NMR Based Metabonomics Study of DAG Treatment in a C2C12 Mouse Skeletal Muscle Cell Line Myotube Model of Burn-Injury

Effects of mechanical trauma on the differentiation and ArfGAP3 expression of C2C12 myoblast and mouse levator ani muscle

INTRODUCTION AND HYPOTHESIS

Severe mechanical injury or inadequate repair of the levator ani muscle (LAM) is a key contributor to the development of pelvic floor dysfunction (PFD). We explored the effects of mechanical stress on myoblasts and LAM at the cellular and animal level and the possible mechanism of PFD induced by mechanical trauma.

METHODS

A C2C12 cell mechanical injury model was established with a four-point bending device, and a LAM injury mouse model was established via vaginal distention and distal traction, a common way of simulating the birth injury. The cells were divided into control, 1333 μ strain for 4-h cyclic mechanical strain (CMS), 1333 μ strain for 8-h CMS, and 5333 μ strain for 4-h CMS groups. Mice were divided into control and injury groups. After treatment, mitochondrial membrane potential (ΔΨm), reactive oxygen species (ROS) levels, indicators of oxidative damage, cell apoptosis, muscle and cell morphology, cell differentiation, and expression of adenosine diphosphate (ADP)-ribosylation factor GTPase activating protein 3 (ArfGAP3) were detected.

RESULTS

5333 μ strain for 4-h CMS loading could induce myoblast injury with a reduction of ΔΨm, increased ROS levels, aggravation of oxidative damage-associated proteins NADPH oxidase 2 (NOX2) and xanthine oxidase (XO), and an increased apoptosis rate of C2C12 cells. At the same time, the injury CMS loading can promote the differentiation of myoblasts and increase the expression of ArfGAP3, a factor regulating intracellular transport. Mechanical trauma could also lead to the oxidative damage of LAM, indicated by 8-hydroxy-2'-deoxyguanosine(8-OHdG), NOX2 and XO protein accumulation, and increase the expression of ArfGAP3 in LAM.

CONCLUSIONS

Oxidative stress caused by mechanical trauma induces dysfunction and damage repairing of LAM and C2C12 myoblast, and ArfGAP3 may promote the repairing process.

Influence of Drying Method on NMR-Based Metabolic Profiling of Human Cell Lines

Metabolic profiling of cell line and tissue extracts involves sample processing that includes a drying step prior to re-dissolving the cell or tissue extracts in a buffer for analysis by GC/LC-MS or NMR. Two of the most commonly used drying techniques are centrifugal evaporation under vacuum (SpeedVac) and lyophilization. Here, NMR spectroscopy was used to determine how the metabolic profiles of hydrophilic extracts of three human pancreatic cancer cell lines, MiaPaCa-2, Panc-1 and AsPC-1, were influenced by the choice of drying technique. In each of the three cell lines, 40-50 metabolites were identified as having statistically significant differences in abundance in redissolved extract samples depending on the drying technique used during sample preparation. In addition to these differences, some metabolites were only present in the lyophilized samples, for example, n-methyl-α-aminoisobutyric acid, n-methylnicotimamide, sarcosine and 3-hydroxyisovaleric acid, whereas some metabolites were only present in SpeedVac dried samples, for example, trimethylamine. This research demonstrates that the choice of drying technique used during the preparation of samples of human cell lines or tissue extracts can significantly influence the observed metabolome, making it important to carefully consider the selection of a drying method prior to preparation of such samples for metabolic profiling.

RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies

INTRODUCTION

The Metabolomics Standards Initiative has recommended four categories for metabolite assignments in NMR-based metabolic profiling studies. The "putatively annotated compound" category is most commonly reported by metabolomics investigators. However, there is significant ambiguity in reliability of "putatively annotated compound" assignments, which can range from low confidence made on minimal corroborating data to high confidence made on substantial corroborating data.

OBJECTIVES

To introduce a new ranking system, Rank and AssigN Confidence to Metabolites (RANCM), to assign confidence levels to "putatively annotated compound" assignments in NMR-based metabolic profiling studies.

METHODS

The ranking system was constructed with three confidence levels ranging from Rank 1 for the lowest confidence assignment level to Rank 3 for the highest confidence assignment level. A decision tree was constructed to guide rank selection for each metabolite assignment.

RESULTS

Examples are provided from experimental data demonstrating how to use the decision tree to make confidence level assignments to "putatively annotated compounds" in each of the three rank levels. A standard Excel sheet template is provided to facilitate decision-making, documentation and submission to data repositories.

CONCLUSION

RANCM is intended to reduce the ambiguity in "putatively annotated compound" assignments, to facilitate effective communication of the degree of confidence in "putatively annotated compound" assignments, and to make it easier for non-experts to evaluate the significance and reliability of NMR-based metabonomics studies. The system is straightforward to implement, based on the most common datasets collected in NMR-based metabolic profiling studies, and can be used with equal rigor and significance with any set of NMR datasets.

NMR-based metabolic profiling of urine, serum, fecal, and pancreatic tissue samples from the Ptf1a-Cre; LSL-KrasG12D transgenic mouse model of pancreatic cancer

Pancreatic cancer is the third leading cause of cancer deaths in the United States with more than 53,000 expected to be diagnosed with the disease in 2018. The median survival time after diagnosis is four to six months. The poor survival statistics are due in part to the fact that pancreatic cancer is typically asymptomatic until it reaches advanced stages of the disease. Although surgical resection provides the best chance of survival, pancreatic cancer is rarely detected when surgery is still possible due, in part, to lack of effective biomarkers for early detection. The goal of the research reported here was to determine if it was possible to identify metabolic biomarkers for detection of pre-cancerous pancreatic intraepithelial neoplasia (PanIN) that precede pancreatic adenocarcinoma. The transgenic Ptf1a-Cre; LSL-KrasG12D mouse strain was used as a model of pancreatic cancer progression. Nuclear magnetic resonance (NMR) spectroscopy was employed to compare metabolic profiles of urine, sera, fecal extracts, and pancreatic tissue extracts collected from control and study mice aged 5, 11, and 15 months, including 47 mice with tumors. We were able to identify the following potential biomarkers: decreased 3-indoxylsulfate, benzoate and citrate in urine, decreased glucose, choline, and lactate in blood, and decreased phenylalanine and benzoate and increased acetoin in fecal extracts. Potential biomarkers were validated by p-values, PLS-DA VIP scores, and accuracies based on area under ROC curve analyses. Essentially, all of the metabolic profiling changes could be explained as being associated with the consequences of bicarbonate wasting caused by a complete substitution of the normal pancreatic acinar tissue by tissue entirely composed of PanIN. Given the nature of the mouse model used here, our results indicate that it may be possible to use NMR-based metabolic profiling to identify biomarkers for detection of precancerous PanIN that immediately precede pancreatic cancer.

Des-acyl-ghrelin (DAG) normalizes hyperlactacidemia and improves survival in a lethal rat model of burn trauma

Critical illness, including burn injury, results in elevated plasma lactate levels. Dysregulation of PI3K/Akt signaling has been shown to play a predominant role in the inactivation of skeletal muscle PDC and, hence, in hyperlactacidemia in rat models of sepsis and endotoxemia. This observation, and our previous finding that DAG can reverse burn-induced skeletal muscle proteolysis through the activation of PI3K/Akt pathway, led us to hypothesize that DAG may also attenuate hyperlactacidemia in burn injury. Our investigations revealed that burn injury significantly elevated both skeletal muscle lactate production and plasma lactate levels. Moreover, this was accompanied in skeletal muscle by a 5-7 fold increase in mRNA expression of pyruvate dehydrogenase kinases (PDK) 2 and 4, and a ∼30% reduction in PDC activity. DAG treatment of burn rats completely normalized not only the mRNA expression of the PDKs and PDC activity, but also hyperlactacidemia within 24h of burn injury. DAG also normalized epinephrine-induced lactate production by isolated skeletal muscles from normal rats. Moreover, DAG also improved survival in a lethal rat model of burn trauma. These findings with DAG may have clinical implications because chances of survival for critically ill patients are greatly improved if plasma lactate levels are normalized within 24h of injury.

Use of 1H-nuclear magnetic resonance to screen a set of biomarkers for monitoring metabolic disturbances in severe burn patients

Introduction

To establish a plasma metabolomics fingerprint spectrum for severe burn patients and to use it to identify a set of biomarkers that could be used for clinical monitoring.

Methods

Twenty-one severe burn patients and three healthy control individuals were enrolled in this study, and the plasma samples from patients and healthy individuals were collected for nuclear magnetic resonance (NMR) measurements. The NMR spectra were analyzed using principal component analysis (PCA) and partial least squares (PLS) in order to establish the metabolomics fingerprint representing the changes in metabolism and to select the major biomarkers.

Results

NMR spectra of the plasma samples showed significant differences between burn patients and healthy individuals. Using metabolomics techniques, we found an Eigen-metabolome that consists of 12 metabolites, which are regulated by 103 enzymes in a global metabolic network. Among these metabolites, α-ketoisovaleric acid, 3-methylhistidine, and β-hydroxybutyric acid were the most important biomarkers that were significantly increased during the early stage of burn injury. These results suggest that the mitochondrial damage and carbohydrate, protein and fatty acid metabolism disturbances occur after burn injury. Our analysis also show that histone deacetylases, which are protein transcription suppressors, were remarkably increased and indicate that protein transcription was inhibited and anabolism was restrained during the early stage of burn injury.

Conclusions

Metabolomics techniques based on NMR can be used to monitor metabolism in severe burn patients. Our study demonstrates that integrated ¹H-NMR metabolome and global metabolic network analysis is useful for visualizing complex metabolic disturbances after severe burn injury and may provide a new quantitative injury severity evaluation for future clinical use.

Trial registration

Chinese Clinical Trial Registry ChiCTR-OCC-12002145. Registered 25 April 2012.

Prolonged antibiotic use induces intestinal injury in mice that is repaired after removing antibiotic pressure: implications for empiric antibiotic therapy

Metabolic profiling of urine and fecal extracts, histological investigation of intestinal ilea, and fecal metagenomics analyses were used to investigate effects of prolonged antibiotic use in mice. The study provides insight into the effects of extended empiric antibiotic therapy in humans. Mice were administered a broad-spectrum antibiotic for four consecutive days followed by oral gavage with Clostridium butyricum, an opportunistic gram-positive pathogenic bacteria commonly isolated in fecal and blood cultures of necrotizing enterocolitis patients. Metagenomics data indicated loss of bacterial diversity after 4 days on antibiotics that was restored after removing antibiotic pressure. Histological analyses indicated damage to ileal villi after antibiotic treatment that underwent repair after lifting antibiotic pressure. Metabolic profiling confirmed intestinal injury in antibiotic-treated mice indicated by increased urinary trans-4-hydroxy-l-proline, a breakdown product of collagen present in connective tissue of ileal villi that may serve as a biomarker for antibiotic-induced injury in at risk populations.

Introduction of a new critical p value correction method for statistical significance analysis of metabonomics data

Nuclear magnetic resonance (NMR) spectroscopy-based metabonomics is of growing importance for discovery of human disease biomarkers. Identification and validation of disease biomarkers using statistical significance analysis (SSA) is critical for translation to clinical practice. SSA is performed by assessing a null hypothesis test using a derivative of the Student's t test, e.g., a Welch's t test. Choosing how to correct the significance level for rejecting null hypotheses in the case of multiple testing to maintain a constant family-wise type I error rate is a common problem in such tests. The multiple testing problem arises because the likelihood of falsely rejecting the null hypothesis, i.e., a false positive, grows as the number of tests applied to the same data set increases. Several methods have been introduced to address this problem. Bonferroni correction (BC) assumes all variables are independent and therefore sacrifices sensitivity for detecting true positives in partially dependent data sets. False discovery rate (FDR) methods are more sensitive than BC but uniformly ascribe highest stringency to lowest p value variables. Here, we introduce standard deviation step down (SDSD), which is more sensitive and appropriate than BC for partially dependent data sets. Sensitivity and type I error rate of SDSD can be adjusted based on the degree of variable dependency. SDSD generates fundamentally different profiles of critical p values compared with FDR methods potentially leading to reduced type II error rates. SDSD is increasingly sensitive for more concentrated metabolites. SDSD is demonstrated using NMR-based metabonomics data collected on three different breast cancer cell line extracts.