Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling

Metabolic Objectives and Trade-Offs: Inference and Applications

Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and environmental constraints. While rapidly proliferating cells like tumors are often assumed to prioritize biomass production, mammalian cell types can exhibit objectives beyond growth, such as supporting tissue functions, developmental processes, and redox homeostasis. Methods: This review addresses the challenge of determining metabolic objectives and trade-offs from multiomics data. Results: Recent advances in single-cell omics, metabolic modeling, and machine/deep learning methods have enabled the inference of cellular objectives at both the transcriptomic and metabolic levels, bridging gene expression patterns with metabolic phenotypes. Conclusions: These in silico models provide insights into how cells adapt to changing environments, drug treatments, and genetic manipulations. We further explore the potential application of incorporating cellular objectives into personalized medicine, drug discovery, tissue engineering, and systems biology.

Inferring metabolic objectives and trade-offs in single cells during embryogenesis

While proliferating cells optimize their metabolism to produce biomass, the metabolic objectives of cells that perform non-proliferative tasks are unclear. The opposing requirements for optimizing each objective result in a trade-off that forces single cells to prioritize their metabolic needs and optimally allocate limited resources. Here, we present single-cell optimization objective and trade-off inference (SCOOTI), which infers metabolic objectives and trade-offs in biological systems by integrating bulk and single-cell omics data, using metabolic modeling and machine learning. We validated SCOOTI by identifying essential genes from CRISPR-Cas9 screens in embryonic stem cells, and by inferring the metabolic objectives of quiescent cells, during different cell-cycle phases. Applying this to embryonic cell states, we observed a decrease in metabolic entropy upon development. We further uncovered a trade-off between glutathione and biosynthetic precursors in one-cell zygote, two-cell embryo, and blastocyst cells, potentially representing a trade-off between pluripotency and proliferation. A record of this paper's transparent peer review process is included in the supplemental information.

From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling

As single-cell omics data sampling and acquisition methods have accumulated at an unprecedented rate, various data analysis pipelines have been developed for the inference of cell types, cell states and their distribution, state transitions, state trajectories, and state interactions. This presents a new opportunity in which single-cell omics data can be utilized to generate high-resolution, high-fidelity computational models. In this review, we discuss how single-cell omics data can be used to build computational models to simulate biological systems at various scales. We propose that single-cell data can be integrated with physiological information to generate organ-specific models, which can then be assembled to generate multi-organ systems pathophysiological models. Finally, we discuss how generic multi-organ models can be brought to the patient-specific level thus permitting their use in the clinical setting.

Single-cell omics analysis with genome-scale metabolic modeling

Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene-protein-reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.

Reconstruction of cell-specific models capturing the influence of metabolism on DNA methylation in cancer

The imbalance of epigenetic regulatory mechanisms such as DNA methylation, which can promote aberrant gene expression profiles without affecting the DNA sequence, may cause the deregulation of signaling, regulatory, and metabolic processes, contributing to a cancerous phenotype. Since some metabolites are substrates and cofactors of epigenetic regulators, their availability can be affected by characteristic cancer cell metabolic shifts, feeding cancer onset and progression through epigenetic deregulation. Hence, there is a need to study the influence of cancer metabolic reprogramming in DNA methylation to design new effective treatments. In this study, a generic Genome-Scale Metabolic Model (GSMM) of a human cell, integrating DNA methylation or demethylation reactions, was obtained and used for the reconstruction of Genome-Scale Metabolic Models enhanced with Enzymatic Constraints using Kinetic and Omics data (GECKOs) of 31 cancer cell lines. Furthermore, cell-line-specific DNA methylation levels were included in the models, as coefficients of a DNA composition pseudo-reaction, to depict the influence of metabolism over global DNA methylation in each of the cancer cell lines. Flux simulations demonstrated the ability of these models to provide simulated fluxes of exchange reactions similar to the equivalent experimentally measured uptake/secretion rates and to make good functional predictions. In addition, simulations found metabolic pathways, reactions and enzymes directly or inversely associated with the gene promoter methylation. Two potential candidates for targeted cancer epigenetic therapy were identified.

Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth

Quiescence, a temporary withdrawal from the cell cycle, plays a key role in tissue homeostasis and regeneration. Quiescence is increasingly viewed as a continuum between shallow and deep quiescence, reflecting different potentials to proliferate. The depth of quiescence is altered in a range of diseases and during aging. Here, we leveraged genome-scale metabolic modeling (GEM) to define the metabolic and epigenetic changes that take place with quiescence deepening. We discovered contrasting changes in lipid catabolism and anabolism and diverging trends in histone methylation and acetylation. We then built a multi-cell type machine learning model that accurately predicts quiescence depth in diverse biological contexts. Using both machine learning and genome-scale flux simulations, we performed high-throughput screening of chemical and genetic modulators of quiescence and identified novel small molecule and genetic modulators with relevance to cancer and aging.

Epigenetic dynamics during capacitation of naïve human pluripotent stem cells

Human pluripotent stem cells (hPSCs) are of fundamental relevance in regenerative medicine. Naïve hPSCs hold promise to overcome some of the limitations of conventional (primed) hPSCs, including recurrent epigenetic anomalies. Naïve-to-primed transition (capacitation) follows transcriptional dynamics of human embryonic epiblast and is necessary for somatic differentiation from naïve hPSCs. We found that capacitated hPSCs are transcriptionally closer to postimplantation epiblast than conventional hPSCs. This prompted us to comprehensively study epigenetic and related transcriptional changes during capacitation. Our results show that CpG islands, gene regulatory elements, and retrotransposons are hotspots of epigenetic dynamics during capacitation and indicate possible distinct roles of specific epigenetic modifications in gene expression control between naïve and primed hPSCs. Unexpectedly, PRC2 activity appeared to be dispensable for the capacitation. We find that capacitated hPSCs acquire an epigenetic state similar to conventional hPSCs. Significantly, however, the X chromosome erosion frequently observed in conventional female hPSCs is reversed by resetting and subsequent capacitation.

Spic regulates one-carbon metabolism and histone methylation in ground-state pluripotency

Understanding mechanisms of epigenetic regulation in embryonic stem cells (ESCs) is of fundamental importance for stem cell and developmental biology. Here, we identify Spic, a member of the ETS family of transcription factors (TFs), as a marker of ground state pluripotency. We show that Spic is rapidly induced in ground state ESCs and in response to extracellular signal-regulated kinase (ERK) inhibition. We find that SPIC binds to enhancer elements and stabilizes NANOG binding to chromatin, particularly at genes involved in choline/one-carbon (1C) metabolism such as Bhmt, Bhmt2, and Dmgdh. Gain-of-function and loss-of-function experiments revealed that Spic controls 1C metabolism and the flux of S-adenosyl methionine to S-adenosyl-L-homocysteine (SAM-to-SAH), thereby, modulating the levels of H3R17me2 and H3K4me3 histone marks in ESCs. Our findings highlight betaine-dependent 1C metabolism as a hallmark of ground state pluripotency primarily activated by SPIC. These findings underscore the role of uncharacterized auxiliary TFs in linking cellular metabolism to epigenetic regulation in ESCs.

Isotopic Tracing of Nucleotide Sugar Metabolism in Human Pluripotent Stem Cells

Metabolism not only produces energy necessary for the cell but is also a key regulator of several cellular functions, including pluripotency and self-renewal. Nucleotide sugars (NSs) are activated sugars that link glucose metabolism with cellular functions via protein N-glycosylation and O-GlcNAcylation. Thus, understanding how different metabolic pathways converge in the synthesis of NSs is critical to explore new opportunities for metabolic interference and modulation of stem cell functions. Tracer-based metabolomics is suited for this challenge, however chemically-defined, customizable media for stem cell culture in which nutrients can be replaced with isotopically labeled analogs are scarcely available. Here, we established a customizable flux-conditioned E8 (FC-E8) medium that enables stem cell culture with stable isotopes for metabolic tracing, and a dedicated liquid chromatography mass-spectrometry (LC-MS/MS) method targeting metabolic pathways converging in NS biosynthesis. By 13C6-glucose feeding, we successfully traced the time-course of carbon incorporation into NSs directly via glucose, and indirectly via other pathways, such as glycolysis and pentose phosphate pathways, in induced pluripotent stem cells (hiPSCs) and embryonic stem cells. Then, we applied these tools to investigate the NS biosynthesis in hiPSC lines from a patient affected by deficiency of phosphoglucomutase 1 (PGM1), an enzyme regulating the synthesis of the two most abundant NSs, UDP-glucose and UDP-galactose.

Targeting Metabolic Vulnerabilities in Epstein-Barr Virus-Driven Proliferative Diseases

The metabolism of cancer cells and Epstein-Barr virus (EBV) infected cells have remarkable similarities. Cancer cells frequently reprogram metabolic pathways to augment their ability to support abnormal rates of proliferation and promote intra-organismal spread through metastatic invasion. On the other hand, EBV is also capable of manipulating host cell metabolism to enable sustained growth and division during latency as well as intra- and inter-individual transmission during lytic replication. It comes as no surprise that EBV, the first oncogenic virus to be described in humans, is a key driver for a significant fraction of human malignancies in the world (~1% of all cancers), both in terms of new diagnoses and attributable deaths each year. Understanding the contributions of metabolic pathways that underpin transformation and virus replication will be important for delineating new therapeutic targets and designing nutritional interventions to reduce disease burden. In this review, we summarise research hitherto conducted on the means and impact of various metabolic changes induced by EBV and discuss existing and potential treatment options targeting metabolic vulnerabilities in EBV-associated diseases.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Protocol for CAROM: A machine learning tool to predict post-translational regulation from metabolic signatures

This protocol describes CAROM, a computational tool that combines genome-scale metabolic networks (GEMs) and machine learning to identify enzyme targets of post-translational modifications (PTMs). Condition-specific enzyme and reaction properties are used to predict targets of phosphorylation and acetylation in multiple organisms. CAROM is influenced by the accuracy of GEMs and associated flux-balance analysis (FBA), which generate the inputs of the model. We demonstrate the protocol using multi-omics data from E. coli. For complete details on the use and execution of this protocol, please refer to Smith et al. (2022).

Targeting the methionine-methionine adenosyl transferase 2A- S -adenosyl methionine axis for cancer therapy

PURPOSE OF REVIEW

In this review, we summarize the biological roles of methionine, methionine adenosyl transferase 2A (MAT2A) and S -adenosyl methionine (SAM) in methylation reactions during tumorigenesis. Newly emerged inhibitors targeting the methionine-MAT2A-SAM axis will be discussed.

RECENT FINDINGS

SAM is the critical and global methyl-donor for methylation reactions regulating gene expression, and in mammalian cells, it is synthesized by MAT2A using methionine. Recent studies have validated methionine and MAT2A as metabolic dependencies of cancer cells because of their essential roles in SAM biosynthesis. MAT2A inhibition leads to synthetic lethality in methylthioadenosine-phosphorylase (MTAP)-deleted cancers, which accounts for 15% of all cancer types. Of note, remarkable progress has been made in developing inhibitors targeting the methionine-MAT2A-SAM axis, as the first-in-class MAT2A inhibitors AG-270 and IDE397 enter clinical trials to treat cancer.

SUMMARY

The methionine-MAT2A-SAM axis plays an important role in tumorigenesis by providing SAM as a critical substrate for abnormal protein as well as DNA and RNA methylation in cancer cells. Targeting SAM biosynthesis through MAT2A inhibition has emerged as a novel and promising strategy for cancer therapy.

Genome-Wide Analysis of Yeast Metabolic Cycle through Metabolic Network Models Reveals Superiority of Integrated ATAC-seq Data over RNA-seq Data

Saccharomyces cerevisiae undergoes robust oscillations to regulate its physiology for adaptation and survival under nutrient-limited conditions. Environmental cues can induce rhythmic metabolic alterations in order to facilitate the coordination of dynamic metabolic behaviors. Of such metabolic processes, the yeast metabolic cycle enables adaptation of the cells to varying nutritional status through oscillations in gene expression and metabolite production levels. In this process, yeast metabolism is altered between diverse cellular states based on changing oxygen consumption levels: quiescent (reductive charging [RC]), growth (oxidative [OX]), and proliferation (reductive building [RB]) phases. We characterized metabolic alterations during the yeast metabolic cycle using a variety of approaches. Gene expression levels are widely used for condition-specific metabolic simulations, whereas the use of epigenetic information in metabolic modeling is still limited despite the clear relationship between epigenetics and metabolism. This prompted us to investigate the contribution of epigenomic information to metabolic predictions for progression of the yeast metabolic cycle. In this regard, we determined altered pathways through the prediction of regulated reactions and corresponding model genes relying on differential chromatin accessibility levels. The predicted metabolic alterations were confirmed via data analysis and literature. We subsequently utilized RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data sets in the contextualization of the yeast model. The use of ATAC-seq data considerably enhanced the predictive capability of the model. To the best of our knowledge, this is the first attempt to use genome-wide chromatin accessibility data in metabolic modeling. The preliminary results showed that epigenomic data sets can pave the way for more accurate metabolic simulations.

IMPORTANCE Dynamic chromatin organization mediates the emergence of condition-specific phenotypes in eukaryotic organisms. Saccharomyces cerevisiae can alter its metabolic profile via regulation of genome accessibility and robust transcriptional oscillations under nutrient-limited conditions. Thus, both epigenetic information and transcriptomic information are crucial in the understanding of condition-specific metabolic behavior in this organism. Based on genome-wide alterations in chromatin accessibility and transcription, we investigated the yeast metabolic cycle, which is a remarkable example of coordinated and dynamic yeast behavior. In this regard, we assessed the use of ATAC-seq and RNA-seq data sets in condition-specific metabolic modeling. To our knowledge, this is the first attempt to use chromatin accessibility data in the reconstruction of context-specific metabolic models, despite the extensive use of transcriptomic data. As a result of comparative analyses, we propose that the incorporation of epigenetic information is a promising approach in the accurate prediction of metabolic dynamics.

Aldh2 is a lineage-specific metabolic gatekeeper in melanocyte stem cells

Melanocyte stem cells (McSCs) in zebrafish serve as an on-demand source of melanocytes during growth and regeneration, but metabolic programs associated with their activation and regenerative processes are not well known. Here, using live imaging coupled with scRNA-sequencing, we discovered that, during regeneration, quiescent McSCs activate a dormant embryonic neural crest transcriptional program followed by an aldehyde dehydrogenase (Aldh) 2 metabolic switch to generate progeny. Unexpectedly, although ALDH2 is well known for its aldehyde-clearing mechanisms, we find that, in regenerating McSCs, Aldh2 activity is required to generate formate – the one-carbon (1C) building block for nucleotide biosynthesis – through formaldehyde metabolism. Consequently, we find that disrupting the 1C cycle with low doses of methotrexate causes melanocyte regeneration defects. In the absence of Aldh2, we find that purines are the metabolic end product sufficient for activated McSCs to generate progeny. Together, our work reveals McSCs undergo a two-step cell state transition during regeneration, and that the reaction products of Aldh2 enzymes have tissue-specific stem cell functions that meet metabolic demands in regeneration.

Summary: In zebrafish melanocyte regeneration, quiescent McSCs respond by re-expressing a neural crest identity, followed by an Aldh2-dependent metabolic switch to generate progeny.

Lipid exposure activates gene expression changes associated with estrogen receptor negative breast cancer

Improved understanding of local breast biology that favors the development of estrogen receptor negative (ER-) breast cancer (BC) would foster better prevention strategies. We have previously shown that overexpression of specific lipid metabolism genes is associated with the development of ER- BC. We now report results of exposure of MCF-10A and MCF-12A cells, and mammary organoids to representative medium- and long-chain polyunsaturated fatty acids. This exposure caused a dynamic and profound change in gene expression, accompanied by changes in chromatin packing density, chromatin accessibility, and histone posttranslational modifications (PTMs). We identified 38 metabolic reactions that showed significantly increased activity, including reactions related to one-carbon metabolism. Among these reactions are those that produce S-adenosyl-L-methionine for histone PTMs. Utilizing both an in-vitro model and samples from women at high risk for ER- BC, we show that lipid exposure engenders gene expression, signaling pathway activation, and histone marks associated with the development of ER- BC.

Activation of MAT2A-ACSL3 pathway protects cells from ferroptosis in gastric cancer

BACKGROUND

Ferroptosis, a unique form of nonapoptotic-regulated cell death caused by overwhelming lipid peroxidation, represents an emerging tumor suppression mechanism. Growing evidence has demonstrated that cell metabolism plays an important role in the regulation of ferroptosis. Specifically, the association between methionine metabolism and ferroptosis remains undefined.

METHODS

We performed in vitro and in vivo experiments to evaluate the influence of methionine metabolism on ferroptosis sensitivity. Pharmacological and genetic blockade of the methionine cycle was utilized and relevant molecular analyses were performed.

RESULTS

We identified MAT2A as a driver of ferroptosis resistance. Mechanistically, MAT2A mediates the production of S-adenosylmethionine (SAM), which upregulates ACSL3 by increasing the trimethylation of lysine-4 on histone H3 (H3K4me3) at the promoter area, resulting in ferroptosis resistance.

CONCLUSIONS

Collectively, these results established a link between methionine cycle activity and ferroptosis vulnerability in gastric cancer.

Toward modeling metabolic state from single-cell transcriptomics

BACKGROUND

Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge.

SCOPE OF REVIEW

We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals.

MAJOR CONCLUSIONS

Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.

Metabolic signatures of regulation by phosphorylation and acetylation

Acetylation and phosphorylation are highly conserved posttranslational modifications (PTMs) that regulate cellular metabolism, yet how metabolic control is shared between these PTMs is unknown. Here we analyze transcriptome, proteome, acetylome, and phosphoproteome datasets in E. coli, S. cerevisiae, and mammalian cells across diverse conditions using CAROM, a new approach that uses genome-scale metabolic networks and machine learning to classify targets of PTMs. We built a single machine learning model that predicted targets of each PTM in a condition across all three organisms based on reaction attributes (AUC>0.8). Our model predicted phosphorylated enzymes during a mammalian cell-cycle, which we validate using phosphoproteomics. Interpreting the machine learning model using game theory uncovered enzyme properties including network connectivity, essentiality, and condition-specific factors such as maximum flux that differentiate targets of phosphorylation from acetylation. The conserved and predictable partitioning of metabolic regulation identified here between these PTMs may enable rational rewiring of regulatory circuits.

Prolactin from Pluripotency to Central Nervous System Development

Prolactin (PRL) is a versatile hormone that exerts more than 300 functions in vertebrates, mainly associated with physiological effects in adult animals. Although the process that regulates early development is poorly understood, evidence suggests a role of PRL in the early embryonic development regarding pluripotency and nervous system development. Thus, PRL could be a crucial regulator in oocyte preimplantation and maturation as well as during diapause, a reversible state of blastocyst development arrest that shares metabolic, transcriptomic, and proteomic similarities with pluripotent stem cells in the naïve state. Thus, we analyzed the role of the hormone during those processes, which involve the regulation of its receptor and several signaling cascades (Jak/Mapk, Jak/Stat, and PI3k/Akt), resulting in either a plethora of physiological actions or their dysregulation, a factor in developmental disorders. Finally, we propose models to improve the knowledge on PRL function during early development.

Metabolic remodelling during early mouse embryo development

During early mammalian embryogenesis, changes in cell growth and proliferation depend on strict genetic and metabolic instructions. However, our understanding of metabolic reprogramming and its influence on epigenetic regulation in early embryo development remains elusive. Here we show a comprehensive metabolomics profiling of key stages in mouse early development and the two-cell and blastocyst embryos, and we reconstructed the metabolic landscape through the transition from totipotency to pluripotency. Our integrated metabolomics and transcriptomics analysis shows that while two-cell embryos favour methionine, polyamine and glutathione metabolism and stay in a more reductive state, blastocyst embryos have higher metabolites related to the mitochondrial tricarboxylic acid cycle, and present a more oxidative state. Moreover, we identify a reciprocal relationship between α-ketoglutarate (α-KG) and the competitive inhibitor of α-KG-dependent dioxygenases, L-2-hydroxyglutarate (L-2-HG), where two-cell embryos inherited from oocytes and one-cell zygotes display higher L-2-HG, whereas blastocysts show higher α-KG. Lastly, increasing 2-HG availability impedes erasure of global histone methylation markers after fertilization. Together, our data demonstrate dynamic and interconnected metabolic, transcriptional and epigenetic network remodelling during early mouse embryo development.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.

Integration of transcriptional and metabolic control in macrophage activation

Macrophages react to microbial and endogenous danger signals by activating a broad panel of effector and homeostatic responses. Such responses entail rapid and stimulus-specific changes in gene expression programs accompanied by extensive rewiring of metabolism, with alterations in chromatin modifications providing one layer of integration of transcriptional and metabolic regulation. A systematic and mechanistic understanding of the mutual influences between signal-induced metabolic changes and gene expression is still lacking. Here, we discuss current evidence, controversies, knowledge gaps, and future areas of investigation on how metabolic and transcriptional changes are dynamically integrated during macrophage activation. The cross-talk between metabolism and inflammatory gene expression is in part accounted for by alterations in the production, usage, and availability of metabolic intermediates that impact the macrophage epigenome. In addition, stimulus-inducible gene expression changes alter the production of inflammatory mediators, such as nitric oxide, that in turn modulate the activity of metabolic enzymes thus determining complex regulatory loops. Critical issues remain to be understood, notably whether and how metabolic rewiring can bring about gene-specific (as opposed to global) expression changes.

The deacylase SIRT5 supports melanoma viability by influencing chromatin dynamics

Cutaneous melanoma remains the most lethal skin cancer, and ranks third among all malignancies in terms of years of life lost. Despite the advent of immune checkpoint and targeted therapies, only roughly half of patients with advanced melanoma achieve a durable remission. Sirtuin 5 (SIRT5) is a member of the sirtuin family of protein deacylases that regulates metabolism and other biological processes. Germline Sirt5 deficiency is associated with mild phenotypes in mice. Here we showed that SIRT5 was required for proliferation and survival across all cutaneous melanoma genotypes tested, as well as uveal melanoma, a genetically distinct melanoma subtype that arises in the eye and is incurable once metastatic. Likewise, SIRT5 was required for efficient tumor formation by melanoma xenografts and in an autochthonous mouse Braf Pten-driven melanoma model. Via metabolite and transcriptomic analyses, we found that SIRT5 was required to maintain histone acetylation and methylation levels in melanoma cells, thereby promoting proper gene expression. SIRT5-dependent genes notably included MITF, a key lineage-specific survival oncogene in melanoma, and the c-MYC proto-oncogene. SIRT5 may represent a druggable genotype-independent addiction in melanoma.

LIN28A inhibits DUSP family phosphatases and activates MAPK signaling pathway to maintain pluripotency in porcine induced pluripotent stem cells

LIN28A, an RNA-binding protein, plays an important role in porcine induced pluripotent stem cells (piPSCs). However, the molecular mechanism underlying the function of LIN28A in the maintenance of pluripotency in piPSCs remains unclear. Here, we explored the function of LIN28A in piPSCs based on its overexpression and knockdown. We performed total RNA sequencing (RNA-seq) of piPSCs and detected the expression levels of relevant genes by quantitative real-time polymerase chain reaction (qRT-PCR), western blot analysis, and immunofluorescence staining. Results indicated that piPSC proliferation ability decreased following LIN28A knockdown. Furthermore, when LIN28A expression in the shLIN28A2 group was lower (by 20%) than that in the negative control knockdown group ( shNC), the pluripotency of piPSCs disappeared and they differentiated into neuroectoderm cells. Results also showed that LIN28A overexpression inhibited the expression of DUSP (dual-specificity phosphatases) family phosphatases and activated the mitogen-activated protein kinase (MAPK) signaling pathway. Thus, LIN28A appears to activate the MAPK signaling pathway to maintain the pluripotency and proliferation ability of piPSCs. Our study provides a new resource for exploring the functions of LIN28A in piPSCs.

Testicular germ cell tumors arise in the absence of sex-specific differentiation

In response to signals from the embryonic testis, the germ cell intrinsic factor NANOS2 coordinates a transcriptional program necessary for the differentiation of pluripotent-like primordial germ cells toward a unipotent spermatogonial stem cell fate. Emerging evidence indicates that genetic risk factors contribute to testicular germ cell tumor initiation by disrupting sex-specific differentiation. Here, using the 129.MOLF-Chr19 mouse model of testicular teratomas and a NANOS2 reporter allele, we report that the developmental phenotypes required for tumorigenesis, including failure to enter mitotic arrest, retention of pluripotency and delayed sex-specific differentiation, were exclusive to a subpopulation of germ cells failing to express NANOS2. Single-cell RNA sequencing revealed that embryonic day 15.5 NANOS2-deficient germ cells and embryonal carcinoma cells developed a transcriptional profile enriched for MYC signaling, NODAL signaling and primed pluripotency. Moreover, lineage-tracing experiments demonstrated that embryonal carcinoma cells arose exclusively from germ cells failing to express NANOS2. Our results indicate that NANOS2 is the nexus through which several genetic risk factors influence tumor susceptibility. We propose that, in the absence of sex specification, signals native to the developing testis drive germ cell transformation.

Activation of MAT2A-RIP1 signaling axis reprograms monocytes in gastric cancer

BACKGROUND

The activation of tumor-associated macrophages (TAMs) facilitates the progression of gastric cancer (GC). Cell metabolism reprogramming has been shown to play a vital role in the polarization of TAMs. However, the role of methionine metabolism in function of TAMs remains to be explored.

METHODS

Monocytes/macrophages were isolated from peripheral blood, tumor tissues or normal tissues from healthy donors or patients with GC. The role of methionine metabolism in the activation of TAMs was evaluated with both in vivo analyses and in vitro experiments. Pharmacological inhibition of the methionine cycle and modulation of key metabolic genes was employed, where molecular and biological analyses were performed.

RESULTS

TAMs have increased methionine cycle activity that are mainly attributed to elevated methionine adenosyltransferase II alpha (MAT2A) levels. MAT2A modulates the activation and maintenance of the phenotype of TAMs and mediates the upregulation of RIP1 by increasing the histone H3K4 methylation (H3K4me3) at its promoter regions.

CONCLUSIONS

Our data cast light on a novel mechanism by which methionine metabolism regulates the anti-inflammatory functions of monocytes in GC. MAT2A might be a potential therapeutic target for cancer cells as well as TAMs in GC.

PluriMetNet: A dynamic electronic model decrypting the metabolic variations in human embryonic stem cells (hESCs) at fluctuating oxygen concentrations

Stem cells are an excellent resource in translational medicine however much is known only in terms of transcriptional and epigenetic regulation of human embryonic stem cells (hESCs). Metabolic regulation of hESCs is still unexplored in many ways, particularly the role of energy metabolism, which is intrinsic to the maintenance of cell viability, however, is very little explored in the past years. Also, there exists no hESC specific core metabolic model of pluripotency as per our knowledge. Through our work, we establish such a metabolic model of hESC using combinatorial in-silico approach of genome scale model reduction and literature curation. Further, through perturbations taking oxygen as a parameter we propose that under lower levels of oxygen concentration there is a significant dynamic change in the energy metabolism of the hESC. We further investigated energy subsystem pathways and their respective reactions in order to locate the direction of energy production along with the dynamic of nutrient metabolites like glucose and glutamine. The output shows a steep increment/decrement at a certain oxygen range. These sharp increments/decrements under hypoxic conditions are termed here as a critical range for hESC metabolic pathway. The data also resonates with the previous experimental studies on hESC energy metabolism confirming the robustness of our model. The model helps to extract range for different pathways in the energy subsystem, making us a little closer in understanding the metabolism of hESC. We also demonstrated the possible range of pathway changes in hESC's energy metabolism that can serve as the crucial preliminary data for further prospective studies. The model also offers a promise in the prediction of the flux behaviour of various metabolites in hESC.Communicated by Ramaswamy H. Sarma.

Chitosan 3D cell culture system promotes naïve-like features of human induced pluripotent stem cells: A novel tool to sustain pluripotency and facilitate differentiation

A simplified and cost-effective culture system for maintaining the pluripotency of human induced pluripotent stem cells (hiPSCs) is crucial for stem cell applications. Although recombinant protein-based feeder-free hiPSC culture systems have been developed, their manufacturing processes are expensive and complicated, which hinders hiPSC technology progress. Chitosan, a versatile biocompatible polysaccharide, has been reported as a biomaterial for three-dimensional (3D) cell culture system that promotes the physiological activities of mesenchymal stem cells and cancer cells. In the current study, we demonstrated that chitosan membranes sustained proliferation and pluripotency of hiPSCs in long-term culture (up to 365 days). Moreover, using vitronectin as the comparison group, the pluripotency of hiPSCs grown on the membranes was altered into a naïve-like state, which, for pluripotent stem cells, is an earlier developmental stage with higher stemness. On the chitosan membranes, hiPSCs self-assembled into 3D spheroids with an average diameter of ~100 μm. These hiPSC spheroids could be directly differentiated into lineage-specific cells from the three germ layers with 3D structures. Collectively, chitosan membranes not only promoted the naïve pluripotent features of hiPSCs but also provided a novel 3D differentiation platform. This convenient biomaterial-based culture system may enable the effective expansion and accessibility of hiPSCs for regenerative medicine, disease modeling, and drug screening.

Stirred suspension bioreactors maintain naïve pluripotency of human pluripotent stem cells

Due to their ability to standardize key physiological parameters, stirred suspension bioreactors can potentially scale the production of quality-controlled pluripotent stem cells (PSCs) for cell therapy application. Because of differences in bioreactor expansion efficiency between mouse (m) and human (h) PSCs, we investigated if conversion of hPSCs, from the conventional "primed" pluripotent state towards the "naïve" state prevalent in mPSCs, could be used to enhance hPSC production. Through transcriptomic enrichment of mechano-sensing signaling, the expression of epigenetic regulators, metabolomics, and cell-surface protein marker analyses, we show that the stirred suspension bioreactor environment helps maintain a naïve-like pluripotent state. Our research corroborates that converting hPSCs towards a naïve state enhances hPSC manufacturing and indicates a potentially important role for the stirred suspension bioreactor's mechanical environment in maintaining naïve-like pluripotency.

Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling

Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications. This metabolism-epigenome cross talk plays a fundamental role in development, immune function, and diseases like cancer. Yet, major challenges remain in understanding the interactions between cellular metabolism and the epigenome. How the levels and fluxes of various metabolites impact epigenetic marks is still unclear. Discussed herein are recent applications and the potential of systems biology methods such as flux tracing and metabolic modeling to address these challenges and to uncover new metabolic-epigenetic interactions. These systems approaches can ultimately help elucidate how nutrients shape the epigenome of microbes and mammalian cells.

Purine metabolism regulates DNA repair and therapy resistance in glioblastoma

Intratumoral genomic heterogeneity in glioblastoma (GBM) is a barrier to overcoming therapy resistance. Treatments that are effective independent of genotype are urgently needed. By correlating intracellular metabolite levels with radiation resistance across dozens of genomically-distinct models of GBM, we find that purine metabolites, especially guanylates, strongly correlate with radiation resistance. Inhibiting GTP synthesis radiosensitizes GBM cells and patient-derived neurospheres by impairing DNA repair. Likewise, administration of exogenous purine nucleosides protects sensitive GBM models from radiation by promoting DNA repair. Neither modulating pyrimidine metabolism nor purine salvage has similar effects. An FDA-approved inhibitor of GTP synthesis potentiates the effects of radiation in flank and orthotopic patient-derived xenograft models of GBM. High expression of the rate-limiting enzyme of de novo GTP synthesis is associated with shorter survival in GBM patients. These findings indicate that inhibiting purine synthesis may be a promising strategy to overcome therapy resistance in this genomically heterogeneous disease.

Targeting genotype-independent abnormalities may overcome therapy resistance in glioblastoma despite intratumoral genomic heterogeneity. Here, the authors show that glioblastoma radiation resistance is promoted by purine metabolism and can be overcome by inhibitors of purine synthesis.

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

Common biochemical properties of metabolic genes recurrently dysregulated in tumors

BACKGROUND

Tumor initiation and progression are associated with numerous metabolic alterations. However, the biochemical drivers and constraints that contribute to metabolic gene dysregulation are unclear.

METHODS

Here, we present MetOncoFit, a computational model that integrates 142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topology, and reaction flux. MetOncoFit uses genome-scale metabolic modeling and machine-learning to quantify the relative importance of various metabolic features in predicting cancer metabolic gene expression, copy number variation, and survival data.

RESULTS

Using MetOncoFit, we performed a meta-analysis of 9 cancer types and over 4500 samples from TCGA, Prognoscan, and COSMIC tumor databases. MetOncoFit accurately predicted enzyme differential expression and its impact on patient survival using the 142 attributes of metabolic enzymes. Our analysis revealed that enzymes with high catalytic activity were frequently upregulated in many tumors and associated with poor survival. Topological analysis also identified specific metabolites that were hot spots of dysregulation.

CONCLUSIONS

MetOncoFit integrates a broad range of datasets to understand how biochemical and topological features influence metabolic gene dysregulation across various cancer types. MetOncoFit was able to achieve significantly higher accuracy in predicting differential expression, copy number variation, and patient survival than traditional modeling approaches. Overall, MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.

Pleiotropic roles of tankyrase/PARP proteins in the establishment and maintenance of human naïve pluripotency

Tankyrase 1 (TNKS1; PARP-5a) and Tankyrase 2 (TNKS2; PARP-5b) are poly-ADP-ribosyl-polymerase (PARP)-domain-containing proteins that regulate the activities of a wide repertoire of target proteins via post-translational addition of poly-ADP-ribose polymers (PARylation). Although tankyrases were first identified as regulators of human telomere elongation, important and expansive roles of tankyrase activity have recently emerged in the development and maintenance of stem cell states. Herein, we summarize the current state of knowledge of the various tankyrase-mediated activities that may promote human naïve and 'extended' pluripotency'. We review the putative role of tankyrase and PARP inhibition in trophectoderm specification, telomere elongation, DNA repair and chromosomal segregation, metabolism, and PTEN-mediated apoptosis. Importantly, tankyrases possess PARP-independent activities that include regulation of MDC1-associated DNA repair by homologous recombination (HR) and autophagy/pexophagy, which is an essential mechanism of protein synthesis in the preimplantation embryo. Additionally, tankyrases auto-regulate themselves via auto-PARylation which augments their cellular protein levels and potentiates their non-PARP tankyrase functions. We propose that these non-PARP-related activities of tankyrase proteins may further independently affect both naïve and extended pluripotency via mechanisms that remain undetermined. We broadly outline a hypothetical framework for how inclusion of a tankyrase/PARP inhibitor in small molecule cocktails may stabilize and potentiate naïve and extended pluripotency via pleiotropic routes and mechanisms.

Tissue of origin dictates GOT1 dependence and confers synthetic lethality to radiotherapy

BACKGROUND

Metabolic programs in cancer cells are influenced by genotype and the tissue of origin. We have previously shown that central carbon metabolism is rewired in pancreatic ductal adenocarcinoma (PDA) to support proliferation through a glutamate oxaloacetate transaminase 1 (GOT1)-dependent pathway.

METHODS

We utilized a doxycycline-inducible shRNA-mediated strategy to knockdown GOT1 in PDA and colorectal cancer (CRC) cell lines and tumor models of similar genotype. These cells were analyzed for the ability to form colonies and tumors to test if tissue type impacted GOT1 dependence. Additionally, the ability of GOT1 to impact the response to chemo- and radiotherapy was assessed. Mechanistically, the associated specimens were examined using a combination of steady-state and stable isotope tracing metabolomics strategies and computational modeling. Statistics were calculated using GraphPad Prism 7. One-way ANOVA was performed for experiments comparing multiple groups with one changing variable. Student's t test (unpaired, two-tailed) was performed when comparing two groups to each other. Metabolomics data comparing three PDA and three CRC cell lines were analyzed by performing Student's t test (unpaired, two-tailed) between all PDA metabolites and CRC metabolites.

RESULTS

While PDA exhibits profound growth inhibition upon GOT1 knockdown, we found CRC to be insensitive. In PDA, but not CRC, GOT1 inhibition disrupted glycolysis, nucleotide metabolism, and redox homeostasis. These insights were leveraged in PDA, where we demonstrate that radiotherapy potently enhanced the effect of GOT1 inhibition on tumor growth.

CONCLUSIONS

Taken together, these results illustrate the role of tissue type in dictating metabolic dependencies and provide new insights for targeting metabolism to treat PDA.

Inferring Metabolic Flux from Time-Course Metabolomics

The metabolic activity of a mammalian cell changes dynamically over time and is tied to the changing metabolic demands of cellular processes such as cell differentiation and proliferation. While experimental tools like time-course metabolomics and flux tracing can measure the dynamics of a few pathways, they are unable to infer fluxes at the whole network level. To address this limitation, we have developed the Dynamic Flux Activity (DFA) algorithm, a genome-scale modeling approach that uses time-course metabolomics to predict dynamic flux rewiring during transitions between metabolic states. This chapter provides a protocol for applying DFA to characterize the dynamic metabolic activity of various cancer cell lines.

Dissecting primate early post-implantation development using long-term in vitro embryo culture

The transition from peri-implantation to gastrulation in mammals entails the specification and organization of the lineage progenitors into a body plan. Technical and ethical challenges have limited understanding of the cellular and molecular mechanisms that underlie this transition. We established a culture system that enabled the development of cynomolgus monkey embryos in vitro for up to 20 days. Cultured embryos underwent key primate developmental stages, including lineage segregation, bilaminar disc formation, amniotic and yolk sac cavitation, and primordial germ cell–like cell (PGCLC) differentiation. Single-cell RNA-sequencing analysis revealed development trajectories of primitive endoderm, trophectoderm, epiblast lineages, and PGCLCs. Analysis of single-cell chromatin accessibility identified transcription factors specifying each cell type. Our results reveal critical developmental events and complex molecular mechanisms underlying nonhuman primate embryogenesis in the early postimplantation period, with possible relevance to human development.

Epstein-Barr-Virus-Induced One-Carbon Metabolism Drives B Cell Transformation

Epstein-Barr virus (EBV) causes Burkitt, Hodgkin, and post-transplant B cell lymphomas. How EBV remodels metabolic pathways to support rapid B cell outgrowth remains largely unknown. To gain insights, primary human B cells were profiled by tandem-mass-tag-based proteomics at rest and at nine time points after infection; >8,000 host and 29 viral proteins were quantified, revealing mitochondrial remodeling and induction of one-carbon (1C) metabolism. EBV-encoded EBNA2 and its target MYC were required for upregulation of the central mitochondrial 1C enzyme MTHFD2, which played key roles in EBV-driven B cell growth and survival. MTHFD2 was critical for maintaining elevated NADPH levels in infected cells, and oxidation of mitochondrial NADPH diminished B cell proliferation. Tracing studies underscored contributions of 1C to nucleotide synthesis, NADPH production, and redox defense. EBV upregulated import and synthesis of serine to augment 1C flux. Our results highlight EBV-induced 1C as a potential therapeutic target and provide a new paradigm for viral onco-metabolism.

The metabolic network model of primed/naive human embryonic stem cells underlines the importance of oxidation-reduction potential and tryptophan metabolism in primed pluripotency

Background

Pluripotency is proposed to exist in two different stages: Naive and Primed. Conventional human pluripotent cells are essentially in the primed stage. In recent years, several protocols have claimed to generate naive human embryonic stem cells (hESCs). To the best of our knowledge, none of these protocols is currently recognized as the gold standard method. Furthermore, the consistency of the resulting cells from these diverse protocols at the molecular level is yet to be shown. Additionally, little is known about the principles that govern the metabolic differences between naive and primed pluripotency. In this work, using a computational approach, we tried to shed light on these basic issues.

Results

We showed that, after batch effect removal, the transcriptome data of eight different protocols which supposedly produce naive hESCs are clustered consistently when compared to the primed ones. Next, by integrating transcriptomes of all hESCs obtained by these protocols, we reconstructed p-hESCNet and n-hESCNet, the first metabolic network models representing hESCs. By exploiting reporter metabolite analysis we showed that the status of NAD\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{+}$$\end{document}+ and the metabolites involved in the TCA cycle are significantly altered between naive and primed hESCs. Furthermore, using flux variability analysis (FVA), the models showed that the kynurenine-mediated metabolism of tryptophan is remarkably downregulated in naive human pluripotent cells.

Conclusion

The aim of the present paper is twofold. Firstly, our findings confirm the applicability of all these protocols for generating naive hESCs, due to their consistency at the transcriptome level. Secondly, we showed that in silico metabolic models of hESCs can be used to simulate the metabolic states of naive and primed pluripotency. Our models confirmed the OXPHOS activation in naive cells and showed that oxidation-reduction potential vary between naive and primed cells. Tryptophan metabolism is also outlined as a key pathway in primed pluripotency and the models suggest that decrements in the activity of this pathway might be an appropriate marker for naive pluripotency.

Tying Metabolic Branches With Histone Tails Using Systems Biology

Histone modifications represent an innate cellular mechanism to link nutritional status to gene expression. Metabolites such as acetyl-CoA and S-adenosyl methionine influence gene expression by serving as substrates for modification of histones. Yet, we lack a predictive model for determining histone modification levels based on cellular metabolic state. The numerous metabolic pathways that intersect with histone marks makes it highly challenging to understand their interdependencies. Here, we highlight new systems biology tools to unravel the impact of nutritional cues and metabolic fluxes on histone modifications.

Folate can promote the methionine-dependent reprogramming of glioblastoma cells towards pluripotency

Methionine dependency of tumor growth, although not well-understood, is detectable by ¹¹C-methionine positron emission tomography and may contribute to the aggressivity of glioblastomas (GBM) and meningiomas. Cytosolic folate cycle is required for methionine synthesis. Its dysregulation may influence cell reprogramming towards pluripotency. We evaluated methionine-dependent growth of monolayer (ML) cells and stem cell-like tumor spheres (TS) derived from 4 GBM (U251, U87, LN299, T98G) and 1 meningioma (IOMM-LEE) cell lines. Our data showed that for all cell lines studied, exogenous methionine is required for TS formation but not for ML cells proliferation. Furthermore, for GBM cell lines, regardless of the addition of folate cycle substrates (folic acid and formate), the level of 3 folate isoforms, 5-methytetrahydrofolate, 5,10-methenyltetrahydrofolate, and 10-formyltetrahydrofolate, were all downregulated in TS relative to ML cells. Unlike GBM cell lines, in IOMM-LEE cells, 5-methyltetrahydrofolate was actually more elevated in TS than ML, and only 5,10-methenyltetrahydrofolate and 10-formyltetrahydrofolate were downregulated. The functional significance of this variation in folate cycle repression was revealed by the finding that Folic Acid and 5-methyltetrahydrofolate promote the growth of U251 TS but not IOMM-LEE TS. Transcriptome-wide sequencing of U251 cells revealed that DHFR, SHMT1, and MTHFD1 were downregulated in TS vs ML, in concordance with the low activity cytosolic folate cycle observed in U251 TS. In conclusion, we found that a repressed cytosolic folate cycle underlies the methionine dependency of GBM and meningioma cell lines and that 5-methyltetrahydrofolate is a key metabolic switch for glioblastoma TS formation. The finding that folic acid facilitates TS formation, although requiring further validation in diseased human tissues, incites to investigate whether excessive folate intake could promote cancer stem cells formation in GBM patients.

Metabolic-Epigenetic Axis in Pluripotent State Transitions

Cell state transition (CST) occurs during embryo development and in adult life in response to different stimuli and is associated with extensive epigenetic remodeling. Beyond growth factors and signaling pathways, increasing evidence point to a crucial role of metabolic signals in this process. Indeed, since several epigenetic enzymes are sensitive to availability of specific metabolites, fluctuations in their levels may induce the epigenetic changes associated with CST. Here we analyze how fluctuations in metabolites availability influence DNA/chromatin modifications associated with pluripotent stem cell (PSC) transitions. We discuss current studies and focus on the effects of metabolites in the context of naïve to primed transition, PSC differentiation and reprogramming of somatic cells to induced pluripotent stem cells (iPSCs), analyzing their mechanism of action and the causal correlation between metabolites availability and epigenetic alteration.

Mitochondrial Akt Signaling Modulated Reprogramming of Somatic Cells

The signaling mechanisms controlling somatic cell reprogramming are not fully understood. In this study, we report a novel role for mitochondrial Akt1 signaling that enhanced somatic cell reprogramming efficiency. The role of mitochondrial Akt1 in somatic cell reprogramming was investigated by transducing fibroblasts with the four reprogramming factors (Oct4, Sox2, Klf4, c-Myc) in conjunction with Mito-Akt1, Mito-dnAkt1, or control virus. Mito-Akt1 enhanced reprogramming efficiency whereas Mito-dnAkt1 inhibited reprogramming. The resulting iPSCs formed embryoid bodies in vitro and teratomas in vivo. Moreover, Oct4 and Nanog promoter methylation was reduced in the iPSCs generated in the presence of Mito-Akt1. Akt1 was activated and translocated into mitochondria after growth factor stimulation in embryonic stem cells (ESCs). To study the effect of mitochondrial Akt in ESCs, a mitochondria-targeting constitutively active Akt1 (Mito-Akt1) was expressed in ESCs. Gene expression profiling showed upregulation of genes that promote stem cell proliferation and survival and down-regulation of genes that promote differentiation. Analysis of cellular respiration indicated similar metabolic profile in the resulting iPSCs and ESCs, suggesting comparable bioenergetics. These findings showed that activation of mitochondrial Akt1 signaling was required during somatic cell reprogramming.

Dynamic genome-scale cell-specific metabolic models reveal novel inter-cellular and intra-cellular metabolic communications during ovarian follicle development

BACKGROUND

The maturation of the female germ cell, the oocyte, requires the synthesis and storing of all the necessary metabolites to support multiple divisions after fertilization. Oocyte maturation is only possible in the presence of surrounding, diverse, and changing layers of somatic cells. Our understanding of metabolic interactions between the oocyte and somatic cells has been limited due to dynamic nature of ovarian follicle development, thus warranting a systems approach.

RESULTS

Here, we developed a genome-scale metabolic model of the mouse ovarian follicle. This model was constructed using an updated mouse general metabolic model (Mouse Recon 2) and contains several key ovarian follicle development metabolic pathways. We used this model to characterize the changes in the metabolism of each follicular cell type (i.e., oocyte, granulosa cells, including cumulus and mural cells), during ovarian follicle development in vivo. Using this model, we predicted major metabolic pathways that are differentially active across multiple follicle stages. We identified a set of possible secreted and consumed metabolites that could potentially serve as biomarkers for monitoring follicle development, as well as metabolites for addition to in vitro culture media that support the growth and maturation of primordial follicles.

CONCLUSIONS

Our systems approach to model follicle metabolism can guide future experimental studies to validate the model results and improve oocyte maturation approaches and support growth of primordial follicles in vitro.

Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors

Histone acetylation plays a central role in gene regulation and is sensitive to the levels of metabolic intermediates. However, predicting the impact of metabolic alterations on acetylation in pathological conditions is a significant challenge. Here, we present a genome-scale network model that predicts the impact of nutritional environment and genetic alterations on histone acetylation. It identifies cell types that are sensitive to histone deacetylase inhibitors based on their metabolic state, and we validate metabolites that alter drug sensitivity. Our model provides a mechanistic framework for predicting how metabolic perturbations contribute to epigenetic changes and sensitivity to deacetylase inhibitors.

S-adenosylmethionine biosynthesis is a targetable metabolic vulnerability of cancer stem cells

PURPOSE

Many transformed cells and embryonic stem cells are dependent on the biosynthesis of the universal methyl-donor S-adenosylmethionine (SAM) from methionine by the enzyme MAT2A to maintain their epigenome. We hypothesized that cancer stem cells (CSCs) rely on SAM biosynthesis and that the combination of methionine depletion and MAT2A inhibition would eradicate CSCs.

METHODS

Human triple (ER/PR/HER2)-negative breast carcinoma (TNBC) cell lines were cultured as CSC-enriched mammospheres in control or methionine-free media. MAT2A was inhibited with siRNAs or cycloleucine. The effects of methionine restriction and/or MAT2A inhibition on the formation of mammospheres, the expression of CSC markers (CD44^hi/C24^low), MAT2A and CSC transcriptional regulators, apoptosis induction and histone modifications were determined. A murine model of metastatic TNBC was utilized to evaluate the effects of dietary methionine restriction, MAT2A inhibition and the combination.

RESULTS

Methionine restriction inhibited mammosphere formation and reduced the CD44^hi/C24^low CSC population; these effects were partly rescued by SAM. Methionine depletion induced MAT2A expression (mRNA and protein) and sensitized CSCs to inhibition of MAT2A (siRNAs or cycloleucine). Cycloleucine enhanced the effects of methionine depletion on H3K4me3 demethylation and suppression of Sox9 expression. Dietary methionine restriction induced MAT2A expression in mammary tumors, and the combination of methionine restriction and cycloleucine was more effective than either alone at suppressing primary and lung metastatic tumor burden in a murine TNBC model.

CONCLUSIONS

Our findings point to SAM biosynthesis as a unique metabolic vulnerability of CSCs that can be targeted by combining methionine depletion with MAT2A inhibition to eradicate drug-resistant CSCs.

Dynamic Network Modeling of Stem Cell Metabolism

Stem cell metabolism is intrinsically tied to stem cell pluripotency and function. Yet, understanding metabolic rewiring in stem cells has been challenging due to the complex and highly interconnected nature of the metabolic network. Genome-scale metabolic network models are increasingly used to holistically model the metabolic behavior of various cells and tissues using transcriptomics data. However, these powerful approaches that model steady-state behavior have limited utility for studying dynamic stem cell state transitions. To address this complexity, we recently developed the dynamic flux activity (DFA) approach; DFA is a genome-scale modeling approach that uses time-course metabolic data to predict metabolic flux rewiring. This protocol outlines the steps for modeling steady-state and dynamic metabolic behavior using transcriptomics and time-course metabolomics data, respectively. Using data from naive and primed pluripotent stem cells, we demonstrate how we can use genome-scale modeling and DFA to comprehensively characterize the metabolic differences between these states.

Metabolomics in Stem Cell Biology Research

Stem cell research has been greatly facilitated by comprehensive and integrative multi-omics studies. As a unique approach of functional analysis, metabolomics measures many metabolites and activities of metabolic pathways which can directly indicate cellular energetic status, cell proliferation and fitness, and stem cell fate choices such as self-renewal versus differentiation. Here we describe the methods of applying metabolomics, ¹³C-labeled glucose and glutamine tracing with mouse embryonic stem cells (ES cells), metabolite analysis using mass spectrometry tools, and the following statistical and computational modeling analysis. Integration of these methods into the more common gene expression and epigenetics analysis toolbox will help to generate a more complete picture and in-depth understanding of one's stem cells of interest.