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.

Spatial variations in gut permeability are linked to type 1 diabetes development in non-obese diabetic mice.

Objectives: To determine if spatial variations in gut permeability play a role in regulating type 1 diabetes (T1D) progression. Research design and methods: Spatially resolved duodenum, jejunum, ileum, and large intestine sections from end-stage T1D non-obese diabetic (NOD) mice were probed by immunohistochemistry to quantify zonulin levels as a measure of gut permeability in early-progressor and late-progressor NOD mice in comparison with non-progressor NOD mice and healthy NOR/LtJ control mice. Results: Zonulin levels were elevated in the small and large intestines in early-progressor and late-progressor NOD mice in comparison with non-progressor NOD mice and healthy NOR control mice. In early-onset mice, elevated zonulin levels were maximum in the duodenum and jejunum and decreased in the ileum and large intestine. In late-progressor mice, zonulin levels were elevated almost evenly along the small and large intestines. In non-progressor NOD mice, zonulin levels were comparable with NOR control levels in both the small and large intestines. Conclusions: Elevated zonulin expression levels indicated that gut permeability was increased both in the small and large intestines in NOD mice that progressed to end-stage T1D in comparison with non-progressor NOD mice and healthy NOR control mice. Highest elevations in zonulin levels were observed in the duodenum and jejunum followed by the ileum and large intestines. Spatial variations in gut permeability appeared to play a role in regulating the rate and severity of T1D progression in NOD mice indicating that spatial variations in gut permeability should be investigated as a potentially important factor in human T1D progression.

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.

HMGA1 expression levels are elevated in pancreatic intraepithelial neoplasia cells in the Ptf1a-Cre; LSL-KrasG12D transgenic mouse model of pancreatic cancer.

BACKGROUND: Pancreatic cancer is currently the third leading cause of cancer deaths in the United States and it is predicted to become the second by the year 2030. High-mobility group A1 protein (HMGA1) is an oncogenic transcription factor, localised and active in cell nuclei, that is linked to tumour progression in many human cancers, including pancreatic cancer. Overexpression of HMGA1 renders cancer cells resistant to chemotherapy. Although the Ptf1a-Cre; LSL-KrasG12D transgenic mouse is perhaps the most widely utilised animal model for human pancreatic cancer, expression levels of HMGA1 in pancreata from this mouse model have not been characterised. METHODS: Quantitative immunohistochemical analysis was used to determine nuclear HMGA1 levels in pancreatic tissue sections from Ptf1a-Cre; LSL-KrasG12D mice aged 5, 11, and 15 months. The H Score method was used for quantitative analysis. RESULTS: The HMGA1 levels were significantly elevated in pancreatic intraepithelial neoplasia (PanIN) epithelia compared with untransformed acinar tissues or fibroinflammatory stroma. CONCLUSIONS: The PanINs have long been regarded as precancerous precursors to pancreatic adenocarcinoma. Significantly elevated HMGA1 levels observed in the nuclei of PanINs in Ptf1a-Cre; LSL-KrasG12D mice validate this animal model for investigating the role that HMGA1 plays in cancer progression and testing therapeutic approaches targeting HMGA1 in human cancers.