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

Transcript annotation tool (TransAT): an R package for retrieving annotations for transcript-specific genetic variants

BACKGROUND

An individual's genetics play a role in how RNA transcripts are generated from DNA and consequently in their translation into protein. Transcriptional and translational profiling of patients furnishes the information that a specific marker is present; however, it fails to provide evidence whether the marker correlates with response to a therapeutic agent. A comparative analysis of the frequency of genetic variants, such as single nucleotide polymorphisms (SNPs), in diseased and general populations can identify pathogenic variants in individual patients. This is in part because SNPs have considerable effects on protein function and gene expression when they occur in coding regions and regulatory sequences, respectively. Therefore, a tool that can help users to obtain the allele frequency for a corresponding transcript is the need of the day. Several annotation tools such as SNPnexus and VariED are publicly available; however, none of them can use transcript IDs as input and provide the corresponding genomic positions of variants.

RESULTS

In this study, we developed an R package, called transcript annotation tool (TransAT), that provides (i) SNP ID and genomic position for a user-provided transcript ID from patients, and (ii) allele frequencies for the SNPs from publicly available global populations. All data elements are extracted, collected, and displayed in an easily downloadable format in two simple command lines. TransAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It is available at https://github.com/ShihChingYu/TransAT and can be downloaded and installed using devtools::install_github("ShihChingYu/TransAT", force=T) on the R execution page. Thereafter, all functions can be executed by loading the package into R with library(TransAT).

CONCLUSIONS

TransAT is a novel tool that seamlessly provides genetic annotations for queried transcripts. Such easily obtainable information would be greatly advantageous for physicians, assisting them to make individualized decisions about specific drug treatments. Moreover, allele frequencies from user-chosen global ethnic populations will highlight the importance of ethnicity and its effect on patient pathogenicity.

EMT-Derived Alterations in Glutamine Metabolism Sensitize Mesenchymal Breast Cells to mTOR Inhibition

Epithelial-to-mesenchymal transition (EMT) is a fundamental developmental process with strong implications in cancer progression. Understanding the metabolic alterations associated with EMT may open new avenues of treatment and prevention. Here we used ¹³C carbon analogs of glucose and glutamine to examine differences in their utilization within central carbon and lipid metabolism following EMT in breast epithelial cell lines. We found that there are inherent differences in metabolic profiles before and after EMT. We observed EMT-dependent re-routing of the TCA-cycle, characterized by increased mitochondrial IDH2-mediated reductive carboxylation of glutamine to lipid biosynthesis with a concomitant lowering of glycolytic rates and glutamine-dependent glutathione (GSH) generation. Using weighted correlation network analysis, we identified cancer drugs whose efficacy against the NCI-60 Human Tumor Cell Line panel is significantly associated with GSH abundance and confirmed these in vitro. We report that EMT-linked alterations in GSH synthesis modulate the sensitivity of breast epithelial cells to mTOR inhibitors. IMPLICATIONS: EMT in breast cells causes an increased demand for glutamine for fatty acid biosynthesis, altering its contribution to glutathione biosynthesis, which sensitizes the cells to mTOR inhibitors.

Comprehensive plasma metabolomic and lipidomic analyses reveal potential biomarkers for heart failure

Heart failure is a syndrome with symptoms or signs caused by cardiac dysfunction. In clinic, four stages (A, B, C, and D) were used to describe heart failure progression. This study was aimed to explore plasma metabolomic and lipidomic profiles in different HF stages to identify potential biomarkers. Metabolomics and lipidomics were performed using plasma of heart failure patients at stages A (n = 49), B (n = 61), and C+D (n = 26). Analysis of Variance (ANOVA) was used for screening dysregulated molecules. Bioinformatics was used to retrieve perturbed metabolic pathways. Univariate and multivariate receiver operating characteristic curve (ROC) analyses were used for potential biomarker screening. Stage A showed significant difference to other stages, and 142 dysregulated lipids and 134 dysregulated metabolites were found belonging to several metabolic pathways. Several marker panels were proposed for the diagnosis of heart failure stage A versus stage B-D. Several molecules, including lysophosphatidylcholine 18:2, cholesteryl ester 18:1, alanine, choline, and Fructose, were found correlated with B-type natriuretic peptide or left ventricular ejection fractions. In summary, using untargeted metabolomic and lipidomic profiling, several dysregulated small molecules were successfully identified between HF stages A and B-D. These molecules would provide valuable information for further pathological researches and biomarker development.

Label-Free SERS Analysis of Urine Using a 3D-Stacked AgNW-Glass Fiber Filter Sensor for the Diagnosis of Pancreatic Cancer and Prostate Cancer

Metabolomics shows tremendous potential for the early diagnosis and screening of cancer. For clinical application as an effective diagnostic tool, however, improved analytical methods for complex biological fluids are required. Here, we developed a reliable rapid urine analysis system based on surface-enhanced Raman spectroscopy (SERS) using 3D-stacked silver nanowires (AgNWs) on a glass fiber filter (GFF) sensor and applied it to the diagnosis of pancreatic cancer and prostate cancer. Urine samples were pretreated with centrifugation to remove large debris and with calcium ion addition to improve the binding of metabolites to AgNWs. The label-free urine-SERS detection using the AgNW-GFF SERS sensor showed different spectral patterns and distinguishable specific peaks in three groups: normal control (n = 30), pancreatic cancer (n = 22), and prostate cancer (n = 22). Multivariate analyses of SERS spectra using unsupervised principal component analysis and supervised orthogonal partial least-squares discriminant analysis showed excellent discrimination between the pancreatic cancer group and the prostate cancer group as well as between the normal control group and the combined cancer groups. The results demonstrate the great potential of the urine-SERS analysis system using the AgNW-GFF SERS sensor for the noninvasive diagnosis and screening of cancers.

One-carbon metabolism in cancer cells: a critical review based on a core model of central metabolism

One-carbon metabolism (1C-metabolism), also called folate metabolism because the carbon group is attached to folate-derived tetrahydrofolate, is crucial in metabolism. It is at the heart of several essential syntheses, particularly those of purine and thymidylate. After a short reminder of the organization of 1C-metabolism, I list its salient features as reported in the literature. Then, using flux balance analysis, a core model of central metabolism and the flux constraints for an 'average cancer cell metabolism', I explore the fundamentals underlying 1C-metabolism and its relationships with the rest of metabolism. Some unreported properties of 1C-metabolism emerge, such as its potential roles in mitochondrial NADH exchange with cytosolic NADPH, participation in NADH recycling, and optimization of cell proliferation.

The diversity and breadth of cancer cell fatty acid metabolism

Tumor cellular metabolism exhibits distinguishing features that collectively enhance biomass synthesis while maintaining redox balance and cellular homeostasis. These attributes reflect the complex interactions between cell-intrinsic factors such as genomic-transcriptomic regulation and cell-extrinsic influences, including growth factor and nutrient availability. Alongside glucose and amino acid metabolism, fatty acid metabolism supports tumorigenesis and disease progression through a range of processes including membrane biosynthesis, energy storage and production, and generation of signaling intermediates. Here, we highlight the complexity of cellular fatty acid metabolism in cancer, the various inputs and outputs of the intracellular free fatty acid pool, and the numerous ways that these pathways influence disease behavior.

A novel method to detect intracellular metabolite alterations in MCF-7 cells by doxorubicin induced cell death

BACKGROUND

Metabolic reprogramming within cancer cells has been recognized as a potential barrier to chemotherapy. Additionally, metabolic tumor heterogeneity is the one of factors behind discernible hallmarks such as drug resistance, relapse of the tumor and the formation of secondary tumors.

METHODS

In this paper, cell-based assays including PI/annexin V staining and immunoblot assay were performed to show the apoptotic cell death in MCF-7 cells treated with DOX. Further, MCF-7 cells were lysed in a hypotonic buffer and the whole cell lysate was purified by a novel and specifically designed metabolite (~ 100 to 1000 Da) fractionation system called vertical tube gel electrophoresis (VTGE). Further, purified intracellular metabolites were subjected to identification by LC-HRMS technique.

RESULTS

Cleaved PARP 1 in MCF-7 cells treated with DOX was observed in the present study. Concomitantly, data showed the absence of active caspase 3 in MCF-7 cells. Novel findings are to identify key intracellular metabolites assisted by VTGE system that include lipid (CDP-DG, phytosphingosine, dodecanamide), non-lipid (N-acetyl-D-glucosamine, N1-acetylspermidine and gamma-L-glutamyl-L-cysteine) and tripeptide metabolites in MCF-7 cells treated by DOX. Interestingly, we reported the first evidence of doxorubicinone, an aglycone form of DOX in MCF-7 cells that are potentially linked to the mechanism of cell death in MCF-7 cells.

CONCLUSION

This paper reported novel methods and processes that involve VTGE system based purification of hypotonically lysed novel intracellular metabolites of MCF-7 cells treated by DOX. Here, these identified intracellular metabolites corroborate to caspase 3 independent and mitochondria induced apoptotic cell death in MCF-7 cells. Finally, these findings validate a proof of concept on the applications of novel VTGE assisted purification and analysis of intracellular metabolites from various cell culture models.

Systematic alteration of in vitro metabolic environments reveals empirical growth relationships in cancer cell phenotypes

Cancer cells, like microbes, live in complex metabolic environments. Recent evidence suggests that microbial behavior across metabolic environments is well described by simple empirical growth relationships, or growth laws. Do such empirical growth relationships also exist in cancer cells? To test this question, we develop a high-throughput approach to extract quantitative measurements of cancer cell behaviors in systematically altered metabolic environments. Using this approach, we examine relationships between growth and three frequently studied cancer phenotypes: drug-treatment survival, cell migration, and lactate overflow. Drug-treatment survival follows simple linear growth relationships, which differ quantitatively between chemotherapeutics and EGFR inhibition. Cell migration follows a weak grow-and-go growth relationship, with substantial deviation in some environments. Finally, lactate overflow is mostly decoupled from growth rate and is instead determined by the cells’ ability to maintain high sugar uptake rates. Altogether, this work provides a quantitative approach for formulating empirical growth laws of cancer.

Kochanowski et al. quantify cancer cell phenotypes across systematically altered in vitro metabolic environments to search for phenotype-growth relationships, similar to the growth laws found in microbes. Three case studies highlight examples in which such growth relationships are clearly operating (cancer drug survival), weakly present (cell migration), or absent (lactate overflow).

Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches

Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.

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.

Metabolite Support of Long-Term Storage of Sperm in the Spermatheca of Honeybee (Apis mellifera) Queens

The polyandrous mating system of honeybees (Apis mellifera L.) has garnered widespread attention. Long-lived honeybee queens only mate early in maturation, and the sperm obtained from the aerial mating is stored in the spermatheca. The maintenance of sperm viability in the spermatheca is an intriguing and complex process. However, the key physiological and biochemical adaptations underlying the long-term storage of sperm remain unclear. Analysis of the metabolite profile could help better understand the biology of the spermatheca and offer insights into the breeding and conservation of honeybees and even pest control strategies. Here, the changes in metabolites in the spermatheca were quantified between virgin queens and new-laying queens (with stored sperm) via liquid chromatography-mass spectrometry. Compared with virgin queens, changes occurred in lipids and lipid-like molecules, including fatty acyls and glycerophospholipids (GPL), prenol lipids, and sterol lipids, during storage of sperm in new-laying honeybee queens. Furthermore, the metabolic pathways that were enriched with the differentially expressed metabolites were identified and included GPL metabolism, biosynthesis of amino acids, and the mTOR signaling pathway. The likely roles of the pathways in the maintenance and protection of sperm are discussed. The study identifies key metabolites and pathways in the complex interplay of substances that contribute to the long-term storage of sperm and ultimately reproductive success of honeybee queens.

Leveraging genome-scale metabolic models for human health applications

Genome-scale metabolic modeling is a scalable and extensible computational method for analyzing and predicting biological function. With the ongoing improvements in computational methods and experimental capabilities, genome-scale metabolic models (GEMs) are demonstrating utility in addressing human health applications. The initial areas of highest impact are likely to be health applications where disease states involve metabolic changes. In this review, we focus on recent application of GEMs to studying cancer and the human microbiome by describing the enabling methodologies and outcomes of these studies. We conclude with proposing some areas of research that are likely to arise as a result of recent methodological advances.

Role of Glutathione in Cancer: From Mechanisms to Therapies

Glutathione (GSH) is the most abundant non-protein thiol present at millimolar concentrations in mammalian tissues. As an important intracellular antioxidant, it acts as a regulator of cellular redox state protecting cells from damage caused by lipid peroxides, reactive oxygen and nitrogen species, and xenobiotics. Recent studies have highlighted the importance of GSH in key signal transduction reactions as a controller of cell differentiation, proliferation, apoptosis, ferroptosis and immune function. Molecular changes in the GSH antioxidant system and disturbances in GSH homeostasis have been implicated in tumor initiation, progression, and treatment response. Hence, GSH has both protective and pathogenic roles. Although in healthy cells it is crucial for the removal and detoxification of carcinogens, elevated GSH levels in tumor cells are associated with tumor progression and increased resistance to chemotherapeutic drugs. Recently, several novel therapies have been developed to target the GSH antioxidant system in tumors as a means for increased response and decreased drug resistance. In this comprehensive review we explore mechanisms of GSH functionalities and different therapeutic approaches that either target GSH directly, indirectly or use GSH-based prodrugs. Consideration is also given to the computational methods used to describe GSH related processes for in silico testing of treatment effects.

(2R,3S)-Dihydroxybutanoic Acid Synthesis as a Novel Metabolic Function of Mutant Isocitrate Dehydrogenase 1 and 2 in Acute Myeloid Leukemia

Simple Summary

Acute myeloid leukemia (AML) is one of several cancers where cancer proliferation occurs under the influence of an aberrant metabolite known as an oncometabolite produced by a mutated enzyme in the cancer cell. In AML, mutant isocitrate dehydrogenases produce the oncometabolite 2-hydroxyglutarate. We screened AML patients with and without mutant isocitrate dehydrogenases by using a technique known as metabolomics, which measures many different metabolites in patient plasma. It was observed that another metabolite, 2,3-dihydroxybutyrate, was produced in larger amounts in patients with mutated isocitrate dehydrogenase and correlated strongly with 2-hydroxyglutarate levels. Moreover, 2,3-dihydroxybutyrate was a better indicator of the presence of mutated isocitrate dehydrogenase in the cancer than the known oncometabolite 2-hydroxyglutarate. These findings may lead to the characterization of 2,3-dihydroxybutyrate as a novel oncometabolite in AML, which would bring a fuller understanding of the etiology of this disease and offer opportunities for the development of novel therapeutic agents.

Abstract

Acute myeloid leukemia (AML) frequently harbors mutations in isocitrate 1 (IDH1) and 2 (IDH2) genes, leading to the formation of the oncometabolite (2R)-hydroxyglutaric acid (2R-HG) with epigenetic consequences for AML proliferation and differentiation. To investigate if broad metabolic aberrations may result from IDH1 and IDH2 mutations in AML, plasma metabolomics was conducted by gas chromatography–mass spectrometry (GC–MS) on 51 AML patients, 29 IDH1/2 wild-type (WT), 9 with IDH1R132, 12 with IDH2R140 and one with IDH2R172 mutations. Distinct metabolic differences were observed between IDH1/2 WT, IDH1R132 and IDH2R140 patients that comprised 22 plasma metabolites that were mainly amino acids. Only two plasma metabolites were statistically significantly different (p < 0.0001) between both IDH1R132 and WT IDH1/2 and IDH2R140 and WT IDH1/2, specifically (2R)-hydroxyglutaric acid (2R-HG) and the threonine metabolite (2R,3S)-dihydroxybutanoic acid (2,3-DHBA). Moreover, 2R-HG correlated strongly (p < 0.0001) with 2,3-DHBA in plasma. One WT patient was discovered to have a D-2-hydroxyglutarate dehydrogenase (D2HGDH) A426T inactivating mutation but this had little influence on 2R-HG and 2,3-DHBA plasma concentrations. Expression of transporter genes SLC16A1 and SLC16A3 displayed a weak correlation with 2R-HG but not 2,3-DHBA plasma concentrations. Receiver operating characteristic (ROC) analysis demonstrated that 2,3-DHBA was a better biomarker for IDH mutation than 2R-HG (Area under the curve (AUC) 0.861; p < 0.0001; 80% specificity; 87.3% sensitivity). It was concluded that 2,3-DHBA and 2R-HG are both formed by mutant IDH1R132, IDH2R140 and IDH2R172, suggesting a potential role of 2,3-DHBA in AML pathogenesis.

The Metabolic Heterogeneity and Flexibility of Cancer Stem Cells

Simple Summary

Cancer stem cells (CSCs) have been shown to be the main cause of therapy resistance and cancer recurrence. An analysis of their biological properties has revealed that CSCs have a particular metabolism that differs from non-CSCs to maintain their stemness properties. In this review, we analyze the flexible metabolic mechanisms of CSCs and highlight the new therapeutics that target CSC metabolism.

Abstract

Numerous findings have indicated that CSCs, which are present at a low frequency inside primary tumors, are the main cause of therapy resistance and cancer recurrence. Although various therapeutic methods targeting CSCs have been attempted for eliminating cancer cells completely, the complicated characteristics of CSCs have hampered such attempts. In analyzing the biological properties of CSCs, it was revealed that CSCs have a peculiar metabolism that is distinct from non-CSCs to maintain their stemness properties. The CSC metabolism involves not only the catabolic and anabolic pathways, but also intracellular signaling, gene expression, and redox balance. In addition, CSCs can reprogram their metabolism to flexibly respond to environmental changes. In this review, we focus on the flexible metabolic mechanisms of CSCs, and highlight the new therapeutics that target CSC metabolism.

FUNCTIONAL METABOLOMICS DECIPHER BIOCHEMICAL FUNCTIONS AND ASSOCIATED MECHANISMS UNDERLIE SMALL-MOLECULE METABOLISM

Metabolism is the collection of biochemical reactions enabled by chemically diverse metabolites, which facilitate different physiological processes to exchange substances and synthesize energy in diverse living organisms. Metabolomics has emerged as a cutting-edge method to qualify and quantify the metabolites in different biological matrixes, and it has the extraordinary capacity to interrogate the biological significance that underlies metabolic modification and modulation. Liquid chromatography combined with mass spectrometry (LC/MS), as a robust platform for metabolomics analysis, has increased in popularity over the past 10 years due to its excellent sensitivity, throughput, and versatility. However, metabolomics investigation currently provides us with only phenotype data without revealing the biochemical functions and associated mechanisms. This limitation indeed weakens the core value of metabolomics data in a broad spectrum of the life sciences. In recent years, the scientific community has actively explored the functional features of metabolomics and translated this cutting-edge approach to be used to solve key multifaceted questions, such as disease pathogenesis, the therapeutic discovery of drugs, nutritional issues, agricultural problems, environmental toxicology, and microbial evolution. Here, we are the first to briefly review the history and applicable progression of LC/MS-based metabolomics, with an emphasis on the applications of metabolic phenotyping. Furthermore, we specifically highlight the next era of LC/MS-based metabolomics to target functional metabolomes, through which we can answer phenotype-related questions to elucidate biochemical functions and associated mechanisms implicated in dysregulated metabolism. Finally, we propose many strategies to enhance the research capacity of functional metabolomics by enabling the combination of contemporary omics technologies and cutting-edge biochemical techniques. The main purpose of this review is to improve the understanding of LC/MS-based metabolomics, extending beyond the conventional metabolic phenotype toward biochemical functions and associated mechanisms, to enhance research capability and to enlarge the applicable scope of functional metabolomics in small-molecule metabolism in different living organisms.

Metabolite Patterns in Human Myeloid Hematopoiesis Result from Lineage-Dependent Active Metabolic Pathways

Assessment of hematotoxicity from environmental or xenobiotic compounds is of notable interest and is frequently assessed via the colony forming unit (CFU) assay. Identification of the mode of action of single compounds is of further interest, as this often enables transfer of results across different tissues and compounds. Metabolomics displays one promising approach for such identification, nevertheless, suitability with current protocols is restricted. Here, we combined a hematopoietic stem and progenitor cell (HSPC) expansion approach with distinct lineage differentiations, resulting in formation of erythrocytes, dendritic cells and neutrophils. We examined the unique combination of pathway activity in glycolysis, glutaminolysis, polyamine synthesis, fatty acid oxidation and synthesis, as well as glycerophospholipid and sphingolipid metabolism. We further assessed their interconnections and essentialness for each lineage formation. By this, we provide further insights into active metabolic pathways during the differentiation of HSPC into different lineages, enabling profound understanding of possible metabolic changes in each lineage caused by exogenous compounds.

Fast and sensitive flow-injection mass spectrometry metabolomics by analyzing sample-specific ion distributions

Mass spectrometry based metabolomics is a widely used approach in biomedical research. However, current methods coupling mass spectrometry with chromatography are time-consuming and not suitable for high-throughput analysis of thousands of samples. An alternative approach is flow-injection mass spectrometry (FI-MS) in which samples are directly injected to the ionization source. Here, we show that the sensitivity of Orbitrap FI-MS metabolomics methods is limited by ion competition effect. We describe an approach for overcoming this effect by analyzing the distribution of ion m/z values and computationally determining a series of optimal scan ranges. This enables reproducible detection of ~9,000 and ~10,000 m/z features in metabolomics and lipidomics analysis of serum samples, respectively, with a sample scan time of ~15 s and duty time of ~30 s; a ~50% increase versus current spectral-stitching FI-MS. This approach facilitates high-throughput metabolomics for a variety of applications, including biomarker discovery and functional genomics screens.

Fast and ergonomic extraction of adherent mammalian cells for NMR-based metabolomics studies

Cellular metabolomics has become key to elucidate mechanistic aspects in various fields such as cancerology or pharmacology, and is rapidly becoming a standard phenotyping tool accessible to the broad biological community. Acquisition of reliable spectroscopic datasets, such as nuclear magnetic resonance (NMR) spectra, to characterize biological systems depends on the elaboration of robust methods for cellular metabolites extraction. Previous studies have addressed many issues raised by these protocols, however with little pondering on ergonomic and practical aspects of the methods that impact their scalability, reproducibility and hence their suitability to high-throughput studies or their use by non-metabolomics experts. Here, we optimize a fast and ergonomic protocol for extraction of metabolites from adherent mammalian cells for NMR metabolomics studies. The proposed extraction protocol, including cell washing, metabolism quenching and actual extraction of intracellular metabolites, was first optimized on HeLa cells. Efficiency of the protocol, in its globality and for the different individual steps, was assessed by NMR quantification of 27 metabolites from cellular extracts. We show that a single PBS wash provides a seemly compromise between contamination from growth medium and leakage of intracellular metabolites. In HeLa cells, extraction using pure methanol, without cell scraping, recovered a higher amount of intracellular metabolites than the reference methanol/water/chloroform method with cell scraping, with yields varying across metabolite classes. Optimized and reference protocols were further tested on eight cell lines of miscellaneous nature, and inter-operator reproducibility was demonstrated. Our results stress the need for tailored extraction protocols and show that fast protocols minimizing time-consuming steps, without compromising extraction yields, are suitable for high-throughput metabolomics studies. Graphical abstract.

Data normalization strategies in metabolomics: Current challenges, approaches, and tools

Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which can lead to misleading and confusing biological results and thereby result in failed application to human healthcare, clinical, and other research avenues. To address this issue, a number of statistical approaches and software tools have been proposed in the literature and implemented over the years, thereby providing a multitude of approaches to choose from - either sample-based or data-based normalization strategies. In recent years, new dedicated software tools for metabolomics data normalization have surfaced as well. In this account article, I summarize the existing approaches and the new discoveries and research findings in this area of metabolomics data normalization, and I introduce some recent tools that aid in data normalization.

The Role of Nuclear Receptor Subfamily 1 Group H Member 4 (NR1H4) in Colon Cancer Cell Survival through the Regulation of c-Myc Stability

Nuclear receptor subfamily group H member 4 (NR1H4), also known as farnesoid X receptor, has been implicated in several cellular processes in the liver and intestine. Preclinical and clinical studies have suggested a role of NR1H4 in colon cancer development; however, how NR1H4 regulates colon cancer cell growth and survival remains unclear. We generated NR1H4 knockout (KO) colon cancer cells using clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein-9 nuclease (CAS9) technology and explored the effects of NR1H4 KO in colon cancer cell proliferation, survival, and apoptosis. Interestingly, NR1H4 KO cells showed impaired cell proliferation, reduced colony formation, and increased apoptotic cell death compared to control colon cancer cells. We identified MYC as an important mediator of the signaling pathway alterations induced by NR1H4 KO. NR1H4 silencing in colon cancer cells resulted in reduced MYC protein levels, while NR1H4 activation using an NR1H4 ligand, chenodeoxycholic acid, resulted in time- and dose-dependent MYC induction. Moreover, NR1H4 KO enhanced the anti-cancer effects of doxorubicin and cisplatin, supporting the role of MYC in the enhanced apoptosis observed in NR1H4 KO cells. Taken together, our findings suggest that modulating NR1H4 activity in colon cancer cells might be a promising alternative approach to treat cancer using MYC-targeting agents.

Anionic Polymers Promote Mitochondrial Targeting of Delocalized Lipophilic Cations

Mitochondria are therapeutic targets in many diseases including cancer, metabolic disorders, and neurodegenerative diseases. Therefore, strategies to deliver therapeutics of interest to mitochondria are important for therapeutic development. As delocalized lipophilic cations (DLCs) preferentially accumulate in mitochondria, DLC-conjugation has been utilized to facilitate therapeutic delivery systems with mitochondrial targeting capability. Here we report that upon DLC-conjugation, anionic polymers exhibit significantly improved mitochondrial targeting when compared to cationic polymers and charge-neutral polymers. Considering that the cell membrane generally bears a net negative charge, the observed phenomenon is unexpected. Notably, the DLC-conjugated anionic polymers circumvent endosomal entrapment. The rapid mitochondrial accumulation of DLC-conjugated anionic polymers is likely a membrane-potential-driven process, along with the involvement of the mitochondrial pyruvate carrier. Moreover, the structural variations on the side chain of DLC-conjugated anionic polymers do not compromise the overall mitochondrial targeting capability, widely extending the applicability of anionic macromolecules in therapeutic delivery systems.

Systems metabolomics: from metabolomic snapshots to design principles

Metabolomics is a rapidly expanding technology that finds increasing application in a variety of fields, form metabolic disorders to cancer, from nutrition and wellness to design and optimization of cell factories. The integration of metabolic snapshots with metabolic fluxes, physiological readouts, metabolic models, and knowledge-informed Artificial Intelligence tools, is required to obtain a system-level understanding of metabolism. The emerging power of multi-omic approaches and the development of integrated experimental and computational tools, able to dissect metabolic features at cellular and subcellular resolution, provide unprecedented opportunities for understanding design principles of metabolic (dis)regulation and for the development of precision therapies in multifactorial diseases, such as cancer and neurodegenerative diseases.

Data libraries - the missing element for modeling biological systems

The primary bottleneck in understanding and modeling biological systems is shifting from data collection to data analysis and integration. This process critically depends on data being available in an organized form, so that they can be accessed, understood, and reused by a broad community of scientists. A proven solution for organizing data is literature curation, which extracts, aggregates, and distributes findings from publications. Here, I describe the benefits of extending curation practices to datasets, especially those that are not deposited in centralized databases. I argue that dataset curation (or ‘data librarianship’ as I suggest we call it) will overcome many barriers in data visibility and reusability and make a unique contribution to integration and modeling.

1H-NMR Based Serum Metabolomics Highlights Different Specific Biomarkers between Early and Advanced Hepatocellular Carcinoma Stages

The application of non-targeted serum metabolomics profiling represents a noninvasive tool to identify new clinical biomarkers and to provide early diagnostic differentiation, and insight into the pathological mechanisms underlying hepatocellular carcinoma (HCC) progression. In this study, we used proton Nuclear Magnetic Resonance (¹H-NMR) Spectroscopy and multivariate data analysis to profile the serum metabolome of 64 HCC patients, in early (n = 28) and advanced (n = 36) disease stages. We found that ¹H-NMR metabolomics profiling could discriminate early from advanced HCC patients with a cross-validated accuracy close to 100%. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed significant changes in serum glucose, lactate, lipids and some amino acids, such as alanine, glutamine, 1-methylhistidine, lysine and valine levels between advanced and early HCC patients. Moreover, in early HCC patients, Kaplan-Meier analysis highlighted the serum tyrosine level as a predictor for overall survival (OS). Overall, our analysis identified a set of metabolites with possible clinical and biological implication in HCC pathophysiology.

Mitochondrial Fusion Via OPA1 and MFN1 Supports Liver Tumor Cell Metabolism and Growth

Metabolic reprogramming universally occurs in cancer. Mitochondria act as the hubs of bioenergetics and metabolism. The morphodynamics of mitochondria, comprised of fusion and fission processes, are closely associated with mitochondrial functions and are often dysregulated in cancer. In this study, we aim to investigate the mitochondrial morphodynamics and its functional consequences in human liver cancer. We observed excessive activation of mitochondrial fusion in tumor tissues from hepatocellular carcinoma (HCC) patients and in vitro cultured tumor organoids from cholangiocarcinoma (CCA). The knockdown of the fusion regulator genes, OPA1 (Optic atrophy 1) or MFN1 (Mitofusin 1), inhibited the fusion process in HCC cell lines and CCA tumor organoids. This resulted in inhibition of cell growth in vitro and tumor formation in vivo, after tumor cell engraftment in mice. This inhibitory effect is associated with the induction of cell apoptosis, but not related to cell cycle arrest. Genome-wide transcriptomic profiling revealed that the inhibition of fusion predominately affected cellular metabolic pathways. This was further confirmed by the blocking of mitochondrial fusion which attenuated oxygen consumption and cellular ATP production of tumor cells. In conclusion, increased mitochondrial fusion in liver cancer alters metabolism and fuels tumor cell growth.

Defining a metabolic landscape of tumours: genome meets metabolism

Cancer is a complex disease of multiple alterations occuring at the epigenomic, genomic, transcriptomic, proteomic and/or metabolic levels. The contribution of genetic mutations in cancer initiation, progression and evolution is well understood. However, although metabolic changes in cancer have long been acknowledged and considered a plausible therapeutic target, the crosstalk between genetic and metabolic alterations throughout cancer types is not clearly defined. In this review, we summarise the present understanding of the interactions between genetic drivers of cellular transformation and cancer-associated metabolic changes, and how these interactions contribute to metabolic heterogeneity of tumours. We discuss the essential question of whether changes in metabolism are a cause or a consequence in the formation of cancer. We highlight two modes of how metabolism contributes to tumour formation. One is when metabolic reprogramming occurs downstream of oncogenic mutations in signalling pathways and supports tumorigenesis. The other is where metabolic reprogramming initiates transformation being either downstream of mutations in oncometabolite genes or induced by chronic wounding, inflammation, oxygen stress or metabolic diseases. Finally, we focus on the factors that can contribute to metabolic heterogeneity in tumours, including genetic heterogeneity, immunomodulatory factors and tissue architecture. We believe that an in-depth understanding of cancer metabolic reprogramming, and the role of metabolic dysregulation in tumour initiation and progression, can help identify cellular vulnerabilities that can be exploited for therapeutic use.

Plasma Metabolomics and Lipidomics Reveal Perturbed Metabolites in Different Disease Stages of Chronic Obstructive Pulmonary Disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a common disease characterized by persistent respiratory symptoms and airflow restriction. It is usually manifested as airway and/or alveolar abnormalities caused by significant exposure to harmful particulates or gases.

OBJECTIVE

We aim to explore plasma metabolomic changes in the acute exacerbation stage of COPD (AECOPD) and stable stage of COPD (Stable COPD) to identify potential biomarkers for diagnosis or prognosis in clinical practice.

METHODS

Untargeted metabolomics and lipidomics analyses were performed to investigate dysregulated molecules in blood plasma of AECOPD patients (n=48) and Stable COPD (n=48), and a cohort of healthy people were included as a control group (n=48). Statistical analysis and bioinformatics analysis were performed to reveal dysregulated metabolites and perturbed metabolic pathways. SVM-based multivariate ROC analysis was used for candidate biomarker screening.

RESULTS

A total of 142 metabolites and 688 lipids were dysregulated in COPD patients. Pathway enrichment analysis showed that several metabolic pathways were perturbed after COPD onset. Several biomarker panels were proposed for diagnosis of COPD vs healthy control and AECOPD vs Stable COPD with AUC greater than 0.9.

CONCLUSION

Numerous plasma metabolites and several metabolic pathways were detected relevant to COPD disease onset or progression. These metabolites may be considered as candidate biomarkers for diagnosis or prognosis of COPD. The perturbed pathways involved in COPD provide clues for further pathological mechanism studies of COPD.

Tumor microenvironmental influences on dendritic cell and T cell function: A focus on clinically relevant immunologic and metabolic checkpoints

Cancer immunotherapy is fast becoming one of the most promising means of treating malignant disease. Cancer vaccines, adoptive cell transfer therapies, and immune checkpoint blockade have all shown varying levels of success in the clinical management of several cancer types in recent years. However, despite the clinical benefits often achieved by these regimens, an ongoing problem for many patients is the inherent or acquired resistance of their cancer to immunotherapy. It is now appreciated that dendritic cells and T lymphocytes both play key roles in antitumor immune responses and that the tumor microenvironment presents a number of barriers to the function of these cells that can ultimately limit the success of immunotherapy. In particular, the engagement of several immunologic and metabolic checkpoints within the hostile tumor microenvironment can severely compromise the antitumor functions of these important immune populations. This review highlights work from both preclinical and clinical studies that has shaped our understanding of the tumor microenvironment and its influence on dendritic cell and T cell function. It focuses on clinically relevant targeted and immunotherapeutic strategies that have emerged from these studies in an effort to prevent or overcome immune subversion within the tumor microenvironment. Emphasis is also placed on the potential of next-generation combinatorial regimens that target metabolic and immunologic impediments to dendritic cell and T lymphocyte function as strategies to improve antitumor immune reactivity and the clinical outcome of cancer immunotherapy going forward.

Systematic identification of metabolites controlling gene expression in E. coli

Metabolism controls gene expression through allosteric interactions between metabolites and transcription factors. These interactions are usually measured with in vitro assays, but there are no methods to identify them at a genome-scale in vivo. Here we show that dynamic transcriptome and metabolome data identify metabolites that control transcription factors in E. coli. By switching an E. coli culture between starvation and growth, we induce strong metabolite concentration changes and gene expression changes. Using Network Component Analysis we calculate the activities of 209 transcriptional regulators and correlate them with metabolites. This approach captures, for instance, the in vivo kinetics of CRP regulation by cyclic-AMP. By testing correlations between all pairs of transcription factors and metabolites, we predict putative effectors of 71 transcription factors, and validate five interactions in vitro. These results show that combining transcriptomics and metabolomics generates hypotheses about metabolism-transcription interactions that drive transitions between physiological states.

Interactions between metabolites and transcription factors are known to control gene expression but analyzing these events at genome-scale is challenging. Here, the authors integrate dynamic metabolome and transcriptome data from E.coli to predict regulatory metabolite-transcription factor interactions.

Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells

Intermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes, including signal transduction and gene expression patterns, arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here, we quantified metabolic features, mostly from the ¹³C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression, and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.

Co-Operation between Aneuploidy and Metabolic Changes in Driving Tumorigenesis

Alterations from the normal set of chromosomes are extremely common as cells progress toward tumourigenesis. Similarly, we expect to see disruption of normal cellular metabolism, particularly in the use of glucose. In this review, we discuss the connections between these two processes: how chromosomal aberrations lead to metabolic disruption, and vice versa. Both processes typically result in the production of elevated levels of reactive oxygen species, so we particularly focus on their role in mediating oncogenic changes.