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Thyroid cancer is a common endocrine malignancy with increasing incidence globally. Although most cases can be treated effectively, some cases are more aggressive and have a higher risk of mortality. Inhibiting RET and BRAF kinases has emerged as a potential therapeutic strategy for the treatment of thyroid cancer, particularly in cases of advanced or aggressive disease. However, the development of resistance mechanisms may limit the efficacy of these kinase inhibitors. Therefore, developing precise strategies to target thyroid cancer cell metabolism and overcome resistance is a critical area of research for advancing thyroid cancer treatment. In the field of cancer therapeutics, researchers have explored combinatorial strategies involving dual metabolic inhibition and metabolic inhibitors in combination with targeted therapy, chemotherapy, and immunotherapy to overcome the challenge of metabolic plasticity. This review highlights the need for new therapeutic approaches for thyroid cancer and discusses promising metabolic inhibitors targeting thyroid cancer. It also discusses the challenges posed by metabolic plasticity in the development of effective strategies for targeting cancer cell metabolism and explores the potential advantages of combined metabolic targeting.

Objective To investigate the expression level and significance of the signal peptidase complex subunit 2 (SPCS2) gene in papillary thyroid carcinoma (PTC). Methods UALCAN and HPA Databases were used to investigate the mRNA expression levels of SPCS2 in PTC tissue and normal tissue. Gepia Database was used to predict the influence of the expression level of SPCS2 on the disease-free survival of PTC patients. The Cox regression model was used to investigate the independent risk factors for the prognosis of PTC patients. GO, KEGG, and GSEA analyses were performed for SPCS2 to identify the important signaling pathways involved in PTC. Results The mRNA expression levels of SPCS2 in PTC tissue were significantly lo-wer than those in normal tissue (Z=-43.219,P

Objective To investigate the protective effect of xylo-oligosaccharides on mice with fatty liver induced by high-fat diet and related mechanisms. Methods A total of 40 male C57/BL6J mice, aged 5 weeks, were randomly divided into low-fat diet control group (LFD group), high-fat diet model group (HFD group), high-fat diet+low-dose xylo-oligosaccharide intervention group (XOS.L group), and high-fat diet+high-dose xylo-oligosaccharide intervention group (XOS.H group), with 10 mice in each group. The mice in the LFD group were fed a low-fat diet containing 10% fat, and those in the HFD group were fed a high-fat diet containing 60% fat; in addition to the treatment in the HFD group, the mice in the XOS.L group were given xylo-oligosaccharides at a dose of 0.35 g/kg by gavage once a day, and those in the XOS.H group were given xylo-oligosaccharides at a dose of 1.0 g/kg by gavage once a day; the mice in the LFD group and the HFD group were given an equal volume of normal saline by gavage. At the end of week 16, blood, liver tissue, and fecal samples were collected from each group of mice; the serum levels of alanine ami-notransferase (ALT), aspartate aminotransferase and triglyceride (TG) were measured; HE staining and oil red O staining were used to perform a histological evaluation of the liver; 16S rRNA microbial sequencing technology and metabolomics were used to measure the changes in fecal gut microbiota and metabolites. Results compared with LFD group, the HFD group showed significant steatosis of the liver, disturbances in various metabolites including arachidonic acid and linoleic acid (LA), significant increases in the abundances of the pathogenic bacteria such as Faecalibaculum, Bacteroidaceae, and significant reductions in the abundances of the probiotic bacteria such as Ruminococcaceae, Bacteroides. Compared with the HFD group, the XOS.H groups showed a significant improvement in liver steatosis and the abundances of related microbiota such as Ruminococcaceae and Actinobacteria, significant reductions in liver SAF scores and the enrichment intensities of the proinflammatory metabolites such as Prostaglandin H2, and significant increases in the enrichment intensities of the metabolites such as Prostaglandin D2, LA, and docosahexaenoic acid. Conclusion Xylo-oligosaccharides can significantly improve liver steatosis, gut microbiota dysbiosis, and metabolite disturbances induced by high-fat diet in mice, possibly by regulating the gut microbiota to interfere with the metabolic pathways such as arachidonic acid and LA.

Context: Gastric cancer is one of the most prevalent types of cancer in developing countries and ranks fourth in terms of death causes. Helicobacter pylori infection is a significant contributor to the emergence of gastric cancer. Lack of early diagnosis of gastric cancer is a leading cause of death. Aims: To identify the key genes and pathways involved in gastric cancer. Methods: This study performed a comprehensive analysis of RNA-Seq data from human gastric cancer and adjacent normal tissues. Raw data passed quality checks with FastQC and were aligned to GRCh38 using HISAT2. Subread’s FeatureCounts handled transcript assembly and quantification. DESeq2 pinpointed significant genes, while ClueGO explored gene ontology and KEGG pathways. Protein-protein interaction networks, constructed with StringApp, aided in identifying hub genes through CytoHubba. This holistic approach yields insights into the molecular mechanisms underpinning gastric cancer. Results: This study detected 711 differentially expressed genes (DEGs) between normal and gastric samples. A total of 594 genes were identified as upregulated and 117 as downregulated. Major DEGs are enriched in signal transduction, stimulus-response regulation, transmembrane signaling receptor activity, and signal transduction pathways involving cytokines. In addition, 20 hub genes from the PPI network were identified based on MCC rank analysis from the CytoHubba plugin, contributing to the progression of gastric cancer. Conclusions: The top six hub genes, CD4, CTLA4, CD28, CD80, CD27, and SELL, are expected to regulate several pathways and may serve as potential biomarkers for the early detection and treatment of gastric cancer patients.

Background Immunoglobulin A nephropathy (IgAN) is the most prevalent form of glomerulonephritis worldwide. Prediction of disease progression in IgAN can help to provide individualized treatment based on accurate risk stratification. Methods We performed proton nuclear magnetic resonance-based metabolomics analyses of serum and urine samples from healthy controls, non-progressor (NP), and progressor (P) groups to identify metabolic profiles of IgAN disease progression. Metabolites that were significantly different between the NP and P groups were selected for pathway analysis. Subsequently, we analyzed multivariate area under the receiver operating characteristic (ROC) curves to evaluate the predictive power of metabolites associated with IgAN progression. Results We observed several distinct metabolic fingerprints of the P group involving the following metabolic pathways: glycolipid metabolism; valine, leucine, and isoleucine biosynthesis; aminoacyl-transfer RNA biosynthesis; glycine, serine, and threonine metabolism; and glyoxylate and dicarboxylate metabolism. In multivariate ROC analyses, the combinations of serum glycerol, threonine, and proteinuria (area under the curve [AUC], 0.923; 95% confidence interval [CI], 0.667–1.000) and of urinary leucine, valine, and proteinuria (AUC, 0.912; 95% CI, 0.667–1.000) showed the highest discriminatory ability to predict IgAN disease progression. Conclusion This study identified serum and urine metabolites profiles that can aid in the identification of progressive IgAN and proposed perturbed metabolic pathways associated with the identified metabolites.

BackgroundCarbapenem-resistant Enterobacteriaceae (CRE) are an emerging concern for global health and are associated with high morbidity and mortality in critically ill patients. Risk factors for CRE acquisition include broad-spectrum antibiotic use and microbiota dysbiosis in critically ill patients. Therefore, we evaluated the alteration of the intestinal microbiota associated with CRE colonization in critically ill patients.MethodsFecal samples of 41 patients who were diagnosed with septic shock or respiratory failure were collected after their admission to the intensive care unit (ICU). The gut microbiota profile determined using 16S rRNA gene sequencing and quantitative measurement of fecal short-chain fatty acids were evaluated in CRE-positive (n = 9) and CRE negative (n = 32) patients. The analysis of bacterial metabolic abundance to identify an association between CRE acquisition and metabolic pathway was performed.ResultsCRE carriers showed a significantly increased proportion of the phyla Proteobacteria and decreased numbers of the phyla Bacteroidetes as compared to the CRE non-carriers. Linear discriminant analysis (LDA) with linear discriminant effect size showed that the genera Erwinia, Citrobacter, Klebsiella, Cronobacter, Kluyvera, Dysgomonas, Pantoea, and Alistipes had an upper 2 LDA score in CRE carriers. The alpha-diversity indices were significantly decreased in CRE carriers, and beta-diversity analysis demonstrated that the two groups were clustered significantly apart. Among short-chain fatty acids, the levels of isobutyric acid and valeric acid were significantly decreased in CRE carriers. Furthermore, the PICRUSt-predicted metabolic pathways revealed significant differences in five features, including ATP-binding cassette transporters, phosphotransferase systems, sphingolipid metabolism, other glycan degradation, and microbial metabolism, in diverse environments between the two groups.ConclusionCritically ill patients with CRE have a distinctive gut microbiota composition and community structure, altered short-chain fatty acid production and changes in the metabolic pathways. Further studies are needed to determine whether amino acids supplementation improves microbiota dysbiosis in patients with CRE.

Background: Cervical cancer (CC) is the fourth leading cause of cancer among women in the world. Metabolomics can provide a deeper understanding of the underlying metabolic alterations associated with its pathophysiology. Objective: To systematically analyze metabolomic approaches and findings used in the study of cervical cancer. Selection Criteria: Studies that included the use of metabolomics, obtained through biological samples, from patients diagnosed with CC. Data collection and Analysis: The review was conducted according to the PRISMA guidelines, and registered in PROSPERO. The terms of Medical Subject Headings (MeSH) and Health Sciences Descriptors (DeCS) corresponding to “Metabolomics” and “Cervical Cancer” were used as descriptors. Article quality was reviewed based on the QUADOMICS criteria. Results: A total of 17 articles were selected for systematic review. Study quality evaluation using QUADOMICS demonstrated heterogeneous results. The main changes in metabolite levels associated with cervical cancer were identified in alanine, creatine, valine, tyrosine, isoleucine, phosphatidylcholine, acetate, lactate and β-glucose. Some amino acid levels were reduced in patients with CC and changes in energy metabolism pathways were observed. Eight articles tested the diagnostic capacity of metabolomics, obtaining results for sensitivity > 90%, specificity between 73% and 99% and AUC between 0.78 and 0.99. Conclusion: The results suggest that patients with CC present alterations in energy metabolism, amino acids and glycerolphospholipids, pointing to a potential group of specific biomarkers.

Ferroptosis is an iron-dependent mode of cell death characterized by intracellular lipid peroxide accumulation and a redox reaction imbalance. Compared with other modes of cell death, ferroptosis has specific biological and morphological features. The iron-dependent lipid peroxidation accumulation is manifested explicitly in the abnormal metabolism of intracellular lipid oxides catalyzed by excessive iron ions with the production of many reactive oxygen species and over-oxidization of polyunsaturated fatty acids. Recent studies have shown that various diseases, which include intestinal diseases and cancer, are associated with ferroptosis, but few studies are related to airway inflammatory diseases. This review provides a comprehensive analysis of the primary damage mechanisms of ferroptosis and summarizes the relationship between ferroptosis and airway inflammatory diseases. In addition to common acute and chronic airway inflammatory diseases, we also focus on the progress of research on COVID-19 in relation to ferroptosis. New therapeutic approaches and current issues to be addressed in the treatment of inflammatory airway diseases using ferroptosis are further proposed.

Wenting Xu,1,2,* Huimin Deng,2,* Song Hu,2,3,* Yiguo Zhang,2,3 Li Zheng,1,2 Meiyun Liu,2 Yuanli Chen,2 Juan Wei,2 Hao Yang,2 Xin Lv1– 3 1Department of Anesthesiology, Fuyang Hospital of Anhui Medical University, Fuyang, Anhui, 236000, People’s Republic of China; 2Department of Anesthesiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, People’s Republic of China; 3Graduate School, Wannan Medical College, Wuhu, AnHui, 241002, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xin Lv; Hao YangDepartment of Anesthesiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Road, Shanghai, 200433, People’s Republic of ChinaEmail xinlvg@126.com; yanghaozunyi@sina.comAbstract: Ferroptosis is a new type of programmed cell death characterized by intracellular iron accumulation and lipid peroxidation that leads to oxidative stress and cell death. The metabolism of iron, lipids, and amino acids and multiple signalling pathways precisely regulate the process of ferroptosis. Emerging evidence has demonstrated that ferroptosis participates in the occurrence and progression of various pathological conditions and diseases, such as infections, neurodegeneration, tissue ischaemia-reperfusion injury and immune diseases. Recent studies have also indicated that ferroptosis plays a critical role in the pathogenesis of acute lung injury, chronic obstructive pulmonary disease, pulmonary fibrosis, pulmonary infection and asthma. Herein, we summarize the latest knowledge on the regulatory mechanism of ferroptosis and its association with iron, lipid and amino acid metabolism as well as several signalling pathways. Furthermore, we review the contribution of ferroptosis to the pathogenesis of lung diseases and discuss ferroptosis as a novel therapeutic target for various lung diseases.Keywords: ferroptosis, metabolic networks and pathways, signalling pathways, lung diseases

Abstract Background Human sperm cryopreservation is a simple and effective approach for male fertility preservation. Methods To identify potential proteomic changes in this process, data-independent acquisition (DIA), a technology with high quantitative accuracy and highly reproducible proteomics, was used to quantitatively characterize the proteomics of human sperm cryopreservation. Results A total of 174 significantly differential proteins were identified between fresh and cryoperservated sperm: 98 proteins decreased and 76 proteins increased in the cryopreservation group. Bioinformatic analysis revealed that metabolic pathways play an important role in cryopreservation, including: propanoate metabolism, glyoxylate and dicarboxylate metabolism, glycolysis/gluconeogenesis, and pyruvate metabolism. Four different proteins involved in glycolysis were identified by Western blotting: GPI, LDHB, ADH5, and PGAM1. Conclusions Our work will provide valuable information for future investigations and pathological studies involving sperm cryopreservation.

The immune function is closely related to iron (Fe) homeostasis and allostasis. The aim of this bioinformatics-assisted review was twofold; (i) to update the current knowledge of Fe metabolism and its relationship to the immune system, and (ii) to perform a prediction analysis of regulatory network hubs that might serve as potential biomarkers during stress-induced immunosuppression. Several literature and bioinformatics databases/repositories were utilized to review Fe metabolism and complement the molecular description of prioritized proteins. The Search Tool for the Retrieval of Interacting Genes (STRING) was used to build a protein-protein interactions network for subsequent network topology analysis. Importantly, Fe is a sensitive double-edged sword where two extremes of its nutritional status may have harmful effects on innate and adaptive immunity. We identified clearly connected important hubs that belong to two clusters: (i) presentation of peptide antigens to the immune system with the involvement of redox reactions of Fe, heme, and Fe trafficking/transport; and (ii) ubiquitination, endocytosis, and degradation processes of proteins related to Fe metabolism in immune cells (e.g., macrophages). The identified potential biomarkers were in agreement with the current experimental evidence, are included in several immunological/biomarkers databases, and/or are emerging genetic markers for different stressful conditions. Although further validation is warranted, this hybrid method (human-machine collaboration) to extract meaningful biological applications using available data in literature and bioinformatics tools should be highlighted.

Proton-coupled monocarboxylate transporters (MCTs), representing the first four isoforms of the SLC16A gene family, mainly participate in the transport of lactate, pyruvate, and other monocarboxylates. Cancer cells exhibit a metabolic shift from oxidative metabolism to an enhanced glycolytic phenotype, leading to a higher production of lactate in the cytoplasm. Excessive accumulation of lactate threatens the survival of cancer cells, and the overexpression of proton-coupled MCTs observed in multiple types of cancer facilitates enhanced export of lactate from highly glycolytic cancer cells. Proton-coupled MCTs not only play critical roles in the metabolic symbiosis between hypoxic and normoxic cancer cells within tumors but also mediate metabolic interaction between cancer cells and cancer-associated stromal cells. Of the four proton-coupled MCTs, MCT1 and MCT4 are the predominantly expressed isoforms in cancer and have been identified as potential therapeutic targets in cancer. Therefore, in this review, we primarily focus on the roles of MCT1 and MCT4 in the metabolic reprogramming of cancer cells under hypoxic and nutrient-deprived conditions. Additionally, we discuss how MCT1 and MCT4 serve as metabolic links between cancer cells and cancer-associated stromal cells via transport of crucial monocarboxylates, as well as present emerging opportunities and challenges in targeting MCT1 and MCT4 for cancer treatment.

Metabolic reprogramming is an emerging hallmark of cancer cells, in which cancer cells exhibit distinct metabolic phenotypes to fuel their proliferation and progression. The significant advancements made in the area of metabolic reprogramming make possible new strategies for overcoming malignant cancer, including triple-negative breast cancer. Triple-negative breast cancer (TNBC) is associated with high histologic grade, aggressive phenotype, and poor prognosis. Even though triple-negative breast cancer patients benefit from standard chemotherapy, they still face high recurrence rates and are more likely to develop resistance to chemotherapeutic drugs. Therefore, there is an urgent need to explore vulnerabilities of triple-negative breast cancer and develop novel therapeutic drugs to improve clinical outcomes for triple-negative breast cancer patients. Metabolic reprogramming may provide promising therapeutic targets for the treatment of triple-negative breast cancer. In this paper, we primarily discuss how triple-negative breast cancer cells reprogram their metabolic phenotype and that of stromal cells in the microenvironment to survive under nutrient-poor conditions. Considering that metastasis and chemoresistance are the main contributors to mortality in triple-negative breast cancer patients, we also focus on the role of metabolic adaption in mediating metastasis and chemoresistance of triple-negative breast cancer tumors.

Metabolic syndrome (METS) is a disorder of energy utilization and storage and increases the risk of developing cardiovascular disease and diabetes. To identify the genetic risk factors of METS, we carried out a genome-wide association study (GWAS) for 2,657 cases and 5,917 controls in Korean populations. As a result, we could identify 2 single nucleotide polymorphisms (SNPs) with genome-wide significance level p-values (

Prostate cancer is the most common non-cutaneous cancer in men in the United States. Cancer metabolism has emerged as a contemporary topic of great interest for improved mechanistic understanding of tumorigenesis. Prostate cancer is a disease model of great interest from a metabolic perspective. Prostatic tissue exhibits unique metabolic activity under baseline conditions. Benign prostate cells accumulate zinc, and this excess zinc inhibits citrate oxidation and metabolism within the citric acid cycle, effectively resulting in citrate production. Malignant cells, however, actively oxidize citrate and resume more typical citric acid cycle function. Of further interest, prostate cancer does not exhibit the Warburg effect, an increase in glucose uptake, seen in many other cancers. These cellular metabolic differences and others are of clinical interest as they present a variety of potential therapeutic targets. Furthermore, understanding of the metabolic profile differences between benign prostate versus low- and high-grade prostate cancers also represents an avenue to better understand cancer progression and potentially develop new diagnostic testing. In this paper, we review the current state of knowledge on the metabolic phenotypes of prostate cancer.

Vibrio cholerae, a Gram-negative bacterium, is the causative agent of pandemic cholera. Previous studies have shown that the survival of the seventh pandemic El Tor biotype V. cholerae strain N16961 requires production of acetoin in a glucose-rich environment. The production of acetoin, a neutral fermentation end-product, allows V. cholerae to metabolize glucose without a pH drop, which is mediated by the production of organic acid. This finding suggests that inhibition of acetoin fermentation can result in V. cholerae elimination by causing a pH imbalance under glucose-rich conditions. Here, we developed a simple high-throughput screening method and identified an inducer of medium acidification (iMAC). Of 8,364 compounds screened, we identified one chemical, 5-(4-chloro-2-nitrobenzoyl)-6-hydroxy-1,3-dimethylpyrimidine-2,4(1H,3H)-dione, that successfully killed glucose-metabolizing N16961 by inducing acidic stress. When N16961 was grown with abundant glucose in the presence of iMAC, acetoin production was completely suppressed and concomitant accumulation of lactate and acetate was observed. Using a beta-galactosidase activity assay with a single-copy palsD::lacZ reporter fusion, we show that that iMAC likely inhibits acetoin production at the transcriptional level. Thin-layer chromatography revealed that iMAC causes a significantly reduced accumulation of intracellular (p)ppGpp, a bacterial stringent response alarmone known to positively regulate acetoin production. In vivo bacterial colonization and fluid accumulation were also markedly decreased after iMAC treatment. Finally, we demonstrate iMAC-induced bacterial killing for 22 different V. cholerae strains belonging to diverse serotypes. Together, our results suggest that iMAC, acting as a metabolic modulator, has strong potential as a novel antibacterial agent for treatment against cholera.

To investigate the natural variability of leaf metabolism and enzymatic activity in a maize inbred population, statistical and network analyses were employed on metabolite and enzyme profiles. The test of coefficient of variation showed that sugars and amino acids displayed opposite trends in their variance within the population, consistently with their related enzymes. The overall higher CV values for metabolites as compared to the tested enzymes are indicative for their greater phenotypic plasticity. H2 tests revealed galactinol (1) and asparagine (0.91) as the highest scorers among metabolites and nitrate reductase (0.73), NAD-glutamate dehydrogenase (0.52), and phosphoglucomutase (0.51) among enzymes. The overall low H2 scores for metabolites and enzymes are suggestive for a great environmental impact or gene-environment interaction. Correlation-based network generation followed by community detection analysis, partitioned the network into three main communities and one dyad, (i) reflecting the different levels of phenotypic plasticity of the two molecular classes as observed for the CV values and (ii) highlighting the concerted changes between classes of chemically related metabolites. Community 1 is composed mainly of enzymes and specialized metabolites, community 2’ is enriched in N-containing compounds and phosphorylated-intermediates. The third community contains mainly organic acids and sugars. Cross-community linkages are supported by aspartate, by the photorespiration amino acids glycine and serine, by the metabolically related GABA and putrescine, and by citrate. The latter displayed the strongest node-betweenness value (185.25) of all nodes highlighting its fundamental structural role in the connectivity of the network by linking between different communities and to the also strongly connected enzyme aldolase.

The central carbon metabolic system is the upstream energy source for microbial fermentation. In addition, it is a master switch for increasing the production of metabolites and an important part of the microbial metabolic network. Investigation into the relationship between genes, environmental factors, and metabolic networks is a main focus of systems biology, which significantly impacts research in biochemistry, metabolic engineering, and synthetic biology. To this end, the central carbon metabolic flux under a variety of growth conditions or using strains with various genetic modifications was previously measured in Saccharomyces cerevisiae using 13C tracer technology. However, the measured values were not integrated and investigated further. In this study, we collected and analyzed the metabolic flux rates of the central carbon metabolic system in S. cerevisiae measured in recent studies. We carried out preliminary analyses of flux values of each pathway, performed regression analyses on relationship between different fluxes, and extracted principal component factors of the flux variables. Based on the results, the general characteristics of pathway flux distribution were clustered and explored, and the effects of environmental and genetic factors on the flux distribution were analyzed. Furthermore, this study explored the relationship between similarity in the enzyme's transcriptional regulation and the correlations in the enzyme's reaction flux. Our results provide a foundation for further studies on the control of the central carbon metabolic flux and facilitate the search for targets in metabolic engineering research.

Recent progress in high-throughput data acquisition has shifted the focus from data generation to processing and understanding of how to integrate collected information. Context specific reconstruction based on generic genome scale models like ReconX (Duarte et al., 2007; Thiele et al., 2013) or HMR (Agren et al., 2013) has the potential to become a diagnostic and treatment tool tailored to the analysis of specific individuals. The respective computational algorithms require a high level of predictive power, robustness and sensitivity. Although multiple context specific reconstruction algorithms were published in the last ten years, only a fraction of them is suitable for model building based on human high-throughput data. Beside other reasons, this might be due to problems arising from the limitation to only one metabolic target function or arbitrary thresholding.This review describes and analyses common validation methods used for testing model building algorithms. Two major methods can be distinguished, consistency testing and comparison based testing. The former includes methods like cross validation or testing with artificial networks. The latter covers methods comparing sets of functionalities, comparison with existing networks or additional databases. We test those methods on several available algorithms and deduce properties of these algorithms, that can be compared with future developments. The set of tests performed, can therefore serve as a benchmarking procedure for future algorithms

Tumor cells alter their metabolism to maintain unregulated cellular proliferation and survival, but this transformation leaves them reliant on constant supply of nutrients and energy. In addition to the widely studied dysregulated glucose metabolism to fuel tumor cell growth, accumulating evidences suggest that utilization of amino acids and lipids contributes significantly to cancer cell metabolism. Also recent progresses in our understanding of carcinogenesis have revealed that cancer is a complex disease and cannot be understood through simple investigation of genetic mutations of cancerous cells. Cancer cells present in complex tumor tissues communicate with the surrounding microenvironment and develop traits which promote their growth, survival and metastasis. Decoding the full scope and targeting dysregulated metabolic pathways that support neoplastic transformations and their preservation requires both the advancement of experimental technologies for more comprehensive measurement of omics as well as the advancement of robust computational methods for accurate analysis of the generated data. Here, we review cancer-associated reprogramming of metabolism and highlight the capability of genome-scale metabolic modeling approaches in perceiving a system-level perspective of cancer metabolism and in detecting novel selective drug targets

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The Phenotype Microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi but it has not been reported for the phenotyping of microalgae. Here we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of D-amino acids, L-dipeptides, and L-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.

Membrane transporters enable efficient cellular metabolism, aid in nutrient sensing, and have been associated with various diseases, such as obesity and cancer. Genome-scale metabolic network reconstructions capture genomic, physiological, and biochemical knowledge of a target organism, along with a detailed representation of the cellular metabolite transport mechanisms. Since the first reconstruction of human metabolism, Recon 1, published in 2007, progress has been made in the field of metabolite transport. Recently, we published an updated reconstruction, Recon 2, which significantly improved the metabolic coverage and functionality. Human metabolic reconstructions have been used to investigate the role of metabolism in disease and to predict biomarkers and drug targets. Given the importance of cellular transport systems in understanding human metabolism in health and disease, we analyzed the coverage of transport systems for various metabolite classes in Recon 2. We will review the current knowledge on transporters (i.e., their preferred substrates, transport mechanisms, metabolic relevance, and disease association for each metabolite class). We will assess missing coverage and propose modifications and additions through a transport module that is functional when combined with Recon 2. This information will be valuable for further refinements. These data will also provide starting points for further experiments by highlighting areas of incomplete knowledge. This review represents the first comprehensive overview of the transporters involved in central metabolism and their transport mechanisms, thus serving as a compendium of metabolite transporters specific for human metabolic reconstructions.