Metabolomics of astaxanthin hyperaccumulation in Haematococcus pluvialis under high light stress

Variation in metabolite profiles of Haematococcus pluvialis(a type of unicellular green algal)under light stress is a key issue of study at the present.To investigate the effect of light intensity on accumulation of astaxanthin in H.pluvialis,a 26-day batch culture experiment of H.pluvialis under the light intensity levels at 73,127,182,236,and 291μmol/(m^(2)·s)was conducted.Therefore,the optimal light intensity and the corresponding metabolic pathways of accumulation in H.pluvialis were determined.Results show that 236μmol/(m^(2)·s)was the optimum light intensity to induce astaxanthin accumulation,at which a maximum content of 9.01 mg/L was achieved on Day 24.A total of 132 metabolites were identified and quantified,of which 38 differential metabolites were highlighted and classified,including 3 fatty acids or intermediates,5 amino acids or derivatives,5 carbohydrates or intermediates,16nucleoside derivatives,and 9 other metabolites using LC-MS/MS technique.Subsequently,16 statistically significant differential metabolic pathways were enriched and annotated based on Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis between the control and the 236μmol/(m^(2)·s)treatment group(P<0.05).In addition,the bioprocesses included cellular basal metabolism and signaling systems,such as carbohydrate metabolism,amino acid metabolism,glycerol and derivatives metabolism,nucleotide and derivative metabolism,and inositol phosphate metabolism were activated and regulated under strong light stress conditions.Moreover,4 hub metabolites containing D-glucose-6-phosphate,L-tyrosine,glycerol-3-phosphate,and L-glutamine were identified,based on which the associated metabolic network was constructed.The study provided a metabolomic view of astaxanthin accumulation in H.pluvialis under strong light stress.

Potential Secretory Transporters and Biosynthetic Precursors of Biological Nitrification Inhibitor 1,9-Decanediol in Rice as Revealed by Transcriptome and Metabolome Analyses

Biological nitrification inhibitors(BNIs)are released from plant roots and inhibit the nitrification activity of microorganisms in soils,reducing NO_(3)^(‒)leaching and N2O emissions,and increasing nitrogenuse efficiency(NUE).Several recent studies have focused on the identification of new BNIs,yet little is known about the genetic loci that govern their biosynthesis and secretion.We applied a combined transcriptomic and metabolomic analysis to investigate possible biosynthetic pathways and transporters involved in the biosynthesis and release of BNI 1,9-decanediol(1,9-D),which was previously identified in rice root exudates.Our results linked four fatty acids,icosapentaenoic acid,linoleate,norlinolenic acid,and polyhydroxy-α,ω-divarboxylic acid,with 1,9-D biosynthesis and three transporter families,namely the ATP-binding cassette protein family,the multidrug and toxic compound extrusion family,and the major facilitator superfamily,with 1,9-D release from roots into the soil medium.Our finding provided candidates for further work on the genes implicated in the biosynthesis and secretion of 1,9-D and pinpoint genetic loci for crop breeding to improve NUE by enhancing 1,9-D secretion,with the potential to reduce NO_(3)^(‒)leaching and N2O emissions from agricultural soils.

Metabolome Comparison of Transgenic and Non-transgenic Rice by Statistical Analysis of FTIR and NMR Spectra

Modern biotechnology, based on recombinant DNA techniques, has made it possible to introduce new traits with great potential for crop improvement. However, concerns about unintended effects of gene transformation that possibly threaten environment or consumer health have persuaded scientists to set up pre-release tests on genetically modified organisms. Assessment of ＇substantial equivalence＇ concept that established by comparison of genetically modified organism with a comparator with a history of safe use could be the first step of a comprehensive risk assessment. Metabolite level is the dchest in performance of changes which stem from genetic or environmental factors. Since assessment of all metabolites in detail is very costly and practically impossible, statistical evaluation of processed data of grain spectroscopic values could be a time and cost effective substitution for complex chemical analysis. To investigate the ability of multivariate statistical techniques in comparison of metabolomes as well as testing a method for such comparisons with available tools, a transgenic rice in combination with its traditionally bred parent were used as test material, and the discriminant analysis were applied as supervised method and principal component analysis as unsupervised classification method on the processed data which were extracted from Fourier transform infrared spectroscopy and nuclear magnetic resonance spectral data of powdered rice and rice extraction and badey grain samples, of which the latter was considered as control. The results confirmed the capability of statistics, even with initial data processing applications in metabolome studies. Meanwhile, this study confirms that the supervised method results in more distinctive results.

Growth,ROS Markers,Antioxidant Enzymes,Osmotic Regulators and Metabolic Changes in Tartary Buckwheat Subjected to Short Drought

Tartary buckwheat(Fagopyrum tataricum)is an important pseudocereal feed crop with medicinal and nutritional value.Drought is one of the main causes of reduced growth and yield in these plants.We investigated the growth,physiological,and metabolic responses of the widely promoted Tartary buckwheat variety Chuan Qiao No.1 to polyethylene glycol(PEG)-mediated drought stress.Drought significantly decreased shoot length,shoot biomass and relative water content.Root length,malondialdehyde content,electrolyte leakage,activities of superoxide dismutase,peroxidase,catalase and amylase,and contents of soluble sugar,soluble protein and proline were increased by PEG-mediated drought.Untargeted metabolomics analysis identified 32 core metabolites in seedlings subjected to PEG-mediated drought,16 of which increased—including quercetin,isovitexin,cyanidin 3-O-beta-D-glucoside,L-arginine,and glycerophosphocholine,while the other 16 decreased—including 3-methoxytyramine,2,6-diaminopimelic acid,citric acid,UDP-alpha-D-glucose,adenosine,keto-D-fructose.The 32 core metabolites were enriched in 29 metabolic pathways,including lysine biosynthesis,citrate(TCA)cycle,anthocyanin biosynthesis,and aminoacyl-tRNA biosynthesis.Among them,taurine and hypotaurine metabolism,flavor and flavor biosynthesis,indole alkaline biosynthesis,and alanine,aspartate and glutamate metabolism were the four main metabolic pathways affected by drought.Our findings provide new insights into the physiological and metabolic response mechanisms of Tartary buckwheat to drought stress.

Metabolomics analysis of poly(L-lactic acid)nanofibers’performance on PC12 cell differentiation

The aim of this article is to reveal the influence of aligned/random poly(L-lactic acid)(PLLA)nanofibers on PC12 cell differentiation from the perspective of metabolic level.First,three materials-PLLA aligned nanofibers(PLLA AF),PLLA random nanofibers(PLLA RF)and PLLA films(control)-were prepared by electrospinning and spin coating.Their surface morphologies were characterized.Subsequently,the cell viability,cell morphology and neurite length of PC12 cells on the surface of the three materials were evaluated,indicating more neurites in the PLLA RF groups but the longer average neurite length in the PLLA AF groups.Next,the metabolite profiles of PC12 cells cultured on the surface of the three nanofibers after 12 h,24 h and 36 h showed that,compared with the control,51,48 and 31 types of differential metabolites were detected at the three time points among the AF groups,respectively;and 56,45 and 41 types among the RF groups,respectively.Furthermore,the bioinformatics analysis of differential metabolites identified two pathways and three metabolites critical to PC12 cell differentiation influenced by the nanofibers.In addition,the verification experiment on critical metabolites and metabolic pathways were performed.The integrative analysis combining cytology,metabolomics and bioinformatics approaches revealed that though both PLLA AF and RF were capable of stimulating the synthesis of neurotransmitters,the PLLA AF were more beneficial for PC12 cell differentiation,whereas the PLLA RF were less effective.

Biotransformation of ethylene glycol by engineered Escherichia coli

There has been extensive research on the biological recycling of PET waste to address the issue of plastic waste pollution,with ethylene glycol(EG)being one of the main components recovered from this process.Therefore,finding ways to convert PET monomer EG into high-value products is crucial for effective PET waste recycling.In this study,we successfully engineered Escherichia coli to utilize EG and produce glycolic acid(GA),expecting to facilitate the biological recycling of PET waste.The engineered E.coli,able to utilize 10 g/L EG to produce 1.38 g/L GA within 96 h,was initially constructed.Subsequently,strategies based on overexpression of key enzymes and knock-out of the competing pathways are employed to enhance EG utilization along with GA biosynthesis.An engineered E.coli,characterized by the highest GA production titer and substrate conversion rate,was obtained.The GA titer increased to 5.1 g/L with a yield of 0.75 g/g EG,which is the highest level in the shake flake experiments.Transcriptional level analysis and metabolomic analysis were then conducted,revealing that overexpression of key enzymes and knock-out of the competing pathways improved the metabolic flow in the EG utilization.The improved metabolic flow also leads to accelerated synthesis and metabolism of amino acids.

Targeted metabolic analysis of nucleotides and identification of biomarkers associated with cancer in cultured cell models

Cancer,like other diseases accompanied by metabolic changes,shows characteristic DNA/RNA modifications and activities of modifying enzymes,resulting in fluctuations in nucleoside levels.In this study,we undertook targeted metabolomic analyses of nucleotides in different cancer cell culture models using a sensitive and reproducible ion-pair HPLC method.The experimental data were analyzed by principal component analysis(PCA)to identify potential biomarkers in cancer cells,and statistical significance was determined by one-way analysis of variance.As a result,a clear differentiation of normal and tumor cells into two clusters was shown,indicating abnormal metabolism of nucleotides in tumor cells.Six variables(AMP,UDP,CTP levels with a significance of Po0.05;ATP,UTP and GMP levels with a significance of Po0.01)were considered as potential biomarkers;the content of AMP,UTP,GMP and ATP was significantly higher in cancer cells.The receiver operating characteristic(ROC)curve analysis allowed us to discriminate normal cells from tumor cells based on area under the curve(AUC).The sequence of their AUC values were:ATP(0.979)4UTP(0.938)4CTP¼GMP(0.896)4AMP(0.812)4UDP(0.792),so we conclude that ATP and UTP are the best potential biomarkers in tumor cells.This study may provide a valuable tool for studying minute alterations of intracellular nucleotide pools induced by anticancer/antiviral drugs,diseases or environmental factors.

Effects of antibiotic antitumor drugs on nucleotide levels in cultured tumor cells:an exploratory method to distinguish the mechanisms of antitumor drug action based on targeted metabolomics

Nucleotide pools in mammalian cells change due to the influence of antitumor drugs,which may help in evaluating the drug effect and understanding the mechanism of drug action.In this study,an ion-pair RP-HPLC method was used for a simple,sensitive and simultaneous determination of the levels of 12 nucleotides in mammalian cells treated with antibiotic antitumor drugs(daunorubicin,epirubicin and dactinomycin D).Through the use of this targeted metabolomics approach to find potential biomarkers,UTP and ATP were verified to be the most appropriate biomarkers.Moreover,a holistic statistical approach was put forward to develop a model which could distinguish 4 categories of drugs with different mechanisms of action.This model can be further validated by evaluating drugs with different mechanismsof action.This targeted metabolomics study may provide a novel approach to predict the mechanism of action of antitumor drugs.