Robust Regression Analysis of GCMS Data Reveals Differential Rewiring of Metabolic Networks in Hepatitis B and C Patients.
Cedric SimillionNasser SemmoJeffrey R IdleDiren BeyoğluPublished in: Metabolites (2017)
About one in 15 of the world's population is chronically infected with either hepatitis virus B (HBV) or C (HCV), with enormous public health consequences. The metabolic alterations caused by these infections have never been directly compared and contrasted. We investigated groups of HBV-positive, HCV-positive, and uninfected healthy controls using gas chromatography-mass spectrometry analyses of their plasma and urine. A robust regression analysis of the metabolite data was conducted to reveal correlations between metabolite pairs. Ten metabolite correlations appeared for HBV plasma and urine, with 18 for HCV plasma and urine, none of which were present in the controls. Metabolic perturbation networks were constructed, which permitted a differential view of the HBV- and HCV-infected liver. HBV hepatitis was consistent with enhanced glucose uptake, glycolysis, and pentose phosphate pathway metabolism, the latter using xylitol and producing threonic acid, which may also be imported by glucose transporters. HCV hepatitis was consistent with impaired glucose uptake, glycolysis, and pentose phosphate pathway metabolism, with the tricarboxylic acid pathway fueled by branched-chain amino acids feeding gluconeogenesis and the hepatocellular loss of glucose, which most probably contributed to hyperglycemia. It is concluded that robust regression analyses can uncover metabolic rewiring in disease states.
Keyphrases
- hepatitis b virus
- hepatitis c virus
- liver failure
- public health
- blood glucose
- gas chromatography mass spectrometry
- human immunodeficiency virus
- end stage renal disease
- electronic health record
- newly diagnosed
- ejection fraction
- amino acid
- chronic kidney disease
- big data
- blood pressure
- genome wide
- peritoneal dialysis
- wastewater treatment
- hiv infected
- prognostic factors
- metabolic syndrome
- adipose tissue
- skeletal muscle
- insulin resistance
- mass spectrometry
- artificial intelligence
- gene expression
- patient reported