An IROA Workflow for correction and normalization of ion suppression in mass spectrometry-based metabolomic profiling data.

Ion suppression is a major problem in mass spectrometry (MS)-based metabolomics; it can dramatically decrease measurement accuracy, precision, and signal-to-noise sensitivity. Here we report a new method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) plus novel companion algorithms to 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. We have evaluated the method across ion chromatography (IC), hydrophilic interaction liquid chromatography (HILIC), and reverse phase liquid chromatography (RPLC)-MS systems in both positive and negative ionization modes, with clean and unclean ion sources, and across different biological matrices. Across the broad range of conditions tested, all detected metabolites exhibited ion suppression ranging from 1% to 90+% and coefficient of variations ranging from 1% to 20%, but the Workflow and companion algorithms were highly effective at nulling out that suppression and error. Overall, the Workflow corrects ion suppression across diverse analytical conditions and produces robust normalization of non-targeted metabolomic data.

Offline Two-dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome.

Compound identification is an essential task in the workflow of untargeted metabolomics since the interpretation of the data in a biological context depends on the correct assignment of chemical identities to the features it contains. Current techniques fall short of identifying all or even most observable features in untargeted metabolomics data, even after rigorous data cleaning approaches to remove degenerate features are applied. Hence, new strategies are required to annotate the metabolome more deeply and accurately. The human fecal metabolome, which is the focus of substantial biomedical interest, is a more complex, more variable, yet lesser-investigated sample matrix compared to widely studied sample types like human plasma. This manuscript describes a novel experimental strategy using multidimensional chromatography to facilitate compound identification in untargeted metabolomics. Pooled fecal metabolite extract samples were fractionated using offline semi-preparative liquid chromatography. The resulting fractions were analyzed by an orthogonal LC-MS/MS method, and the data were searched against commercial, public, and local spectral libraries. Multidimensional chromatography yielded more than a 3-fold improvement in identified compounds compared to the typical single-dimensional LC-MS/MS approach and successfully identified several rare and novel compounds, including atypical conjugated bile acid species. Most features identified by the new approach could be matched to features that were detectable but not identifiable in the original single-dimension LC-MS data. Overall, our approach represents a powerful strategy for deeper annotation of the metabolome that can be implemented with commercially-available instrumentation, and should apply to any dataset requiring deeper annotation of the metabolome.

Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics.

Untargeted metabolomics is an essential component of systems biology research, but it is plagued by a high proportion of detectable features not identified with a chemical structure. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments produce spectra that can be searched against databases to help identify or classify these unknowns, but many features do not generate spectra of sufficient quality to enable successful annotation. Here, we explore alterations to gradient length, mass loading, and rolling precursor ion exclusion parameters for reversed phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) that improve compound identification performance for human plasma samples. A manual review of spectral matches from the HILIC data set was used to determine reasonable thresholds for search score and other metrics to enable semi-automated MS/MS data analysis. Compared to typical LC-MS/MS conditions, methods adapted for compound identification increased the total number of unique metabolites that could be matched to a spectral database from 214 to 2052. Following data alignment, 68.0% of newly identified features from the modified conditions could be detected and quantitated using a routine 20-min LC-MS run. Finally, a localized machine learning model was developed to classify the remaining unknowns and select a subset that shared spectral characteristics with successfully identified features. A total of 576 and 749 unidentified features in the HILIC and RPLC data sets were classified by the model as high-priority unknowns or higher-importance targets for follow-up analysis. Overall, our study presents a simple strategy to more deeply annotate untargeted metabolomics data for a modest additional investment of time and sample.

Cyst fluid metabolites distinguish malignant from benign pancreatic cysts.

OBJECTIVES: Current standard of care imaging, cytology, or cystic fluid analysis cannot reliably differentiate malignant from benign pancreatic cystic neoplasms. This study sought to determine if the metabolic profile of cystic fluid could distinguish benign and malignant lesions, as well as mucinous and non-mucinous lesions. METHODS: Metabolic profiling by untargeted mass spectrometry and quantitative nuclear magnetic resonance was performed in 24 pancreatic cyst fluid from surgically resected samples with pathological diagnoses and clinicopathological correlation. RESULTS: (Iso)-butyrylcarnitine distinguished malignant from benign pancreatic cysts, with a diagnostic accuracy of 89%. (Iso)-butyrylcarnitine was 28-fold more abundant in malignant cyst fluid compared with benign cyst fluid (P=.048). Furthermore, 5-oxoproline (P=.01) differentiated mucinous from non-mucinous cysts with a diagnostic accuracy of 90%, better than glucose (82% accuracy), a previously described metabolite that distinguishes mucinous from non-mucinous cysts. Combined analysis of glucose and 5-oxoproline did not improve the diagnostic accuracy. In comparison, standard of care cyst fluid carcinoembryonic antigen (CEA) and cytology had a diagnostic accuracy of 40% and 60% respectively for mucinous cysts. (Iso)-butyrylcarnitine and 5-oxoproline correlated with cyst fluid CEA levels (P<.0001 and P<.05 respectively). For diagnosing malignant pancreatic cysts, the diagnostic accuracies of cyst size > 3 cm, ≥ 1 high-risk features, cyst fluid CEA, and cytology are 38%, 75%, 80%, and 75%, respectively. CONCLUSIONS: (Iso)-butyrylcarnitine has potential clinical application for accurately distinguishing malignant from benign pancreatic cysts, and 5-oxoproline for distinguishing mucinous from non-mucinous cysts.

Quantitative Metabolomics Using Isotope Residue Outlier Analysis (IROA®) with Internal Standards.

Various research strategies involving biomarker discovery and mechanistic studies in system biology depend on reproducible and reliable quantification of all metabolites from tissue(s) of interest. Contemporary analytical methods rely on mass spectrometry-based targeted and/or untargeted metabolomics platforms. The robustness of these analyses depends on the cleanliness of the samples, accuracy of the database, resolution of the instrument, and, the most variable of the list, the personal preferences of the researcher and the instrument operator. In this chapter, we introduce a simple method to prepare murine liver samples and carry it through the Isotope Ratio Outlier Analysis (IROA®) pipeline. This pipeline encompasses sample preparation, LC-MS-based peak acquisition, proprietary software-based library creation, normalization, and quantification of metabolites. IROA® offers a unique platform to create and normalize a local library and account for run-to-run variability over years of acquisition using the internal standards (IROA®-IS) and long-term reference standards (IROA®-LTRS).

BatMass: a Java Software Platform for LC-MS Data Visualization in Proteomics and Metabolomics.

Mass spectrometry (MS) coupled to liquid chromatography (LC) is a commonly used technique in metabolomic and proteomic research. As the size and complexity of LC-MS-based experiments grow, it becomes increasingly more difficult to perform quality control of both raw data and processing results. In a practical setting, quality control steps for raw LC-MS data are often overlooked, and assessment of an experiment's success is based on some derived metrics such as "the number of identified compounds". The human brain interprets visual data much better than plain text, hence the saying "a picture is worth a thousand words". Here, we present the BatMass software package, which allows for performing quick quality control of raw LC-MS data through its fast visualization capabilities. It also serves as a testbed for developers of LC-MS data processing algorithms by providing a data access library for open mass spectrometry file formats and a means of visually mapping processing results back to the original data. We illustrate the utility of BatMass with several use cases of quality control and data exploration.

Integrated phosphoproteomic and metabolomic profiling reveals NPM-ALK-mediated phosphorylation of PKM2 and metabolic reprogramming in anaplastic large cell lymphoma.

The mechanisms underlying the pathogenesis of the constitutively active tyrosine kinase nucleophosmin-anaplastic lymphoma kinase (NPM-ALK) expressing anaplastic large cell lymphoma are not completely understood. Here we show using an integrated phosphoproteomic and metabolomic strategy that NPM-ALK induces a metabolic shift toward aerobic glycolysis, increased lactate production, and biomass production. The metabolic shift is mediated through the anaplastic lymphoma kinase (ALK) phosphorylation of the tumor-specific isoform of pyruvate kinase (PKM2) at Y105, resulting in decreased enzymatic activity. Small molecule activation of PKM2 or expression of Y105F PKM2 mutant leads to reversal of the metabolic switch with increased oxidative phosphorylation and reduced lactate production coincident with increased cell death, decreased colony formation, and reduced tumor growth in an in vivo xenograft model. This study provides comprehensive profiling of the phosphoproteomic and metabolomic consequences of NPM-ALK expression and reveals a novel role of ALK in the regulation of multiple components of cellular metabolism. Our studies show that PKM2 is a novel substrate of ALK and plays a critical role in mediating the metabolic shift toward biomass production and tumorigenesis.

Statistical methods in metabolomics.

Metabolomics is the relatively new field in bioinformatics that uses measurements on metabolite abundance as a tool for disease diagnosis and other medical purposes. Although closely related to proteomics, the statistical analysis is potentially simpler since biochemists have significantly more domain knowledge about metabolites. This chapter reviews the challenges that metabolomics poses in the areas of quality control, statistical metrology, and data mining.

Expression in multigene families. Analysis of chloroplast and mitochondrial proteases.

The proteolytic machinery of chloroplasts and mitochondria in Arabidopsis consists primarily of three families of ATP-dependent proteases, Clp, Lon, and FtsH, and one family of ATP-independent proteases, DegP. However, the functional significance of the multiplicity of their genes is not clear. To test whether expression of specific isomers could be differently affected by growth conditions, we analyzed transcript abundance following short-term exposure to different environmental stimuli, using 70-mer oligonucleotide arrays. This analysis revealed variability in the response to high light and different temperatures within members of each family. Thirty out of the 41 tested genes were up-regulated in response to high light, including both chloroplast and mitochondrial isozymes, whereas only six and five genes responded to either high or low temperature, respectively. The extent of response was variable, ranging from 2- to 20-fold increase in the steady-state levels. Absolute transcript levels of the tested genes, compiled from one-channel arrays, were also variable. In general, transcripts encoding mitochondrial isozymes were accumulated to a lower level than chloroplastic ones. Within the FtsH family, transcript abundance of most genes correlated with the severity of mutant phenotypes in the relevant genes. This correlation was also evident at the protein level. Analysis of FtsH isozymes revealed that FtsH2 was the most abundant species, followed by FtsH5 and 8, with FtsH1 being accumulated to only 10% of FtsH2 level. These results suggest that, unlike previous expectations, the relative importance of different chloroplast protease isozymes, evidenced by mutant phenotypes at least in the FtsH family, is determined by their abundance, and not necessarily by different specific functions or specialized expression under certain conditions.

A consensus-binding structure for adenine at the atomic level permits searching for the ligand site in a wide spectrum of adenine-containing complexes.

Attempts to derive structural features of ligand-binding sites have traditionally involved seeking commonalities at the residue level. Recently, structural studies have turned to atomic interactions of small molecular fragments to extract common binding-site properties. Here, we explore the use of larger ligand elements to derive a consensus binding structure for the ligand as a whole. We superimposed multiple molecular structures from a nonredundant set of adenosine-5'-triphosphate (ATP) protein complexes, using the adenine moiety as template. Clustered binding-site atoms of compatible atomic classes forming attractive contacts with the adenine probe were extracted. A set of atomic clusters characterizing the adenine binding pocket was then derived. Among the clusters are three vertices representing the interactions of adenine atom N6 with its protein-binding niche. These vertices, together with atom C6 of the purine ring system, complete the set of four vertices for the pyramid-like structure of the N6 anchor atom. Also, the sequence relationship for the adenine-binding loop interacting with the C2-N6 end of the conjugated ring system is expanded to include a third hydrophilic cluster interacting with atom N1. A search procedure involving interatomic distances between cluster centers was formulated and applied to seek putative binding sites in test cases. The results show that a consensus network of clusters, based on an adenine probe and an ATP-complexed training set of proteins, is sufficient to recognize the experimental cavity for adenine in a wide spectrum of ligand-protein complexes.