To investigate the metabolic changes in type C hepatic encephalopathy (CHE) rats after reducing manganese (Mn) intake. A total of 80 Sprague-Dawley rats were divided into control group and CHE groups (induced by intraperitoneal injection of thioacetamide at a dose of 250 mg/kg of body weight twice a week for 6 weeks). CHE rats were subdivided into 1Mn group (fed a standard diet, with 10 mg Mn/kg feed), 0.5Mn group (half-Mn diet), 0.25Mn group (quarter-Mn diet) and 0Mn group (no-Mn diet) for 4 to 8 weeks. Morris water maze (MWM), Y maze and narrow beam test (NBT) were used to evaluate cognitive and motor functions. Blood ammonia, brain Mn content, the number of GS-positive cells, and glutamine synthetase (GS) activity were measured. The metabolic changes of CHE rats were investigated using hydrogen-nuclear magnetic resonance and mass spectrometry. Multivariate statistical analysis was used to analyze the results. Significantly decreased numbers of entries in target area of MWM and Y maze, longer NBT latency and total time, higher blood ammonia, brain Mn content and GS activity were found in CHE rats. After reducing Mn intake, CHE rats had better behavioral performance, significantly lower blood ammonia, brain Mn content and GS activity. The main up-regulated metabolites were Ala, GABA, Glu, Gln, Lac, Tyr, Phe in 1Mn rats. After reducing Mn intake, metabolites returned to normal level at different degrees. Reducing Mn intake could reduce brain Mn content and blood ammonia, regulate GS activity and amino acid metabolism, ultimately improve behavioral performance in CHE rats.

Drug-induced liver injury (DILI) is a potentially fatal adverse event and the leading cause of acute liver failure in the US and in the majority of Europe. The liver can be affected directly, in a dose-dependent manner, or idiosyncratically, independently of the dose, and therefore unpredictably. Currently, DILI is a diagnosis of exclusion that physicians should suspect in patients with unexplained elevated liver enzymes. Therefore, new diagnostic and prognostic biomarkers are necessary to achieve an early and reliable diagnosis of DILI and thus improve the prognosis. Although several DILI biomarkers have been found through analytical and genetic tests and pharmacokinetic approaches, none of them have been able to display enough specificity and sensitivity, so new approaches are needed. In this sense, metabolomics is a strongly and promising emerging field that, from biofluids collected through minimally invasive procedures, can obtain early biomarkers of toxicity, which may constitute specific indicators of liver damage.

To identify plasma metabolites used as biomarkers in order to distinguish cirrhotics from controls and encephalopathics.

A clinical study involving stable cirrhotic patients with and without overt hepatic encephalopathy was designed. A control group of healthy volunteers was used. Plasma from those patients was analysed using (1)H - nuclear magnetic resonance spectroscopy. We used the Carr Purcell Meiboom Gill sequence to process the sample spectra at ambient probe temperature. We used a gated secondary irradiation field for water signal suppression. Samples were calibrated and referenced using the sodium trimethyl silyl propionate peak at 0.00 ppm. For each sample 128 transients (FID's) were acquired into 32 K complex data points over a spectral width of 6 KHz. 30 degree pulses were applied with an acquisition time of 4.0 s in order to achieve better resolution, followed by a recovery delay of 12 s, to allow for complete relaxation and recovery of the magnetisation. A metabolic profile was created for stable cirrhotic patients without signs of overt hepatic encephalopathy and encephalopathic patients as well as healthy controls. Stepwise discriminant analysis was then used and discriminant factors were created to differentiate between the three groups.

Eighteen stabled cirrhotic patients, eighteen patients with overt hepatic encephalopathy and seventeen healthy volunteers were recruited. Patients with cirrhosis had significantly impaired ketone body metabolism, urea synthesis and gluconeogenesis. This was demonstrated by higher concentrations of acetoacetate (0.23 ± 0.02 vs 0.05 ± 0.00, P < 0.01), and b-hydroxybutarate (0.58 ± 0.14 vs 0.08 ± 0.00, P < 0.01), lower concentrations of glutamine (0.44 ± 0.08 vs 0.63 ± 0.03, P < 0.05), histidine (0.16 ± 0.01 vs 0.36 ± 0.04, P < 0.01) and arginine (0.08 ± 0.01 vs 0.14 ± 0.02, P < 0.03) and higher concentrations of glutamate (1.36 ± 0.25 vs 0.58 ± 0.04, P < 0.01), lactate (1.53 ± 0.11 vs 0.42 ± 0.05, P < 0.01), pyruvate (0.11 ± 0.02 vs 0.03 ± 0.00, P < 0.01) threonine (0.39 ± 0.02 vs 0.08 ± 0.01, P < 0.01) and aspartate (0.37 ± 0.03 vs 0.03 ± 0.01). A five metabolite signature by stepwise discriminant analysis could separate between controls and cirrhotic patients with an accuracy of 98%. In patients with encephalopathy we observed further derangement of ketone body metabolism, impaired production of glycerol and myoinositol, reversal of Fischer's ratio and impaired glutamine production as demonstrated by lower b-hydroxybutyrate (0.58 ± 0.14 vs 0.16 ± 0.02, P < 0.0002), higher acetoacetate (0.23 ± 0.02 vs 0.41 ± 0.16, P < 0.05), leucine (0.33 ± 0.02 vs 0.49 ± 0.05, P < 0.005) and isoleucine (0.12 ± 0.02 vs 0.27 ± 0.02, P < 0.0004) and lower glutamine (0.44 ± 0.08 vs 0.36 ± 0.04, P < 0.013), glycerol (0.53 ± 0.03 vs 0.19 ± 0.02, P < 0.000) and myoinositol (0.36 ± 0.04 vs 0.18 ± 0.02, P < 0.010) concentrations. A four metabolite signature by stepwise discriminant analysis could separate between encephalopathic and cirrhotic patients with an accuracy of 87%.

Patients with cirrhosis and patients with hepatic encephalopathy exhibit distinct metabolic abnormalities and the use of metabonomics can select biomarkers for these diseases.

The emergent discipline of metabolomics has attracted considerable research effort in hepatology. Here we review the metabolomic data for non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), cirrhosis, hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), alcoholic liver disease (ALD), hepatitis B and C, cholecystitis, cholestasis, liver transplantation, and acute hepatotoxicity in animal models. A metabolomic window has permitted a view into the changing biochemistry occurring in the transitional phases between a healthy liver and hepatocellular carcinoma or cholangiocarcinoma. Whether provoked by obesity and diabetes, alcohol use or oncogenic viruses, the liver develops a core metabolomic phenotype (CMP) that involves dysregulation of bile acid and phospholipid homeostasis. The CMP commences at the transition between the healthy liver (Phase 0) and NAFLD/NASH, ALD or viral hepatitis (Phase 1). This CMP is maintained in the presence or absence of cirrhosis (Phase 2) and whether or not either HCC or CCA (Phase 3) develops. Inflammatory signalling in the liver triggers the appearance of the CMP. Many other metabolomic markers distinguish between Phases 0, 1, 2 and 3. A metabolic remodelling in HCC has been described but metabolomic data from all four Phases demonstrate that the Warburg shift from mitochondrial respiration to cytosolic glycolysis foreshadows HCC and may occur as early as Phase 1. The metabolic remodelling also involves an upregulation of fatty acid β-oxidation, also beginning in Phase 1. The storage of triglycerides in fatty liver provides high energy-yielding substrates for Phases 2 and 3 of liver pathology. The metabolomic window into hepatobiliary disease sheds new light on the systems pathology of the liver.

In the study, gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) techniques coupled with principal components analysis (PCA) were used to investigate metabolite perturbations in the serum of the rats exposed to 0.75, 3 or 8 Gy gamma rays. Male standard deviation rats were gamma-irradiated at doses of 0.75, 3 and 8 Gy (1.9 Gy min(-1)) or sham-irradiated. Serum samples were collected over the first 24 h under the exposure to irradiation in order to analyse the samples by GC/TOFMS. And multivariate data were analysed by PCA. The composition of metabolites in serum yielded distinct metabolomic phenotypes for 0.75, 3 and 8 Gy at 24 h after irradiation. Nine serum metabolites were significantly altered as a result of radiation exposure. Up-regulated metabolites included inositol, serine, lysine, glycine, threonine and glycerol; down-regulated metabolites included isocitrate, gluconic acid and stearic acid. The nine metabolites were significantly altered after ionising radiation for they may be the potential biomarkers for the diagnosis of radiation injury.

In this paper, we compare the performance of six different feature selection methods for LC-MS-based proteomics and metabolomics biomarker discovery-t test, the Mann-Whitney-Wilcoxon test (mww test), nearest shrunken centroid (NSC), linear support vector machine-recursive features elimination (SVM-RFE), principal component discriminant analysis (PCDA), and partial least squares discriminant analysis (PLSDA)-using human urine and porcine cerebrospinal fluid samples that were spiked with a range of peptides at different concentration levels. The ideal feature selection method should select the complete list of discriminating features that are related to the spiked peptides without selecting unrelated features. Whereas many studies have to rely on classification error to judge the reliability of the selected biomarker candidates, we assessed the accuracy of selection directly from the list of spiked peptides. The feature selection methods were applied to data sets with different sample sizes and extents of sample class separation determined by the concentration level of spiked compounds. For each feature selection method and data set, the performance for selecting a set of features related to spiked compounds was assessed using the harmonic mean of the recall and the precision (f-score) and the geometric mean of the recall and the true negative rate (g-score). We conclude that the univariate t test and the mww test with multiple testing corrections are not applicable to data sets with small sample sizes (n = 6), but their performance improves markedly with increasing sample size up to a point (n > 12) at which they outperform the other methods. PCDA and PLSDA select small feature sets with high precision but miss many true positive features related to the spiked peptides. NSC strikes a reasonable compromise between recall and precision for all data sets independent of spiking level and number of samples. Linear SVM-RFE performs poorly for selecting features related to the spiked compounds, even though the classification error is relatively low.

High-mobility group box 1 (HMGB1) is a late mediator of lethal systemic inflammation. Acute liver failure (ALF) has been shown to trigger systemic inflammation in clinical and animal studies. To evaluate the possibility of HMGB1 cytoplasmic translocation in ALF, we determined whether HMGB1 is released in hepatocytes and end organ in patients with liver failure/injury.

HepG2 cell were stimulated with LPS or TNF-α, the increase of HMGB1 extracellularly in the culture medium and intracellularly in various cellular fractions were determined by western blot or immunocytochemistry. To observe sub-cellular location of HMGB1 in hepatocytes, liver specimens were obtained from 6 patients with ALF caused by HBV infection, 10 patients with chronic viral hepatitis B, 6 healthy controls, as well as animals model of ALF by intraperitoneal administration of D-GalN (600 mg/kg) and LPS (0.5 mg/kg).

In HepG2 cell culture, LPS or TNF actively induced HMGB1 cytoplasmic translocation and release in a time- and dose-dependent fashion. In animal model of ALF, cytoplasmic HMGB1 translocation was observed in hepatocyts as early as 3 hours post onset of ALF. In patients with ALF caused by HBV infection, cytoplasmic HMGB1 translocation was similarly observed in some hepatocytes of the liver specimen.

Cytoplasmic HMGB1 translocation may occur during ALF, which may potentially contribute to the pathogenesis of liver inflammatory diseases.