utilised FFAs, glycerol, and FAAs directly or indirectly to increase glucose in the hemolymph. Taken together, 20E via KLF15 integrated autophagy and gluconeogenesis by promoting autophagy-related and gluconeogenesis-related genes expression.

Glucose is the direct substrate for energy production in animal and humans. Autophagy and gluconeogenesis are known to help organisms maintaining energy substrates; however, the mechanism of integration of autophagy and gluconeogenesis is unclear. Holometabolous insects stop feeding during metamorphosis under steroid hormone 20-hydroxyecdysone (20E) regulation, providing a good model for the study. Using lepidopteran insect Helicoverpa armigera, cotton bollworm, as a model, we revealed that Krüppel-like factor 15 (KLF15) integrated autophagy and gluconeogenesis to maintain glucose homeostasis under 20E regulation. 20E increased Klf15 expression, and KLF15 in turn promoted autophagy-related and gluconeogenesis-related genes expression during metamorphosis. Autophagy and apoptosis of the fat body provided substrates for gluconeogenesis. This work clarified the important functions and mechanisms of KLF15 in autophagy and glycometabolism reprogramming for glucose homeostasis after feeding stop during insect metamorphosis.

Combination strategies to target metabolic flexibility in cancer

Therapeutically interfering with metabolic pathways has great merit to curtail tumor growth because the demand for copious amounts of energy for growth-supporting biomass production is common to all cancer entities. A major impediment to a straight implementation of metabolic cancer therapy is the metabolic flexibility and plasticity of cancer cells (and their microenvironment) resulting in therapy resistance and evasion. Metabolic combination therapies, therefore, are promising as they are designed to target several energetic routes simultaneously and thereby diminish the availability of alternative substrates. Thus, dietary restrictions, specific nutrient limitations, and/or pharmacological interventions impinging on metabolic pathways can be combined to improve cancer treatment efficacy, to overcome therapy resistance, or even act as a preventive measure. Here, we review the most recent developments in metabolic combination therapies particularly highlighting in vivo reports of synergistic effects and available clinical data. We close with identifying the challenges of the field (metabolic tumor heterogeneity, immune cell interactions, inter-patient variabilities) and suggest a "metabo-typing" strategy to tailor evidence-based metabolic combination therapies to the energetic requirements of the tumors and the patient's nutritional habits and status.

Differences in glucose concentration shows new perspectives in gastric cancer metabolism

Gastric cancer (GC) is among the deadliest cancers worldwide despite available therapies, highlighting the need for novel therapies and pharmacological agents. Metabolic deregulation is a potential study area for new anticancer targets, but the in vitro metabolic studies are controversial, as different ranges of glucose used in the culture medium can influence results. In this study, we evaluated cellular viability, glucose uptake, and LDH activity in gastric cell lines when exposed to different glucose concentrations: high (HG, 25 mM), low (LG, 5.5 mM), and free (FG, 0 mM) glucose mediums. Moreover, we evaluated how glucose variations may influence cellular phenotype and the expression of genes related to epithelial-mesenchymal transition (EMT), metabolism, and cancer development in metastatic GC cells (AGP-01). Results showed that in the FG metastatic cells evidenced higher viability when compared with other cell lines and that when exposed to either LG or HG mediums most of the phenotypic assays did not differ. However, cells exposed to LG increased colony formation and mRNA levels of metabolic-related genes when compared to HG medium. Our results recommend LG medium to metabolic studies once glucose concentration is closer to physiological levels. These findings are important to point out new relevant targets in metabolic reprogramming that can be alternatives to current chemotherapies in patients with metastatic GC.

Gene expression is a poor predictor of steady-state metabolite abundance in cancer cells

Metabolic reprogramming is a hallmark of cancer characterized by global changes in metabolite levels. However, compared with the study of gene expression, profiling of metabolites in cancer samples remains relatively understudied. We obtained metabolomic profiling and gene expression data from 454 human solid cancer cell lines across 24 cancer types from the Cancer Cell Line Encyclopedia (CCLE) database, to evaluate the feasibility of inferring metabolite levels from gene expression data. For each metabolite, we trained multivariable LASSO regression models to identify gene sets that are most predictive of the level of each metabolite profiled. Even when accounting for cell culture conditions or cell lineage in the model, few metabolites could be accurately predicted. In some cases, the inclusion of the upstream and downstream metabolites improved prediction accuracy, suggesting that gene expression is a poor predictor of steady-state metabolite levels. Our analysis uncovered a single robust relationship between the expression of nicotinamide N-methyltransferase (NNMT) and 1-methylnicotinamide (MNA), however, this relationship could only be validated in cancer samples with high purity, as NNMT is not expressed in immune cells. Together, we have trained models that use gene expression profiles to predict the level of individual metabolites. Our analysis suggests that inferring metabolite levels based on the expression of genes is generally challenging in cancer.

Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines

The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).

Evolution of plasticity: metabolic compensation for fluctuating energy demands at the origin of life

Phenotypic plasticity of physiological functions enables rapid responses to changing environments and may thereby increase the resilience of organisms to environmental change. Here, we argue that the principal hallmarks of life itself, self-replication and maintenance, are contingent on the plasticity of metabolic processes ('metabolic plasticity'). It is likely that the Last Universal Common Ancestor (LUCA), 4 billion years ago, already possessed energy-sensing molecules that could adjust energy (ATP) production to meet demand. The earliest manifestation of metabolic plasticity, switching cells from growth and storage (anabolism) to breakdown and ATP production (catabolism), coincides with the advent of Darwinian evolution. Darwinian evolution depends on reliable translation of information from information-carrying molecules, and on cell genealogy where information is accurately passed between cell generations. Both of these processes create fluctuating energy demands that necessitate metabolic plasticity to facilitate replication of genetic material and (proto)cell division. We propose that LUCA possessed rudimentary forms of these capabilities. Since LUCA, metabolic networks have increased in complexity. Generalist founder enzymes formed the basis of many derived networks, and complexity arose partly by recruiting novel pathways from the untapped pool of reactions that are present in cells but do not have current physiological functions (the so-called 'underground metabolism'). Complexity may thereby be specific to environmental contexts and phylogenetic lineages. We suggest that a Boolean network analysis could be useful to model the transition of metabolic networks over evolutionary time. Network analyses can be effective in modelling phenotypic plasticity in metabolic functions for different phylogenetic groups because they incorporate actual biochemical regulators that can be updated as new empirical insights are gained.

The Potential of Metabolomics in Biomedical Applications

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

The POU2F1-ALDOA axis promotes the proliferation and chemoresistance of colon cancer cells by enhancing glycolysis and the pentose phosphate pathway activity

Cancer metabolic reprogramming enhances its malignant behaviors and drug resistance, which is regulated by POU domain transcription factors. This study explored the effect of POU domain class 2 transcription factor 1 (POU2F1) on metabolic reprogramming in colon cancer. The POU2F1 expression was analyzed in GEO dataset, TCGA cohorts and human colon cancer tissues by bioinformatics and immunohistochemistry. The effects of altered POU2F1 expression on proliferation, glucose metabolism and oxaliplatin sensitivity of colon cancer cells were tested. The impacts of POU2F1 on aldolase A (ALDOA) expression and malignant behaviors of colon cancer cells were examined. We found that up-regulated POU2F1 expression was associated with worse prognosis and oxaliplatin resistance in colon cancer. POU2F1 enhanced the proliferation, aerobic glycolysis and the pentose phosphate pathway (PPP) activity, but reduced oxidative stress and apoptosis in colon cancer cells, dependent on up-regulating ALDOA expression. Mechanistically, POU2F1 directly bound to the ALDOA promoter to enhance the ALDOA promoter activity in colon cancer cells. Moreover, activation of the POU2F1-ALDOA axis decreased the sensitivity to oxaliplatin in colon cancer cells. These data indicate that the POU2F1-ALDOA axis promotes the progression and oxaliplatin resistance by enhancing metabolic reprogramming in colon cancer. Our findings suggest that the POU2F1-ALDOA axis may be new therapeutic targets to overcome oxaliplatin resistance in colon cancer.

Identification of Biomarker in Brain-specific Gene Regulatory Network Using Structural Controllability Analysis

Brain tumor research has been stapled for human health while brain network research is crucial for us to understand brain activity. Here the structural controllability theory is applied to study three human brain-specific gene regulatory networks, including forebrain gene regulatory network, hindbrain gene regulatory network and neuron associated cells cancer related gene regulatory network, whose nodes are neural genes and the edges represent the gene expression regulation among the genes. The nodes are classified into two classes: critical nodes and ordinary nodes, based on the change of the number of driver nodes upon its removal. Eight topological properties (out-degree DO, in-degree DI, degree D, betweenness B, closeness CA, in-closeness CI, out-closeness CO and clustering coefficient CC) are calculated in this paper and the results prove that the critical genes have higher score of topological properties than the ordinary genes. Then two bioinformatic analysis are used to explore the biologic significance of the critical genes. On the one hand, the enrichment scores in several kinds of gene databases are calculated and reveal that the critical nodes are richer in essential genes, cancer genes and the neuron related disease genes than the ordinary nodes, which indicates that the critical nodes may be the biomarker in brain-specific gene regulatory network. On the other hand, GO analysis and KEGG pathway analysis are applied on them and the results show that the critical genes mainly take part in 14 KEGG pathways that are transcriptional misregulation in cancer, pathways in cancer and so on, which indicates that the critical genes are related to the brain tumor. Finally, by deleting the edges or routines in the network, the robustness analysis of node classification is realized, and the robustness of node classification is proved. The comparison of neuron associated cells cancer related GRN (Gene Regulatory Network) and normal brain-specific GRNs (including forebrain and hindbrain GRN) shows that the neuron-related cell cancer-related gene regulatory network is more robust than other types.

Heat Shock Proteins in Benign Prostatic Hyperplasia and Prostate Cancer

Two out of three diseases of the prostate gland affect aging men worldwide. Benign prostatic hyperplasia (BPH) is a noncancerous enlargement affecting millions of men. Prostate cancer (PCa) in turn is the second leading cause of cancer death. The factors influencing the occurrence of BPH and PCa are different; however, in the course of these two diseases, the overexpression of heat shock proteins is observed. Heat shock proteins (HSPs), chaperone proteins, are known to be one of the main proteins playing a role in maintaining cell homeostasis. HSPs take part in the process of the proper folding of newly formed proteins, and participate in the renaturation of damaged proteins. In addition, they are involved in the transport of specific proteins to the appropriate cell organelles and directing damaged proteins to proteasomes or lysosomes. Their function is to protect the proteins against degradation factors that are produced during cellular stress. HSPs are also involved in modulating the immune response and the process of apoptosis. One well-known factor affecting HSPs is the androgen receptor (AR)—a main player involved in the development of BPH and the progression of prostate cancer. HSPs play a cytoprotective role and determine the survival of cancer cells. These chaperones are often upregulated in malignancies and play an indispensable role in tumor progression. Therefore, HSPs are considered as one of the therapeutic targets in anti-cancer therapies. In this review article, we discuss the role of different HSPs in prostate diseases, and their potential as therapeutic targets.

Fatty Acid Metabolism in Myeloid-Derived Suppressor Cells and Tumor-Associated Macrophages: Key Factor in Cancer Immune Evasion

The tumor microenvironment (TME) comprises various cell types, soluble factors, viz, metabolites or cytokines, which together play in promoting tumor metastasis. Tumor infiltrating immune cells play an important role against cancer, and metabolic switching in immune cells has been shown to affect activation, differentiation, and polarization from tumor suppressive into immune suppressive phenotypes. Macrophages represent one of the major immune infiltrates into TME. Blood monocyte-derived macrophages and myeloid derived suppressor cells (MDSCs) infiltrating into the TME potentiate hostile tumor progression by polarizing into immunosuppressive tumor-associated macrophages (TAMs). Recent studies in the field of immunometabolism focus on metabolic reprogramming at the TME in polarizing tumor-associated macrophages (TAMs). Lipid droplets (LD), detected in almost every eukaryotic cell type, represent the major source for intra-cellular fatty acids. Previously, LDs were mainly described as storage sites for fatty acids. However, LDs are now recognized to play an integral role in cellular signaling and consequently in inflammation and metabolism-mediated phenotypical changes in immune cells. In recent years, the role of LD dependent metabolism in macrophage functionality and phenotype has been being investigated. In this review article, we discuss fatty acids stored in LDs, their role in modulating metabolism of tumor-infiltrating immune cells and, therefore, in shaping the cancer progression.