Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group.As a prominent strain in the fields of agriculture and bioengineering,there is still a lack of comprehensive understanding regarding its metabolic capabilities,specifically in terms of central metabolism and substrate utilization.Therefore,further exploration and extensive studies are required to gain a detailed insight into these aspects.This study reconstructed a genome-scale metabolic network model for P.stutzeri A1501 and conducted extensive curations,including correcting energy generation cycles,respiratory chains,and biomass composition.The final model,iQY1018,was successfully developed,covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890.The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality.The model prediction accuracy of substrate utilization for P.stutzeri A1501 reached 90%.The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products.This work provides an updated,high-quality model of P.stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.

Unraveling the mechanism of action of Shangxia Liangji formula for treating insomnia:a metabolomics and network pharmacology approach

Background:Insomnia is a prevalent clinical condition and Shangxia Liangji formula(SXLJF)is a well-established method of treatment.Nevertheless,the specific mechanism of action of SXLJF remains unclear.Methods:The mouse model of insomnia was established by intraperitoneal injection of para-chlorophenylalanine.Forty-two mice were randomly divided into a negative control group,model group,SXLJF group(18.72 g/kg/day),and positive control group(diazepam,2 mg/kg)and treated with the corresponding drugs for 7 consecutive days.The open field test and pentobarbital-induced sleeping test were conducted.LC-MS-based untargeted metabolomics and network pharmacology were applied to explore the potential targets of SXLJF for treating insomnia.Finally,key targets were validated using RT-qPCR.Results:Behavioral tests demonstrated that SXLJF reduced the total distance,average velocity,central distance,and sleep latency,and prolonged sleep duration.Metabolomics and network pharmacology revealed potential targets,signaling pathways,metabolic pathways,and metabolites associated with the anti-insomnia effects of SXLJF.Specifically,tyrosine hydroxylase(TH)and tyrosine metabolism emerged as crucial metabolic pathways and targets,respectively.RT-qPCR results supported the role of TH in the mechanism of SXLJF in treating insomnia.Conclusion:In conclusion,TH and tyrosine metabolism may represent significant targets and pathways for SXLJF in treating insomnia.

Effects of different doses of glucose and fructose on central metabolic pathways and intercellular wireless communication networks in humans

Fructose and glucose are often widely used in food processing and may contribute to many metabolic diseases.To observe the effects of different doses of glucose and fructose on human metabolism and cellular communication,volunteers were given low,medium,and high doses of glucose and fructose.Serum cytokines,glucose,lactate,nicotinamide adenine dinucleotide(NADH)and metabolic enzymes were assayed,and central carbon metabolic pathway networks and cytokine communication networks were constructed.The results showed that the glucose and fructose groups basically maintained the trend of decreasing catabolism and increasing anabolism with increasing dose.Compared with glucose,low-dose fructose decreased catabolism and increased anabolism,significantly enhanced the expression of the inflammatory cytokine interferon-γ(IFN-γ),macrophage-derived chemokine(MDC),induced protein-10(IP-10),and eotaxin,and significantly reduced the activity of isocitrate dehydrogenase(ICDH)and pyruvate dehydrogenase complexes(PDHC).Both medium and high doses of fructose increase catabolism and anabolism,and there are more cytokines and enzymes with significant changes.Furthermore,multiple cytokines and enzymes show strong relevance to metabolic regulation by altering the transcription and expression of enzymes in central carbon metabolic pathways.Therefore,excessive intake of fructose should be reduced to avoid excessive inflammatory responses,allergic reactions and autoimmune diseases.

Identification of key gene networks controlling organic acid and sugar metabolism during watermelon fruit development by integrating metabolic phenotypes and gene expression profiles

The organoleptic qualities of watermelon fruit are defined by the sugar and organic acid contents,which undergo considerable variations during development and maturation.The molecular mechanisms underlying these variations remain unclear.In this study,we used transcriptome profiles to investigate the coexpression patterns of gene networks associated with sugar and organic acid metabolism.We identified 3 gene networks/modules containing 2443 genes highly correlated with sugars and organic acids.Within these modules,based on intramodular significance and Reverse Transcription Quantitative polymerase chain reaction(RT-qPCR),we identified 7 genes involved in the metabolism of sugars and organic acids.Among these genes,Cla97C01G000640,Cla97C05G087120 and Cla97C01G018840(r^(2)=0.83 with glucose content)were identified as sugar transporters(SWEET,EDR6 and STP)and Cla97C03G064990(r^(2)=0.92 with sucrose content)was identified as a sucrose synthase from information available for other crops.Similarly,Cla97C07G128420,Cla97C03G068240 and Cla97C01G008870,having strong correlations with malic(r^(2)=0.75)and citric acid(r^(2)=0.85),were annotated as malate and citrate transporters(ALMT7,CS,and ICDH).The expression profiles of these 7 genes in diverse watermelon genotypes revealed consistent patterns of expression variation in various types of watermelon.These findings add significantly to our existing knowledge of sugar and organic acid metabolism in watermelon.

Application of the back-error propagation artificial neural network(BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga- tion artificial neural network （BPANN）. We established the model based on data gathered from metabolic syndrome patients （n = 1012） and normal controls （n = 1069） by BPANN. Mean impact value （MIV） for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction （GMDR） confirmed a joint effect of PPAR-9＂ and RXR-a based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk fac- tors were in the order of body mass index （BMI）, serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 （rs4240711, rs4842194, rs2920502 and rs3856806） models were the best models （cross-validation consistency 10/10, P = 0.0107） with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.

Mechanistic study of lipid metabolism disorders in diabetic kidney disease treated with GLQMP based on network pharmacology,molecular docking and in vitro experiments

Background:In this study,we used network pharmacology and molecular docking combined with vitro experiments to explore the potential mechanism of action of Gualou Qumai pill(GLQMP)against DKD.Methods:We screened effective compounds and drug targets using Chinese medicine systemic pharmacology database and analysis platform and Chinese medicine molecular mechanism bioinformatics analysis tools;and searched for DKD targets using human online Mendelian genetics and gene cards.The potential targets of GLQMP for DKD were obtained through the intersection of drug targets and disease targets.Cytoscape software was applied to build herbal medicine-active compound-target-disease networks and analyze them;protein-protein interaction networks were analyzed using the STRING database platform;gene ontology and Kyoto Encyclopedia of Genes and Genomes were used for gene ontology and gene and genome encyclopedia to enrich potential targets using the DAVID database;and the AutoDock Vina 1.1.2 software for molecular docking of key targets with corresponding key components.In vitro experiments were validated by CCK8,oil red O staining,TC,TG,RT-qPCR,and Western blot.Results:Through network pharmacology analysis,a total of 99 potential therapeutic targets of GLQMP for DKD and the corresponding 38 active compounds were obtained,and 5 core compounds were identified.By constructing the protein-protein interaction network and performing network topology analysis,we found that PPARA and PPARG were the key targets,and then we molecularly docked these two key targets with the 38 active compounds,especially the 5 core compounds,and found that PPARA and PPARG had good binding ability with a variety of compounds.In vitro experiments showed that GLQMP was able to ameliorate HK-2 cell injury under high glucose stress,improve cell viability,reduce TC and TG levels as well as decrease the accumulation of lipid droplets,and RT-qPCR and Western blot confirmed that GLQMP was able to promote the expression levels of PPARA and PPARG.Conclusion:Overall,this study revealed the active compounds,important targets and possible mechanisms of GLQMP treatment for DKD,and conducted preliminary verification experiments on its correctness,provided novel insights into the treatment of DKD by GLQMP.

Ethanol extract of cassia seed alleviates metabolic dysfunction-associated steatotic liver disease by acting on multiple lipid metabolism-related pathways

Background and objective:In northern China's cold regions,the prevalence of metabolic dysfunction-associated steatotic liver disease(MASLD)exceeds 50%,significantly higher than the national and global rates.MASLD is an important risk factor for cardiovascular and cerebrovascular diseases,including coronary heart disease,stroke,and tumors,with no specific therapeutic drugs currently available.The ethanol extract of cassia seed(CSEE)has shown promise in lowering blood lipids and improving hepatic steatosis,but its mechanism in treating MASLD remains underexplored.This study aims to investigate the therapeutic effects and mechanisms of CSEE.Methods:MASLD models were established in male Wistar rats and golden hamsters using a high fat diet(HFD).CSEE(10,50,250 mg/kg)was administered via gavage for six weeks.Serum levels of total cholesterol(TC),triglyceride(TG),low-density lipoprotein cholesterol(LDL-C),high-density lipoprotein cholesterol(HDL-C),aspartate aminotransferase(AST),and alanine aminotransferase(ALT),as well as liver TC and TG,were measured using biochemical kits.Histopathological changes in the liver were evaluated using Oil Red O staining,Hematoxylin-eosin(H&E)staining,and transmission electron microscopy(TEM).HepG2 cell viability was assessed using the cell counting kit-8(CCK8)and Calcein-AM/PI staining.Network pharmacology was used to analyze drug-disease targets,and western blotting was used to confirm these predictions.Results:CSEE treatment significantly reduced serum levels of TC,TG,LDL-C,ALT,and AST,and improved liver weight,liver index,and hepatic lipid deposition in rats and golden hamsters.In addition,CSEE alleviated free fatty acid(FFA)-induced lipid deposition in HepG2 cells.Molecular biology experiments demonstrated that CSEE increased the protein levels of p-AMPK,p-ACC,PPARα,CPT1A,PI3K P110 and p-AKT,while decreasing the protein levels of SREBP1,FASN,C/EBPα,and PPARγ,thus improving hepatic lipid metabolism and reducing lipid deposition.The beneficial effects of CSEE were reversed by small molecule inhibitors of the signaling pathways in vitro.Conclusion:CSEE improves liver lipid metabolism and reduces lipid droplet deposition in Wistar rats and golden hamsters with MASLD by activating hepatic AMPK,PPARα,and PI3K/AKT signaling pathways.

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.

Noise effect in metabolic networks

Constraint-based models such as flux balance analysis （FBA） are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term‘noise＇, and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.

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.

Transcriptome Profile Based on Protein-Protein Interaction Networks Provides a Set of Core Genes for Understanding the Metabolic Mechanisms of the Egg-Protecting Behavior in Amphioctopus fangsiao

Marine organisms cannot grow and reproduce without proper metabolic regulation.Within a metabolic network,problems with a given link will affect the normal life activities of the organism.Many metabolic mechanisms associated with behaviors of Am-phioctopus fangsiao are still unclear.Moreover,as a factor affecting the normal growth of A.fangsiao,egg protection has rarely been considered in previous behavioral studies.In this research,we analyzed the transcriptome profile of gene expression in A.fangsiao egg-unprotected larvae and egg-protected larvae,and identified 818 differentially expressed genes(DEGs).We used GO and KEGG enrichment analyses to search for metabolism-related DEGs.Protein-protein interaction networks were constructed to examine the interactions between metabolism-related genes.Twenty hub genes with multiple protein-protein interaction relationships or that were involved in multiple KEGG signaling pathways were obtained and verified by quantitative RT-PCR.We first studied the effects of egg protection on the metabolism of A.fangsiao larvae by means of protein-protein interaction networks,and the results provide va-luable gene resources for understanding the metabolism of invertebrate larvae.The data serve as a foundation for further research on the egg-protecting behavior of invertebrates.

Comparative transcriptomics and gene network analysis revealed secondary metabolism as preeminent metabolic pathways for heat tolerance in hard fescue

The molecular mechanisms underlying genetic variations in heat tolerance,one of the important turfgrass traits,for fine fescue are not well-understood.In the present study,our objective was to identify molecular constituents and metabolic interactions involved in heat tolerance in two genotypes of hard fescue(Festuca trachyphylla)contrasting in heat tolerance by comparative transcriptomics and gene comparison network analysis.Two cultivars of hard fescue,'Reliant IV'(heat-tolerant),and'Predator'(heat-sensitive),were subjected to heat stress temperature at 35/30℃(day/night)or maintained at optimal temperature at 22/18℃(day/night)(non-stress control)for 21 d.At 14 and 21 d of heat stress,'Reliant IV'maintained significantly higher photochemical efficiency(F_(v)/F_(m)),chlorophyll(Chl)content,and lower cell membrane electrolyte leakage(EL)compared to'Predator',suggesting its superiority in heat tolerance.Comparative transcriptomic profiles,gene functional enrichment analysis,and weighted gene comparison network analysis revealed central hub genes(BBE22 and ALPLD)and their connecting genes involved in secondary metabolism for biosynthesis of oxylipins(LOX1 and LOX3),phenolic compounds(PAL2),and dhurrin(C79A1 and C71E1).These genes were up-regulated in heat-tolerant'Reliant IV'under heat stress but not in heat-sensitive'Predator',while a majority of heat-regulated genes involved in primary metabolism responded similarly to heat stress in both cultivars.Those unique genes in the secondary metabolic pathways enriched in only the heat-tolerant cultivar could be critical for mediating the protection of hard fescue against heat stress and are potentially useful as candidate genes or molecular markers for augmenting heat tolerance in other temperate species of grass.

Constrain-based analysis of gene deletion on the metabolic flux redistribution of Saccharomyces Cerevisiae

Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.

Energy intake,metabolic homeostasis,and human health

The energy substances(mainly carbohydrates and fats)are the basis and guarantee of life activity,especially the oxidative phosphorylation for energy supply.However,excessive absorption and accumulation of these substances can lead to metabolic diseases such as obesity,hyperlipidemia,diabetes,and cancers.A large amount of studies demonstrate that G protein-coupled receptors(GPCRs)play a key role in identification and absorption of energy substances,and the signaling network of nerves,immune,and endocrine regulates their storage and utilization.The gastrointestinal mucus layer not only identifies these substances through identification in diet components but also transfers immune,metabolic,and endocrine signals of hormones,cytokines,and chemokines by promoting interactions between receptors and ligands.These signaling molecules are transferred to corresponding organs,tissues,and cells by the circulatory system,and cell activity is regulated by amplifying of cell signals that constitute the wireless communication network among cells in the body.Absorption,accumulation,and utilization of energy substances in the body obey the law of energy conservation.Energy is stored in the form of fat,and meets the demand of body via two coupled mechanisms:catabolism and oxidative phosphorylation.Under normal physiological conditions,fat consumption involves ketone body metabolism through the circulatory system and glucose consumption requires blood lactic acid cycle.Accumulation of excessive energy leads to the abnormal activation of mammalian target of rapamycin(mTOR),thus promoting the excretion of glucose or glycogen in the form of blood glucose and urine glucose.Alternatively,the body cancels the intercellular contact inhibition and promotes cell proliferation to induce carcinogenesis,which can induce the consumption of large amounts of glucose.Intercellular communication is performed by signaling molecules via sensing,absorption,accumulation,and utilization of energy substances,and anabolism and catabolism are controlled by the central metabolic pathway.Therefore,slower catabolism will result in longer life expectancy,whereas faster catabolism results in shorter life expectancy.Energy substances in diet influence the balance between energy and metabolism in the body through the sensing function of the gastrointestinal system at two levels:cellular communication network and metabolic network.The present review of studies aims to strengthen our knowledge on cellular communication and metabolic networks to offer a dietary guidance on the metabolism and communication role of various foods.

Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

A novel hyper-cube shrink algorithm to predict metabolic fluxes based on enzyme activity only

OBJECTIVE One of the long-expected goals of genome-scale metabolic modeling is to evaluate the influence of the perturbed enzymes to the yield of an expected end product.METHDOS Metabolic control analysis(MCA)performs such role to calculate the sensitivity of flux change upon that of enzymes under the framework of ordinary differential equation(ODE)models,which are restricted in small-scale networks and require explicit kinetic parameters.The constraint-based models,like flux balance analysis(FBA),lack of the room of performing MCA because they are parameters-free.In this study,we developed a hyper-cube shrink algorithm(HCSA)to incorporate the enzymatic properties to the FBA model by introducing a pair of parameters for each reaction.Our algorithm was able to handle not only prediction of knockout strains but also strains with an adjustment of expression level of certain enzymes.RESULTS We first showed the concept by applying HCSA to a simplest three-nodes network.Then we show the HCSA possesses Michaelis-Menten like behaviors characterized by steady state of ODE.We obtained good prediction of a synthetic network in Saccharomyces cerevisiae producing voilacein and analogues.Finally we showed its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast.CONCLUSION We have developed an algorithm the impact on fluxes when certain enzymes were inhibited or activated.It provides us a powerful tool to evaluate the consequences of enzyme inhibitor or activator.

Ethylene accelerates maize leaf senescence in response to nitrogen deficiency by regulating chlorophyll metabolism and autophagy

Leaf senescence is an orderly and highly coordinated process,and finely regulated by ethylene and nitrogen(N),ultimately affecting grain yield and nitrogen-use efficiency(NUE).However,the underlying regulatory mechanisms on the crosstalk between ethylene-and N-regulated leaf senescence remain a mystery in maize.In this study,ethylene biosynthesis gene ZmACS7 overexpressing(OE-ZmACS7)plants were used to study the role of ethylene regulating leaf senescence in response to N deficiency,and they exhibited the premature leaf senescence accompanied by increased ethylene release,decreased chlorophyll content and F_v/F_m ratio,and accelerated chloroplast degradation.Then,we investigated the dynamics changes of transcriptome reprogramming underlying ethylene-accelerated leaf senescence in response to N deficiency.The differentially expressed genes(DEGs)involved in chlorophyll biosynthesis were significantly down-regulated,while DEGs involved in chlorophyll degradation and autophagy processes were significantly up-regulated,especially in OE-ZmACS7 plants in response to N deficiency.A gene regulatory network(GRN)was predicted during ethylene-accelerated leaf senescence in response to N deficiency.Three transcription factors(TFs)ZmHSF4,Zmb HLH106,and ZmEREB147 were identified as the key regulatory genes,which targeted chlorophyll biosynthesis gene ZmLES22,chlorophyll degradation gene ZmNYC1,and autophagy-related gene ZmATG5,respectively.Furthermore,ethylene signaling key genes might be located upstream of these TFs,generating the signaling cascade networks during ethylene-accelerated leaf senescence in response to N deficiency.Collectively,these findings improve our molecular knowledge of ethylene-accelerated maize leaf senescence in response to N deficiency,which is promising to improve NUE by manipulating the progress of leaf senescence in maize.

An integrated strategy for comprehensive characterization of metabolites and metabolic profiles of bufadienolides from Venenum Bufonis in rats

Comprehensive characterization of metabolites and metabolic profiles in plasma has considerable significance in determining the efficacy and safety of traditional Chinese medicine(TCM)in vivo.However,this process is usually hindered by the insufficient characteristic fragments of metabolites,ubiquitous matrix interference,and complicated screening and identification procedures for metabolites.In this study,an effective strategy was established to systematically characterize the metabolites,deduce the metabolic pathways,and describe the metabolic profiles of bufadienolides isolated from Venenum Bufonis in vivo.The strategy was divided into five steps.First,the blank and test plasma samples were injected into an ultra-high performance liquid chromatography/linear trap quadrupole-orbitrap-mass spectrometry(MS)system in the full scan mode continuously five times to screen for valid matrix compounds and metabolites.Second,an extension-mass defect filter model was established to obtain the targeted precursor ions of the list of bufadienolide metabolites,which reduced approximately 39%of the interfering ions.Third,an acquisition model was developed and used to trigger more tandem MS(MS/MS)fragments of precursor ions based on the targeted ion list.The acquisition mode enhanced the acquisition capability by approximately four times than that of the regular data-dependent acquisition mode.Fourth,the acquired data were imported into Compound Discoverer software for identification of metabolites with metabolic network prediction.The main in vivo metabolic pathways of bufadienolides were elucidated.A total of 147 metabolites were characterized,and the main biotransformation reactions of bufadienolides were hydroxylation,dihydroxylation,and isomerization.Finally,the main prototype bufadienolides in plasma at different time points were determined using LC-MS/MS,and the metabolic profiles were clearly identified.This strategy could be widely used to elucidate the metabolic profiles of TCM preparations or Chinese patent medicines in vivo and provide critical data for rational drug use.

Systemic omics analysis of the hub genes, proteins, metabolites and metabolic pathways related to the hypoxia preconditioning in mice

Hypoxia preconditioning （HPC） is associated with many complicated pathophysiological and biochemical processes that integrated and regulated via molecular levels. HPC could protect cells, tissues, organs and systems from hypoxia injury, but up to date, the molecular mechanism still remained unclear. The acute and repetitive hy- poxia preconditioning model was constructed and the related parameters were observed. The high-throughput mi- croarray analysis and multiple bioinformatics were used to explore the differentially expressed genes in HPC mice brain and the related gene network, pathways and biological processes related to HPC. The 2D-DIGE coupled with MALDI-TOF/TOF-MS was performed to identify these proteins that were differentially expressed during HPC. The UPLC-HRMS based metabolomics method was utilized to explore the key endogenous metabolites and metabolic pathways related to HPC. The results showed that （1） 1175 differentially expressed genes in HPC mice brain were identified. Fourteen of these genes were the related hub genes for HPC, including Cacna2dl, Grin2a, Npylr, Mef2c, Epha4, Rxfpl, Chrm3, Pdela, Atp2b4, Glral, Idil , Fgfl, Grin2b and Cda. The change trends of all the detected genes by RT-PCR were consistent with the data of gene chips. There were 113 significant functions up- regulated and 138 significant functions down-regulated in HPC mice. （2） About 2100 proteins were revealed via the gel imaging and spot detection. 66, 45 and 70 of proteins were found to have significantly difference between the control group and three times of HPC group, the control and six times of HPC, and the three times of HPC and six times of HPC group. （3）Some endogenous metabolites such as phenylalanine, valine, proline, leucine and glu- tamine were increased, while ereatine was decreased, both in HPC brain and heart; in addition, y-aminobutyric acid was markedly decreased in brain. The sphingolipid metabolic pathways were noticed due to the low p-value and high pathway impact. Especially, the sphingolipid compound sphingomyelin, ceramide, glucosyleeramide, galactosylceramide and laetosylceramide were mapping in this metabolic pathway. Interestingly, these sphingolipid metabolites with olefinic bond in the long fatty chain were up-regulated, while those sphingolipids without olefinic bond were down-regulated. The functions of these differentially expressed genes mainly involved the cellular proces- ses including MAPK pathway, ion transport, neurotransmitter transport and neuropeptide signal pathway. The pro- tein levels related the ATP synthesis and citric acid cycle decreased while the proteins with the glycolysis and oxy- gen-binding increased. Glutathione, GNBP-1 and GPD1L were related to preventing hypoxic damage. The results indicated that C24：l-Cers played a critical role in HPC and had potential in endogenous protective mechanism. The combinations of the system omies data of the different molecules were sufficient to give a further understanding of the molecular pathways affected by HPC. Our data provided an important insight to reveal the protection mechanism of HPC.