scholarly journals Metabolomic signatures associated with disease severity in multiple sclerosis

2017 ◽  
Vol 4 (2) ◽  
pp. e321 ◽  
Author(s):  
Pablo Villoslada ◽  
Cristina Alonso ◽  
Ion Agirrezabal ◽  
Ekaterina Kotelnikova ◽  
Irati Zubizarreta ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Amino Acids ◽  
Disease Severity ◽  
High Performance ◽  
Severe Disease ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Serum Samples ◽  
Prospective Assessment

Objective:To identify differences in the metabolomic profile in the serum of patients with multiple sclerosis (MS) compared to controls and to identify biomarkers of disease severity.Methods:We studied 2 cohorts of patients with MS: a retrospective longitudinal cohort of 238 patients and 74 controls and a prospective cohort of 61 patients and 41 controls with serial serum samples. Patients were stratified into active or stable disease based on 2 years of prospective assessment accounting for presence of clinical relapses or changes in disability measured with the Expanded Disability Status Scale (EDSS). Metabolomic profiling (lipids and amino acids) was performed by ultra-high-performance liquid chromatography coupled to mass spectrometry in serum samples. Data analysis was performed using parametric methods, principal component analysis, and partial least square discriminant analysis for assessing the differences between cases and controls and for subgroups based on disease severity.Results:We identified metabolomics signatures with high accuracy for classifying patients vs controls as well as for classifying patients with medium to high disability (EDSS >3.0). Among them, sphingomyelin and lysophosphatidylethanolamine were the metabolites that showed a more robust pattern in the time series analysis for discriminating between patients and controls. Moreover, levels of hydrocortisone, glutamic acid, tryptophan, eicosapentaenoic acid, 13S-hydroxyoctadecadienoic acid, lysophosphatidylcholines, and lysophosphatidylethanolamines were associated with more severe disease (non-relapse-free or increase in EDSS).Conclusions:We identified metabolomic signatures composed of hormones, lipids, and amino acids associated with MS and with a more severe course.

scholarly journals Serum Metabolomic Signatures Associated with Disease Severity in Allergic Rhinitis

2020 ◽  
Author(s):  
Shaobing Xie ◽  
Hua Zhang ◽  
Zhihai Xie ◽  
Yongzhen Liu ◽  
Kelei Gao ◽  
...  
Keyword(s):  
Allergic Rhinitis ◽  
Disease Severity ◽  
High Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Sphingolipid Metabolism ◽  
Healthy Controls ◽  
Serum Samples ◽  
Metabolomic Profiling ◽  
Serum Metabolomics

Abstract Background: Allergic rhinitis (AR) is a global healthcare problem with obscure pathogenesis, and few studies have evaluated the association between AR and metabolomics. The aim of this study was to identify differences in serum metabolomics profiling of AR patients compared to healthy controls and to explore novel biomarkers reflecting disease severity.Methods: Serum samples were collected form 29 healthy controls and 72 AR patients, including 30 mild AR patients (MAR) and 42 moderate to severe AR patients (MSAR). Metabolomic profiling was performed by ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) in serum samples. Orthogonal partial least square-discriminate analysis (OPLS-DA) was applied to assess the differences between AR patients and controls and for subgroups based on disease severity. Results: These analysis results successfully revealed distinct metabolite signatures which distinguished MAR patients and MSAR patients from health controls. In addition, MSAR patients also could be discriminated from MAR patients basing on their metabolic fingerprints. Most observed metabolite changes were related to pathways associated with glycine, serine and threonine metabolism, pyrimidine metabolism, sphingolipid metabolism, arginine and proline metabolism and fatty acid metabolism. Among these metabolites from dysregulated metabolic pathways, levels of sarcosine, sphingosine-1-phosphate (S1P), cytidine and linoleic acid significantly correlated with AR total nasal symptom score (TNSS) and visual analogue scale (VAS).Conclusions: MSAR patients have a distinctive serum metabolomics profile compared to MAR and health controls. These results suggest that metabolomic profiling may provide novel insights into pathophysiological mechanisms of AR and contribute to its evaluation of disease severity.

scholarly journals A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1546
Author(s):  
Ioanna Dagla ◽  
Anthony Tsarbopoulos ◽  
Evagelos Gikas
Keyword(s):  
Design Of Experiments ◽  
High Performance ◽  
Similarity Index ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Distance Measures ◽  
Linear Regression Models ◽  
Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

scholarly journals Metabolomics profiling of Polygoni Multiflori Radix and Polygoni Multiflori Radix Preparata extracts using UPLC-Q/TOF-MS

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Zhaoyan Zhang ◽  
Liang Yang ◽  
Xiaoyan Huang ◽  
Yue Gao
Keyword(s):  
High Performance ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Alpha Linolenic Acid ◽  
Multivariate Statistical ◽  
Biochemical Indicators ◽  
Flight Mass Spectrometry ◽  
Underlying Mechanisms ◽  
Tof Ms

Abstract Background The side effects caused by Polygoni Multiflori Radix (PMR) and Polygoni Multiflori Radix Praeparata (PMRP) have often appeared globally. There is no research on the changes of endogenous metabolites among PMR- and PMRP-treated rats. The aim of this study was to evaluate the varying metabolomic effects between PMR- and PMRP-treated rats. We tried to discover relevant differences in biomarkers and endogenous metabolic pathways. Methods Hematoxylin and eosin staining and immunohistochemistry staining were performed to find pathological changes. Biochemical indicators were also measured, one-way analysis of variance with Dunnett’s multiple comparison test was used for biochemical indicators comparison among various groups. Metabolomics analysis based on ultra-high performance liquid chromatography-quadrupole time of flight mass spectrometry (UPLC-Q/TOF-MS) was performed to find the changes in metabolic biomarkers. Multivariate statistical approaches such as principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA) were applied to reveal group clustering trend, evaluate and maximize the discrimination between the two groups. MetaboAnalyst 4.0 was performed to find and confirm the pathways. Results PMR extracts exhibited slight hepatotoxic effects on the liver by increasing aspartate and alanine aminotransferase levels. Twenty-nine metabolites were identified as biomarkers, belonging to five pathways, including alpha-linolenic acid metabolism, taurine and hypotaurine metabolism, glycerophospholipid metabolism, arginine and proline metabolism, and primary bile acid biosynthesis. Conclusion This study provided a comprehensive description of metabolomic changes between PMR- and PMRP-treated rats. The underlying mechanisms require further research.

scholarly journals Authentication of Greek PDO Kalamata Table Olives: A Novel Non-Target High Resolution Mass Spectrometric Approach

Molecules ◽  
2020 ◽  
Vol 25 (12) ◽  
pp. 2919
Author(s):  
Natasa P. Kalogiouri ◽  
Reza Aalizadeh ◽  
Marilena E. Dasenaki ◽  
Nikolaos S. Thomaidis
Keyword(s):  
High Performance ◽  
Model Development ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Table Olives ◽  
Method Performance ◽  
Validation Parameters ◽  
Targeted Screening ◽  
Mass Spectrometric Approach

Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography–quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins.

scholarly journals A Study on Gentiana dahurica Fisch Ethanol Extract Alleviating Alcoholic Liver Disease in Mice: A Metabolomic Analysis of the Liver

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Houkang Cao ◽  
Yanxiu Guo ◽  
Ling Jin
Keyword(s):  
High Performance ◽  
Principal Component ◽  
Ethanol Extract ◽  
Partial Least Square ◽  
Least Square ◽  
Control Group ◽  
Metabolomic Analysis ◽  
Pyrimidine Metabolism ◽  
Alcohol Group ◽  
Glucose And Lipid Metabolism

We clarified the hepatoprotective effect of Gentiana dahurica Fisch ethanol extract (GDEE) in our previous study, and we further revealed the mechanism with the help of metabolomics technology in this study. The livers from Control group, Alcohol group, and Alcohol + GDEE group were analyzed by metabolomics. The metabolites in the liver were separated by ultra-high-performance liquid chromatography (UHPLC) and were tentatively identified using mass spectrometry (MS)/MS analysis. Differential metabolites were defined with VIP > 1 and P < 0.05 . Principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to analyze differences among these groups. The results showed that the groups could be clearly distinguished by PCA and OPLS-DA analysis. Alcohol and GDEE could change the overall profile of liver metabolites. Alterations in liver tissues of ALD mice induced by alcohol were mainly involved in the dipeptides, purine and pyrimidine metabolism and glucose and lipid metabolism, which could be partly affected by GDEE. This study revealed that the mechanism of GDEE in alleviating ALD had the characteristics of multitarget and multipathway.

scholarly journals Feed forensic strategy: Incorporating multivariate data analysis with high-performance liquid chromatography refractive index detector and differential scanning calorimeter for authentication of fish feed sources

2021 ◽  
Author(s):  
Mohamed Haniff Hanafy Idris ◽  
Muhamad Shirwan Abdullah Sani ◽  
Amalia Mohd Hashim ◽  
Nor Nadiha Mohd Zaki ◽  
Yanty Noorzianna Abdul Manaf ◽  
...  
Keyword(s):  
Mean Square Error ◽  
High Performance ◽  
Principal Component ◽  
Factor Loading ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
High Loading ◽  
Fish Feed ◽  
Mean Square

Abstract This study authenticated fish feed sources and determined lard adulteration using dataset pre-processing, principal component analysis (PCA), discriminant analysis (DA) and partial least square regression (PLSR) on 19 triacylglycerols (TAGs) and 16 thermal properties (TPs). At cumulative variability (90.625%) and Keiser-Meyer Olkin (KMO) value (0.811), the PCA identified strong factor loading variables, i.e., OLL, PLL, OOL, POL, PPL, POO, PPO, PSO, ICT and FHT in PC1 and LLLn, OOO and CT2 in PC2. These variables were significantly (p < 0.05) contributing to lard-palm-oil (L-PO) clusters: (1) POO, PPO and PPL (high loading) and OLL, PLL, OOL, ICT, POL, PSO and FHT (low loading) in 0:100 and 25:75 L-PO clusters; (2) CT2, OOO and LLLn (high loading) in 50:50 L-PO cluster; and (3) OLL, PLL, OOL, ICT, POL, PSO and FHT (high loading) and POO, PPO and PPL (low loading) in 72:25 and 100:0 L-PO clusters. Training, validation and testing datasets had 100%, 84.44% and 100% correct-classification, respectively at p < 0.0001 of Wilks' lambda and p < 0.0001 Fisher distance. The DA selected PLL, OOL, POL, PPL, PSO, ICT and FHT as the significantly authenticating biomarkers (p < 0.05). With determination coefficient (R²) (0.9693), mean square error (MSE) (38.382) and root mean square error (RMSE) (6.195), the PLSR's variable importance in the projection (VIP) identified the most influential biomarkers, i.e., PPL, POL, PPO, OOL, ICT, PLL, FHT, POO and OLL. The Z-test result (p > 0.05) indicated that the PLSR could determine the lard adulteration percentage in fish feed.

Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Khairunnisa Khairunnisa ◽  
Rizka Pitri ◽  
Victor P Butar-Butar ◽  
Agus M Soleh
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Principal Component Regression ◽  
Circulation Model ◽  
Principal Component ◽  
Meteorological Station ◽  
Grid Size ◽  
Partial Least Square ◽  
Least Square ◽  
Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

scholarly journals Different Blood Metabolomics Profiles in Infants Consuming a Meat- or Dairy-Based Complementary Diet

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 388
Author(s):  
Minghua Tang ◽  
Nicholas E. Weaver ◽  
Lillian M. Berman ◽  
Laura D. Brown ◽  
Audrey E. Hendricks ◽  
...  
Keyword(s):  
Amino Acids ◽  
Dietary Intervention ◽  
Principal Component ◽  
Essential Amino Acids ◽  
Infant Growth ◽  
Serum Samples ◽  
Z Scores ◽  
Protein Rich ◽  
Over Time ◽  
Complementary Diet

Background: Research is limited in evaluating the mechanisms responsible for infant growth in response to different protein-rich foods; Methods: Targeted and untargeted metabolomics analysis were conducted on serum samples collected from an infant controlled-feeding trial that participants consumed a meat- vs. dairy-based complementary diet from 5 to 12 months of age, and followed up at 24 months. Results: Isoleucine, valine, phenylalanine increased and threonine decreased over time among all participants; Although none of the individual essential amino acids had a significant impact on changes in growth Z scores from 5 to 12 months, principal component heavily weighted by BCAAs (leucine, isoleucine, valine) and phenylalanine had a positive association with changes in length-for-age Z score from 5 to 12 months. Concentrations of acylcarnitine-C4, acylcarnitine-C5 and acylcarnitine-C5:1 significantly increased over time with the dietary intervention, but none of the acylcarnitines were associated with infant growth Z scores. Quantitative trimethylamine N-oxide increased in the meat group from 5 to 12 months; Conclusions: Our findings suggest that increasing total protein intake by providing protein-rich complementary foods was associated with increased concentrations of certain essential amino acids and short-chain acyl-carnitines. The sources of protein-rich foods (e.g., meat vs. dairy) did not appear to differentially impact serum metabolites, and comprehensive mechanistic investigations are needed to identify other contributors or mediators of the diet-induced infant growth trajectories.

scholarly journals The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mika Jönsson ◽  
Björn Gerdle ◽  
Bijar Ghafouri ◽  
Emmanuel Bäckryd
Keyword(s):  
Cerebrospinal Fluid ◽  
Neuropathic Pain ◽  
Pain Intensity ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Therapeutic Interventions ◽  
Healthy Controls ◽  
Inflammatory Profile

Abstract Background Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls. Methods The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant. Results We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1). Conclusions Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

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