Towards applications of genome-scale metabolic model-based approaches in designing synthetic microbial communities

Background:Synthetic microbial communities,with different strains brought together by balancing their nutrition and promoting their interactions,demonstrate great advantages for exploring complex performance of communities and for further biotechnology applications.The potential of such microbial communities has not been explored,due to our limited knowledge of the extremely complex microbial interactions that are involved in designing and controlling effective and stable communities.Results:Genome-scale metabolic models(GEM)have been demonstrated as an effective tool for predicting and guiding the investigation and design of microbial communities,since they can explicitly and efficiently predict the phenotype of organisms from their genotypic data and can be used to explore the molecular mechanisms of microbehabitats and microbe-microbe interactions.In this work,we reviewed two main categories of GEM-based approaches and three uses related to design of synthetic microbial communities:predicting multi-species interactions,exploring environmental impacts on microbial phenotypes,and optimizing community-level performance.Conclusions:Although at the infancy stage,GEM-based approaches exhibit an increasing scope of applications in designing synthetic microbial communities.Compared to other methods,especially the use of laboratory cultures,GEM-based approaches can greatly decrease the trial-and-error cost of various procedures for designing synthetic communities and improving their functionality,such as identifying community members,determining media composition,evaluating microbial interaction potential or selecting the best community configuration.Future efforts should be made to overcome the limitations of the approaches,ranging from quality control of GEM reconstructions to community-level modeling algorithms,so that more applications of GEMs in studying phenotypes of microbial communities can be expected.

Construction and application of high-quality genome-scale metabolic model of Zymomonas mobilis to guide rational design of microbial cell factories

High-quality genome-scale metabolic models(GEMs)could play critical roles on rational design of microbial cell factories in the classical Design-Build-Test-Learn cycle of synthetic biology studies.Despite of the constant establishment and update of GEMs for model microorganisms such as Escherichia coli and Saccharomyces cerevisiae,high-quality GEMs for non-model industrial microorganisms are still scarce.Zymomonas mobilis subsp.mobilis ZM4 is a non-model ethanologenic microorganism with many excellent industrial characteristics that has been developing as microbial cell factories for biochemical production.Although five GEMs of Z.mobilis have been constructed,these models are either generating ATP incorrectly,or lacking information of plasmid genes,or not providing standard format file.In this study,a high-quality GEM iZM516 of Z.mobilis ZM4 was constructed.The information from the improved genome annotation,literature,datasets of Biolog Phenotype Microarray studies,and recently updated Gene-Protein-Reaction information was combined for the curation of iZM516.Finally,516 genes,1389 reactions,1437 metabolites,and 3 cell compartments are included in iZM516,which also had the highest MEMOTE score of 91%among all published GEMs of Z.mobilis.Cell growth was then predicted by iZM516,which had 79.4%agreement with the experimental results of the substrate utilization.In addition,the potential endogenous succinate synthesis pathway of Z.mobilis ZM4 was proposed through simulation and analysis using iZM516.Furthermore,metabolic engineering strategies to produce succinate and 1,4-butanediol(1,4-BDO)were designed and then simulated under anaerobic condition using iZM516.The results indicated that 1.68 mol/mol succinate and 1.07 mol/mol 1,4-BDO can be achieved through combinational metabolic engineering strategies,which was comparable to that of the model species E.coli.Our study thus not only established a high-quality GEM iZM516 to help understand and design microbial cell factories for economic biochemical production using Z.mobilis as the chassis,but also provided guidance on building accurate GEMs for other non-model industrial microorganisms.

Genome-scale metabolic models applied for human health and biopharmaceutical engineering

Over the last 15 years,genome-scale metabolic models(GEMs)have been reconstructed for human and model animals,such as mouse and rat,to systematically understand metabolism,simulate multicellular or multi-tissue interplay,understand human diseases,and guide cell factory design for biopharmaceutical protein production.Here,we describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints for flux simulation.Then,we review the history of GEM development for quantitative understanding of Homo sapiens and other relevant animals,together with their applications.We describe how model develops from H.sapiens to other animals and from generic purpose to precise context-specific simulation.The progress of GEMs for animals greatly expand our systematic understanding of metabolism in human and related animals.We discuss the difficulties and present perspectives on the GEM development and the quest to integrate more biological processes and omics data for future research and translation.We truly hope that this review can inspire new models developed for other mammalian organisms and generate new algorithms for integrating big data to conduct more in-depth analysis to further make progress on human health and biopharmaceutical engineering.

FibroScan-aspartate transaminase:A superior non-invasive model for diagnosing high-risk metabolic dysfunction-associated steatohepatitis

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)with hepatic histological NAFLD activity score≥4 and fibrosis stage F≥2 is regarded as“at risk”non-alcoholic steatohepatitis(NASH).Based on an international consensus,NAFLD and NASH were renamed as metabolic dysfunction-associated steatotic liver disease(MASLD)and metabolic dysfunction-associated steatohepatitis(MASH),respectively;hence,we introduced the term“high-risk MASH”.Diagnostic values of seven non-invasive models,including FibroScan-aspartate transaminase(FAST),fibrosis-4(FIB-4),aspartate transaminase to platelet ratio index(APRI),etc.for high-risk MASH have rarely been studied and compared in MASLD.AIM To assess the clinical value of seven non-invasive models as alternatives to liver biopsy for diagnosing high-risk MASH.METHODS A retrospective analysis was conducted on 309 patients diagnosed with NAFLD via liver biopsy at Beijing Ditan Hospital,between January 2012 and December 2020.After screening for MASLD and the exclusion criteria,279 patients wereincluded and categorized into high-risk and non-high-risk MASH groups.Utilizing threshold values of each model,sensitivity,specificity,positive predictive value(PPV),and negative predictive values(NPV),were calculated.Receiver operating characteristic curves were constructed to evaluate their diagnostic efficacy based on the area under the curve(AUROC).RESULTS MASLD diagnostic criteria were met by 99.4%patients with NAFLD.The MASLD population was analyzed in two cohorts:Overall population(279 patients)and the subgroup(117 patients)who underwent liver transient elastography(FibroScan).In the overall population,FIB-4 showed better diagnostic efficacy and higher PPV,with sensitivity,specificity,PPV,NPV,and AUROC of 26.9%,95.2%,73.5%,72.2%,and 0.75.APRI,Forns index,and aspartate transaminase to alanine transaminase ratio(ARR)showed moderate diagnostic efficacy,whereas S index and gamma-glutamyl transpeptidase to platelet ratio(GPR)were relatively weaker.In the subgroup,FAST had the highest diagnostic efficacy,its sensitivity,specificity,PPV,NPV,and AUROC were 44.2%,92.3%,82.1%,67.4%,and 0.82.The FIB-4 AUROC was 0.76.S index and GPR exhibited almost no diagnostic value for high-risk MASH.CONCLUSION FAST and FIB-4 could replace liver biopsy as more effectively diagnostic methods for high-risk MASH compared to APRI,Forns index,ARR,S index,and GPR;FAST is superior to FIB-4.

Rifaximin on epigenetics and autophagy in animal model of hepatocellular carcinoma secondary to metabolic-dysfunction associated steatotic liver disease

BACKGROUND Prevalence of hepatocellular carcinoma(HCC)is increasing,especially in patients with metabolic dysfunctionassociated steatotic liver disease(MASLD).AIM To investigate rifaximin(RIF)effects on epigenetic/autophagy markers in animals.METHODS Adult Sprague-Dawley rats were randomly assigned(n=8,each)and treated from 5-16 wk:Control[standard diet,water plus gavage with vehicle(Veh)],HCC[high-fat choline deficient diet(HFCD),diethylnitrosamine(DEN)in drinking water and Veh gavage],and RIF[HFCD,DEN and RIF(50 mg/kg/d)gavage].Gene expression of epigenetic/autophagy markers and circulating miRNAs were obtained.RESULTS All HCC and RIF animals developed metabolic-dysfunction associated steatohepatitis fibrosis,and cirrhosis,but three RIF-group did not develop HCC.Comparing animals who developed HCC with those who did not,miR-122,miR-34a,tubulin alpha-1c(Tuba-1c),metalloproteinases-2(Mmp2),and metalloproteinases-9(Mmp9)were significantly higher in the HCC-group.The opposite occurred with Becn1,coactivator associated arginine methyltransferase-1(Carm1),enhancer of zeste homolog-2(Ezh2),autophagy-related factor LC3A/B(Map1 Lc3b),and p62/sequestosome-1(p62/SQSTM1)-protein.Comparing with controls,Map1 Lc3b,Becn1 and Ezh2 were lower in HCC and RIF-groups(P<0.05).Carm1 was lower in HCC compared to RIF(P<0.05).Hepatic expression of Mmp9 was higher in HCC in relation to the control;the opposite was observed for p62/Sqstm1(P<0.05).Expression of p62/SQSTM1 protein was lower in the RIF-group compared to the control(P=0.024).There was no difference among groups for Tuba-1c,Aldolase-B,alpha-fetoprotein,and Mmp2(P>0.05).miR-122 was higher in HCC,and miR-34a in RIF compared to controls(P<0.05).miR-26b was lower in HCC compared to RIF,and the inverse was observed for miR-224(P<0.05).There was no difference among groups regarding miR-33a,miR-143,miR-155,miR-375 and miR-21(P>0.05).CONCLUSION RIF might have a possible beneficial effect on preventing/delaying liver carcinogenesis through epigenetic modulation in a rat model of MASLD-HCC.

Ornithine aspartate effects on bacterial composition and metabolic pathways in a rat model of steatotic liver disease

BACKGROUND Metabolic-dysfunction associated steatotic liver disease(MASLD)is a hepatic manifestation of metabolic syndrome.Studies suggest ornithine aspartate(LOLA)as drug therapy.AIM To analyze the influence of LOLA intake on gut microbiota using a nutritional model of MASLD.METHODS Adult male Sprague Dawley rats were randomized into three groups:Control(10 rats fed with a standard diet),MASLD(10 rats fed with a high-fat and choline-deficient diet),and LOLA(10 rats receiving 200 mg/kg/d LOLA,after the 16th week receiving high-fat and choline-deficient diet).After 28 wk of the experiment,animals were euthanized,and feces present in the intestine were collected.Following fecal DNA extraction,the V4 region of the 16S rRNA gene was amplified followed by sequencing in an Ion S5™system.RESULTS Alpha and beta diversity metrics were comparable between MASLD and LOLA.3 OTUs were differentially abundant between MASLD and LOLA,which belong to the species Helicobacter rodentium,Parabacteroides goldsteinii,and Parabacteroides distasonis.The functional prediction provided two different metabolic profiles between MASLD and LOLA.The 9 pathways differentially abundant in MASLD are related to a change in energy source,adenosine/purine nucleotides degradation as well as guanosine and adenosine deoxyribonucleotides biosynthesis.The 14 pathways differentially abundant in LOLA are associated with four major metabolic functions primarily influenced by L-aspartate,including tricarboxylic acid cycle pathways,purine/guanosine nucleotides biosynthesis,pyrimidine ribonucleotides biosynthesis and salvage as well as lipid IVA biosynthesis.CONCLUSION Although LOLA had no influence on alpha and beta diversity in this nutritional model of MASLD,it was associated with changes in specific gut microbes and their related metabolic pathways.

Genome-scale metabolic modeling in antimicrobial pharmacology

The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades.This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant micro-bial pathogens.However,the detailed mechanisms of action,resistance,and toxicity of many antimicrobials remain uncertain,significantly hampering the development of novel antimicrobials.Genome-scale metabolic model(GSMM)has been increasingly employed to investigate microbial metabolism.In this review,we discuss the latest progress of GSMM in antimicrobial pharmacology,particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity,resistance,and toxicity.We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities(e.g.,gene expression),identi-fication of potential drug targets,and integration with machine learning and pharmacokinetic/pharmacodynamic modeling.Overall,GSMM has significant potential in elucidating the critical role of metabolic changes in antimi-crobial pharmacology,providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.

Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study

Herbal medicines are popular natural medicines that have been used for decades.The use of alternative medicines continues to expand rapidly across the world.The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications,as their therapeutic potential varies between different geographic origins,plant species,and varieties.Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach.Their quality should be considered based on a complete metabolic profile,as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds.A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization.In this study,a rapid and effective quality assessment system for geographical traceability,species,and variety-specific authenticity of the widely used natural medicines turmeric,Ocimum,and Withania somnifera was investigated using Fourier transform near-infrared(FT-NIR)spectroscopy-based metabolic fingerprinting.Four different geographical origins of turmeric,five different Ocimum species,and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches.Extremely good discrimination(R^(2)>0.98,Q^(2)>0.97,and accuracy=1.0)with sensitivity and specificity of 100%was achieved using this metabolic fingerprinting strategy.Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.

Construction and validation of a severity prediction model for metabolic associated fatty liver disease

Objective:To analyze the independent risk factors for the occurrence of moderate-to-severe metabolic-associated fatty liver disease(MAFLD),to construct a prediction model for moderate-to-severe MAFLD,and to verify the validity of the model.Methods:In the first part,278 medical examiners who were diagnosed with MAFLD in Medical Examination Center at the Second Affiliated Hospital of Hainan University from January to May 2022 were taken as the study subjects(training set),and they were divided into mild MAFLD group(200)and moderate-severe MAFLD group(78)based on ultrasound results.Demographic data and laboratory indexes were collected,and risk factors were screened by univariate and multifactor analysis.In the second part,a dichotomous logistic regression equation was used to construct a prediction model for moderate-to-severe MAFLD,and the model was visualized in a line graph.In the third part,the MAFLD population(200 people in the external validation set)from our physical examination center from November to December 2022 was collected as the moderate-to-severe MAFLD prediction model,and the risk factors in both groups were compared.The receiver operating characteristic(ROC)curves,calibration curves,and clinical applicability of the model were plotted to represent model discrimination for internal and external validation.Results:The risk factors of moderate-to-severe MAFLD were fasting glucose(FPG),blood uric acid(UA),triglycerides(TG),triglyceride glucose index(TyG),total cholesterol(CHOL),and high-density lipoprotein(HDL-C).UA[OR=1.021,95%CI(1.015,1.027),P<0.001]and FPG[OR=1.575,95%CI(1.158,2.143),P=0.004]were independent risk factors for people with moderate to severe MAFLD.The visualized line graph model showed that UA was the factor contributing more to the risk of moderate to severe MAFLD in this model.The ROC curves showed AUC values of 0.8701,0.8686 and 0.7991 for the training set,internal validation set and external validation set,respectively.The curves almost coincided with the reference line after calibration of the model calibration degree with P>0.05 in Hosmer-Lemeshow test.The decision curve analysis(DCA)plotted by the clinical applicability of the model was higher than the two extreme curves,predicting that patients with moderate to severe MAFLD would benefit from the prediction model.Conclusion:The prediction model constructed by combining FPG with UA has higher accuracy and better clinical applicability,and can be used for clinical diagnosis.

Prognostic model and treatment plan analysis of hepatocellular carcinoma based on genes related to glutamine metabolism

Objective:To identify the prognosis of hepatocellular carcinoma(HCC)and the effect of anti-cancer drug therapy by screening glutamine metabolism-related signature genes because glutamine metabolism plays an important role in tumor development.Methods:We obtained gene expression samples of normal liver tissue and hepatocellular carcinoma from the TCGA database and GEO database,screened for differentially expressed glutamine metabolismrelated genes(GMRGs),constructed a prognostic model by lasso regression and step cox analysis,and assessed the differences in drug sensitivity between high-and low-risk groups.Results:We screened 23 differentially expressed GMRGs by differential analysis,and correlation loop plots and PPI protein interaction networks indicated that these differential genes were strongly correlated.The four most characterized genes(CAD,PPAT,PYCR3,and SLC7A11)were obtained by lasso regression and step cox,and a risk model was constructed and confirmed to have reliable predictive power in the TCGA dataset and GEO dataset.Finally,immunotherapy is better in the high-risk group than in the low-risk group,and chemotherapy and targeted drug therapy are better in the low-risk group than in the high-risk group.Conclusion:In conclusion,we have developed a reliable prognostic risk model characterized by glutamine metabolism-related genes,which may provide a viable basis for the prognosis and Treatment options of HCC patients.

Towards Kinetic Modeling of Global Metabolic Networks： Methylobacterium extorquens AM1 Growth as Validation

Here we report a systematic method for constructing a large scale kinetic metabolic model and its initial application to the modeling of central metabolism of Methylobacterium extorquens AM1, a methylotrophic and environmental important bacterium. Its central metabolic network includes formaldehyde metabolism, serine cycle, citric acid cycle, pentose phosphate pathway, gluconeogensis, PHB synthesis and acetyl-CoA conversion pathway, respiration and energy metabolism. Through a systematic and consistent procedure of finding a set of parameters in the physiological range we overcome an outstanding difficulty in large scale kinetic modeling: the requirement for a massive number of enzymatic reaction parameters. We are able to construct the kinetic model based on general biological considerations and incomplete experimental kinetic parameters. Our method consists of the following major steps: 1) using a generic enzymatic rate equation to reduce the number of enzymatic parameters to a minimum set while still preserving their characteristics; 2) using a set of steady state fluxes and metabolite concentrations in the physiological range as the expected output steady state fluxes and metabolite concentrations for the kinetic model to restrict the parametric space of enzymatic reactions; 3) choosing enzyme constants K's and K'eqs optimized for reactions under physiological concentrations, if their experimental values are unknown; 4) for models which do not cover the entire metabolic network of the organisms, designing a dynamical exchange for the coupling between the metabolism represented in the model and the rest not included.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

The bio-active components of the Mongolian medicine Horcha-6 and therapeutic mechanism in the rat migraine model

Background:The active components of Horcha-6 were identified using liquid chromatography with tandem mass spectrometry.Also,we investigated the potential mechanisms that explain why Horcha-6 may be effective in treating migraines through the use of network pharmacology and a rat migraine model.Methods:After identifying the active components of Horcha-6,the corresponding genes of the active components’target were obtained from the Universal Protein database,and a“compound-target-disease”network was constructed using Cytoscape 3.9.0 software.For the in vivo experiments,nitroglycerin was injected intraperitoneally into rats to create a migraine model.Pre-treatment with Horcha-6 was administered orally for 14 days,and rats were subjected to migraine-related behavior tests.RNA sequencing was performed to identify the gene expression regulated by Horcha-6 in the trigeminal nerve.Results:A total of 903 chemical components of Horcha-6 have been collected in the liquid chromatography with tandem mass spectrometry.We discovered 55 of the Horcha-6 bio-active components that were evaluated based on their Percent Human Oral Absorption(≥30%)and DL values(≥0.185)on the traditional Chinese medicine systems pharmacology database.The“compound-target-disease”network contained 163 intersection targets with the migraine state.Gene Ontology analysis indicated that these components significantly regulated the immune response,vascular function,oxidative stress,etc.When Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed,we observed that most of the target genes were significantly enriched in the inflammation and neuro-related signaling pathway,toll-like receptor signaling pathway,neuroactive ligand-receptor interaction,etc.These predictions were further demonstrated via in vivo animal model experiments.The RNA sequencing results showed that 41 genes were down-regulated(P<0.05)and 86 genes were up-regulated(P<0.05)in the Horcha-6 treated group compared with the untreated group.Those genes were mainly involved in neuromodulation,vascular function,and hormone metabolism.Conclusion:The 55 bio-active components in Horcha-6 regulate inflammation,hormone metabolism,and neurotransmitters and have potential as a therapy to treat migraines.

Analysis of the key networks,metabolic pathways,and regulation substances of hypoxia based on the omics and zebrafish model

OBJECTIVE Hypoxia is associated with many complicated pathophysiological and biochemical processes that integrated and regulated via the key gene,protein and endogenous metabolite levels.Up to date,the exact molecular mechanism of hypoxia still remains unclear.In this work,we further explore the molecular mechanism of hypoxia and adaption to attenuate the damage in zebrafish model that have potential to resist hypoxic environment.METHODS The hypoxic zebrafish model was established in different concentration of oxygen with 3%,5%,10%,21%in water.The brain tissue was separated and the RNA-seq was used to identify the differentially expressed genes.The related endogenous metabolites profiles were obtained by LC-HDMS,and the multivariate statistics was applied to discover the important metabolites candidates in hypoxic zebrafish.The candidates were searched in HMDB,KEGG and Lipid Maps databases.RESULTS The zebrafish hypoxic model was successfully constructed via the different concentration of oxygen,temperature and hypoxic time.The activities of the related hypoxic metabolic enzymes and factors including HIF-1a,actate dehydrogenase(LDH)and citrate synthase(CS)were evaluated.Significant differences(P<0.05 and fold change>2)in the expression of 422 genes were observed between the normal and 3% hypoxic model.Among them,201 genes increased depended on the lower concentration of oxygen.53 metabolites were identified that had significant difference between the hypoxia and control groups(P<0.05,fold change>1.5 and VIP>1.5).The ten key metabolites were increased gradually while six compounds were decreased.The endogenous hypoxic metabolites of phenylalanine,D-glucosamine-6P and several important lipids with the relevant hub genes had similar change in hypoxic model.In addition,the metabolic pathways of phenylalanine,glutamine and glycolipid were influenced in both the levels of genes and metabolites.CONCLUSION The up-regulation of phenylalanine,D-glucosamine-6P and lipid may have further understanding of protective effect in hypoxia.Our data provided an insight to further reveal the hypoxia and adaptation mechanism.

Analysis of Data on Xanthan Fermentation in Stationary Phase Using Black Box and Metabolic Network Models

The xanthan fermentation data in the stationary phase was analyzed using the black box and the metabolic network models. The data consistency ls checked through the elemental balance in the black box model. In the metabolic network model, the metabolic flux distribution in the cell is calculated using the metabolic flux analysis method, then the maintenance coefficients is calculated.

Application of Gray Metabolic GM (1,1) Model in Prediction of Annual Total Yields of Chinese Aquatic Products

To predict the annual total yields of Chinese aquatic products in future five years ( 2011-2015) ,based on the theory and method of gray system,this paper firstly establishes a conventional GM ( 1,1) model and a gray metabolic GM ( 1,1) model respectively to predict the annual total yields of Chinese aquatic products in 2006-2009 and compare the prediction accuracy between these two models. Then,it selects the model with higher accuracy to predict the annual total yields of Chinese aquatic products in future five years. The comparison indicates that gray metabolic GM ( 1,1) model has higher prediction accuracy and smaller error,thus it is more suitable for prediction of annual total yields of aquatic products. Therefore,it adopts the gray metabolic GM ( 1,1) model to predict annual total yields of Chinese aquatic products in 2011-2015. The prediction results of annual total yields are 55. 32,57. 46,59. 72,62. 02 and 64. 43 million tons respectively in future five years with annual average increase rate of about 3. 7% ,much higher than the objective of 2. 2% specified in the Twelfth Five-Year Plan of the National Fishery Development ( 2011 to 2015) . The results of this research show that the gray metabolic GM ( 1,1) model is suitable for prediction of yields of aquatic products and the total yields of Chinese aquatic products in 2011-2015 will totally be able to realize the objective of the Twelfth Five-Year Plan.

Large-Scale Kinetic Parameter Identification of Metabolic Network Model of <i>E. coli</i>Using PSO

In metabolic network modelling, the accuracy of kinetic parameters has become more important over the last two decades. Even a small perturbation in kinetic parameters may cause major changes in a model’s response. The focus of this study is to identify the kinetic parameters, using two distinct approaches: firstly, a One-at-a-Time Sensitivity Measure, performed on 185 kinetic parameters, which represent glycolysis, pentose phosphate, TCA cycle, gluconeogenesis, glycoxylate pathways, and acetate formation. Time profiles for sensitivity indices were calculated for each parameter. Seven kinetic parameters were found to be highly affected in the model response;secondly, particle swarm optimization was applied for kinetic parameter identification of a metabolic network model. The simulation results proved the effectiveness of the proposed method.

Mathematical Modeling of a Metabolic Network to Study the Impact of Food Contaminants on Genomic Methylation and DNA Instability

Environmental contamination of food is a worldwide public health problem. Folate mediated one- carbon metabolism plays an important role in epigenetic regulation of gene expression and mutagenesis. Many contaminants in food cause cancer through epigenetic mechanisms and/or DNA instability i.e. default methylation of uracil to thymine, subsequent to the decrease of 5-methylte- trahydrofolate (5 mTHF) pool in the one-carbon metabolism network. Evaluating consequences of an exposure to food contaminants based on systems biology approaches is a promising alternative field of investigation. This report presents a dynamic mathematical modeling for the study of the alteration in the one-carbon metabolism network by environmental factors. It provides a model for predicting “the impact of arbitrary contaminants that can induce the 5 mTHF deficiency. The model allows for a given experimental condition, the analysis of DNA methylation activity and dumping methylation in the de novo pathway of DNA synthesis.

A nutraceutical combination improves insulin sensitivity in patients with metabolic syndrome

AIM:To test the efficacy of a proprietary nutraceutical combination in reducing insulin resistance associated with the metabolic syndrome(MetS).METHODS:Sixty-four patients with MetS followed at a tertiary outpatient clinic were randomly assigned to receive either placebo or a proprietary nutraceutical combination(AP)consisting of berberine,policosanol and red yeast rice,in a prospective,double-blind,placebo-controlled study.Evaluations were performed at baseline and after 18 wk of treatment.The homeostasis model assessment of insulin resistance(HOMAIR)index was the primary outcome measure.Secondary endpoints included lipid panel,blood glucose and insulin fasting,after a standard mixed meal and after an oral glucose tolerance test(OGTT),ow-mediated dilation(FMD),and waist circumference.RESULTS:Fifty nine patients completed the study,2 withdrew because of adverse effects.After 18 wk there was a signif icant reduction in the HOMA-IR index in the AP group compared with placebo(ΔHOMA respectively-0.6 ± 1.2 vs 0.4 ± 1.9;P < 0.05).Total and low density lipoprotein cholesterol also significantly decreased in the treatment arm compared with placebo(Δlow density lipoprotein cholesterol-0.82 ± 0.68 vs-0.13 ± 0.55 mmol/L;P < 0.001),while triglycerides,high density lipoprotein cholesterol,and the OGTT were not affected.In addition,there were significant reductions in blood glucose and insulin after the standard mixed meal,as well as an increase in FMD(ΔFMD 1.9 ± 4.2 vs 0 ± 1.9 %;P < 0.05)and a significant reduction in arterial systolic blood pressure in the AP arm.CONCLUSION:This short-term study shows that AP has relevant beneficial effects on insulin resistance and many other components of MetS.

Developmental programming of the metabolic syndrome critical windows for intervention

Metabolic disease results from a complex interaction of many factors,including genetic,physiological,behavioral and environmental influences.The recent rate at which these diseases have increased suggests that environmental and behavioral influences,rather than genetic causes,are fuelling the present epidemic.In this context,the developmental origins of health and disease hypothesis has highlighted the link between the periconceptual,fetal and early infant phases of life and the subsequent development of adult obesity and the metabolic syndrome.Although the mechanisms are yet to be fully elucidated,this programming was generally considered an irreversible change in developmental trajectory.Recent work in animal models suggests that developmental programming of metabolic disorders is potentially reversible by nutritional or targeted therapeutic interventions during the period of developmental plasticity.This review will discuss critical windows of developmental plasticity and possible avenues to ameliorate the development of postnatal metabolic disorders following an adverse early life environment.