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

Joesten, William C; Kennedy, Michael A.

doi:10.1007/s11306-018-1465-2

Cited by 9 publications

(7 citation statements)

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“…Metabolite identifications were confirmed using the Biological Magnetic Resonance Data Bank (BMRB) [ 41 ] and the Human Metabolome Database (HMDB) [ 42 – 45 ]. If a metabolite was considered to be present in the spectra, a concentration was recorded and identity was further confirmed using 2D spectra and ranked according to an ordinal ranking system developed in our laboratory [ 46 ]. The ranking system can be used to assign confidence levels to all metabolite identifications.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Methodsmentioning

confidence: 99%

“…The rank levels range from 1 to 5, with 1 being the lowest confidence and 5 being the highest confidence in the metabolite identification. Assignment of rank levels of confidence to metabolite identifications is guided by a detailed decision tree as previously described [ 46 ].…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2018

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“…Finally, it is worth discussing how important the contribution of the f1 trace comparison (multiplicity and coupling constants) is for the JRES-STOCSY and database hit, toward the assignment of confidence levels (ranks) when performing the putative annotation of a compound, in relation to the recently reported recommendations from the group of Kennedy (RANCM). 40 While the decision tree proposed by RANCM includes a differentiation for "singlets only" versus multiplets when analyzing a 1D 1 H spectral set, the use of the JRES-STOCSY projections gives equal importance to any type of multiplet. In addition, RANCM specifically excludes the use of STOCSY in its flowchart, which means that the mere presence of a peak at a chemical shift might confound annotation at tier 1 levels.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D ¹H NMR Database Search

et al. 2020

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NMR-based metabolomics requires proper identification of metabolites to draw conclusions from the system under study. Normally, multivariate data analysis is performed using 1D 1H NMR spectra, and identification of peaks (and then compounds) relevant to the classification is accomplished using database queries as a first step. 1D 1H NMR spectra of complex mixtures often suffer from peak overlap. To overcome this issue, several studies employed the projections of the (tilted and symmetrized) 2D 1H J-resolved (JRES) spectra, p-JRES, which are similar to 1D 1H decoupled spectra. Nonetheless, there are no public databases available that allow searching for chemical shift spectral data for multiplets. We present the Chemical Shift Multiplet Database (CSMDB), built utilizing JRES spectra obtained from the Birmingham Metabolite Library. The CSMDB provides scoring accounting for both matched and unmatched peaks from a query list and the database hits. This input list is generated from a projection of a 2D statistical correlation analysis on the JRES spectra, p-(JRES-STOCSY), being able to compare the multiplets for the matched peaks, in essence, the f1 traces from the JRES-STOCSY spectrum and from the database hit. The inspection of the unmatched peaks for the database hit allows the retrieval of peaks in the query list that have a decreased correlation coefficient due to low intensities. The CSMDB is coupled to “ConQuer ABC”, which permits the assessment of biological correlation by means of consecutive queries with the unmatched peaks in the first and subsequent queries.

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“…Two-dimensional NMR data were analyzed using the COLMAR software [70,71] () to confirm metabolite assignments. The confidence in metabolite assignments was ranked using the RANCM scheme developed in our laboratory [72].…”

Section: Methodsmentioning

confidence: 99%

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

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Joesten

et al. 2019

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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.

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

Cited by 9 publications

References 37 publications

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

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

Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D ¹H NMR Database Search

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

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

Cited by 9 publications

References 37 publications

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

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

Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D 1H NMR Database Search

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

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Product

Resources

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Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D ¹H NMR Database Search