Displaying publications 21 - 40 of 172 in total

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  1. Jamil NAM, Rahmad N, Rosli NHM, Al-Obaidi JR
    Electrophoresis, 2018 12;39(23):2954-2964.
    PMID: 30074628 DOI: 10.1002/elps.201800185
    Wax apple is one of the underutilized fruits that is considered a good source of fibers, vitamins, minerals as well as antioxidants. In this study, a comparative analysis of the developments of wax fruit ripening at the proteomic and metabolomic level was reported. 2D electrophoresis coupled with MALDI-TOF/TOF was used to compare the proteome profile from three developmental stages named immature, young, and mature fruits. In general, the protein expression profile and the identified proteins function were discussed for their potential roles in fruit physiological development and ripening processes. The metabolomic investigation was also performed on the same samples using quadrupole LC-MS (LC-QTOF/MS). Roles of some of the differentially expressed proteins and metabolites are discussed in relation to wax apple ripening during the development. This is the first study investigating the changes in the proteins and metabolites in wax apple at different developmental stages. The information obtained from this research will be helpful in developing biomarkers for breeders and help the plant researchers to avoid wax apple cultivation problems such as fruit cracking.
    Matched MeSH terms: Metabolomics/methods*
  2. Baharum SN, Azizan KA
    Adv Exp Med Biol, 2018 11 2;1102:51-68.
    PMID: 30382568 DOI: 10.1007/978-3-319-98758-3_4
    Over the last decade, metabolomics has continued to grow rapidly and is considered a dynamic technology in envisaging and elucidating complex phenotypes in systems biology area. The advantage of metabolomics compared to other omics technologies such as transcriptomics and proteomics is that these later omics only consider the intermediate steps in the central dogma pathway (mRNA and protein expression). Meanwhile, metabolomics reveals the downstream products of gene and expression of proteins. The most frequently used tools are nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). Some of the common MS-based analyses are gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These high-throughput instruments play an extremely crucial role in discovery metabolomics to generate data needed for further analysis. In this chapter, the concept of metabolomics in the context of systems biology is discussed and provides examples of its application in human disease studies, plant responses towards stress and abiotic resistance and also microbial metabolomics for biotechnology applications. Lastly, a few case studies of metabolomics analysis are also presented, for example, investigation of an aromatic herbal plant, Persicaria minor metabolome and microbial metabolomics for metabolic engineering applications.
    Matched MeSH terms: Metabolomics*
  3. Amin AM, Mostafa H, Arif NH, Abdul Kader MAS, Kah Hay Y
    Clin Chim Acta, 2019 Jun;493:112-122.
    PMID: 30826371 DOI: 10.1016/j.cca.2019.02.030
    BACKGROUND: Coronary artery disease (CAD) claims lives yearly. Nuclear magnetic resonance (1H NMR) metabolomics analysis is efficient in identifying metabolic biomarkers which lend credence to diagnosis. We aimed to identify CAD metabotypes and its implicated pathways using 1H NMR analysis.

    METHODS: We analysed plasma and urine samples of 50 stable CAD patients and 50 healthy controls using 1H NMR. Orthogonal partial least square discriminant analysis (OPLS-DA) followed by multivariate logistic regression (MVLR) models were developed to indicate the discriminating metabotypes. Metabolic pathway analysis was performed to identify the implicated pathways.

    RESULTS: Both plasma and urine OPLS-DA models had specificity, sensitivity and accuracy of 100%, 96% and 98%, respectively. Plasma MVLR model had specificity, sensitivity, accuracy and AUROC of 92%, 86%, 89% and 0.96, respectively. The MVLR model of urine had specificity, sensitivity, accuracy and AUROC of 90%, 80%, 85% and 0.92, respectively. 35 and 12 metabolites were identified in plasma and urine metabotypes, respectively. Metabolic pathway analysis revealed that urea cycle, aminoacyl-tRNA biosynthesis and synthesis and degradation of ketone bodies pathways were significantly disturbed in plasma, while methylhistidine metabolism and galactose metabolism pathways were significantly disturbed in urine. The enrichment over representation analysis against SNPs-associated-metabolite sets library revealed that 85 SNPs were significantly enriched in plasma metabotype.

    CONCLUSIONS: Cardiometabolic diseases, dysbiotic gut-microbiota and genetic variabilities are largely implicated in the pathogenesis of CAD.

    Matched MeSH terms: Metabolomics*
  4. Ahamad Bustamam MS, Pantami HA, Shaari K, Min CC, Mediani A, Ismail IS
    Fish Shellfish Immunol, 2023 Jan;132:108455.
    PMID: 36464078 DOI: 10.1016/j.fsi.2022.108455
    Tilapia is one of the most common fish species that is intensively produced all over the world. However, significant measures at improving aquaculture health must be taken since disease outbreaks are often encountered in the rapidly developing aquaculture industry. Therefore, the objective of the study was designed to evaluate the metabolite changes in tilapia' sera through 1H NMR metabolomics in identifying the potential biomarkers responsible for immunomodulatory effect by the indigenous species of Malaysian microalgae Isochrysis galbana (IG). The results showed that IG-incorporated diet mainly at 5.0% has improved the immune response of innate immunity as observed in serum bactericidal activity (SBA) and serum lysozyme activity (SLA). The orthogonal partial least squares (OPLS) analysis indicated 5 important metabolites significantly upregulated namely as ethanol, lipoprotein, lipid, α-glucose and unsaturated fatty acid (UFA) in the 5.0% IG-incorporated diet compared to control. In conclusion, this study had successfully determined IG in improving aquaculture health through its potential use as an immune modulator. This work also demonstrated the effective use of metabolomics approach in the development of alternative nutritious diet from microalgae species to boost fish health in fulfilling the aquaculture's long-term goals.
    Matched MeSH terms: Metabolomics/methods
  5. Badamasi IM, Maulidiani M, Lye MS, Ibrahim N, Shaari K, Stanslas J
    Curr Neuropharmacol, 2022;20(5):965-982.
    PMID: 34126904 DOI: 10.2174/1570159X19666210611095320
    BACKGROUND: The evaluation of metabolites that are directly involved in the physiological process, few steps short of phenotypical manifestation, remains vital for unravelling the biological moieties involved in the development of the (MDD) and in predicting its treatment outcome.

    METHODOLOGY: Eight (8) urine and serum samples each obtained from consenting healthy controls (HC), twenty-five (25) urine and serum samples each from first episode treatment naïve MDD (TNMDD) patients, and twenty (22) urine and serum samples each s from treatment naïve MDD patients 2 weeks after SSRI treatment (TWMDD) were analysed for metabolites using proton nuclear magnetic resonance (1HNMR) spectroscopy. The evaluation of patients' samples was carried out using Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Square- Discriminant Analysis (OPLSDA) models.

    RESULTS: In the serum, decreased levels of lactate, glucose, glutamine, creatinine, acetate, valine, alanine, and fatty acid and an increased level of acetone and choline in TNMDD or TWMDD irrespective of whether an OPLSDA or PLSDA evaluation was used were identified. A test for statistical validations of these models was successful.

    CONCLUSION: Only some changes in serum metabolite levels between HC and TNMDD identified in this study have potential values in the diagnosis of MDD. These changes included decreased levels of lactate, glutamine, creatinine, valine, alanine, and fatty acid, as well as an increased level of acetone and choline in TNMDD. The diagnostic value of these changes in metabolites was maintained in samples from TWMDD patients, thus reaffirming the diagnostic nature of these metabolites for MDD.

    Matched MeSH terms: Metabolomics/methods
  6. Ma NL, Rahmat Z, Lam SS
    Int J Mol Sci, 2013 Apr 08;14(4):7515-41.
    PMID: 23567269 DOI: 10.3390/ijms14047515
    Physiological and ecological constraints that cause the slow growth and depleted production of crops have raised a major concern in the agriculture industry as they represent a possible threat of short food supply in the future. The key feature that regulates the stress signaling pathway is always related to the reactive oxygen species (ROS). The accumulation of ROS in plant cells would leave traces of biomarkers at the genome, proteome, and metabolome levels, which could be identified with the recent technological breakthrough coupled with improved performance of bioinformatics. This review highlights the recent breakthrough in molecular strategies (comprising transcriptomics, proteomics, and metabolomics) in identifying oxidative stress biomarkers and the arising opportunities and obstacles observed in research on biomarkers in rice. The major issue in incorporating bioinformatics to validate the biomarkers from different omic platforms for the use of rice-breeding programs is also discussed. The development of powerful techniques for identification of oxidative stress-related biomarkers and the integration of data from different disciplines shed light on the oxidative response pathways in plants.
    Matched MeSH terms: Metabolomics/methods*
  7. Deng L, Ma L, Cheng KK, Xu X, Raftery D, Dong J
    J Proteome Res, 2021 06 04;20(6):3204-3213.
    PMID: 34002606 DOI: 10.1021/acs.jproteome.1c00064
    Metabolite set enrichment analysis (MSEA) has gained increasing research interest for identification of perturbed metabolic pathways in metabolomics. The method incorporates predefined metabolic pathways information in the analysis where metabolite sets are typically assumed to be mutually exclusive to each other. However, metabolic pathways are known to contain common metabolites and intermediates. This situation, along with limitations in metabolite detection or coverage leads to overlapping, incomplete metabolite sets in pathway analysis. For overlapping metabolite sets, MSEA tends to result in high false positives due to improper weights allocated to the overlapping metabolites. Here, we proposed an extended partial least squares (PLS) model with a new sparse scheme for overlapping metabolite set enrichment analysis, named overlapping group PLS (ogPLS) analysis. The weight vector of the ogPLS model was decomposed into pathway-specific subvectors, and then a group lasso penalty was imposed on these subvectors to achieve a proper weight allocation for the overlapping metabolites. Two strategies were adopted in the proposed ogPLS model to identify the perturbed metabolic pathways. The first strategy involves debiasing regularization, which was used to reduce inequalities amongst the predefined metabolic pathways. The second strategy is stable selection, which was used to rank pathways while avoiding the nuisance problems of model parameter optimization. Both simulated and real-world metabolomic datasets were used to evaluate the proposed method and compare with two other MSEA methods including Global-test and the multiblock PLS (MB-PLS)-based pathway importance in projection (PIP) methods. Using a simulated dataset with known perturbed pathways, the average true discovery rate for the ogPLS method was found to be higher than the Global-test and the MB-PLS-based PIP methods. Analysis with a real-world metabolomics dataset also indicated that the developed method was less prone to select pathways with highly overlapped detected metabolite sets. Compared with the two other methods, the proposed method features higher accuracy, lower false-positive rate, and is more robust when applied to overlapping metabolite set analysis. The developed ogPLS method may serve as an alternative MSEA method to facilitate biological interpretation of metabolomics data for overlapping metabolite sets.
    Matched MeSH terms: Metabolomics
  8. Rosli MAF, Azizan KA, Baharum SN, Goh HH
    Data Brief, 2017 Oct;14:295-297.
    PMID: 28795107 DOI: 10.1016/j.dib.2017.07.068
    Hybridisation plays a significant role in the evolution and diversification of plants. Hybridisation among Nepenthes species is extensive, either naturally or man-made. To investigate the effects of hybridisation on the chemical compositions, we carried out metabolomics study on pitcher tissue of Nepenthes ampullaria, Nepenthes rafflesiana and their hybrid, Nepenthes × hookeriana. Pitcher samples were harvested and extracted in methanol:chloroform:water via sonication-assisted extraction before analysed using LC-TOF-MS. MS data were analysed using XCMS online version 2.2.5. This is the first MS data report towards the profiling, identification and comprehensive comparison of metabolites present in Nepenthes species.
    Matched MeSH terms: Metabolomics
  9. Dhanapal ACTA, Wuni R, Ventura EF, Chiet TK, Cheah ESG, Loganathan A, et al.
    Nutrients, 2022 Dec 01;14(23).
    PMID: 36501140 DOI: 10.3390/nu14235108
    Nutritional epidemiological studies show a triple burden of malnutrition with disparate prevalence across the coexisting ethnicities in Malaysia. To tackle malnutrition and related conditions in Malaysia, research in the new and evolving field of nutrigenetics and nutrigenomics is essential. As part of the Gene-Nutrient Interactions (GeNuIne) Collaboration, the Nutrigenetics and Nutrigenomics Research and Training Unit (N2RTU) aims to solve the malnutrition paradox. This review discusses and presents a conceptual framework that shows the pathway to implementing and strengthening precision nutrition strategies in Malaysia. The framework is divided into: (1) Research and (2) Training and Resource Development. The first arm collects data from genetics, genomics, transcriptomics, metabolomics, gut microbiome, and phenotypic and lifestyle factors to conduct nutrigenetic, nutrigenomic, and nutri-epigenetic studies. The second arm is focused on training and resource development to improve the capacity of the stakeholders (academia, healthcare professionals, policymakers, and the food industry) to utilise the findings generated by research in their respective fields. Finally, the N2RTU framework foresees its applications in artificial intelligence and the implementation of precision nutrition through the action of stakeholders.
    Matched MeSH terms: Metabolomics
  10. Windarsih A, Riswanto FDO, Bakar NKA, Yuliana ND, Dachriyanus, Rohman A
    Molecules, 2022 Nov 29;27(23).
    PMID: 36500423 DOI: 10.3390/molecules27238325
    Adulteration of high-quality meat products using lower-priced meats, such as pork, is a crucial issue that could harm consumers. The consumption of pork is strictly forbidden in certain religions, such as Islam and Judaism. Therefore, the objective of this research was to develop untargeted metabolomics using liquid chromatography-high resolution mass spectrometry (LC-HRMS) combined with chemometrics for analysis of pork in beef meatballs for halal authentication. We investigated the use of non-targeted LC-HRMS as a method to detect such food adulteration. As a proof of concept using six technical replicates of pooled samples from beef and pork meat, we could show that metabolomics using LC-HRMS could be used for high-throughput screening of metabolites in meatballs made from beef and pork. Chemometrics of principal component analysis (PCA) was successfully used to differentiate beef meatballs and pork meatball samples. Partial least square-discriminant analysis (PLS-DA) clearly discriminated between halal and non-halal beef meatball samples with 100% accuracy. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) perfectly discriminated and classified meatballs made from beef, pork, and a mixture of beef-pork with a good level of fitness (R2X = 0.88, R2Y = 0.71) and good predictivity (Q2 = 0.55). Partial least square (PLS) and orthogonal PLS (OPLS) were successfully applied to predict the concentration of pork present in beef meatballs with high accuracy (R2 = 0.99) and high precision. Thirty-five potential metabolite markers were identified through VIP (variable important for projections) analysis. Metabolites of 1-(1Z-hexadecenyl)-sn-glycero-3-phosphocholine, acetyl-l-carnitine, dl-carnitine, anserine, hypoxanthine, linoleic acid, and prolylleucine had important roles for predicting pork in beef meatballs through S-line plot analysis. It can be concluded that a combination of untargeted metabolomics using LC-HRMS and chemometrics is promising to be developed as a standard analytical method for halal authentication of highly processed meat products.
    Matched MeSH terms: Metabolomics
  11. Au A
    Adv Clin Chem, 2018 03 08;85:31-69.
    PMID: 29655461 DOI: 10.1016/bs.acc.2018.02.002
    Ischemic stroke is a sudden loss of brain function due to the reduction of blood flow. Brain tissues cease to function with subsequent activation of the ischemic cascade. Metabolomics and lipidomics are modern disciplines that characterize the metabolites and lipid components of a biological system, respectively. Because the pathogenesis of ischemic stroke is heterogeneous and multifactorial, it is crucial to establish comprehensive metabolomic and lipidomic approaches to elucidate these alterations in this disease. Fortunately, metabolomic and lipidomic studies have the distinct advantages of identifying tissue/mechanism-specific biomarkers, predicting treatment and clinical outcome, and improving our understanding of the pathophysiologic basis of disease states. Therefore, recent applications of these analytical approaches in the early diagnosis of ischemic stroke were discussed. In addition, the emerging roles of metabolomics and lipidomics on ischemic stroke were summarized, in order to gain new insights into the mechanisms underlying ischemic stroke and in the search for novel metabolite biomarkers and their related pathways.
    Matched MeSH terms: Metabolomics/instrumentation; Metabolomics/methods*
  12. Maulidiani, Abas F, Khatib A, Perumal V, Suppaiah V, Ismail A, et al.
    J Ethnopharmacol, 2016 Mar 2;180:60-9.
    PMID: 26775274 DOI: 10.1016/j.jep.2016.01.001
    'Pegaga' is a traditional Malay remedy for a wide range of complaints. Among the 'pegaga', Centella asiatica has been used as a remedy for diabetes mellitus. Thus, we decided to validate this claim by evaluating the in vivo antidiabetic property of C. asiatica (CA) on T2DM rat model using the holistic (1)H NMR-based metabolomics approach.
    Matched MeSH terms: Metabolomics
  13. Mazlan O, Aizat WM, Aziz Zuddin NS, Baharum SN, Noor NM
    Data Brief, 2018 Dec;21:2221-2223.
    PMID: 30555858 DOI: 10.1016/j.dib.2018.11.072
    Metabolic regulation is important during seed germination for the establishment of seedling. The germination strategy of mangosteen (Garcinia mangostana L.) seed is thought to be unique due to its recalcitrant characteristic (sensitive to coldness and drying). To investigate the metabolic changes during seed germination, we performed metabolomics analysis on germinating mangosteen seed sown after zero, one, three, five, seven and nine days. Sampled mangosteen seeds were subjected to methanol extraction prior analysis using Liquid Chromatography-Time of Flight-Mass Spectrometry (LC-TOF-MS). MS data were further analyzed using ProfileAnalysis (version 2.1). This is one of the earliest reports in metabolite identification and profiling of mangosteen seed at different germination stages. This data article refers to the article entitled "Metabolite profiling of mangosteen seed germination highlights metabolic changes related to carbon utilization and seed protection" (Mazlan et al., 2019) [1].
    Matched MeSH terms: Metabolomics
  14. Marina Mohd Bakri
    MyJurnal
    Over the past decade, research involving immunometabolism, has been gaining much interest. The immune cell re-sponses of an individual may be influenced by metabolites released by the host or derived from the microbiota. How-ever, the immune response of an individual may vary depending on the health condition of an individual. During infection, the metabolic processes derived from the infectious diseases can effect the function of immune cells and thus determine the response or survival of the host during infection. Immunometabolism also has a role in tumor development although the mechanism of how tumor cells influence immune cell function is not well understood. Among the major meatbolic pathways that have been studied in immune cells include glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, fatty acid oxidation, fatty acid synthesis and amino acid metabolism. Understanding the tight connection between metabolomics and immunity in health and disease will be crucial as this could lead to therapeutic interventions or in developing metabolomic biomarkers in immunology.
    Matched MeSH terms: Metabolomics
  15. Deng L, Guo F, Cheng KK, Zhu J, Gu H, Raftery D, et al.
    J Proteome Res, 2020 05 01;19(5):1965-1974.
    PMID: 32174118 DOI: 10.1021/acs.jproteome.9b00793
    In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.
    Matched MeSH terms: Metabolomics
  16. Zolkeflee NKZ, Isamail NA, Maulidiani M, Abdul Hamid NA, Ramli NS, Azlan A, et al.
    Phytochem Anal, 2021 Jan;32(1):69-83.
    PMID: 31953888 DOI: 10.1002/pca.2917
    INTRODUCTION: Muntingia calabura from the Muntingiaceae family has been documented for several medicinal uses. The combinations of drying treatment and extracting solvents for a plant species need to be determined and optimised to ensure that the extracts contain adequate amounts of the bioactive metabolites.

    OBJECTIVE: Evaluate the metabolite variations and antioxidant activity among M. calabura leaves subjected to different drying methods and extracted with different ethanol ratios using proton nuclear magnetic resonance (1 H-NMR)-based metabolomics. Methodology The antioxidant activity of M. calabura leaves dried with three different drying methods and extracted with three different ethanol ratios was determined by using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and nitric oxide (NO) scavenging assays. The metabolites variation among the extracts and correlation with antioxidant activity were analysed by 1 H-NMR-based metabolomics.

    RESULTS: Muntingia calabura leaves extracted with 50% and 100% ethanol from air-drying and freeze-drying methods had the highest total phenolic content and the lowest IC50 value for the DPPH scavenging activity. Meanwhile, oven-dried leaves extracted with 100% ethanol had the lowest IC50 value for the NO scavenging activity. A total of 43 metabolites, including sugars, organic acids, amino acids, phytosterols, phenolics and terpene glycoside were tentatively identified. A noticeable discrimination was observed in the different ethanol ratios by the principal component analysis. The partial least-squares analysis suggested that 32 compounds out of 43 compounds identified were the contributors to the bioactivities.

    CONCLUSION: The results established set the preliminary steps towards developing this plant into a high value product for phytomedicinal preparations.

    Matched MeSH terms: Metabolomics
  17. Jamil IN, Remali J, Azizan KA, Nor Muhammad NA, Arita M, Goh HH, et al.
    Front Plant Sci, 2020;11:944.
    PMID: 32754171 DOI: 10.3389/fpls.2020.00944
    Across all facets of biology, the rapid progress in high-throughput data generation has enabled us to perform multi-omics systems biology research. Transcriptomics, proteomics, and metabolomics data can answer targeted biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration (MOI) can comprehensively assimilate, annotate, and model these large data sets. Previous MOI studies and reviews have detailed its usage and practicality on various organisms including human, animals, microbes, and plants. Plants are especially challenging due to large poorly annotated genomes, multi-organelles, and diverse secondary metabolites. Hence, constructive and methodological guidelines on how to perform MOI for plants are needed, particularly for researchers newly embarking on this topic. In this review, we thoroughly classify multi-omics studies on plants and verify workflows to ensure successful omics integration with accurate data representation. We also propose three levels of MOI, namely element-based (level 1), pathway-based (level 2), and mathematical-based integration (level 3). These MOI levels are described in relation to recent publications and tools, to highlight their practicality and function. The drawbacks and limitations of these MOI are also discussed for future improvement toward more amenable strategies in plant systems biology.
    Matched MeSH terms: Metabolomics
  18. Sari E, Mahira KF, Patel DN, Chua LS, Pratami DK, Sahlan M
    Heliyon, 2021 May;7(5):e06912.
    PMID: 34013079 DOI: 10.1016/j.heliyon.2021.e06912
    Royal jellies (RJs) possess moisturizing, emulsifying, and stabilizing properties, and several pharmacological activities have also been found to be present, which make them an ideal component for cosmetic and skin care products. However, despite the abundant efficacies, there is a lack of studies that explore the chemical composition of RJ using metabolome analysis. Furthermore, an evaluation of the chemical composition of Indonesian RJs collected from different regions has yet to be carried out. Therefore, the main objective of this study was to identify any differences in the chemical composition of such RJs. Chemical profiling was also carried out to enable more targeted utilization based on the actual compositions. Chemical profiling is also important given the rich Indonesian biodiversity and the high dependence of the RJ compositions on the botanical source. In this research, ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was used as part of an untargeted metabolomics approach. From the chemical profiling, >30 compounds were identified across four RJ samples. The major constituents of the samples were found to be oligosaccharides, fatty acids, and adenosine monophosphate derivatives. Meanwhile, sucrose and planteose were found to be highest in the samples from Banjarnegara and Kediri, whereas dimethyloctanoic acid was found to be unique to the sample from Banjarnegara. It was also discovered that the RJs from Demak and Tuban contained more organic fatty acids and oligosaccharides than the other samples. Although the sample from Demak demonstrated good potential for use in the cosmetic, skin care, and bio-supplement industries, the higher abundance of fatty acids and oligosaccharides in the sample from Tuban indicated that it is perhaps the most suitable RJ for use in this field.
    Matched MeSH terms: Metabolomics
  19. Yan D, Wong YF, Whittock SP, Koutoulis A, Shellie RA, Marriott PJ
    Anal Chem, 2018 04 17;90(8):5264-5271.
    PMID: 29575899 DOI: 10.1021/acs.analchem.8b00142
    A novel sequential three-dimensional gas chromatography-high-resolution time-of-flight mass spectrometry (3D GC-accTOFMS) approach for profiling secondary metabolites in complex plant extracts is described. This integrated system incorporates a nonpolar first-dimension (1Dnp) separation step, prior to a microfluidic heart-cut (H/C) of a targeted region(s) to a cryogenic trapping device, directly followed by the rapid reinjection of a trapped solute into a polar second-dimension (2DPEG) column for multidimensional separation (GCnp-GCPEG). For additional separation, the effluent from 2DPEG can then be modulated according to a comprehensive 2D GC process (GC×GC), using an ionic liquid phase as a third-dimension (3DIL) column, to produce a sequential GCnp-GCPEG×GCIL separation. Thus, the unresolved or poorly resolved components, or regions that require further separation, can be precisely selected and rapidly transferred for additional separation on 2D or 3D columns, based on the greater separation realized by these steps. The described integrated system can be used in a number of modes, but one useful approach is to target specific classes of compounds for improved resolution. This is demonstrated through the separation and detection of the oxygenated sesquiterpenes in hop ( Humulus lupulus L.) essential oil and agarwood ( Aquilaria malaccensis) oleoresin. Improved resolution and peak capacity were illustrated through the progressive comparison of the tentatively identified components for GCnp-GCPEG and GCnp-GCPEG×GCIL methods. Relative standard deviations of intraday retentions (1 tR, 2 tR,, and 3 tR) and peak areas of ≤0.01, 0.07, 0.71, and 7.5% were achieved. This analytical approach comprising three GC column selectivities, hyphenated with high-resolution TOFMS detection, should be a valuable adjunct for the improved characterization of complex plant samples, particularly in the area of plant metabolomics.
    Matched MeSH terms: Metabolomics
  20. Zuther E, Lee YP, Erban A, Kopka J, Hincha DK
    Adv Exp Med Biol, 2018 10 6;1081:81-98.
    PMID: 30288705 DOI: 10.1007/978-981-13-1244-1_5
    During low-temperature exposure, temperate plant species increase their freezing tolerance in a process termed cold acclimation. The molecular mechanisms involved in cold acclimation have been mostly investigated in Arabidopsis thaliana. In addition, other Brassicaceae species related to A. thaliana have been employed in recent years to study plant stress responses on a phylogenetically broader basis and in some cases with extremophile species with a much higher stress tolerance. In this paper, we briefly summarize cold acclimation responses in A. thaliana and current knowledge about cold acclimation in A. thaliana relatives with special emphasis on Eutrema salsugineum and two closely related Thellungiella species. We then present a transcriptomic and metabolomic analysis of cold acclimation in five A. thaliana and two E. salsugineum accessions that differ widely in their freezing tolerance. Differences in the cold responses of the two species are discussed.
    Matched MeSH terms: Metabolomics
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