An integrated Qual/Quan strategy for ganglioside lipidomics using high-resolution mass spectrometry and Skyline software

RATIONALE

Gangliosides (GS) are attractive targets in biomarker discovery because of their physiological significance in numerous human diseases including certain cancers and developmental and metabolic disorders. The robust strategy described here enables the profiling of numerous GS while obtaining quantitative data of exploratory biomarkers present in human plasma and whole blood.

METHOD

The GS from human blood, human plasma, and several cell lines were extracted using a mixture of methanol and isopropanol/0.1% formic acid followed by direct analysis of the supernatant. The simultaneous Qualitative and Quantitative (Qual/Quan) approach involves micro flow (20 μL/min) high pressure liquid chromatography (HPLC)/high-resolution mass spectrometry (HRMS) and post-acquisition data processing with Skyline software for profiling numerous GS in biological matrices. The quantitative assay involves reverse-phase liquid chromatography/HRMS and calibration curves using commercially available GS.

RESULTS

Protein precipitation resulted in ~60%-80% GS recovery from biological matrices. Direct injection of the extract allowed for quantification of targeted GS in human blood, plasma, and cancer cell lines. The lower limit of detection for the target analytes, GM1, GT1, GD1, spiked into 1% BSA/PBS, ranged from 1 to 10 ng/mL. Human lung cancer cell lines contained variable amounts (1-130 ng/mL) of soluble Fuc-GM1 analogs, potential biomarkers of lung cancer.

CONCLUSIONS

A combination of simple extraction and micro-HPLC/HRMS allowed for quantification of GS in human serum and whole blood. Integration of HRMS with Skyline allowed for GS profiling in the same samples using post-acquisition HRMS data without the need for reanalysis. The strategy presented here is expected to play an important role in profiling exploratory GS biomarkers in discovery bioanalytical research.

A High-Resolution Mass Spectrometry-Based Quantitative Metabolomic Workflow Highlights Defects in 5-Fluorouracil Metabolism in Cancer Cells with Acquired Chemoresistance

Currently, 5-fluorouracil (5-FU)-based combination chemotherapy is the mainstay in the treatment of metastatic colorectal cancer (CRC), which benefits approximately 50% of the patients. However, these tumors inevitably acquire chemoresistance resulting in treatment failure. The molecular mechanisms driving acquired chemotherapeutic drug resistance in CRC is fundamental for the development of novel strategies for circumventing resistance. However, the specific phenomenon that drives the cancer cells to acquire resistance is poorly understood. Understanding the molecular mechanisms that regulate chemoresistance will uncover new avenues for the treatment of CRC. Among the various mechanisms of acquired chemoresistance, defects in the drug metabolism pathways could play a major role. In the case of 5-FU, it gets converted into various active metabolites, which, directly or indirectly, interferes with the replication and transcription of dividing cells causing DNA and RNA damage. In this project, we developed a high-resolution mass spectrometry-based method to effectively extract and quantify levels of the 5-FU metabolites in cell lysates and media of parental and 5-FU resistant LIM1215 CRC cells. The analysis highlighted that the levels of 5-FU metabolites are significantly reduced in 5-FU resistant cells. Specifically, the level of the nucleotide fluorodeoxyuridine monophosphate (FdUMP) is reduced with treatment of 5-FU clarifying the compromised 5-FU metabolism in resistant cells. Corroborating the metabolomic analysis, treatment of the resistant cells with FdUMP, an active metabolite of 5-FU, resulted in effective killing of the resistant cells. Overall, in this study, an effective protocol was developed for comparative quantitation of polar metabolites and nucleotide analogues from the adherent cells efficiently. Furthermore, the utility of FdUMP as an alternative for CRC therapy is highlighted.

Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry

Advances in liquid chromatography-mass spectrometry (LC-MS) instruments and analytical strategies have brought about great progress in targeted metabolomics analysis. This methodology is now capable of performing precise targeted measurement of dozens or hundreds of metabolites in complex biological samples. Classic targeted quantification assay using the multiple reaction monitoring (MRM) mode has been the foundation of high-quality metabolite quantitation. However, utilization of this strategy in biological studies has been limited by its relatively low metabolite coverage and throughput capacity. A number of methods for large-scale targeted metabolomics assay which have been developed overcome these limitations. These strategies have enabled extended metabolite coverage which is defined as targeting of large numbers of metabolites, while maintaining reliable quantification performance. These recently developed techniques thus bridge the gap between traditional targeted metabolite quantification and untargeted metabolomics profiling, and have proven to be powerful tools for metabolomics study. Although the LC-MRM-MS strategy has been used widely in large-scale metabolomics quantification analysis due to its fast scan speed and ideal analytic stability, there are still drawbacks which are due to the low resolution of the triple quadrupole instruments used for MRM assays. New approaches have been developed to expand the options for large-scale targeted metabolomics study, using high-resolution instruments such as parallel reaction monitoring (PRM). MRM and PRM-based techniques are now attractive strategies for quantitative metabolomics analysis and high-throughput biomarker discovery. Here we provide an overview of the major developments in LC-MS-based strategies for large-scale targeted metabolomics quantification in biological samples. The advantages of LC-MRM/PRM-MS based analytical strategies which may be used in multiplexed and high throughput quantitation for a wide range of metabolites are highlighted. In particular, PRM and MRM strategies are compared, and we summarize the work flow commonly used for large-scale targeted metabolomics analysis including sample preparation, LC separation and data analysis, as well as recent applications in biological studies.

Chemical profiling of Di-Wu-Yang-Gan Granules by ultra performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry with MSE technology

Di-Wu-Yang-Gan Granules is a Traditional Chinese Medicine prescription used for the treatment of HBeAg-negative chronic hepatitis B patients in China. It consists of five commonly used Chinese herbs. However, the chemical constituents of the whole prescription had not been clarified yet. Hence, in this study, the chemical profiling of Di-Wu-Yang-Gan Granules was explored by ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry, which can provide accurate molecular weight within 5-ppm error and sufficient MS/MS fragment ions without the need for precursor ion selection. As a result, 116 compounds were identified, including lignans, triterpenesaponins, flavonoids, coumarins, iridoids, nortriterpenoids, phenolic acids, and sesquiterpenes. All compounds were further assigned to the individual herbs. In conclusion, this established method was reliable and effective for the separation and identification of the constituents in Di-Wu-Yang-Gan Granules. The findings are beneficial for quality control of the prescription during production and provide helpful chemical information for exploring its efficacy and the mechanism of action. The fragmentation regularity summarized in this study also provided important information for the rapid identification of the chemical composition in herbal medicines or their prescription.

The secreted metabolome of Streptomyces chartreusis and implications for bacterial chemistry

Actinomycetes are known for producing diverse secondary metabolites. Combining genomics with untargeted data-dependent tandem MS and molecular networking, we characterized the secreted metabolome of the tunicamycin producer Streptomyces chartreusis NRRL 3882. The genome harbors 128 predicted biosynthetic gene clusters. We detected >1,000 distinct secreted metabolites in culture supernatants, only 22 of which were identified based on standards and public spectral libraries. S. chartreusis adapts the secreted metabolome to cultivation conditions. A number of metabolites are produced iron dependently, among them 17 desferrioxamine siderophores aiding in iron acquisition. Eight previously unknown members of this long-known compound class are described. A single desferrioxamine synthesis gene cluster was detected in the genome, yet different sets of desferrioxamines are produced in different media. Additionally, a polyether ionophore, differentially produced by the calcimycin biosynthesis cluster, was discovered. This illustrates that metabolite output of a single biosynthetic machine can be exquisitely regulated not only with regard to product quantity but also with regard to product range. Compared with chemically defined medium, in complex medium, total metabolite abundance was higher, structural diversity greater, and the average molecular weight almost doubled. Tunicamycins, for example, were only produced in complex medium. Extrapolating from this study, we anticipate that the larger part of bacterial chemistry, including chemical structures, ecological functions, and pharmacological potential, is yet to be uncovered.

N-linked glycosite profiling and use of Skyline as a platform for characterization and relative quantification of glycans in differentiating xylem of Populus trichocarpa

Our greater understanding of the importance of N-linked glycosylation in biological systems has spawned the field of glycomics and development of analytical tools to address the many challenges regarding our ability to characterize and quantify this complex and important modification as it relates to biological function. One of the unmet needs of the field remains a systematic method for characterization of glycans in new biological systems. This study presents a novel workflow for identification of glycans using Individuality Normalization when Labeling with Isotopic Glycan Hydrazide Tags (INLIGHT™) strategy developed in our lab. This consists of monoisotopic mass extraction followed by peak pair identification of tagged glycans from a theoretical library using an in-house program. Identification and relative quantification could then be performed using the freely available bioinformatics tool Skyline. These studies were performed in the biological context of studying the N-linked glycome of differentiating xylem of the poplar tree, a widely studied model woody plant, particularly with respect to understanding lignin biosynthesis during wood formation. Through our workflow, we were able to identify 502 glycosylated proteins including 12 monolignol enzymes and 1 peroxidase (PO) through deamidation glycosite analysis. Finally, our novel semi-automated workflow allowed for rapid identification of 27 glycans by intact mass and by NAT/SIL peak pairing from a library containing 1573 potential glycans, eliminating the need for extensive manual analysis. Implementing Skyline for relative glycan quantification allowed for improved accuracy and precision of quantitative measurements over current processing tools which we attribute to superior algorithms correction for baseline variation and MS1 peak filtering. Graphical abstract Workflow for FANGS-INLIGHT glycosite profiling of plant xylem and monolignol proteins followed by INLIGHT tagging with semi-automated identification of glycans by light-heavy peak pairs. Finally, manual validation and relative quantification was performed in Skyline.

Stepped collisional energy MS(All) : an analytical approach for optimal MS/MS acquisition of complex mixture with diverse physicochemical properties

The analysis of complex mixtures is becoming increasingly important in various fields, such as nutrition, medicinal plants and metabolomics. The components contained in such complex mixtures are always characterized with diverse physiochemical properties that pose a major challenge during the optimization of various parameters using liquid chromatography-mass spectrometer (LC-MS). The parameter 'CE energy' that is normally set at a fixed value with a moderate range of CE spread during data-dependent acquisition (DDA) analysis, a prevalent approach for untargeted identification, often fails to generate sufficient MS/MS fragment ions for untargeted identification of components from complex mixtures. Here we developed a simple and generally applicable acquisition method named stepped MS(All) (sMS(All) ) in this study, aiming to obtain optimal MS/MS spectra for identification of chemically diverse compounds from complex mixtures. sMS(All) collects serial MS(All) scans acquired at low CE to gradually ramped-up high CE values in a cycle that conventional DDA scans cannot afford. The resultant MS/MS spectra of each compound were compared and evaluated among serial MS(All) scans, and the optimal spectra were used for identification. An untargeted data analysis strategy was then employed to analyze these optimal MS/MS spectra by searching common diagnostic ions and connecting the diagnostic ion families into a network via bridging components. This sMS(All) -based route enables identification of 71 natural products from a herbal preparation, whereas only 53 out of 71 compounds were identified using the classical DDA approach. Therefore, the sMS(All) -based approach is expected to find its wide applications for characterization of vastly diverse compounds with no priori knowledge from various complex mixtures. Copyright © 2016 John Wiley & Sons, Ltd.

In-depth evaluation of software tools for data-independent acquisition based label-free quantification

Label-free quantification (LFQ) based on data-independent acquisition workflows currently experiences increasing popularity. Several software tools have been recently published or are commercially available. The present study focuses on the evaluation of three different software packages (Progenesis, synapter, and ISOQuant) supporting ion mobility enhanced data-independent acquisition data. In order to benchmark the LFQ performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, Escherichia coli). This model dataset simulates complex biological samples containing large numbers of both unregulated (background) proteins as well as up- and downregulated proteins with exactly known ratios between samples. We determined the number and dynamic range of quantifiable proteins and analyzed the influence of applied algorithms (retention time alignment, clustering, normalization, etc.) on quantification results. Analysis of technical reproducibility revealed median coefficients of variation of reported protein abundances below 5% for MS(E) data for Progenesis and ISOQuant. Regarding accuracy of LFQ, evaluation with synapter and ISOQuant yielded superior results compared to Progenesis. In addition, we discuss reporting formats and user friendliness of the software packages. The data generated in this study have been deposited to the ProteomeXchange Consortium with identifier PXD001240 (http://proteomecentral.proteomexchange.org/dataset/PXD001240).

Assessment of BAK1 activity in different plant receptor-like kinase complexes by quantitative profiling of phosphorylation patterns

Plant receptor-like kinases (RLKs) constitute a large family of receptors coordinating developmental programs with adaptation to environmental stresses including immune defenses. BRI1-ASSOCIATED KINASE 1 (BAK1), a member of the plant RLK family, forms receptor complexes with multiple RLK proteins including BRI1, FLS2, EFR and BIK1 to regulate responses to growth hormones or PAMPs. RLK activation and signal initiation involve protein complex formation and phosphorylation/dephosphorylation between BAK1 and its interacting partners. To gain new insight into how phosphorylation contributes to BAK1-mediated signaling specificity, we first mapped the phosphorylation patterns of BAK1 associated with different RLK partners (BRI1, FLS2, EFR and BIK1). Quantitative phospho-pattern profiling by label-free mass spectrometry revealed that differential phosphorylation patterns of RLK partners resulted from altered BAK1 phosphorylation status. More interestingly, the study of two BAK1 mutants (T450A and C408Y) both showing severe defect in immune defense yet normal growth phenotype suggested that varied phosphorylation patterns of RLK partners by BAK1 could be the molecular basis for selective regulation of multiple BAK1-dependent pathways. Taken together, this phospho-pattern profiling strategy allowed for explicit assessment of BAK1 kinase activity in different RLK complexes, which would facilitate elucidation of BAK1 diverse functions in plant development, defense, and adaptation.

BIOLOGICAL SIGNIFICANCE

BAK1 is a functionally important co-receptor known to interact with different receptor-like kinases (RLKs) to coordinate plant development and immune defenses. Our study first mapped the phosphorylation patterns of BAK1 associated with four RLK partners (BRI1, FLS2, EFR and BIK1), and further revealed that differential phosphorylation patterns of multiple RLK partners resulted from altered BAK1 phosphorylation status. More interestingly, the study of two BAK1 mutants suggested that varied phosphorylation patterns of RLK partners by BAK1 could be the basis for selective regulation of signaling pathways. Taken together, this phospho-pattern profiling strategy allowed for explicit assessment of BAK1 kinase activity in different RLK complexes, which would facilitate elucidation of BAK1 diverse functions in plant development, defense, and adaptation.