Orthogonal Projections To Latent Structures Discriminant Analysis Research Articles

Efficient Discovery of Quality Control Markers for Gastrodia elata Tuber by Fingerprint‐Efficacy Relationship Modelling

Gastrodia elata tuber (GET) has been widely used in China as a famous herbal medicine. However, the quality control markers (QCMs) for GET still need further investigation. To develop a rational strategy based on fingerprint-efficacy relationship modelling to discover the efficacy-related QCMs, using GET as a case study. The high-performance liquid chromatography (HPLC) fingerprints of 13 batches of GET extracts were established and 10 common peaks were structurally characterised by HPLC coupled with quadrupole time-of-flight mass spectrometry (QTOF-MS). The neuroprotective effects of GET samples were evaluated using the in vitro model of β-amyloid peptide (Aβ25-35 )-induced PC12 cell death. The fingerprint-efficacy relationship of chemical fingerprints and neuroprotective effects was linked by orthogonal projection to latent structure-discriminant analysis (OPLS-DA) model. The chemical combination of 5-hydroxymethyl-2-furaldehyde (5-HMF), parishin B (PB) and parishin C (PC) was discovered and confirmed as QCMs of GET by the OPLS-DA method. The selected QCMs allowed to evaluate the relative quality of GET samples. This study has demonstrated that the rational strategy based on fingerprint-efficacy relationship modelling might be generally applicable for the quality control of herbal medicines. Copyright © 2017 John Wiley & Sons, Ltd.

Metabolic profiling reveals new serum biomarkers of lupus nephritis.

Metabolomics has been applied to explore altered metabolite profiles in disease and identify unique metabolic signatures specific to certain pathologies. The aim of the current study is to characterize the metabolic profile of patients diagnosed with lupus nephritis (LN) and explore new insights into underlying disease processes. A metabolomic approach using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS) was developed in serum samples from 32 LN patients, 30 idiopathic nephrotic syndrome (INS) patients and 28 healthy controls (HCs). Potential biomarkers were screened from orthogonal projection to latent structures discriminate analysis (OPLS-DA) and further evaluated by receiver operating characteristic analysis (ROC). A total of 14 potential biomarkers were screened and tentatively identified for LN patients compared to HCs. Compared to HCs and INS patients, the LN patients had increased serum levels of sorbitol and glycocholic acid metabolites and decreased levels of cortisol, creatinine and L-aspartyl-L-phenylalanine. A panel of three metabolomics (theophylline, oxidized glutathione and capric acid) was identified as biomarkers of LN with a sensitivity of 87.50% and a specificity of 67.86% using ROC analysis. Our results suggest that UPLC-HRMS based quantification of circulating metabolites was a useful tool for identification of biomarkers with the ability to segregate LN patients from INS patients and HCs. The potential biomarkers indicated that the LN metabolic disturbance may be closely associated with inflammation injury, oxidative stress and phospholipid metabolism.

NMR Metabolomics Investigates the Influence of Flavonoid-Enriched Rations on Chicken Plasma.

The use of flavonoids as dietary supplements is well established, mainly due to their intense antioxidant and anti-inflammatory properties. In the present study, hesperidin, naringin, and vitamin E were used as additives at different concentrations in poultry rations in order to achieve meat of improved quality. NMR metabolomics was applied to chicken blood serum samples to discern whether and how the enriched rations affected the animals' metabolic profile. Variations in the metabolic patterns according to sustenance consumption were traced by orthogonal projections to latent structures discriminant analysis (OPLS-DA) models and were attributed to specific metabolites by using S-line plots. In particular, serum samples from chickens fed with vitamin E displayed higher concentrations of glycine and succinic acid compared to control samples, which were mainly characterized by betaine, formic acid, and lipoproteins. Samples from chickens fed with hesperidin were characterized by increased levels of lactic acid, citric acid, creatine, carnosine, creatinine, phosphocreatine, anserine, glucose, and alanine compared to control samples. Lastly, naringin samples exhibited increased levels of citric and acetic acids. Results verify the scalability of NMR metabolomics to highlight metabolite variations among chicken serum samples in relation to food rations.

Evaluation and Application of a Novel Quantitative Antioxidant Activity Assay Based on Cellular Metabolomics

A novel quantitative antioxidant activity assay based on cellular metabolomics, named cellular metabolomics antioxidant activity (CMAA) assay, was established and applied in this paper. CMAA assay evaluates antioxidant activity by considering the change rules of the cellular metabolites of different treatment groups of human liver carcinoma cell line HepG2 cells subjected to oxidative stress. This assay considers cellular metabolites as the main analytical index, principal components analysis (PCA), and orthogonal projections to latent structures discriminant analysis (OPLS-DA) as the main multi- and megavariate data analysis method and vitamin E (VE) as a standard substance to quantify antioxidant activities. The antioxidant activities of flavonoids extracted from Cynomorium songaricum Rupr. (CS) were measured by the CMAA assay. The results showed that the antioxidant activity of flavonoids extracted from CS was successfully evaluated using the CMAA assay, and the antioxidant activity of flavonoids extracted from CS was about 1/5 of the same concentration of VE. It indicated that the proposed approach, as an effective tool for evaluating the antioxidant capacity, was feasible.

Changes in plasma protein levels as an early indication of a bloodstream infection.

Blood culture is the primary diagnostic test performed in a suspicion of bloodstream infection to detect the presence of microorganisms and direct the treatment. However, blood culture is slow and time consuming method to detect blood stream infections or separate septic and/or bacteremic patients from others with less serious febrile disease. Plasma proteomics, despite its challenges, remains an important source for early biomarkers for systemic diseases and might show changes before direct evidence from bacteria can be obtained. We have performed a plasma proteomic analysis, simultaneously at the time of blood culture sampling from ten blood culture positive and ten blood culture negative patients, and quantified 172 proteins with two or more unique peptides. Principal components analysis, Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) and ROC curve analysis were performed to select protein(s) features which can classify the two groups of samples. We propose a number of candidates which qualify as potential biomarkers to select the blood culture positive cases from negative ones. Pathway analysis by two methods revealed complement activation, phagocytosis pathway and alterations in lipid metabolism as enriched pathways which are relevant for the condition. Data are available via ProteomeXchange with identifier PXD005022.

Accumulation of Carotenoids and Metabolic Profiling in Different Cultivars of Tagetes Flowers.

Species of Tagetes, which belong to the family Asteraceae show different characteristics including, bloom size, shape, and color; plant size; and leaf shape. In this study, we determined the differences in primary metabolites and carotenoid yields among six cultivars from two Tagetes species, T. erecta and T. patula. In total, we detected seven carotenoids in the examined cultivars: violaxanthin, lutein, zeaxanthin, α-carotene, β-carotene, 9-cis-β-carotene, and 13-cis-β-carotene. In all the cultivars, lutein was the most abundant carotenoid. Furthermore, the contents of each carotenoid in flowers varied depending on the cultivar. Principal component analysis (PCA) facilitated metabolic discrimination between Tagetes cultivars, with the exception of Inca Yellow and Discovery Orange. Moreover, PCA and orthogonal projection to latent structure-discriminant analysis (OPLS-DA) results provided a clear discrimination between T. erecta and T. patula. Primary metabolites, including xylose, citric acid, valine, glycine, and galactose were the main components facilitating separation of the species. Positive relationships were apparent between carbon-rich metabolites, including those of the TCA cycle and sugar metabolism, and carotenoids.

A Chemometrics Approach to the Investigation of the Intraspecific Variability of the Volatile Oil of Eupatorium tremulum from Southern Brazil.

Eupatorium tremulum is a South American shrub reported to cause cattle digestive intoxication, of which the volatile oil, mainly composed by bisabolane- and amorphane-type sesquiterpenoids, exhibits high quantitative variability. This report describes the application of chemometric tools for the identification of volatile compounds that characterize phenophasical changes in the plant. Preblooming, blooming, and postblooming specimens were paired-sampled and submitted to hydrodistillation and GC-MS analysis. Differential results were analyzed by orthogonal projection to latent structures-discriminant analysis (OPLS-DA), and the substances with different distribution in each phase were highlighted. Mean results between phases were submitted to factor analysis (FA), and correlations between the variables were demonstrated. Preblooming to blooming phase change was characterized by decreased levels of amorpha-4-en-7-ol (13) and epi-α-bisabolol (19) and increased amounts of amorpha-4,7(11)-diene (1). Blooming to postblooming change was characterized by decreases in 1, germacrene D (2), and β-bisabolene (4) and increases in 13 and 19. Finally, enhanced levels of 1, 4, and 2 reflected the change from the postblooming to the preblooming phase. FA revealed a strong correlation in the variability between the bisabolane hydrocarbons, possibly related to its common enzymatic origin. Another strong source of negative correlation showed bisabolane- and amorphane-type alcohols, on one side, and amorphane-type furans, on the other side, to occur in two alternative oxidation routes. Finally, 1 was strongly negatively correlated to its oxidized furan and ketofuran derivatives [verboccidentafuran (16) and 3-oxo-verboccidentafuran (23)] and additionally to a third compound, putatively identified as a biosynthetic intermediate between this hydrocarbon and the furans, amorpha-4,7(11)-dien-8-one (20).

Comparison of the meat metabolite composition of Linwu and Pekin ducks using 600 MHz 1H nuclear magnetic resonance spectroscopy

In an effort to further understand of the differences of meat flavor and texture between Linwu ducks and Pekin ducks at market age, we investigated the meat metabolite composition of the two breeds of ducks using 600 MHz 1 H nuclear magnetic resonance (NMR) spectroscopy. Comprehensive multivariate data analysis including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal projection to latent structure-discriminant analysis (OPLS-DA) were applied to analyze the 1 H-NMR profiling data to identify the distinguishing metabolites of breast meat between two breeds of ducks. Compared with 42-d-old Pekin duck meat, breast from 72-d-old Linwu duck has higher concentration of anserine, carnosine, homocarnosine, and nicotinamide, but significantly lower concentration of succinate, creatine, and myo-inositol. These results contribute to a better understanding of the differences in meat metabolite composition between 72-d-old Linwu and 42-d-old Pekin ducks, which could be used to help assess the quality of duck meat as a food.

Urine Fingerprints of Stanozolol Treated Horses by Liquid Chromatography High Resolution Mass Spectrometry

The current detection methods for stanozolol are all based on targeted approach. The present study aimed to assess the global biological effect of stanozolol-treatment by means of chemometric models, after generating and comparing horse urine LC-HRMS fingerprints collected from control and stanozolol-treated horses. The animal study was conducted according to an ethically approved protocol at two different places in France: Chamberet and Coye la Foret. The total duration of the animal phase was seven months and only females were selected to partake. The sixteen mares in this study were not actively racing horses, but were in good physical condition. SIMCA-P+ software and R free software environment were used for multivariate data analysis. Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA) were applied to build some descriptive and predictive models. The analyzed horse urine fingerprints based on the 220 features selected after suppression of confounding factors show changes in metabolic states after chronic stanozolol treatment. This proof of concept study confirms the power of untargeted approach in doping control since the changes are present over seven months after anabolic administration.

Pathway Analysis and Metabolites Identification by Metabolomics of Etiolation Substrate from Fresh-Cut Chinese Water Chestnut (Eleocharis tuberosa).

Fresh-cut Chinese water chestnuts (CWC) turn yellow after being peeled, reducing their shelf life and commercial value. Metabolomics, the systematic study of the full complement of small molecular metabolites, was useful for clarifying the mechanism of fresh-cut CWC etiolation and developing methods to inhibit yellowing. In this study, metabolic alterations associated with etiolation at different growth stages (0 day, 2 days, 3 days, 4 days, 5 days) from fresh-cut CWC were investigated using LC–MS and analyzed by pattern recognition methods (principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal projection to latent structures-discriminant analysis (OPLS-DA)). The metabolic pathways of the etiolation molecules were elucidated. The main metabolic pathway appears to be the conversion of phenylalanine to p-coumaroyl-CoA, followed by conversion to naringenin chalcone, to naringenin, and naringenin then following different pathways. Firstly, it can transform into apigenin and its derivatives; secondly, it can produce eriodictyol and its derivatives; and thirdly it can produce dihydrokaempferol, quercetin, and myricetin. The eriodictyol can be further transformed to luteolin, cyanidin, dihydroquercetin, dihydrotricetin, and others. This is the first reported use of metabolomics to study the metabolic pathways of the etiolation of fresh-cut CWC.

Metabolites profiling reveals for antimicrobial compositional differences and action mechanism in the toothbrushing stick “miswak” Salvadora persica

Among many plant species suitable for preparing toothbrushing sticks, miswak (Salvadora persica, family Salvadoraceae) is found the most effective tool for oral hygiene. S. persica possesses antibacterial, antiviral and antifungal effects against oral microbes, mostly due to its benzyl isothiocyanate content. To provide insight into S. persica chemical composition, volatile constituents from roots and stems of S. persica grown in Egypt and Saudi Arabia were profiled using solid-phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC–MS). A total of 21 volatiles were identified with sulfur compounds amounting for the major volatile class. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) revealed for benzyl isothiocyanate (BITC) enrichment in roots versus stems. Primary metabolites contributing to S. persica taste viz. sugars and organic acids were profiled using GC–MS with silylation. Polyols (sugars) viz. arabitol, meso-erythritol, and mannitol were found to predominate sugars composition in S. persica stems being most enriched in meso-erythritol. The impact of saliva on S. persica aroma profile was further assessed and revealing for no enhancement in BITC production with salivation, and further not being detected in toothpaste preparation claimed to contain S. persica extract. This study provides the most complete profile of volatiles, sugars, and organic acids in S. persica organs and more rationalizing its use as a toothbrush.

A targeted proteomic multiplex CSF assay identifies increased malate dehydrogenase and other neurodegenerative biomarkers in individuals with Alzheimer's disease pathology.

Alzheimer's disease (AD) is the most common cause of dementia. Biomarkers are required to identify individuals in the preclinical phase, explain phenotypic diversity, measure progression and estimate prognosis. The development of assays to validate candidate biomarkers is costly and time-consuming. Targeted proteomics is an attractive means of quantifying novel proteins in cerebrospinal and other fluids, and has potential to help overcome this bottleneck in biomarker development. We used a previously validated multiplexed 10-min, targeted proteomic assay to assess 54 candidate cerebrospinal fluid (CSF) biomarkers in two independent cohorts comprising individuals with neurodegenerative dementias and healthy controls. Individuals were classified as ‘AD' or ‘non-AD' on the basis of their CSF T-tau and amyloid Aβ1–42 profile measured using enzyme-linked immunosorbent assay; biomarkers of interest were compared using univariate and multivariate analyses. In all, 35/31 individuals in Cohort 1 and 46/36 in Cohort 2 fulfilled criteria for AD/non-AD profile CSF, respectively. After adjustment for multiple comparisons, five proteins were elevated significantly in AD CSF compared with non-AD CSF in both cohorts: malate dehydrogenase; total APOE; chitinase-3-like protein 1 (YKL-40); osteopontin and cystatin C. In an independent multivariate orthogonal projection to latent structures discriminant analysis (OPLS-DA), these proteins were also identified as major contributors to the separation between AD and non-AD in both cohorts. Independent of CSF Aβ1–42 and tau, a combination of these biomarkers differentiated AD and non-AD with an area under curve (AUC)=0.88. This targeted proteomic multiple reaction monitoring (MRM)-based assay can simultaneously and rapidly measure multiple candidate CSF biomarkers. Applying this technique to AD we demonstrate differences in proteins involved in glucose metabolism and neuroinflammation that collectively have potential clinical diagnostic utility.

Characterization and Comparison of Commercial Chinese Cereal and European Grape Vinegars Using 1H NMR Spectroscopy Combined with Multivariate Analysis

AbstractA holistic and comparative quality assessment of vinegars from different countries is needed with international trade of vinegar become frequent. In this study, compounds characterization and comparison of commercial‐grade Chinese cereal and European grape vinegars were performed using 1H NMR spectroscopy coupled with principal component analysis (PCA) and orthogonal projection to latent structures discriminate analysis (OPLS‐DA). The results showed that Balsamic vinegars of Modena were clearly discriminated by higher amount of fructose and glucose, while Chinese aromatic vinegar and aged vinegars were characterized by higher amount of amino acids, volatile compounds, succinate and betaine. On the other hand, flavoring compounds in Chinese rice vinegar and European wine vinegars are less than the others. These characteristic components are associated with the special raw materials and producing process of each types of vinegar and endow them special flavor. The results obtained in this study provide a global insight into vinegar through a 1H NMR based compounds analysis that allows a holistic quality assessment and comparison of vinegars from different manufacture origins.

Metabolite profiling in Trigonella seeds via UPLC-MS and GC-MS analyzed using multivariate data analyses.

Trigonella foenum-graecum is a plant of considerable value for its nutritive composition as well as medicinal effects. This study aims to examine Trigonella seeds using a metabolome-based ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) in parallel to gas chromatography-mass spectrometry (GC-MS) coupled with multivariate data analyses. The metabolomic differences of seeds derived from three Trigonella species, i.e., T. caerulea, T. corniculata, and T. foenum-graecum, were assessed. Under specified conditions, we were able to identify 93 metabolites including 5 peptides, 2 phenolic acids, 22 C/O-flavonoid conjugates, 26 saponins, and 9 fatty acids using UPLC-MS. Several novel dipeptides, saponins, and flavonoids were found in Trigonella herein for the first time. Samples were classified via unsupervised principal component analysis (PCA) followed by supervised orthogonal projection to latent structures-discriminant analysis (OPLS-DA). A distinct separation among the investigated Trigonella species was revealed, with T. foenum-graecum samples found most enriched in apigenin-C-glycosides, viz. vicenins 1/3 and 2, compared to the other two species. In contrast to UPLC-MS, GC-MS was less efficient to classify specimens, with differences among specimens mostly attributed to fatty acyl esters. GC-MS analysis of Trigonella seed extracts led to the identification of 91 metabolites belonging mostly to fatty acyl esters, free fatty acids followed by organic acids, sugars, and amino acids. This study presents the first report on primary and secondary metabolite compositional differences among Trigonella seeds via a metabolomics approach and reveals that, among the species examined, the official T. foenum-graecum presents a better source of Trigonella secondary bioactive metabolites.

The Role of High-Resolution Magic Angle Spinning 1H Nuclear Magnetic Resonance Spectroscopy for Predicting the Invasive Component in Patients with Ductal Carcinoma In Situ Diagnosed on Preoperative Biopsy.

The purpose of this study was to evaluate the role of high-resolution magic angle spinning (HR-MAS) 1H nuclear magnetic resonance (NMR) spectroscopy in patients with ductal carcinoma in situ (DCIS) diagnosed on preoperative biopsy. We investigated whether the metabolic profiling of tissue samples using HR-MAS 1H NMR spectroscopy could be used to distinguish between DCIS lesions with or without an invasive component. Our institutional review board approved this combined retrospective and prospective study. Tissue samples were collected from 30 patients with pure DCIS and from 30 with DCIS accompanying invasive carcinoma. All patients were diagnosed with DCIS by preoperative core-needle biopsy and underwent surgical resection. The metabolic profiling of tissue samples was performed by HR-MAS 1H NMR spectroscopy. All observable metabolite signals were identified and quantified in all tissue samples. Metabolite intensity normalized by total spectral intensities was compared according to the tumor type using the Mann-Whitney test. Multivariate analysis was performed with orthogonal projections to latent structure-discriminant analysis (OPLS-DA). By univariate analysis, the metabolite concentrations of choline-containing compounds obtained with HR-MAS 1H NMR spectroscopy did not differ significantly between the pure DCIS and DCIS accompanying invasive carcinoma groups. However, the GPC/PC ratio was higher in the pure DCIS group than in the DCIS accompanying invasive carcinoma group (p = 0.004, Bonferroni-corrected p = 0.064), as well as the concentration of myo-inositol and succinate. By multivariate analysis, the OPLS-DA models built with HR-MAS MR metabolic profiles could clearly discriminate between pure DCIS and DCIS accompanying invasive carcinoma. Our preliminary results suggest that HR-MAS MR metabolomics on breast tissue may be able to distinguish between DCIS lesions with or without an invasive component.

PLS-Based and Regularization-Based Methods for the Selection of Relevant Variables in Non-targeted Metabolomics Data

Non-targeted metabolomics constitutes a part of the systems biology and aims at determining numerous metabolites in complex biological samples. Datasets obtained in the non-targeted metabolomics studies are high-dimensional due to sensitivity of mass spectrometry-based detection methods as well as complexity of biological matrices. Therefore, a proper selection of variables which contribute into group classification is a crucial step, especially in metabolomics studies which are focused on searching for disease biomarker candidates. In the present study, three different statistical approaches were tested using two metabolomics datasets (RH and PH study). The orthogonal projections to latent structures-discriminant analysis (OPLS-DA) without and with multiple testing correction as well as the least absolute shrinkage and selection operator (LASSO) with bootstrapping, were tested and compared. For the RH study, OPLS-DA model built without multiple testing correction selected 46 and 218 variables based on the VIP criteria using Pareto and UV scaling, respectively. For the PH study, 217 and 320 variables were selected based on the VIP criteria using Pareto and UV scaling, respectively. In the RH study, OPLS-DA model built after correcting for multiple testing, selected 4 and 19 variables as in terms of Pareto and UV scaling, respectively. For the PH study, 14 and 18 variables were selected based on the VIP criteria in terms of Pareto and UV scaling, respectively. In the RH and PH study, the LASSO selected 14 and 4 variables with reproducibility between 99.3 and 100%, respectively. In the light of PLS-based models, the larger the search space the higher the probability of developing models that fit the training data well with simultaneous poor predictive performance on the validation set. The LASSO offers potential improvements over standard linear regression due to the presence of the constrain, which promotes sparse solutions. This paper is the first one to date utilizing the LASSO penalized logistic regression in untargeted metabolomics studies.

PCA as a practical indicator of OPLS-DA model reliability.

Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) are powerful statistical modeling tools that provide insights into separations between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. However, when used without validation, these tools may lead investigators to statistically unreliable conclusions. This danger is especially real for Partial Least Squares (PLS) and OPLS, which aggressively force separations between experimental groups. As a result, OPLS-DA is often used as an alternative method when PCA fails to expose group separation, but this practice is highly dangerous. Without rigorous validation, OPLS-DA can easily yield statistically unreliable group separation. A Monte Carlo analysis of PCA group separations and OPLS-DA cross-validation metrics was performed on NMR datasets with statistically significant separations in scores-space. A linearly increasing amount of Gaussian noise was added to each data matrix followed by the construction and validation of PCA and OPLS-DA models. With increasing added noise, the PCA scores-space distance between groups rapidly decreased and the OPLS-DA cross-validation statistics simultaneously deteriorated. A decrease in correlation between the estimated loadings (added noise) and the true (original) loadings was also observed. While the validity of the OPLS-DA model diminished with increasing added noise, the group separation in scores-space remained basically unaffected. Supported by the results of Monte Carlo analyses of PCA group separations and OPLS-DA cross-validation metrics, we provide practical guidelines and cross-validatory recommendations for reliable inference from PCA and OPLS-DA models.

GC-TOF/MS-based metabolomics approach to study the cellular immunotoxicity of deoxynivalenol on murine macrophage ANA-1 cells

Gas chromatography-time of fly/mass spectrum (GC-TOF/MS) based complete murine macrophage ANA-1 cell metabolome strategy, including the endo-metabolome and the exo-metabolome, ANA-1 cell viability assays and apoptosis induced by diverse concentrations of DON were evaluated for selection of an optimized dose for in-depth metabolomic research. Using the optimized chromatography and mass spectrometry parameters, the metabolites detected by GC-TOF/MS were identified and processed with multivariate statistical analysis, including principal componentanalysis (PCA) and orthogonal projection to latent structures-discriminant analysis (OPLS-DA) analysis. The data sets were screened with a t-test (P) value < 0.05, VIP value > 1, similarity value > 500, leaving 16 exo-metabolite variables and 11 endo-metabolite variables for further pathway analysis. Implementing the integration of key metabolic pathways, the metabolism pathways were categorized into two dominating types, metabolism of amino acid and glycometabolism. Glycine, serine and threonine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis and phenylalanine metabolism were the significant amino acids affected by the metabolic pathways, indicating statistically significant fold changes including pyruvate, serine, glycine, lactate and threonine. Glycolysis or gluconeogenesis, starch and sucrose metabolism, and galactose metabolism, belonging to glycometabolism, were the pathways that were found to be primarily affected, resulting in abnormal metabolites such as glucose-1P, Glucose, gluconic acid, myo-inositol, sorbitol and glycerol.

Efficacy assessment of local doxycycline treatment in periodontal patients using multivariate chemometric approach

The aim of our study was application of chemometric algorithms for multivariate data analysis in efficacy assessment of the local periodontal treatment with doxycycline (DOX). Treatment efficacy was evaluated by monitoring inflammatory biomarkers in gingival crevicular fluid (GCF) samples and clinical indices before and after the local treatment as well as by determination of DOX concentration in GCF after the local treatment.The experimental values from these determinations were submitted to several chemometric algorithms: principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and orthogonal projection to latent structures-discriminant analysis (OPLS-DA). The data structure and the mutual relations of the selected variables were thoroughly investigated by PCA. The PLS-DA model identified variables responsible for discrimination of classes of data, before and after DOX treatment. The OPLS-DA model compared the efficacy of the two commonly used medications in periodontal treatment, chlorhexidine (CHX) and DOX, at the same time providing insight in their mechanism of action. The obtained results indicate that application of multivariate chemometric algorithms can be used as a valuable approach for assessment of treatment efficacy.

Metabolomic analysis reveals metabolic disturbances in the prefrontal cortex of the lipopolysaccharide-induced mouse model of depression

Major depressive disorder (MDD) is a debilitating illness. However, the underlying molecular mechanisms of depression remain largely unknown. Increasing evidence supports that inflammatory cytokine disturbances may be associated with the pathophysiology of depression in humans. Systemic administration of lipopolysaccharide (LPS) has been used to study inflammation-associated neurobehavioral changes in rodents, but no metabonomic study has been conducted to assess differential metabolites in the prefrontal cortex (PFC) of a LPS-induced mouse model of depression. Here, we employed a gas chromatography–mass spectrometry-based metabonomic approach in the LPS-induced mouse model of depression to investigate any significant metabolic changes in the PFC. Multivariate statistical analysis, including principal component analysis (PCA), partial least squares-discriminate analysis (PLS-DA), and pair-wise orthogonal projections to latent structures discriminant analysis (OPLS-DA), was implemented to identify differential PFC metabolites between LPS-induced depressed mice and healthy controls. A total of 20 differential metabolites were identified. Compared with control mice, LPS-treated mice were characterized by six lower level metabolites and 14 higher level metabolites. These molecular changes were closely related to perturbations in neurotransmitter metabolism, energy metabolism, oxidative stress, and lipid metabolism, which might be evolved in the pathogenesis of MDD. These findings provide insight into the pathophysiological mechanisms underlying MDD and could be of valuable assistance in the clinical diagnosis of MDD.