Metabolic modeling of endosymbiont genome reduction on a temporal scale

A fundamental challenge in Systems Biology is whether a cell‐scale metabolic model can predict patterns of genome evolution by realistically accounting for associated biochemical constraints. Here, we study the order in which genes are lost in an in silico evolutionary process, leading from the metabolic network of Eschericia coli to that of the endosymbiont Buchnera aphidicola. We examine how this order correlates with the order by which the genes were actually lost, as estimated from a phylogenetic reconstruction. By optimizing this correlation across the space of potential growth and biomass conditions, we compute an upper bound estimate on the model's prediction accuracy (R=0.54). The model's network‐based predictive ability outperforms predictions obtained using genomic features of individual genes, reflecting the effect of selection imposed by metabolic stoichiometric constraints. Thus, while the timing of gene loss might be expected to be a completely stochastic evolutionary process, remarkably, we find that metabolic considerations, on their own, make a marked 40% contribution to determining when such losses occur.     

Modular,synthetic chromosomes as new tools for large scale engineering of metabolism

The construction of powerful cell factories requires intensive genetic engineering for the addition of new functionalities and the remodeling of native pathways and processes. The present study demonstrates the feasibility of extensive genome reprogramming using modular, specialized de novo-assembled neochromosomes in yeast. The in vivo assembly of linear and circular neochromosomes, carrying 20 native and 21 heterologous genes, enabled the first de novo production in a microbial cell factory of anthocyanins, plant compounds with a broad range of pharmacological properties. Turned into exclusive expression platforms for heterologous and essential metabolic routes, the neochromosomes mimic native chromosomes regarding mitotic and genetic stability, copy number, harmlessness for the host and editability by CRISPR/Cas9. This study paves the way for future microbial cell factories with modular genomes in which core metabolic networks, localized on satellite, specialized neochromosomes can be swapped for alternative configurations and serve as landing pads for the addition of functionalities.     

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing

Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O₂). This review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learning and transfer learning), which may facilitate “Learn and Design” for strain development.     

Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction

Growth is a fundamental process of life. Growth requirements are well‐characterized experimentally for many microbes; however, we lack a unified model for cellular growth. Such a model must be predictive of events at the molecular scale and capable of explaining the high‐level behavior of the cell as a whole. Here, we construct an ME‐Model for Escherichia coli—a genome‐scale model that seamlessly integrates metabolic and gene product expression pathways. The model computes ～80% of the functional proteome (by mass), which is used by the cell to support growth under a given condition. Metabolism and gene expression are interdependent processes that affect and constrain each other. We formalize these constraints and apply the principle of growth optimization to enable the accurate prediction of multi‐scale phenotypes, ranging from coarse‐grained (growth rate, nutrient uptake, by‐product secretion) to fine‐grained (metabolic fluxes, gene expression levels). Our results unify many existing principles developed to describe bacterial growth.     

Dysregulated lipid metabolism in cancer

Alteration of lipid metabolism has been increasingly recognized as a hallmark of cancer cells. The changes of expression and activity of lipid metabolizing enzymes are directly regulated by the activity of oncogenic signals. The dependence of tumor cells on the dysregulated lipid metabolism suggests that proteins involved in this process are excellent chemotherapeutic targets for cancer treatment. There are currently several drugs under development or in clinical trials that are based on specifically targeting the altered lipid metabolic pathways in cancer cells. Further understanding of dysregulated lipid metabolism and its associated signaling pathways will help us to better design efficient cancer therapeutic strategy.     

Energy metabolism in cancer stem cells

Normal cells mainly rely on oxidative phosphorylation as an effective energy source in the presence of oxygen. In contrast, most cancer cells use less efficient glycolysis to produce ATP and essential biomolecules. Cancer cells gain the characteristics of metabolic adaptation by reprogramming their metabolic mechanisms to meet the needs of rapid tumor growth. A subset of cancer cells with stem characteristics and the ability to regenerate exist throughout the tumor and are therefore called cancer stem cells (CSCs). New evidence indicates that CSCs have different metabolic phenotypes compared with differentiated cancer cells. CSCs can dynamically transform their metabolic state to favor glycolysis or oxidative metabolism. The mechanism of the metabolic plasticity of CSCs has not been fully elucidated, and existing evidence indicates that the metabolic phenotype of cancer cells is closely related to the tumor microenvironment. Targeting CSC metabolism may provide new and effective methods for the treatment of tumors. In this review, we summarize the metabolic characteristics of cancer cells and CSCs and the mechanisms of the metabolic interplay between the tumor microenvironment and CSCs, and discuss the clinical implications of targeting CSC metabolism.     

Complete genome sequence,metabolic model construction and phenotypic characterization of Geobacillus LC300, an extremely thermophilic,fast growing,xylose-utilizing bacterium

We have isolated a new extremely thermophilic fast-growing Geobacillus strain that can efficiently utilize xylose, glucose, mannose and galactose for cell growth. When grown aerobically at 72 °C, Geobacillus LC300 has a growth rate of 2.15 h⁻¹ on glucose and 1.52 h⁻¹ on xylose (doubling time less than 30 min). The corresponding specific glucose and xylose utilization rates are 5.55 g/g/h and 5.24 g/g/h, respectively. As such, Geobacillus LC300 grows 3-times faster than E. coli on glucose and xylose, and has a specific xylose utilization rate that is 3-times higher than the best metabolically engineered organism to date. To gain more insight into the metabolism of Geobacillus LC300 its genome was sequenced using PacBio׳s RS II single-molecule real-time (SMRT) sequencing platform and annotated using the RAST server. Based on the genome annotation and the measured biomass composition a core metabolic network model was constructed. To further demonstrate the biotechnological potential of this organism, Geobacillus LC300 was grown to high cell-densities in a fed-batch culture, where cells maintained a high xylose utilization rate under low dissolved oxygen concentrations. All of these characteristics make Geobacillus LC300 an attractive host for future metabolic engineering and biotechnology applications.     

A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration

Over the last decade, the field of cancer metabolism has mainly focused on studying the role of tumorigenic metabolic rewiring in supporting cancer proliferation. Here, we perform the first genome‐scale computational study of the metabolic underpinnings of cancer migration. We build genome‐scale metabolic models of the NCI‐60 cell lines that capture the Warburg effect (aerobic glycolysis) typically occurring in cancer cells. The extent of the Warburg effect in each of these cell line models is quantified by the ratio of glycolytic to oxidative ATP flux (AFR), which is found to be highly positively associated with cancer cell migration. We hence predicted that targeting genes that mitigate the Warburg effect by reducing the AFR may specifically inhibit cancer migration. By testing the anti‐migratory effects of silencing such 17 top predicted genes in four breast and lung cancer cell lines, we find that up to 13 of these novel predictions significantly attenuate cell migration either in all or one cell line only, while having almost no effect on cell proliferation. Furthermore, in accordance with the predictions, a significant reduction is observed in the ratio between experimentally measured ECAR and OCR levels following these perturbations. Inhibiting anti‐migratory targets is a promising future avenue in treating cancer since it may decrease cytotoxic‐related side effects that plague current anti‐proliferative treatments. Furthermore, it may reduce cytotoxic‐related clonal selection of more aggressive cancer cells and the likelihood of emerging resistance.     

Unraveling the mystery of cancer metabolism in the genesis of tumor-initiating cells and development of cancer

Robust anaerobic metabolism plays a causative role in the origin of cancer cells; however, the oncogenic metabolic genes, factors, pathways, and networks in genesis of tumor-initiating cells (TICs) have not yet been systematically summarized. In addition, the mechanisms of oncogenic metabolism in the genesis of TICs are enigmatic. In this review, we discussed multiple cancer metabolism-related genes (MRGs) that are overexpressed in TICs and are responsible for inducing pluripotent stem cells. Moreover, we summarized that oncogenic metabolic genes and onco-metabolites induce metabolic reprogramming, which switches normal mitochondrial oxidative phosphorylation to cancer anaerobic metabolism, triggers epigenetic, genetic, and environmental alterations, drives the generation of TICs, and boosts the development of cancer. Importantly, cancer metabolism is controlled by positive and negative metabolic regulators. Positive oncogenic metabolic regulators, including key oncogenic metabolic genes, onco-metabolites, hypoxia, and an acidic environment, promote oncogenic metabolic reprogramming and anaerobic metabolism. However, dysfunction of negative metabolic regulators, including defects in p53, PTEN, and LKB1-AMPK-mTOR pathways, enhances cancer metabolism. Loss of the metabolic balance results in oncogenic metabolic reprogramming, genesis of TICs, and tumorigenesis. Collectively, this review provides new insight into the role and mechanism of these oncogenic metabolisms in the genesis of TICs and tumorigenesis. Accordingly, targeting key oncogenic genes, onco-metabolites, pathways, networks, and the acidic cancer microenvironment appears to be an attractive strategy for novel anti-tumor treatment.     

LMW-PTP modulates glucose metabolism in cancer cells

Background

Low Molecular Weight Phosphotyrosine Protein Phosphatase (LMW-PTP) is an enzyme involved not only in tumor onset and progression but also in type 2 diabetes. A recent review shows that LMW-PTP acts on several RTK (receptor tyrosine kinase) such as PDGFR, EGFR, EphA2, Insulin receptor. It is well described also its interaction with cSrc. It is noteworthy that most of these conclusions are based on the use of cell lines expressing low levels of LMW-PTP. The aim of the present study was to discover new LMW-PTP substrates in aggressive human tumors where the over-expression of this phosphatase is a common feature.

Dissecting the energy metabolism in Mycoplasma pneumoniae through genome‐scale metabolic modeling

Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint‐based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time‐dependent changes, albeit using a static model. By performing an in silico knock‐out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms.     

Tumor metabolism,cancer cell transporters,and microenvironmental resistance

Cancer cells reprogram their metabolic machineries to enter into permanent glycolytic pathways. The full reason for such reprogramming takes place is unclear. However, this metabolic switch is not made in vain for the lactate that is generated and exported outside cells is reused by other cells. This results in the generation of a pH gradient between the low extracellular pH that is acidic (pHe) and the higher cytosolic alkaline or near neutral pH (pHi) environments that are tightly regulated by the overexpression of several pumps and ion channels (e.g. NHE-1, MCT-1, V-ATPase, CA9, and CA12). The generation of this unique pH gradient serves as a determining factor in defining “tumor fitness”. Tumor fitness is the capacity of the tumor to invade and metastasize due to its ability to reduce the efficiency of the immune system and confer resistance to chemotherapy. In this article, we highlight the importance of tumor microenvironment in mediating the failure of chemotherapeutic agents.     

Interplay between genome organization and epigenomic alterations of pericentromeric DNA in cancer

In eukaryotic genome biology, the genomic organization inside the three-dimensional(3 D) nucleus is highly complex, and whether this organization governs gene expression is poorly understood. Nuclear lamina(NL)is a filamentous meshwork of proteins present at the lining of inner nuclear membrane that serves as an anchoring platform for genome organization. Large chromatin domains termed as lamina-associated domains(LADs), play a major role in silencing genes at the nuclear periphery. The interaction of the NL and genome is dynamic and stochastic. Furthermore, many genes change their positions during developmental processes or under disease conditions such as cancer, to activate certain sorts of genes and/or silence others. Pericentromeric heterochromatin(PCH) is mostly in the silenced region within the genome, which localizes at the nuclear periphery. Studies show that several genes located at the PCH are aberrantly expressed in cancer. The interesting question is that despite being localized in the pericentromeric region,how these genes still manage to overcome pericentromeric repression. Although epigenetic mechanisms control the expression of the pericentromeric region, recent studies about genome organization and genome-nuclear lamina interaction have shed light on a new aspect of pericentromeric gene regulation through a complex and coordinated interplay between epigenomic remodeling and genomic organization in cancer.     

An update on therapeutic opportunities offered by cancer glycolytic metabolism

Almost all invasive cancers, regardless of tissue origin, are characterized by specific modifications of their cellular energy metabolism. In fact, a strong predominance of aerobic glycolysis over oxidative phosphorylation (Warburg effect) is usually associated with aggressive tumour phenotypes. This metabolic shift offers a survival advantage to cancer cells, since they may continue to produce energy and anabolites even when they are exposed to either transient or permanent hypoxic conditions. Moreover, it ensures a high production rate of glycolysis intermediates, useful as building blocks for fast cell proliferation of cancer cells. This peculiar metabolic profile may constitute an ideal target for therapeutic interventions that selectively hit cancer cells with minimal residual systemic toxicity. In this review we provide an update about some of the most recent advances in the discovery of new bioactive molecules that are able to interfere with cancer glycolysis.     

Tumor bioenergetics: An emerging avenue for cancer metabolism targeted therapy

Cell proliferation is a delicately regulated process that couples growth signals and metabolic demands to produce daughter cells. Interestingly, the proliferation of tumor cells immensely depends on glycolysis, the Warburg effect, to ensure a sufficient amount of metabolic flux and bioenergetics for macromolecule synthesis and cell division. This unique metabolic derangement ould provide an opportunity for developing cancer therapeutic strategy, particularly when other diverse anti-cancer treatments have been proved ineffective in achieving durable response, largely due to the emergence of resistance. Recent advances in deeper understanding of cancer metabolism usher in new horizons of the next generation strategy for cancer therapy. Here, we discuss the focused review of cancer energy metabolism, and the therapeutic exploitation of glycolysis and OXPHOS as a novel anti-cancer strategy, with particular emphasis on the promise of this approach, among other cancer metabolism targeted therapies that reveal unexpected complexity and context-dependent metabolic adaptability, complicating the development of effective strategies. [BMB Reports 2014; 47(3): 158-166]     

Close interactions between lncRNAs,lipid metabolism and ferroptosis in cancer

Abnormal lipid metabolism including synthesis, uptake, modification, degradation and transport has been considered a hallmark of malignant tumors and contributes to the supply of substances and energy for rapid cell growth. Meanwhile, abnormal lipid metabolism is also associated with lipid peroxidation, which plays an important role in a newly discovered type of regulated cell death termed ferroptosis. Long noncoding RNAs (lncRNAs) have been proven to be associated with the occurrence and progression of cancer. Growing evidence indicates that lncRNAs are key regulators of abnormal lipid metabolism and ferroptosis in cancer. In this review, we mainly summarized the mechanism by which lncRNAs regulate aberrant lipid metabolism in cancer, illustrated that lipid metabolism can also influence the expression of lncRNAs, and discussed the mechanism by which lncRNAs affect ferroptosis. A comprehensive understanding of the interactions between lncRNAs, lipid metabolism and ferroptosis could help us to develop novel strategies for precise cancer treatment in the future.     

Genome-wide metabolic re-annotation of Ashbya gossypii: new insights into its metabolism through a comparative analysis with Saccharomyces cerevisiae and Kluyveromyces lactis

Background

Ashbya gossypii is an industrially relevant microorganism traditionally used for riboflavin production. Despite the high gene homology and gene order conservation comparatively with Saccharomyces cerevisiae, it presents a lower level of genomic complexity. Its type of growth, placing it among filamentous fungi, questions how close it really is from the budding yeast, namely in terms of metabolism, therefore raising the need for an extensive and thorough study of its entire metabolism. This work reports the first manual enzymatic genome-wide re-annotation of A. gossypii as well as the first annotation of membrane transport proteins.

Results

After applying a developed enzymatic re-annotation pipeline, 847 genes were assigned with metabolic functions. Comparatively to KEGG’s annotation, these data corrected the function for 14% of the common genes and increased the information for 52 genes, either completing existing partial EC numbers or adding new ones. Furthermore, 22 unreported enzymatic functions were found, corresponding to a significant increase in the knowledge of the metabolism of this organism. The information retrieved from the metabolic re-annotation and transport annotation was used for a comprehensive analysis of A. gossypii’s metabolism in comparison to the one of S. cerevisiae (post-WGD – whole genome duplication) and Kluyveromyces lactis (pre-WGD), suggesting some relevant differences in several parts of their metabolism, with the majority being found for the metabolism of purines, pyrimidines, nitrogen and lipids. A considerable number of enzymes were found exclusively in A. gossypii comparatively with K. lactis (90) and S. cerevisiae (13). In a similar way, 176 and 123 enzymatic functions were absent on A. gossypii comparatively to K. lactis and S. cerevisiae, respectively, confirming some of the well-known phenotypes of this organism.

Conclusions

This high quality metabolic re-annotation, together with the first membrane transporters annotation and the metabolic comparative analysis, represents a new important tool for the study and better understanding of A. gossypii’s metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-810) contains supplementary material, which is available to authorized users.