Liquid Chromatography-mass Spectrometry Profiling Research Articles

Glycerol Extraction of Bioactive Compounds from Thanaka (Hesperethusa crenulata) Bark through LCMS Profiling and Their Antioxidant Properties.

Organic solvents are hazardous to human and environmental health. The emergence of interest in finding greener solvents to replace organic solvents has sparked a series of studies in the use of glycerol for extracting bioactive compounds from natural products. In this study, we will first identify the bioactive compounds of glycerol- and nonglycerol-based Thanaka (Hesperethusa crenulata) bark extracts using liquid chromatography-mass spectrometry profiles; then, we will determine their antioxidant capacity, free radical scavenging activity, and total phenolic and flavonoid contents. Thanaka bark powder was extracted using solvents, namely, ethanol (TKE), water (TKW), glycerol (TKG), glycerol/water (1:1, v/v) (TKGW), and glycerol/ethanol (1:1, v/v) (TKGE). Among the five extracts, the extract of TKG has the highest number of bioactive compounds, as well as the highest total flavonoid content. TKGE possessed the highest total phenolic content and highest antioxidant activity shown in azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) and ferric-reducing antioxidant power assays among the five extracts. Overall, glycerol has better efficiency in extracting bioactive compounds from Thanaka bark as compared to ethanol and water. Hence, from the phytochemical content and antioxidant properties of Thanaka extracts, we conclude that glycerol is a good green solvent alternative to replace organic solvents.

Cytotoxicity assessment and LC-MS profiling of Adansonia digitata on human gastric and osteosarcoma cancer cell lines

Adansonia digitata (Baobab) is a large, iconic tree with rich medicinal and nutritional values and its parts are used to treat various ailments. The leaves, which are edible, serve as food. In addition to its dietary use, studies on its anti-cancer properties and the specialised metabolites responsible for those properties are limited. Therefore, this study aims to characterise the anticancer properties and chemical constituents of A. digitata L. leaves. The activity of the aqueous extract was evaluated against mouse fibroblast (L929), human gastric (AGS) and osteosarcoma (MG-63) cancer cell lines using an in-vitro MTT assay. A Brine Shrimp Lethality Assay (BSLA) was performed to determine the cytotoxicity of A. digitata extract. Liquid Chromatography-Mass Spectrometry (LC-MS) analysis was employed to identify the major chemical constituents. The in-vitro MTT assay showed that the extract has anticancer potential by sensitising cell proliferation, as the IC50 values for mouse L929, AGS and MG-63 were obtained as 2923 ± 4.504, 190.9 ± 10.81 and 1834 ± 6.194 µg/mL, respectively. BSLA elicited that there was no lethality found up to 100 µg/mL. The LC-MS analysis showed the presence of catechin, vitexin, kaempherol glycoside, rutin, and other phytocompounds. Overall, this study indicated that the potential anticancer activity of the plant extract of A. digitata against AGS cell lines, in addition to its dietary use, may be significantly influenced by its specialised metabolites.

Investigating the multitargeted anti-diabetic potential of cucurbitane-type triterpenoid from Momordica charantia: an LC-MS, docking-based MM\\GBSA and MD simulation study

Type 2 diabetes accounts for the largest percentage of all diabetic cases worldwide. Cucurbitane-type triterpenes are mainly found in Momordica charantia and possess excellent pharmacological activities. This study was designed to identify cucurbitane-type triterpene from Momordica charantia using Liquid Chromatography-Mass Spectrometry (LC-MS) analysis, examine its anti-diabetic property with molecular docking against diabetes enzymes (alpha-amylase, alpha-glucosidase, dipeptidyl dipeptidase IV and peroxisome proliferator-activated receptor gamma). The stability and interactions of the docked complexes were investigated using molecular dynamics simulation, while the pharmacokinetic and toxicity profile of the ligand was examined using an ADMET server. (23E)-Cucurbita-5,23,25-triene-3,7-dione (CUB) was identified from the LC-MS profiling of the methanolic extract of M. charantia. The molecular docking studies showed that the identified phytochemical elicited good binding energy against all the target receptors. The RMSD and RMSF plots obtained from the 100 ns molecular dynamics simulation showed that the ligand was stable and established substantial interactions with the amino acid residues of the diabetes enzymes which were confirmed by the MM\\GBSA computations. The pharmacokinetic and toxicity properties of the ligand showed it was safer as an anti-diabetic drug candidate. Extensive isolation, in vitro and in vivo studies of the ligand against the diabetic enzymes is recommended.Communicated by Ramaswamy H. Sarma

Predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning

Lameness in dairy cattle is a highly prevalent condition that impacts on the health and welfare of dairy cows. Prompt detection and implementation of effective treatment is important for managing lameness. However, major limitations are associated with visual assessment of lameness, which is the most commonly used method to detect lameness. The aims of this study were to investigate the use of metabolomics and machine learning to develop novel methods to detect lameness. Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) alongside machine learning models and a stability selection method were utilized to evaluate the predictive accuracy of differences in the metabolomics profile of first-lactation dairy cows before (during the transition period) and at the time of lameness (based on visual assessment using the 0-3 scale of the Agriculture and Horticulture Development Board). Urine samples were collected from 2 cohorts of dairy heifers and stored at -86°C before analysis using LC-MS. Cohort 1 (n = 90) cows were recruited as current first-lactation cows with weekly mobility scores recorded over a 4-mo timeframe, from which newly lame and nonlame cows were identified. Cohort 2 (n = 30) cows were recruited within 3 wk before calving, and lameness events (based on mobility score) were recorded through lactation until a minimum of 70 d in milk (DIM). All cows were matched paired by DIM ± 14 d. The median DIM at lameness identification was 187.5 and 28.5 for cohort 1 and 2, respectively. The best performing machine learning models predicted lameness at the time of lameness with an accuracy of between 81 and 82%. Using stability selection, the prediction accuracy at the time of lameness was 80 to 81%. For samples collected before and after calving, the best performing machine learning model predicted lameness with an accuracy of 71 and 75%, respectively. The findings from this study demonstrate that untargeted LC-MS profiling combined with machine learning methods can be used to predict lameness as early as before calving and before observable changes in gait in first-lactation dairy cows. The methods also provide accuracies for detecting lameness at the time of observable changes in gait of up to 82%. The findings demonstrate that these methods could provide substantial advancements in the early prediction and prevention of lameness risk. Further external validation work is required to confirm these findings are generalizable; however, this study provides the basis from which future work can be conducted.

Identification of dipeptidyl peptidase-4 inhibitory compounds of Paederia foetida twigs by nuclear magnetic resonance metabolomics, in silico models, and liquid chromatography–mass spectrometry profiling

Identification of dipeptidyl peptidase-4 inhibitory compounds of Paederia foetida twigs by nuclear magnetic resonance metabolomics, in silico models, and liquid chromatography–mass spectrometry profiling

A versatile untargeted metabolomics-driven technology for rapid phytochemical profiling of stem barks of Zanthoxylum species with antioxidant and antimicrobial activities

Zanthoxylum species are credited with various uses in ethnomedicine due to their rich metabolite composition. In Kenya, these include management of cancer and microbial related ailments. However, there are limited reports showing how the bioactivity of Kenyan Zanthoxylum species is linked to their phytochemical profiles. This study therefore aimed at examining the chemical variation among five Zanthoxylum species found in Kenya (Z. chalybeum, Z. gilletii, Z. holtzianum, Z. paracanthum and Z. usambarense) using metabolomics approaches and the anti-oxidant and antimicrobial activities of these species. In a Folin–Ciocalteu test, the phenolic content of the stem bark extracts of these species were 73.083–145.272 mg TAE/g, while the alkaloids (in bromothymol blue chromogenic test) and flavonoids (in aluminium chloride test) were found to be 152.39–207.19 mg ME/g, and 109.416–186.413 mg CE/g, respectively. These extracts also exerted strong antioxidant activities in the 2,2-iphenyl-1-picrylhydrazyl (DPPH) and ferric ion reducing antioxidant power assays. In a broth dilution assay, the extract of the stem bark of Z. holtzianum ability showed the highest antimicrobial activity, followed by Z. chalybeum stem bark extract. The activities were positively correlated to both flavonoids and alkaloids concentrations, while the concentration of phenolics had weak negative correlation to antimicrobial activities. A chemometric analysis of the liquid-chromatography mass spectrometry profiles led to grouping of the species into three clusters. This study illustrates the variation in the bioactivity of Zanthoxylum species based on metabolite composition and justifies the wide usage of Zanthoxylum species in Kenyan traditional medicinal practices.Graphical abstract

Hopomics: Humulus lupulus Brewing Cultivars Classification Based on LC-MS Profiling and Nested Feature Selection.

Omics approaches in plant analysis find many different applications, from classification to new bioactive compounds discovery. Metabolomics seems to be one of the most informative ways of describing plants’ phenotypes, since commonly used methods such as liquid chromatography–mass spectrometry (LC-MS) and nuclear magnetic resonance spectroscopy (NMR) could provide a huge amount of information about samples. However, due to high efficiency, many disadvantages arise with the complexity of the experimental design. In the present work, we demonstrate an untargeted metabolomics pipeline with the example of a Humulus lupulus classification task. LC-MS profiling of brewing cultivars samples was carried out as a starting point. Hierarchical cluster analysis (HCA)-based classification in combination with nested feature selection was provided for sample discrimination and marker compounds discovery. Obtained metabolome-based classification showed an expected difference compared to genetic-based classification data. Nine compounds were found to have the biggest classification power during nested feature selection. Using database search and molecular network construction, five of them were identified as known hops bitter compounds.

Correlation of chemical profiles obtained from 1 H-NMR and LC-MS metabolomics with α-glucosidase inhibition activity for varietal selections of Ficus deltoidea.

Ficus deltoidea Jack (Moraceae) is a plant used in Malaysia to treat various ailments, including diabetes. The presence of several varieties raises essential questions regarding which is the potential bioactive variety and what are the bioactive metabolites. Here, we explored the phytochemical diversity of the seven varieties from Peninsular Malaysia using Nuclear Magnetic Resonance (NMR) and Liquid Chromatography-Mass Spectrometry (LC-MS) analyses and correlated it with the α-glucosidase inhibitory activity. The Nuclear Overhauser Effect Spectroscopy (NOESY) One-Dimensional (1D)-NMR and LC-MS data were processed, annotated, and correlated with in vitro α-glucosidase inhibitory using multivariate data analysis. The α-glucosidase results demonstrated that different varieties have varying inhibitory effects, with the highest inhibition rate being F. deltoidea var. trengganuensis and var. kunstleri. Furthermore, diverse habitats and plant ages could also influence the inhibitory rate. The heat map from NMR and LC-MS profiles showed unique patterns according to varying levels of α-glucosidase inhibition rate. The Partial Least Squares (PLS) model constructed from both NMR and LC-MS further confirmed the correlation between the α-glucosidase inhibition rate of F. deltoidea varieties and its metabolite profiles. The Variable Influence on Projection (VIP) and correlation coefficient (p(corr)) values values were used to determine the highly relevant metabolites for explaining the anticipated inhibitory action. NMR and LC-MS annotations allow the identification of flavan-3-ols and proanthocyanidins as the key bioactive factors. Our current results demonstrated the value of multivariate data analysis to predict the quality of herbal materials from both biological and chemical aspects.

Antioxidant, Antimicrobial, and Metabolomic Characterization of Blanched Pomegranate Peel Extracts: Effect of Cultivar.

Hot water blanching at 80 °C for 3 min can be used as a novel pre-treatment step in pomegranate peel to preserve the integrity of the phytochemical content within the peel extracts by lowering or inactivating enzymes such as polyphenol (PPO) oxidase and peroxidase (POD) that are responsible for the break-down of phytochemicals within the peel. The aim of this study was to investigate the effect of hot water blanching pre-treatment on yield, bioactive compounds, antioxidants, enzyme inactivation, and antibacterial activity of ‘Wonderful’, ‘Acco’, and ‘Herskawitz’ pomegranate peel extracts. We used a variety of spectrophotometric-based assays and liquid chromatography mass spectrometry (LC-MS)-based approach to characterize and quantify metabolites within the peel extracts. Blanching significantly (p < 0.05) reduced PPO activity in all peel extracts, with the highest PPO reduction in ‘Herskawitz’ peel extracts at 0.25 U/mL. Furthermore, higher antioxidant activity in ‘Herskawitz’ blanched peel extracts using 2,2-diphenyl-1-picryl hydrazyl (DPPH) antioxidant activity, ferric ion reducing antioxidant power (FRAP), and 2,2-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid (ABTS) radical scavenging activity at 567.78 ± 9.47 µmol Trolox/g DM, 800.05 ± 1.60 µmol Trolox/g DM, and 915.27 ± 0.61 µmol Trolox/g DM, respectively, was noted. ‘Herskawitz’ blanched peel extracts were recorded with the lowest minimum inhibitory concentration (MIC) value of 80 µg/mL for Gram-positive Bacillus subtilis and Gram-negative Klebsiella pneumoniae bacteria strains. A total of 30 metabolites were present in ‘Acco’ and ‘Herskawitz’ peel extracts and were tentatively identified after LC-MS profiling. This study demonstrates that blanched peel extracts from ‘Herskawitz’ cultivar have great potential for commercial use in value-added products in the nutraceutical, cosmeceutical, and pharmacological industries.

Cocoa bean fingerprinting via correlation networks

Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence particularly challenging for cocoa. Here we provide an illustration how network science can support food fingerprinting and food authenticity research. Using a large dataset of 140 cocoa samples comprising three cocoa fermentation/processing stages and eight countries, we obtain correlation networks between the cocoa samples by computing measures of pairwise correlation from their liquid chromatography-mass spectrometry (LC-MS) profiles. We find that the topology of correlation networks derived from untargeted LC-MS profiles is indicative of the fermentation and processing stage as well as the origin country of cocoa samples. Progressively increasing the correlation threshold firstly reveals network clusters based on processing stage and later country-based clusters. We present both, qualitative and quantitative evidence through network visualization, network statistics and concepts from machine learning. In our view, this network-based approach for classifying mass spectrometry data has broad applicability beyond cocoa.

Vasorelaxant-Mediated Antihypertensive Effect of the Leaf Aqueous Extract from Stephania abyssinica (Dillon & A. Rich) Walp (Menispermaceae) in Rat.

Stephania abyssinica is a medicinal plant used in Cameroon alternative medicine to treat arterial hypertension (AHT). Previous in vitro studies demonstrated the endothelium nitric oxide-independent vasorelaxant property of the aqueous extract from Stephania abyssinica (AESA). But its effect on AHT is unknown. The present study was undertaken to explore other vasorelaxant mechanisms and to determine the antihypertensive effects of AESA in male Wistar rats. Phytochemical analysis of AESA was carried out using the liquid chromatography-mass spectrometry (LC-MS) method. The vasorelaxant effects of AESA (1-1000 μg/mL) were studied on rat isolated thoracic aorta rings, in the absence or presence of indomethacin (10 μM) or methylene blue (10 μM). The inhibitory effect of AESA on phenylephrine (PE, 10 μM) or KCl- (60 mM) induced contraction as well as the intracellular calcium release was also evaluated. The in vivo antihypertensive activity of AESA (43, 86, or 172 mg/kg/day) or captopril (20 mg/kg/day) administered orally was assessed in L-NAME- (40 mg/kg/day) treated rats. Blood pressure and heart rate (HR) were measured at the end of each week while serum or urinary nitric oxide (NO), creatinine, and glomerular filtration rate (GFR) were determined at the end of the 6 weeks of treatment, as well as histological analysis of the heart and the kidney. The LC-MS profiling of AESA identified 9 compounds including 7 alkaloids. AESA produced a concentration-dependent relaxation on contraction induced either by PE and KCl, which was significantly reduced in endothelium-denuded vessels, as well as in vessels pretreated with indomethacin and methylene blue. Moreover, AESA inhibited the intracellular Ca2+ release-induced contraction. In vivo, AESA reduced the AHT, heart rate (HR), and ventricular hypertrophy and increased serum NO, urine creatinine, and GFR. AESA also ameliorated heart and kidney lesions as compared to the L-NAME group. These findings supported the use of AESA as a potential antihypertensive drug.

Comparison of Different Labeling Techniques for the LC-MS Profiling of Human Milk Oligosaccharides.

Human milk oligosaccharides (HMOs) exhibit various biological activities for infants, such as serving as prebiotics, blocking pathogens, and aiding in brain development. HMOs are a complex mixture of hetero-oligosaccharides that are generally highly branched, containing multiple structural isomers and no intrinsic chromophores, presenting a challenge to both their resolution and quantitative detection. While liquid chromatography-mass spectrometry (LC-MS) has become the primary strategy for analysis of various compounds, the very polar and chromophore-free properties of native glycans hinder their separation in LC and ionization in MS. Various labeling approaches have been developed to achieve separation of glycans with higher resolution and greater sensitivity of detection. Here, we compared five commonly used labeling techniques [by 2-aminobenzamide, 2-aminopyridine, 2-aminobenzoic acid (2-AA), 2,6-diaminopyridine, and 1-phenyl-3-methyl-5-pyrazolone] for analyzing HMOs specifically under hydrophilic-interaction chromatography-mass spectrometry (HILIC-MS) conditions. The 2-AA labeling showed the most consistent deprotonated molecular ions, the enhanced sensitivity with the least structural selectivity, and the sequencing-informative tandem MS fragmentation spectra for the widest range of HMOs; therefore, this labeling technique was selected for further optimization under the porous graphitized carbon chromatography-mass spectrometry (PGC-MS) conditions. The combination strategy of 2-AA labeling and PGC-MS techniques provided online decontamination (removal of excess 2-AA, salts, and lactose) and resolute detection of many HMOs, enabling us to characterize the profiles of complicated HMO mixtures comprehensively in a simple protocol.

Blanching Pre-Treatment Promotes High Yields, Bioactive Compounds, Antioxidants, Enzyme Inactivation and Antibacterial Activity of 'Wonderful' Pomegranate Peel Extracts at Three Different Harvest Maturities.

‘Wonderful’ pomegranate (Punica granatum L.) peel contains a wide range of phytochemicals including vitamins, dietary fibre, phenolic compounds, and antioxidant properties. Yet, it is often used as animal feed or discarded in landfills, which is not the best eco-friendly way to utilize this phenolic-rich bioresource. Finding novel ways of utilizing pomegranate peel waste could prove a more profitable and eco-friendlier alternative that is far more beneficial to the economy. Adding a blanching pre-treatment step at optimal conditions prior to processing of pomegranate peel aids in the inactivation of quality changing enzymes such as polyphenol oxidase (PPO) and peroxidase (POD), which are accountable for the degradation reactions that cause breakdown of nutrients and phytochemicals. This study aimed to determine the effect of blanching at 80 °C for 3 min on the yield, polyphenol content, antioxidant properties, enzyme inactivation, and antibacterial activity of ‘Wonderful’ pomegranate peel ethanolic extracts from three different harvest maturities (unripe, ripe, and over ripe), including a comprehensive characterization and quantification using liquid chromatography-mass spectrometry (LC-MS). The blanched unripe peel extracts exhibited the highest total phenolic content, total tannin content, 2,2-diphenyl-1-picryl hydrazyl (DPPH) antioxidant activity, 2,2-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid (ABTS) radical scavenging activity and ferric ion reducing antioxidant power (FRAP) at 14.0 mg gallic acid equivalent (GAE)/g dry mass (DM), 1.0 mg GAE/g DM, 359.1 µmol Trolox/g DM, 912.2 µmol Trolox/g DM and 802.5 µmol Trolox/g DM, respectively. There was significant (p < 0.05) decrease in PPO and POD activity of all blanched pomegranate peel extracts. The blanched unripe peel extracts had the lowest PPO activity at 0.2 U/g fresh weight (FW), with a 70% PPO inactivation compared to ripe and over ripe harvest, whereas the highest POD inactivation was recorded at 67% in over ripe peel extracts. All blanched peel extracts, irrespective of harvest maturity, had minimum inhibitory concentration (MIC) values at 160 µg/mL against all four bacteria strains tested, which included two Gram-positive bacterial strains (Bacillus subtilis ATCC 6051 and Staphylococcus aureus ATCC 12600) and two Gram-negative bacteria (Escherichia coli 11775 and Klebsiella pneumonia ATCC 13883). A total of 25 metabolites including phenolic acids (4), organic acids (1), flavonoids (4), ellagitannins (13), and other polyphenols (3) in all three pomegranate peel samples were tentatively identified after LC-MS profiling. The blanched unripe peel extracts showed significantly higher punicalin α and β, β punicalagin, catechin, epicatechin content at 414 mg/g, and 678 mg/g, 151 mg/g, 229 mg/g, respectively, compared to peel extracts from other harvest maturities. This study provides supportive information for the commercial utilization of pomegranate fruit peel as source of value-added ingredients for the development of novel food, cosmetics, and pharmacological products.

Metabolomic Study of Normal and Modified Nucleosides in the Urine of Mice with Lipopolysaccharide‐Induced Sepsis by LC–MS/MS

Endogenous normal and modified nucleosides have been studied as potential biomarkers of various inflammatory diseases including sepsis. However, no systematic nucleoside profiling and pattern analysis study has been performed in sepsis. In this study, simultaneous liquid chromatography–mass spectrometry profiling of normal and modified nucleosides was performed to characterize altered nucleoside metabolism in sepsis mice.

Mass Spectrometry-Based Profiling of Plant Sphingolipids from Typical and Aberrant Metabolism.

Mass spectrometry has increasingly been used as a tool to complement studies of sphingolipid metabolism and biological functions in plants and other eukaryotes. Mass spectrometry is now essential for comprehensive sphingolipid analytical profiling because of the huge diversity of sphingolipid classes and molecular species in eukaryotes, particularly in plants. This structural diversity arises from large differences in polar head group glycosylation as well as carbon-chain lengths of fatty acids and desaturation and hydroxylation patterns of fatty acids and long-chain bases that together comprise the ceramide hydrophobic backbone of glycosphingolipids. The standard methods for liquid chromatography-mass spectrometry (LC-MS)-based analyses of Arabidopsis thaliana leaf sphingolipids profile >200 molecular species of four sphingolipid classes and free long-chain bases and their phosphorylated forms. While these methods have proven valuable for A. thaliana based sphingolipid research, we have recently adapted them for use with ultraperformance liquid chromatography separations of molecular species and to profile aberrant sphingolipid forms in pollen, transgenic lines, and mutants. This chapter provides updates to standard methods for LC-MS profiling of A. thaliana sphingolipids to expand the utility of mass spectrometry for plant sphingolipid research.

Cocoa origin classifiability through LC-MS data: A statistical approach for large and long-term datasets

Classification of food samples based upon their countries of origin is an important task in food industry for quality assurance and development of fine flavor products. Liquid chromatography –mass spectrometry (LC-MS) provides a fast technique for obtaining in-depth information about chemical composition of foods. However, in a large dataset that is gathered over a period of few years, multiple, incoherent and hard to avoid sources of variations e.g., experimental conditions, transportation, batch and instrumental effects, etc. pose technical challenges that make the study of origin classification a difficult problem. Here, we use a large dataset gathered over a period of four years containing 297 LC-MS profiles of cocoa sourced from 10 countries to demonstrate these challenges by using two popular multivariate analysis methods: principal component analysis (PCA) and linear discriminant analysis (LDA). We show that PCA provides a limited separation in bean origin, while LDA suffers from a strong non-linear dependence on the set of compounds. Further, we show for LDA that a compound selection criterion based on Gaussian distribution of intensities across samples dramatically enhances origin clustering of samples thereby suggesting possibilities for studying marker compounds in such a disparate dataset through this approach. In essence, we show and develop a new approach that maximizes, avoiding overfitting, the utility of multivariate analysis in a highly complex dataset.

Do Metabolomics and Taxonomic Barcode Markers Tell the Same Story about the Evolution of Saccharomyces sensu stricto Complex in Fermentative Environments?

Yeast taxonomy was introduced based on the idea that physiological properties would help discriminate species, thus assuming a strong link between physiology and taxonomy. However, the instability of physiological characteristics within species configured them as not ideal markers for species delimitation, shading the importance of physiology and paving the way to the DNA-based taxonomy. The hypothesis of reconnecting taxonomy with specific traits from phylogenies has been successfully explored for Bacteria and Archaea, suggesting that a similar route can be traveled for yeasts. In this framework, thirteen single copy loci were used to investigate the predictability of complex Fourier Transform InfaRed spectroscopy (FTIR) and High-performance Liquid Chromatography–Mass Spectrometry (LC-MS) profiles of the four historical species of the Saccharomyces sensu stricto group, both on resting cells and under short-term ethanol stress. Our data show a significant connection between the taxonomy and physiology of these strains. Eight markers out of the thirteen tested displayed high correlation values with LC-MS profiles of cells in resting condition, confirming the low efficacy of FTIR in the identification of strains of closely related species. Conversely, most genetic markers displayed increasing trends of correlation with FTIR profiles as the ethanol concentration increased, according to their role in the cellular response to different type of stress.

Hypoxia and hypoxia mimetics differentially modulate histone post-translational modifications

ABSTRACT Post-translational modifications (PTMs) to the tails of the core histone proteins are critically involved in epigenetic regulation. Hypoxia affects histone modifications by altering the activities of histone-modifying enzymes and the levels of hypoxia-inducible factor (HIF) isoforms. Synthetic hypoxia mimetics promote a similar response, but how accurately the hypoxia mimetics replicate the effects of limited oxygen availability on the levels of histone PTMs is uncertain. Here we report studies on the profiling of the global changes to PTMs on intact histones in response to hypoxia/hypoxia-related stresses using liquid chromatography-mass spectrometry (LC-MS). We demonstrate that intact protein LC-MS profiling is a relatively simple and robust method for investigating potential effects of drugs on histone modifications. The results provide insights into the profiles of PTMs associated with hypoxia and inform on the extent to which hypoxia and hypoxia mimetics cause similar changes to histones. These findings imply chemically-induced hypoxia does not completely replicate the substantial effects of physiological hypoxia on histone PTMs, highlighting that caution should be used in interpreting data from their use.

Chemometrics-Assisted Identification of Anti-Inflammatory Compounds from the Green Alga Klebsormidium flaccidum var. zivo.

The green alga Klebsormidium flaccidum var. zivo is a rich source of proteins, polyphenols, and bioactive small-molecule compounds. An approach involving chromatographic fractionation, anti-inflammatory activity testing, ultrahigh performance liquid chromatography-mass spectrometry profiling, chemometric analysis, and subsequent MS-oriented isolation was employed to rapidly identify its small-molecule anti-inflammatory compounds including hydroxylated fatty acids, chlorophyll-derived pheophorbides, carotenoids, and glycoglycerolipids. Pheophorbide a, which decreased intracellular nitric oxide production by inhibiting inducible nitric oxide synthase, was the most potent compound identified with an IC50 value of 0.24 µM in lipopolysaccharides-induced macrophages. It also inhibited nuclear factor kappaB activation with an IC50 value of 32.1 µM in phorbol 12-myristate 13-acetate-induced chondrocytes. Compared to conventional bioassay-guided fractionation, this approach is more efficient for rapid identification of multiple chemical classes of bioactive compounds from a complex natural product mixture.

Abstract No. 422 Biomarker discovery using established and primary hepatocellular carcinoma cell lines

Hepatocellular carcinoma (HCC) is the fastest rising cause of cancer related death. The limited role of biopsy in diagnosis and treatment monitoring emphasizes the importance of developing biomarkers for incurable disease. The identification of biomarkers from serum in patients with HCC is limited by the high levels of background resulting from albumin. The purpose of this study was to overcome this limitation in order to identify novel biomarkers using unbiased proteomic profiling of HCC secreted proteins derived from established and primary HCC cell lines. Following 48-hour incubations, media was collected from 5 HCC established and primary HCC cell lines as well as 5 non-HCC cell lines including normal hepatocytes, fibroblasts and a colorectal cancer line. Unbiased profiling of secreted proteins was performed using liquid chromatography-mass spectrometry (LC-MS). The removal of serum from culture media overcame the high background signal that results from albumin and has limited proteomic profiling of secreted proteins in patient serum. Unbiased LC-MS profiling of secreted HCC proteins by the assayed cell lines enabled the identification of 36 proteins that were present at detectable levels in HCC cell lines but at limited or undetectable levels in non-HCC cell lines (Table 1). While several of these proteins were previously identified as candidate biomarkers of HCC based on a literature search, others have not been reported. Unbiased proteomic profiling of secreted proteins derived from serum-free culture media of established and primary HCC cell lines enabled the discovery of secreted proteins that represent promising biomarker candidates.Tabled 1Top 36 Hits in HCC cell lines vs non-HCC & non-liver cell linesAccessionDescriptionP01011-1Alpha-l-antichymotrypsinP02771Alpha-fetoproteinP10451-1OsteopontinP10451-5Isoform 5 of OsteopontinP09237MatrilysinP00995Serine protease inhibitor Kazal-type 1P56470galectin-4P05026Sodium/potassium-transporting ATPase subunit beta-1P05089Arginase-1P08294Extracellular superoxide dismutase [Cu-Zn]P78509ReelinP60022Beta-defensin 1P36268Inactive glutathione hydrolase 2Q8NFZ8Cell adhesion molecule 4095671-1N-acetylserotonin O-methyltransferase-like proteinP41240tyrosine-protein kinase CSKQ9H479fructosamine-3-kinaseP52926-1high mobility group protein HMGI-CQ9BX67Junctional adhesion molecule CP18846Cyclic AMP-dependent transcription factor ATF-1P51654-1Glypican-3P38571lysosomal acid lipase/cholesteryl ester hydrolase060256-1Phosphoribosyl pyrophosphate synthase-associated protein 2Q9C0C9(E3-independent) E2 ubiquitin-conjugating enzymeP02790HemopexinQ9Y6N7Roundabout homolog 1Q9HAT2sialate O-acetylesteraseQ969Q060S ribosomal protein L36a-likeQ58WW2DDB1- and CUL4-associated factor 6O94868F-BAR and double SH3 domains protein 2P09467Fructose-l,6-bisphosphatase 1P07902Galactose-l-phosphate uridylyltransferaseQ8N392-1Rho GTPase-activating protein 18Q96PK6-1RNA-binding protein 14Q5R3I4tetratricopeptide repeat protein 38P98155-1very low-density lipoprotein receptor Open table in a new tab