Systems approach to characterize the metabolism of liver cancer stem cells expressing CD133

Applications of genome-scale metabolic models to the study of human diseases: A systematic review

BACKGROUND AND OBJECTIVES

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

RESULTS

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

CONCLUSIONS

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

Multidimensional analysis of tumor stem cells: from biological properties, metabolic adaptations to immune escape mechanisms

As a key factor in tumorigenesis, progression, recurrence and metastasis, the biological properties, metabolic adaptations and immune escape mechanisms of CSCs are the focus of current oncological research. CSCs possess self-renewal, multidirectional differentiation and tumorigenicity, and their mechanisms of action can be elucidated by the clonal evolution, hierarchical model and the dynamic CSCs model, of which the dynamic model is widely recognized due to its better explanation of the function and origin of CSCs. The origin hypothesis of CSCs involves cell-cell fusion, horizontal gene transfer, genomic instability and microenvironmental regulation, which together shape the diversity of CSCs. In terms of classification, CSCs include primary CSCs (pri-CSCs), precancerous stem cells (pre-CSCs), migratory CSCs (mig-CSCs), and chemo-radiotherapy-resistant CSCs (cr-CSCs and rr-CSCs), with each type playing a specific role in tumor progression. Surface markers of CSCs, such as CD24, CD34, CD44, CD90, CD133, CD166, EpCAM, and LGR5, offer the possibility of identifying, isolating, and targeting CSCs, but the instability and heterogeneity of their expression increase the difficulty of treatment. CSCs have adapted to their survival needs through metabolic reprogramming, showing the ability to flexibly switch between glycolysis and oxidative phosphorylation (OXPHOS), as well as adjustments to amino acid and lipid metabolism. The Warburg effect typifies their metabolic profiles, and altered glutamine and fatty acid metabolism further contributes to the rapid proliferation and survival of CSCs. CSCs are able to maintain their stemness by regulating the metabolic networks to maintain their stemness characteristics, enhance antioxidant defences, and adapt to therapeutic stress. Immune escape is another strategy for CSCs to maintain their survival, and CSCs can effectively evade immune surveillance through mechanisms such as up-regulating PD-L1 expression and promoting the formation of an immunosuppressive microenvironment. Together, these properties reveal the multidimensional complexity of CSCs, underscoring the importance of a deeper understanding of the biology of CSCs for the development of more effective tumor therapeutic strategies. In the future, therapies targeting CSCs will focus on precise identification of surface markers, intervention of metabolic pathways, and overcoming immune escape, with the aim of improving the relevance and efficacy of cancer treatments, and ultimately improving patient prognosis.

The Impact of Glycosylation on the Functional Activity of CD133 and the Accuracy of Its Immunodetection

The membrane glycoprotein CD133 (prominin-1) is widely regarded as the main molecular marker of cancer stem cells, which are the most malignant cell subpopulation within the tumor, responsible for tumor growth and metastasis. For this reason, CD133 is considered a promising prognostic biomarker and molecular target for antitumor therapy. Under normal conditions, CD133 is present on the cell membrane in glycosylated form. However, in malignancies, altered glycosylation apparently leads to changes in the functional activity of CD133 and the availability of some of its epitopes for antibodies. This review focuses on CD133's glycosylation in human cells and its impact on the function of this glycoprotein. The association of CD133 with proliferation, differentiation, apoptosis, autophagy, epithelial-mesenchymal transition, the organization of plasma membrane protrusions and extracellular trafficking is discussed. In this review, particular attention is paid to the influence of CD133's glycosylation on its immunodetection. A list of commercially available and custom antibodies with their characteristics is provided. The available data indicate that the development of CD133-based biomedical technologies should include an assessment of CD133's glycosylation in each tumor type.

Cancer stem cell-immune cell crosstalk in the tumor microenvironment for liver cancer progression

Crosstalk between cancer cells and the immune microenvironment is determinant for liver cancer progression. A tumor subpopulation called liver cancer stem cells (CSCs) significantly accounts for the initiation, metastasis, therapeutic resistance, and recurrence of liver cancer. Emerging evidence demonstrates that the interaction between liver CSCs and immune cells plays a crucial role in shaping an immunosuppressive microenvironment and determining immunotherapy responses. This review sheds light on the bidirectional crosstalk between liver CSCs and immune cells for liver cancer progression, as well as the underlying molecular mechanisms after presenting an overview of liver CSCs characteristic and their microenvironment. Finally, we discuss the potential application of liver CSCs-targeted immunotherapy for liver cancer treatment.

Can CD133 Be Regarded as a Prognostic Biomarker in Oncology: Pros and Cons

The CD133 cell membrane glycoprotein, also termed prominin-1, is expressed on some of the tumor cells of both solid and blood malignancies. The CD133-positive tumor cells were shown to exhibit higher proliferative activity, greater chemo- and radioresistance, and enhanced tumorigenicity compared to their CD133-negative counterparts. For this reason, CD133 is regarded as a potential prognostic biomarker in oncology. The CD133-positive cells are related to the cancer stem cell subpopulation in many types of cancer. Recent studies demonstrated the involvement of CD133 in the regulation of proliferation, autophagy, and apoptosis in cancer cells. There is also evidence of its participation in the epithelial-mesenchymal transition associated with tumor progression. For a number of malignant tumor types, high CD133 expression is associated with poor prognosis, and the prognostic significance of CD133 has been confirmed in a number of meta-analyses. However, some published papers suggest that CD133 has no prognostic significance or even demonstrate a certain correlation between high CD133 levels and a positive prognosis. This review summarizes and discusses the existing evidence for and against the prognostic significance of CD133 in cancer. We also consider possible reasons for conflicting findings from the studies of the clinical significance of CD133.

Metabolic reprogramming and its clinical implication for liver cancer

Cancer cells often encounter hypoxic and hypo-nutrient conditions, which force them to make adaptive changes to meet their high demands for energy and various biomaterials for biomass synthesis. As a result, enhanced catabolism (breakdown of macromolecules for energy production) and anabolism (macromolecule synthesis from bio-precursors) are induced in cancer. This phenomenon is called "metabolic reprogramming," a cancer hallmark contributing to cancer development, metastasis, and drug resistance. HCC and cholangiocarcinoma (CCA) are 2 different liver cancers with high intertumoral heterogeneity in terms of etiologies, mutational landscapes, transcriptomes, and histological representations. In agreement, metabolism in HCC or CCA is remarkably heterogeneous, although changes in the glycolytic pathways and an increase in the generation of lactate (the Warburg effect) have been frequently detected in those tumors. For example, HCC tumors with activated β-catenin are addicted to fatty acid catabolism, whereas HCC tumors derived from fatty liver avoid using fatty acids. In this review, we describe common metabolic alterations in HCC and CCA as well as metabolic features unique for their subsets. We discuss metabolism of NAFLD as well, because NAFLD will likely become a leading etiology of liver cancer in the coming years due to the obesity epidemic in the Western world. Furthermore, we outline the clinical implication of liver cancer metabolism and highlight the computation and systems biology approaches, such as genome-wide metabolic models, as a valuable tool allowing us to identify therapeutic targets and develop personalized treatments for liver cancer patients.

The Emerging Role of Tumor Microenvironmental Stimuli in Regulating Metabolic Rewiring of Liver Cancer Stem Cells

Primary liver cancer (PLC) is one of the most devastating cancers worldwide. Extensive phenotypical and functional heterogeneity is a cardinal hallmark of cancer, including PLC, and is related to the cancer stem cell (CSC) concept. CSCs are responsible for tumor growth, progression, relapse and resistance to conventional therapies. Metabolic reprogramming represents an emerging hallmark of cancer. Cancer cells, including CSCs, are very plastic and possess the dynamic ability to constantly shift between different metabolic states depending on various intrinsic and extrinsic stimuli, therefore amplifying the complexity of understanding tumor heterogeneity. Besides the well-known Warburg effect, several other metabolic pathways including lipids and iron metabolism are altered in PLC. An increasing number of studies supports the role of the surrounding tumor microenvironment (TME) in the metabolic control of liver CSCs. In this review, we discuss the complex metabolic rewiring affecting liver cancer cells and, in particular, liver CSCs. Moreover, we highlight the role of TME cellular and noncellular components in regulating liver CSC metabolic plasticity. Deciphering the specific mechanisms regulating liver CSC-TME metabolic interplay could be very helpful with respect to the development of more effective and innovative combinatorial therapies for PLC treatment.

Reconstructed Genome-Scale Metabolic Model Characterizes Adaptive Metabolic Flux Changes in Peripheral Blood Mononuclear Cells in Severe COVID-19 Patients

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) poses a mortal threat to human health. The elucidation of the relationship between peripheral immune cells and the development of inflammation is essential for revealing the pathogenic mechanism of COVID-19 and developing related antiviral drugs. The immune cell metabolism-targeting therapies exhibit a desirable anti-inflammatory effect in some treatment cases. In this study, based on differentially expressed gene (DEG) analysis, a genome-scale metabolic model (GSMM) was reconstructed by integrating transcriptome data to characterize the adaptive metabolic changes in peripheral blood mononuclear cells (PBMCs) in severe COVID-19 patients. Differential flux analysis revealed that metabolic changes such as enhanced aerobic glycolysis, impaired oxidative phosphorylation, fluctuating biogenesis of lipids, vitamins (folate and retinol), and nucleotides played important roles in the inflammation adaptation of PBMCs. Moreover, the main metabolic enzymes such as the solute carrier (SLC) family 2 member 3 (SLC2A3) and fatty acid synthase (FASN), responsible for the reactions with large differential fluxes, were identified as potential therapeutic targets. Our results revealed the inflammation regulation potentials of partial metabolic reactions with differential fluxes and their metabolites. This study provides a reference for developing potential PBMC metabolism-targeting therapy strategies against COVID-19.

Targeted Anti-Mitochondrial Therapy: The Future of Oncology

Like living organisms, cancer cells require energy to survive and interact with their environment. Mitochondria are the main organelles for energy production and cellular metabolism. Recently, investigators demonstrated that cancer cells can hijack mitochondria from immune cells. This behavior sheds light on a pivotal piece in the cancer puzzle, the dependence on the normal cells. This article illustrates the benefits of new functional mitochondria for cancer cells that urge them to hijack mitochondria. It describes how functional mitochondria help cancer cells’ survival in the harsh tumor microenvironment, immune evasion, progression, and treatment resistance. Recent evidence has put forward the pivotal role of mitochondria in the metabolism of cancer stem cells (CSCs), the tumor components responsible for cancer recurrence and metastasis. This theory highlights the mitochondria in cancer biology and explains how targeting mitochondria may improve oncological outcomes.

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.

Mechanism of cancer stemness maintenance in human liver cancer

Primary liver cancer mainly includes the following four types: hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), hepatoblastoma (HB), and combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CCA). Recent studies have indicated that there are differences in cancer stem cell (CSC) properties among different types of liver cancer. Liver cancer stem cells (LCSCs), also called liver tumor-initiating cells, have been viewed as drivers of tumor initiation and metastasis. Many mechanisms and factors, such as mitophagy, mitochondrial dynamics, epigenetic modifications, the tumor microenvironment, and tumor plasticity, are involved in the regulation of cancer stemness in liver cancer. In this review, we analyze cancer stemness in different liver cancer types. Moreover, we further evaluate the mechanism of cancer stemness maintenance of LCSCs and discuss promising treatments for eradicating LCSCs.

Reconstruction of tissue-specific genome-scale metabolic models for human cancer stem cells

Cancer Stem Cells (CSCs) contribute to cancer aggressiveness, metastasis, chemo/radio-therapy resistance, and tumor recurrence. Recent studies emphasized the importance of metabolic reprogramming of CSCs for the maintenance and progression of the cancer phenotype through both the fulfillment of the energetic requirements and the supply of substrates fundamental for fast-cell growth, as well as through metabolite-induced epigenetic regulation. Therefore, it is of paramount importance to develop therapeutic strategies tailored to target the metabolism of CSCs. In this work, we built computational Genome-Scale Metabolic Models (GSMMs) for CSCs of different tissues. Flux simulations were then used to predict metabolic phenotypes, identify potential therapeutic targets, and spot already-known Transcription Factors (TFs), miRNAs and antimetabolites that could be used as part of drug repurposing strategies against cancer. Results were in accordance with experimental evidence, provided insights of new metabolic mechanisms for already known agents, and allowed for the identification of potential new targets and compounds that could be interesting for further in vitro and in vivo validation.

Agent Clustering Strategy Based on Metabolic Flux Distribution and Transcriptome Expression for Novel Drug Development

The network module-based method has been used for drug repositioning. The traditional drug repositioning method only uses the gene characteristics of the drug but ignores the drug-triggered metabolic changes. The metabolic network systematically characterizes the connection between genes, proteins, and metabolic reactions. The differential metabolic flux distribution, as drug metabolism characteristics, was employed to cluster the agents with similar MoAs (mechanism of action). In this study, agents with the same pharmacology were clustered into one group, and a total of 1309 agents from the CMap database were clustered into 98 groups based on differential metabolic flux distribution. Transcription factor (TF) enrichment analysis revealed the agents in the same group (such as group 7 and group 26) were confirmed to have similar MoAs. Through this agent clustering strategy, the candidate drugs which can inhibit (Japanese encephalitis virus) JEV infection were identified. This study provides new insights into drug repositioning and their MoAs.

Targeting Cancer Stem Cells by Dietary Agents: An Important Therapeutic Strategy against Human Malignancies

As a multifactorial disease, treatment of cancer depends on understanding unique mechanisms involved in its progression. The cancer stem cells (CSCs) are responsible for tumor stemness and by enhancing colony formation, proliferation as well as metastasis, and these cells can also mediate resistance to therapy. Furthermore, the presence of CSCs leads to cancer recurrence and therefore their complete eradication can have immense therapeutic benefits. The present review focuses on targeting CSCs by natural products in cancer therapy. The growth and colony formation capacities of CSCs have been reported can be attenuated by the dietary agents. These compounds can induce apoptosis in CSCs and reduce tumor migration and invasion via EMT inhibition. A variety of molecular pathways including STAT3, Wnt/β-catenin, Sonic Hedgehog, Gli1 and NF-κB undergo down-regulation by dietary agents in suppressing CSC features. Upon exposure to natural agents, a significant decrease occurs in levels of CSC markers including CD44, CD133, ALDH1, Oct4 and Nanog to impair cancer stemness. Furthermore, CSC suppression by dietary agents can enhance sensitivity of tumors to chemotherapy and radiotherapy. In addition to in vitro studies, as well as experiments on the different preclinical models have shown capacity of natural products in suppressing cancer stemness. Furthermore, use of nanostructures for improving therapeutic impact of dietary agents is recommended to rapidly translate preclinical findings for clinical use.

Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment

The tumor's physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations.

The Heterogeneity of Liver Cancer Metabolism

Primary liver cancer is the fourth leading cause of cancer death around the world. Histologically, it can be divided into two major groups, hepatocellular carcinoma (75% of all liver cancer) and intrahepatic cholangiocarcinoma (15% of all liver cancer) [1, 2]. Primary liver cancer usually happens in liver disease or cirrhosis patients [1], and the risk factors for developing HCC depend on the etiology [3] and the country of provenance [1]. There is an urgent need for an accurate diagnostic test given the high proportion of false positives and false negatives for alpha-fetoprotein (AFP), a common HCC biomarker [4]. Due to often being diagnosed in advanced stages, HCCrelated deaths per year have doubled since 1999 [3]. With the use of metabolomics technologies [5], the aberrant metabolism characteristics of cancer tissues can be discovered and exploited for the new biomarkers and new therapies to treat HCC [6, 7].

Advances in Liver Cancer Stem Cell Isolation and their Characterization

Over the last decade research on cancer stem cells (CSC) significantly contributed to a better understanding of tumor biology. Given their similarity to normal stem cells, i.e. self-renewal and pluripotency the need arises to develop robust protocols for the isolation and characterization of CSCs. As with other malignancies, hepatic tumors are composed of a heterogeneous population of cells including liver cancer stem cells (LCSC). Yet, a precise understanding of why stem cells become cancerous is still lacking. There is unmet need to develop robust protocols for the successful isolation of LCSCs from human tissue resection material as to assist in the development of molecular targeted therapies. Here we review the research progress made in the isolation and characterization of LCSCs by considering a wide range of cell surface markers and sorting methods, as applied to side populations, microsphere cultures and the gradient centrifugation method. We emphasize the different fluorescence activated cell sorting methods and the possibility to enrich LCSCs by immunomagnetic beads. We review the specificity of functional assays by considering ABCG transporter and ALDH1 enzyme activities and evaluate the in vivo tumorigenicity of LCSCs in highly sensitive bioassays. Finally, we evaluate different LCSC markers in association with viral and non-viral liver disease and explore the potential of novel drug delivery systems targeting CD133, EpCAM, CD13 and CD90 for the development of molecular targeted therapies. Graphical Abstract.

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.

Targeting Liver Cancer Stem Cells: An Alternative Therapeutic Approach for Liver Cancer

The first report of cancer stem cell (CSC) from Bruce et al. has demonstrated the relatively rare population of stem-like cells in acute myeloid leukemia (AML). The discovery of leukemic CSCs prompted further identification of CSCs in multiple types of solid tumor. Recently, extensive research has attempted to identity CSCs in multiple types of solid tumors in the brain, colon, head and neck, liver, and lung. Based on these studies, we hypothesize that the initiation and progression of most malignant tumors rely largely on the CSC population. Recent studies indicated that stem cell-related markers or signaling pathways, such as aldehyde dehydrogenase (ALDH), CD133, epithelial cell adhesion molecule (EpCAM), Wnt/β-catenin signaling, and Notch signaling, contribute to the initiation and progression of various liver cancer types. Importantly, CSCs are markedly resistant to conventional therapeutic approaches and current targeted therapeutics. Therefore, it is believed that selectively targeting specific markers and/or signaling pathways of hepatic CSCs is an effective therapeutic strategy for treating chemotherapy-resistant liver cancer. Here, we provide an overview of the current knowledge on the hepatic CSC hypothesis and discuss the specific surface markers and critical signaling pathways involved in the development and maintenance of hepatic CSC subpopulations.

CCND1 silencing suppresses liver cancer stem cell differentiation and overcomes 5-Fluorouracil resistance in hepatocellular carcinoma

OBJECTIVE

Chemoresistance is one of the major barriers in chemotherapy-based hepatocellular carcinoma (HCC) intervention. 5-Fluorouracil (5-Fu) is a widely used as an anticancer drug. Liver cancer stem cells (LCSCs) are considered the origin of tumor recurrence and resistance. CCND1 (Cyclin D1) plays an important role in tumorigenesis and metastasis in multiple cancers including HCC. Herein, this study was designed to explore the role of CCND1 in regulating LCSCs differentiation and 5-Fu resistance in HCC cells.

METHODS

The CCND1 mRNA level was examined by qRT-PCR. The protein levels of γ-H2AX (a DNA damage marker) and RAD51 (a DNA repair protein) were examined by Western blot. CD133 was used as a LCSC marker and CD133+ cell percentage in HCC cells was detected by flow cytometry.

RESULTS

CCND1 silencing decreased CD133+ cell percentage in HepG2 and SMMC-7721 cells. Furthermore, CCND1 silencing significantly increased protein level of γ-H2AX and decreased that of RAD51 under 5-Fu exposure. Moreover, CCND1 silencing enhanced the sensitivity of HepG2 and SMMC-7721 cells to 5-Fu, which was effectively abrogated by RAD51 upregulation.

CONCLUSION

Collectively, CCND1 silencing suppresses LCSCs differentiation and overcomes 5-Fu resistance in HCC.

Metabolic rearrangements in primary liver cancers: cause and consequences

Primary liver cancer (PLC) is the fourth most frequent cause of cancer-related death. The high mortality rates arise from late diagnosis and the limited accuracy of diagnostic and prognostic biomarkers. The liver is a major regulator, orchestrating the clearance of toxins, balancing glucose, lipid and amino acid uptake, managing whole-body metabolism and maintaining metabolic homeostasis. Tumour onset and progression is frequently accompanied by rearrangements of metabolic pathways, leading to dysregulation of metabolism. The limitation of current therapies targeting PLCs, such as hepatocellular carcinoma and cholangiocarcinoma, points towards the importance of deciphering this metabolic complexity. In this Review, we discuss the role of metabolic liver disruptions and the implications of these processes in PLCs, emphasizing their clinical relevance and value in early diagnosis and prognosis and as putative therapeutic targets. We also describe system biology approaches able to reconstruct the metabolic complexity of liver diseases. We also discuss whether metabolic rearrangements are a cause or consequence of PLCs, emphasizing the opportunity to clinically exploit the rewired metabolism. In line with this idea, we discuss circulating metabolites as promising biomarkers for PLCs.

Mitochondrial metabolic reprogramming: An important player in liver cancer progression

Mitochondria are known as essential biosynthetic, bioenergetic and signaling organelles, and play a critical role in cell differentiation, proliferation, and death. Nowadays, cancer is emergingly considered as a mitochondrial metabolic disease. Mitochondria also play an essential role in liver carcinogenesis. Liver cells are highly regenerative and require high energy. For that reason, a large number of mitochondria are present and functional in liver cells. Abnormalities in mitochondrial metabolism in human liver are known to be one of the carcinogenic factors. Interestingly, immune checkpoints regulate mitochondrial metabolic energetics of the tumor, the tumor microenvironment, as well as the tumor-specific immune response. This regulation forms a positive loop between the metabolic reprogramming of both cancer cells and immune cells. In this review, we discuss the evidence and mechanisms that mitochondria interplay with immune checkpoints to influence different steps of oncogenesis, as well as the potential of mitochondria as therapeutic targets for liver cancer therapy.

Current status and applications of genome-scale metabolic models

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.

Roles of mitochondria in liver cancer stem cells

Primary liver cancer (PLC) is heterogeneous and it is an aggressive malignancy with a poor prognostic outcome. Current evidence suggests that PLC tumorigenesis is driven by rare subpopulations of cancer stem cells (CSCs), which contribute to tumor initiation, progression, and therapy resistance through particular molecular mechanisms. Energy metabolism and mitochondrial function play an important role in the regulation of cancer stemness and stem cell specifications. Since the role of mitochondrial function as central hubs in cell growth and survival, studies on the critical physiological mechanisms of the liver underlying their therapy-resistant phenotype is important. In this review, we focus on liver CSC-related mitochondrial metabolism that contributes to the liver CSC features, in terms of enhanced drug-resistance and increased tumorigenicity, and to discuss their roles on potential therapies windows for PLC therapies.

Experimental models to unravel the molecular pathogenesis, cell of origin and stem cell properties of cholangiocarcinoma

Human cholangiocarcinoma (CCA) is an aggressive tumour entity arising from the biliary tree, whose molecular pathogenesis remains largely undeciphered. Over the last decade, the advent of high-throughput and cell-based techniques has significantly increased our knowledge on the molecular mechanisms underlying this disease while, at the same time, unravelling CCA complexity. In particular, it becomes clear that CCA displays pronounced inter- and intratumoural heterogeneity, which is presumably the consequence of the interplay between distinct tissues and cells of origin, the underlying diseases, and the associated molecular alterations. To better characterize these events and to design novel and more effective therapeutic strategies, a number of CCA experimental and preclinical models have been developed and are currently generated. This review summarizes the current knowledge and understanding of these models, critically underlining their translational usefulness and limitations. Furthermore, this review aims to provide a comprehensive overview on cells of origin, cancers stem cells and their dynamic interplay within CCA tissue.

The oncometabolite d‑2‑hydroxyglutarate induces angiogenic activity through the vascular endothelial growth factor receptor 2 signaling pathway

The mutation of isocitrate dehydrogenase (IDH)1 (R132H) and IDH2 (R172K) and the induction of hypoxia in various solid tumors results in alterations in metabolic profiles, including the production of the d‑ or l‑forms of 2‑hydroxyglutarate (2HG) from α‑ketoglutarate in aerobic metabolism in the tricarboxylic acid (TCA) cycle. However, it is unclear whether the oncometabolite d‑2HG increases angiogenesis in endothelial cells. Therefore, in this study, we analyzed the levels of various metabolites, including d‑2HG, under hypoxic conditions and in IDH2R172K mutant breast cancer cells by mass spectrometry. We then further evaluated the effects of this metabolite on angiogenesis in breast cancer cells. The results revealed that treatment with d‑2HG increased the levels of secreted vascular endothelial growth factor (VEGF) in cancer cells and enhanced endothelial cell proliferation in a concentration‑dependent manner. Wound healing and cell migration (examined by Transwell assay) were significantly increased by d‑2HG to a level similar to that induced by VEGF. Tube formation was significantly stimulated by d‑2HG, and chick chorioallantoic membrane angiogenesis was also enhanced by d‑2HG. d‑2HG activated VEGF receptor (VEGFR)2 and VEGFR2 downstream signaling, extracellular signal‑regulated kinase 1/2, focal adhesion kinase, AKT and matrix metalloproteinase (MMP)2. Taken together, the findings of this study suggested that d‑2HG induced angiogenic activity via VEGFR2 signaling and increased MMP2 activity.

RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/RAVEN and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).

B7-H4 overexpression is essential for early hepatocellular carcinoma progression and recurrence

B7-H4, another member of costimulatory molecule, has been shown to be overexpressed in multiple types of tumors, including hepatocellular carcinoma (HCC). However, the specific biological role of B7-H4 in HCC still needs to be further explored. In this study, we observed that B7-H4 was highly overexpressed in HCC tissues and cells, and its overexpression strongly correlated with patient's TNM stage, overall survival and early recurrence. Downregulation of B7-H4 significantly suppressed cell growth, invasion, and stemness of HCC by inducing apoptosis in the in vitro experiment. In addition, depletion of B7-H4 could help restore CD8⁺ T anti-tumor immunity by elevating the expression and secretion levels of CD107a, granzyme A, granzyme B, perforin and IFN-γ. In a xenografted mouse model of HCC, stable depletion of B7-H4 resulted in significantly smaller mean tumor volume and less mean tumor weight after 30 days of growth, compared to the control group. Together, our results provide insights into the diverse functions of B7-H4 involved in the pathogenesis, recurrence and anti-tumor immunity of HCC, indicating B7-H4 as a novel and effective approach for future treatment strategies that benefits anticancer therapy.

Systems metabolic engineering as an enabling technology in accomplishing sustainable development goals

With pressing issues arising in recent years, the United Nations proposed 17 Sustainable Development Goals (SDGs) as an agenda urging international cooperations for sustainable development. In this perspective, we examine the roles of systems metabolic engineering (SysME) and its contribution to improving the quality of life and protecting our environment, presenting how this field of study offers resolutions to the SDGs with relevant examples. We conclude with offering our opinion on the current state of SysME and the direction it should move forward in the generations to come, explicitly focusing on addressing the SDGs.