scholarly journals Metabolomics Coupled with Multivariate Data and Pathway Analysis on Potential Biomarkers in Cholestasis and Intervention Effect of Paeonia lactiflora Pall.

2016 ◽  
Vol 7 ◽  
Author(s):  
Xiao Ma ◽  
Yong-Hui Chi ◽  
Ming Niu ◽  
Yun Zhu ◽  
Yan-Ling Zhao ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Multivariate Data ◽  
Paeonia Lactiflora ◽  
Intervention Effect ◽  
Potential Biomarkers

scholarly journals Identification of potential plasma biomarkers and metabolic dysfunction for unstable angina pectoris and its complication based on global metabolomics

2019 ◽  
Vol 39 (3) ◽  
Author(s):  
Juan Wang ◽  
Wenjuan Xu ◽  
Huihui Zhao ◽  
Jianxin Chen ◽  
Bin Zhu ◽  
...  
Keyword(s):  
Angina Pectoris ◽  
Unstable Angina ◽  
Pathway Analysis ◽  
Clinical Symptoms ◽  
Area Under The Curve ◽  
Unstable Angina Pectoris ◽  
High Morbidity ◽  
Coronary Syndrome ◽  
Global Metabolomics ◽  
Potential Biomarkers

Abstract Unstable angina pectoris (UA) is one of the most dangerous clinical symptoms of acute coronary syndrome due to the risk of myocardial ischemia, which can lead to high morbidity and mortality worldwide. Though there are many advantages in understanding the pathophysiology of UA, the identification of biomarkers for the diagnosis, prognosis, and treatment of UA remains a challenge in the clinic. A global metabolomics research based on ultra-performance liquid chromatography (UPLC) combined with Q-TOF/MS was performed to discover the metabolic profile of health controls, UA patients, and UA patients with diabetes mellitus (DM), and screen for potential biomarkers. Twenty-seven potential biomarkers were determined using pattern recognition. These biomarkers, which include free fatty acids, amino acids, lysoPE and lysoPC species, and organic acids, can benefit the clinical diagnosis of UA. Pathway analysis indicated that arginine and proline metabolism, glycerophospholipid metabolism, and purine metabolism were affected in the UA patients, uniquely. Additionally, alterations in the metabolic signatures between UA and UA-complicated DM were also explored. As a result, six differential metabolites with an area under the curve (AUC) of more than 0.85 were identified as biomarkers for the diagnosis of UA and UA complicated with DM. Pathway analysis implied tryptophan metabolism was a key metabolic pathway in UA patients with DM, which provides new insights into the pathological study and drug discovery of UA.

scholarly journals Analysis of Mucopolysaccharidosis Type VI through Integrative Functional Metabolomics

2019 ◽  
Vol 20 (2) ◽  
pp. 446 ◽  
Author(s):  
Abdellah Tebani ◽  
Lenaig Abily-Donval ◽  
Isabelle Schmitz-Afonso ◽  
Monique Piraud ◽  
Jérôme Ausseil ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Pathway Analysis ◽  
Multivariate Data ◽  
Data Modeling ◽  
Integrative Analysis ◽  
Untargeted Metabolomics ◽  
Metabolomics Data ◽  
Metabolic Phenotyping ◽  
Mps Vi

Metabolic phenotyping is poised as a powerful and promising tool for biomarker discovery in inherited metabolic diseases. However, few studies applied this approach to mcopolysaccharidoses (MPS). Thus, this innovative functional approach may unveil comprehensive impairments in MPS biology. This study explores mcopolysaccharidosis VI (MPS VI) or Maroteaux–Lamy syndrome (OMIM #253200) which is an autosomal recessive lysosomal storage disease caused by the deficiency of arylsulfatase B enzyme. Urine samples were collected from 16 MPS VI patients and 66 healthy control individuals. Untargeted metabolomics analysis was applied using ultra-high-performance liquid chromatography combined with ion mobility and high-resolution mass spectrometry. Furthermore, dermatan sulfate, amino acids, carnitine, and acylcarnitine profiles were quantified using liquid chromatography coupled to tandem mass spectrometry. Univariate analysis and multivariate data modeling were used for integrative analysis and discriminant metabolites selection. Pathway analysis was done to unveil impaired metabolism. The study revealed significant differential biochemical patterns using multivariate data modeling. Pathway analysis revealed that several major amino acid pathways were dysregulated in MPS VI. Integrative analysis of targeted and untargeted metabolomics data with in silico results yielded arginine-proline, histidine, and glutathione metabolism being the most affected. This study is one of the first metabolic phenotyping studies of MPS VI. The findings might shed light on molecular understanding of MPS pathophysiology to develop further MPS studies to enhance diagnosis and treatments of this rare condition.

Sign in / Sign up

Export Citation Format

Share Document