bims-mascan 2022-06-19 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2022‒06‒19
twenty-one papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh

New dawn for cancer cell death: emerging role of lipid metabolism.
Unbiased spatial proteomics with single-cell resolution in tissues.
Optimal analytical strategies for sensitive and quantitative phosphoproteomics using TMT-based multiplexing.
Methods for Quantifying the Metabolic Boundary Fluxes of Cell Cultures in Large Cohorts by High-Resolution Hydrophilic Liquid Chromatography Mass Spectrometry.
Combining metabolic phenotype determination with metabolomics and transcriptional analyses to reveal pathways regulated by hydroxycarboxylic acid receptor 2.
Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas.
Reducing Peptide Sequence Bias in Quantitative Mass Spectrometry Data with Machine Learning.
O-GlcNAc transferase maintains metabolic homeostasis in response to CDK9 inhibition.
An optimization method for untargeted MS-based isotopic tracing investigations of metabolism.
Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling.
Optimizing data-independent acquisition (DIA) spectral library workflows for plasma proteomics studies.
Quantitative analysis of specific androgens in serum and urine samples from male, pre, and postmenopausal subjects using LC-MRM-MS.
Targeting the methionine addiction of cancer.
Rapid immunoprecipitation mass spectrometry of endogenous protein (RIME) to identify chromatin-interactome in prostate cancer cells.
Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data.
Dissecting the relationship between plasma and tissue metabolome in a cohort of women with obesity: Analysis of subcutaneous and visceral adipose, muscle, and liver.
Detection and Quantification of RNA Modifications on RNA-DNA Hybrids Using SID-UPLC-MS/MS.
PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management.
Deletion of Lactate Dehydrogenase-A Impairs Oncogene-Induced Mouse Hepatocellular Carcinoma Development.
A derivatization strategy for comprehensive identification of 2- and 3-hydroxyl fatty acids by LC-MS.
Advances in ionisation techniques for mass spectrometry-based omics research.

Mol Metab. 2022 Jun 14. pii: S2212-8778(22)00098-9. [Epub ahead of print] 101529

New dawn for cancer cell death: emerging role of lipid metabolism.

Chanjuan Zhang, Neng Zhu, Hongfang Li, Yongzhen Gong, Jia Gu, Yaning Shi, Duanfang Liao, Wei Wang, Aiguo Dai, Li Qin.

  BACKGROUND: Resistance to cell death, a protective mechanism for removing damaged cells, is a "Hallmark of Cancer" that is essential for cancer progression. Increasing attention to cancer lipid metabolism has revealed a number of pathways that induce cancer cell death.SCOPE OF REVIEW: We summarize emerging concepts regarding lipid metabolic reprogramming in cancer that is mainly involved in lipid uptake and trafficking, de novo synthesis and esterification, fatty acid synthesis and oxidation, lipogenesis, and lipolysis. During carcinogenesis and progression, continuous metabolic adaptations are co-opted by cancer cells, to maximize their fitness to the ever-changing environmental. Lipid metabolism and the epigenetic modifying enzymes interact in a bidirectional manner which involves regulating cancer cell death. Moreover, lipids in the tumor microenvironment play unique roles beyond metabolic requirements that promote cancer progression. Finally, we posit potential therapeutic strategies targeting lipid metabolism to improve treatment efficacy and survival of cancer patient.
MAJOR CONCLUSIONS: The profound comprehension of past findings, current trends, and future research directions on resistance to cancer cell death will facilitate the development of novel therapeutic strategies targeting the lipid metabolism.

Keywords:  Lipid metabolism; cancer; cell death; therapeutic strategy

DOI:  https://doi.org/10.1016/j.molmet.2022.101529
Mol Cell. 2022 Jun 16. pii: S1097-2765(22)00489-0. [Epub ahead of print]82(12): 2335-2349

Unbiased spatial proteomics with single-cell resolution in tissues.

Andreas Mund, Andreas-David Brunner, Matthias Mann.

Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify the entire complement of proteins in cells or tissues. Here, we review challenges and recent advances in the LC-MS-based analysis of minute protein amounts, down to the level of single cells. Application of this technology revealed that single-cell transcriptomes are dominated by stochastic noise due to the very low number of transcripts per cell, whereas the single-cell proteome appears to be complete. The spatial organization of cells in tissues can be studied by emerging technologies, including multiplexed imaging and spatial transcriptomics, which can now be combined with ultra-sensitive proteomics. Combined with high-content imaging, artificial intelligence and single-cell laser microdissection, MS-based proteomics provides an unbiased molecular readout close to the functional level. Potential applications range from basic biological questions to precision medicine.

DOI: https://doi.org/10.1016/j.molcel.2022.05.022
Proteomics. 2022 Jun 17. e2100245

Optimal analytical strategies for sensitive and quantitative phosphoproteomics using TMT-based multiplexing.

Claire Koenig, Ana Martinez-Val, Giulia Franciosa, Jesper V Olsen.

  In large-scale quantitative mass spectrometry (MS)-based phosphoproteomics, isobaric labeling with tandem mass tags (TMTs) coupled with offline high-pH reversed-phase peptide chromatographic fractionation maximizes depth of coverage. To investigate to what extent limited sample amounts affect sensitivity and dynamic range of the analysis due to sample losses, we benchmarked TMT-based fractionation strategies against single-shot label-free approach with spectral library-free data independent acquisition (LFQ-DIA), for different peptide input per sample. To systematically examine how peptide input amounts influence TMT-fractionation approaches in a phosphoproteomics workflow, we compared two different high-pH reverse-phase fractionation strategies, microflow (MF) and stage-tip fractionation (STF), while scaling the peptide input amount down from 12.5 μg to 1 μg per sample. Our results indicate that, for input amounts higher than 5 μg per sample, TMT labeling, followed by microflow fractionation and phospho-enrichment (MF), achieves the deepest phosphoproteome coverage, even compared to single shot direct-DIA analysis. Conversely, stage-tip fractionation of enriched phosphopeptides (STF) is optimal for lower amounts, below 5 μg/peptide per sample. As a result, we provide a decision tree to help phosphoproteomics users to choose the best workflow as a function of on sample amount. This article is protected by copyright. All rights reserved.

Keywords:  high-pH fractionation; isobaric labeling; phosphoproteomics; scale-down; tandem mass tags

DOI:  https://doi.org/10.1002/pmic.202100245
Anal Chem. 2022 Jun 14.

Methods for Quantifying the Metabolic Boundary Fluxes of Cell Cultures in Large Cohorts by High-Resolution Hydrophilic Liquid Chromatography Mass Spectrometry.

Ryan A Groves, Maryam Mapar, Raied Aburashed, Luis F Ponce, Stephanie L Bishop, Thomas Rydzak, Marija Drikic, Dominique G Bihan, Hallgrimur Benediktsson, Fiona Clement, Daniel B Gregson, Ian A Lewis.

Metabolomics is a mainstream approach for investigating the metabolic underpinnings of complex biological phenomena and is increasingly being applied to large-scale studies involving hundreds or thousands of samples. Although metabolomics methods are robust in smaller-scale studies, they can be challenging to apply to larger cohorts due to the inherent variability of liquid chromatography mass spectrometry (LC-MS). Much of this difficulty results from the time-dependent changes in the LC-MS system, which affects both the qualitative and quantitative performances of the instrument. Herein, we introduce an analytical strategy for addressing this problem in large-scale microbial studies. Our approach quantifies microbial boundary fluxes using two zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) columns that are plumbed to enable offline column equilibration. Using this strategy, we show that over 397 common metabolites can be resolved in 4.5 min per sample and that metabolites can be quantified with a median coefficient of variation of 0.127 across 1100 technical replicates. We illustrate the utility of this strategy via an analysis of 960 strains of Staphylococcus aureus isolated from bloodstream infections. These data capture the diversity of metabolic phenotypes observed in clinical isolates and provide an example of how large-scale investigations can leverage our novel analytical strategy.

DOI: https://doi.org/10.1021/acs.analchem.2c00078
Discov Oncol. 2022 Jun 13. 13(1): 47

Combining metabolic phenotype determination with metabolomics and transcriptional analyses to reveal pathways regulated by hydroxycarboxylic acid receptor 2.

Philipp Rabe, Mareike Gehmlich, Anna Peters, Petra Krumbholz, Anders Nordström, Claudia Stäubert.

  BACKGROUND: The adaptation of cellular metabolism is considered a hallmark of cancer. Oncogenic signaling pathways support tumorigenesis and cancer progression through the induction of certain metabolic phenotypes associated with altered regulation of key metabolic enzymes. Hydroxycarboxylic acid receptor 2 (HCA2) is a G protein-coupled receptor previously shown to act as a tumor suppressor. Here, we aimed to unveil the connection between cellular metabolism and HCA2 in BT-474 cells. Moreover, we intend to clarify how well this metabolic phenotype is reflected in transcriptional changes and metabolite levels as determined by global metabolomics analyses.METHODS: We performed both, siRNA mediated knockdown of HCA2 and stimulation with the HCA2-specific agonist monomethyl fumarate. Seahorse technology was used to determine the role of HCA2 in BT-474 breast cancer cell metabolism and its potential to induce a switch in the metabolic phenotype in the presence of different energy substrates. Changes in the mRNA expression of metabolic enzymes were detected with real-time quantitative PCR (RT-qPCR). Untargeted liquid chromatography-mass spectrometry (LC-MS) metabolic profiling was used to determine changes in metabolite levels.
RESULTS: Knockdown or stimulation of HCA2 induced changes in the metabolic phenotype of BT474 cells dependent on the availability of energy substrates. The presence of HCA2 was associated with increased glycolytic flux with no fatty acids available. This was reflected in the increased mRNA expression of the glycolytic enzymes PFKFB4 and PKM2, which are known to promote the Warburg effect and have been described as prognostic markers in different types of cancer. With exogenous palmitate present, HCA2 caused elevated fatty acid oxidation and likely lipolysis. The increase in lipolysis was also detectable at the transcriptional level of ATGL and the metabolite levels of palmitic and stearic acid.
CONCLUSIONS: We combined metabolic phenotype determination with metabolomics and transcriptional analyses and identified HCA2 as a regulator of glycolytic flux and fatty acid metabolism in BT-474 breast cancer cells. Thus, HCA2, for which agonists are already widely used to treat diseases such as psoriasis or hyperlipidemia, may prove useful as a target in combination cancer therapy.

Keywords:  Cancer metabolism; GPR109A; HCA2; LC-MS; Metabolite profile; Metabolite-sensing GPCR

DOI:  https://doi.org/10.1007/s12672-022-00503-3
Sci Data. 2022 Jun 14. 9(1): 335

Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas.

Mathias Walzer, David García-Seisdedos, Ananth Prakash, Paul Brack, Peter Crowther, Robert L Graham, Nancy George, Suhaib Mohammed, Pablo Moreno, Irene Papatheodorou, Simon J Hubbard, Juan Antonio Vizcaíno.

The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.

DOI: https://doi.org/10.1038/s41597-022-01380-9
J Proteome Res. 2022 Jun 13.

Reducing Peptide Sequence Bias in Quantitative Mass Spectrometry Data with Machine Learning.

Ayse B Dincer, Yang Lu, Devin K Schweppe, Sewoong Oh, William Stafford Noble.

  Quantitative mass spectrometry measurements of peptides necessarily incorporate sequence-specific biases that reflect the behavior of the peptide during enzymatic digestion and liquid chromatography and in a mass spectrometer. These sequence-specific effects impair quantification accuracy, yielding peptide quantities that are systematically under- or overestimated. We provide empirical evidence for the existence of such biases, and we use a deep neural network, called Pepper, to automatically identify and reduce these biases. The model generalizes to new proteins and new runs within a related set of tandem mass spectrometry experiments, and the learned coefficients themselves reflect expected physicochemical properties of the corresponding peptide sequences. The resulting adjusted abundance measurements are more correlated with mRNA-based gene expression measurements than the unadjusted measurements. Pepper is suitable for data generated on a variety of mass spectrometry instruments and can be used with labeled or label-free approaches and with data-independent or data-dependent acquisition.

Keywords:  deep learning; machine learning; neural networks; quantitative mass spectrometry; tandem mass spectrometry

DOI:  https://doi.org/10.1021/acs.jproteome.2c00211
Glycobiology. 2022 Jun 16. pii: cwac038. [Epub ahead of print]

O-GlcNAc transferase maintains metabolic homeostasis in response to CDK9 inhibition.

Aishwarya Gondane, Ninu Poulose, Suzanne Walker, Ian G Mills, Harri M Itkonen.

  Co-targeting of O-GlcNAc transferase (OGT) and the transcriptional kinase CDK9 is toxic to prostate cancer cells. As OGT is an essential glycosyltransferase, identifying an alternative target showing similar effects is of great interest. Here, we used a multiomics approach (transcriptomics, metabolomics and proteomics) to better understand the mechanistic basis of the combinatorial lethality between OGT and CDK9 inhibition. CDK9 inhibition preferentially affected transcription. In contrast, depletion of OGT activity predominantly remodeled the metabolome. Using an unbiased systems biology approach (weighted gene correlation network analysis), we discovered that CDK9 inhibition alters mitochondrial activity / flux, and high OGT activity is essential to maintain mitochondrial respiration when CDK9 activity is depleted. Our metabolite profiling data revealed that pantothenic acid (vitamin B5) is the metabolite that is most robustly induced by both OGT and OGT+CDK9 inhibitor treatments, but not by CDK9 inhibition alone. Finally, supplementing prostate cancer cell lines with vitamin B5 in the presence of CDK9 inhibitor mimics the effects of co-targeting OGT and CDK9.

Keywords:  Cyclin-dependent kinase 9; O-GlcNAc transferase; metabolism; prostate cancer; systems biology

DOI:  https://doi.org/10.1093/glycob/cwac038
Metabolomics. 2022 Jun 16. 18(7): 41

An optimization method for untargeted MS-based isotopic tracing investigations of metabolism.

Noémie Butin, Cécilia Bergès, Jean-Charles Portais, Floriant Bellvert.

  INTRODUCTION: Stable isotope tracer studies are increasingly applied to explore metabolism from the detailed analysis of tracer incorporation into metabolites. Untargeted LC/MS approaches have recently emerged and provide potent methods for expanding the dimension and complexity of the metabolic networks that can be investigated. A number of software tools have been developed to process the highly complex MS data collected in such studies; however, a method to optimize the extraction of valuable isotopic data is lacking.OBJECTIVES: To develop and validate a method to optimize automated data processing for untargeted MS-based isotopic tracing investigations of metabolism.
METHODS: The method is based on the application of a suitable reference material to rationally perform parameter optimization throughout the complete data processing workflow. It was applied in the context of 13C-labelling experiments and with two different software, namely geoRge and X13CMS. It was illustrated with the study of a E. coli mutant impaired for central metabolism.
RESULTS: The optimization methodology provided significant gain in the number and quality of extracted isotopic data, independently of the software considered. Pascal triangle samples are well suited for such purpose since they allow both the identification of analytical issues and optimization of data processing at the same time.
CONCLUSION: The proposed method maximizes the biological value of untargeted MS-based isotopic tracing investigations by revealing the full metabolic information that is encoded in the labelling patterns of metabolites.

Keywords:  Isotope labelling experiments; LC/MS; Parameter optimization; Untargeted analysis

DOI:  https://doi.org/10.1007/s11306-022-01897-5
Methods Mol Biol. 2022 ;2499 1-41

Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling.

Anthony A Iannetta, Leslie M Hicks.

  Post-translational modifications (PTMs) regulate complex biological processes through the modulation of protein activity, stability, and localization. Insights into the specific modification type and localization within a protein sequence can help ascertain functional significance. Computational models are increasingly demonstrated to offer a low-cost, high-throughput method for comprehensive PTM predictions. Algorithms are optimized using existing experimental PTM data, thus accurate prediction performance relies on the creation of robust datasets. Herein, advancements in mass spectrometry-based proteomics technologies to maximize PTM coverage are reviewed. Further, requisite experimental validation approaches for PTM predictions are explored to ensure that follow-up mechanistic studies are focused on accurate modification sites.

Keywords:  Bioinformatics; Bottom-up proteomics; Database searching; Liquid chromatography–tandem mass spectrometry; PTM enrichment; Post-translational modifications

DOI:  https://doi.org/10.1007/978-1-0716-2317-6_1
Proteomics. 2022 Jun 16. e2200125

Optimizing data-independent acquisition (DIA) spectral library workflows for plasma proteomics studies.

Shawn J Rice, Chandra P Belani.

  Traditional data-independent acquisition (DIA) workflows employ off-column fractionation with data-dependent acquisition (DDA) to generate spectral libraries for data extraction. Recent advances have led to the establishment of library-independent approaches for DIA analyses. The selection of a DIA workflow may affect the outcome of plasma proteomics studies. Here we establish a gas-phase fractionation (GPF) workflow to create DIA libraries for DIA Parallel Accumulation and Serial Fragmentation (diaPASEF). This workflow along with three other workflows, fractionated DDA libraries, fractionated DIA libraries and predicted spectra libraries, were evaluated on 20 plasma samples from nonsmall cell lung cancer patients with low or high levels of IL-6. We sought to optimize protein identification and total experiment time. The novel GPF workflow for diaPASEF outperformed the traditional ddaPASEF workflow in the number of identified and quantified proteins. A library-independent workflow based on predicted spectra identified and quantified the most proteins in our experiment at the cost of computational power. Overall, the choice of DIA library workflow seemed to have a limited effect on the overall outcome of a plasma proteomics experiment, but it can affect the number of proteins identified and the total experiment time. This article is protected by copyright. All rights reserved.

Keywords:  GPF; diaPASEF; lung cancer; plasma

DOI:  https://doi.org/10.1002/pmic.202200125
Steroids. 2022 Jun 08. pii: S0039-128X(22)00098-8. [Epub ahead of print]185 109060

Quantitative analysis of specific androgens in serum and urine samples from male, pre, and postmenopausal subjects using LC-MRM-MS.

M Athar Masood, Rafia Khatoon, Timothy D Veenstra.

  Androgens are endogenous hormones that play a crucial role in the paracrine and intracrine hormone system to perform and maintain vital physiological functions. Altered levels of androgens are implicated in many diseases such as sexual dysregulation, breast cancer, prostate cancer, and heart diseases etc. In this manuscript we describe a liquid chromatography-mass spectrometry (LC-MS) method using multiple reaction monitoring (MRM) for quantitatively measuring specific androgens such as dehydroepiandrosterone, testosterone, androsterone sulphate, androstenedione, and dihydrotestosterone in serum and urine samples. Serum acquired from nine different subjects (three pre-menopausal women, three postmenopausal women, and three healthy males) were used to evaluate the developed methods. In the sample preparation methods for serum either protein precipitation or liquid-liquid extraction (LLE) was used while the analysis of urinary androgens used LLE. The extracted androgens were quantitatively measured using LC-MRM-MS to which known amounts of stable isotope labeled standards were added. This manuscript also presents a LC-MRM-MS method mode for the analysis of oxime derivatized androgens potentially to enhance the sensitivity of the assay if required, from urine and venous-drawn serum samples.

Keywords:  Androgens; Isotopic dilution; Liquid chromatography mass spectrometry; Method development; Plasma; Postmenopausal; Premenopausal; Urine

DOI:  https://doi.org/10.1016/j.steroids.2022.109060
Am J Cancer Res. 2022 ;12(5): 2249-2276

Targeting the methionine addiction of cancer.

Joni C Sedillo, Vincent L Cryns.

Methionine is the initiator amino acid for protein synthesis, the methyl source for most nucleotide, chromatin, and protein methylation, and the carbon backbone for various aspects of the cellular antioxidant response and nucleotide biosynthesis. Methionine is provided in the diet and serum methionine levels fluctuate based on dietary methionine content. Within the cell, methionine is recycled from homocysteine via the methionine cycle, which is linked to nutrient status via one-carbon metabolism. Unlike normal cells, many cancer cells, both in vitro and in vivo, show high methionine cycle activity and are dependent on exogenous methionine for continued growth. However, the molecular mechanisms underlying the methionine dependence of diverse malignancies are poorly understood. Methionine deprivation initiates widespread metabolic alterations in cancer cells that enable them to survive despite limited methionine availability, and these adaptive alterations can be specifically targeted to enhance the activity of methionine deprivation, a strategy we have termed "metabolic priming". Chemotherapy-resistant cell populations such as cancer stem cells, which drive treatment-resistance, are also sensitive to methionine deprivation, suggesting dietary methionine restriction may inhibit metastasis and recurrence. Several clinical trials in cancer are investigating methionine restriction in combination with other agents. This review will explore new insights into the mechanisms of methionine dependence in cancer and therapeutic efforts to translate these insights into enhanced clinical activity of methionine restriction in cancer.

Keywords: Methionine; cancer therapy; epigenetics; metabolism; nutrition; one-carbon; oxidative stress
STAR Protoc. 2022 Jun 17. 3(2): 101434

Rapid immunoprecipitation mass spectrometry of endogenous protein (RIME) to identify chromatin-interactome in prostate cancer cells.

Charlotte Scholtes, Catherine R Dufour, Vincent Giguère.

  Rapid immunoprecipitation mass spectrometry of endogenous protein (RIME) is a technique to study protein complexes on chromatin. The protocol below describes specific steps for RIME analysis of the male human-derived prostate cancer cell line LNCaP. This approach can also be applied to other prostate cancer cell lines such as 22Rv1, DU145, and PC3. For other cell types, we recommend optimizing the number of cell culture plates to ensure adequate sample for mass spectrometry protein detection. For complete details on the use and execution of this protocol, please refer to Mohammed et al. (2016) and Dufour et al. (2022).

Keywords:  Cancer; Chromatin immunoprecipitation (ChIP); Mass Spectrometry; Proteomics

DOI:  https://doi.org/10.1016/j.xpro.2022.101434
Nat Protoc. 2022 Jun 17.

Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data.

Zhiqiang Pang, Guangyan Zhou, Jessica Ewald, Le Chang, Orcun Hacariz, Niladri Basu, Jianguo Xia.

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become a workhorse in global metabolomics studies with growing applications across biomedical and environmental sciences. However, outstanding bioinformatics challenges in terms of data processing, statistical analysis and functional interpretation remain critical barriers to the wider adoption of this technology. To help the user community overcome these barriers, we have made major updates to the well-established MetaboAnalyst platform ( www.metaboanalyst.ca ). This protocol extends the previous 2011 Nature Protocol by providing stepwise instructions on how to use MetaboAnalyst 5.0 to: optimize parameters for LC-HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata. Parameter optimization may take ~2 h to complete depending on the server load, and the remaining three stages may be executed in ~60 min.

DOI: https://doi.org/10.1038/s41596-022-00710-w
FASEB J. 2022 Jul;36(7): e22371

Dissecting the relationship between plasma and tissue metabolome in a cohort of women with obesity: Analysis of subcutaneous and visceral adipose, muscle, and liver.

Zhanxuan E Wu, Marlena C Kruger, Garth J S Cooper, Ivana R Sequeira, Anne-Thea McGill, Sally D Poppitt, Karl Fraser.

  Untargeted metabolomics of blood samples has become widely applied to study metabolic alterations underpinning disease and to identify biomarkers. However, understanding the relevance of a blood metabolite marker can be challenging if it is unknown whether it reflects the concentration in relevant tissues. To explore this field, metabolomic and lipidomic profiles of plasma, four sites of adipose tissues (ATs) from peripheral or central depot, two sites of muscle tissue, and liver tissue from a group of nondiabetic women with obesity who were scheduled to undergo bariatric surgery (n = 21) or other upper GI surgery (n = 5), were measured by liquid chromatography coupled with mass spectrometry. Relationships between plasma and tissue profiles were examined using Pearson correlation analysis subject to Benjamini-Hochberg correction. Plasma metabolites and lipids showed the highest number of significantly positive correlations with their corresponding concentrations in liver tissue, including lipid species of ceramide, mono- and di-hexosylceramide, sphingomyelin, phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysophosphatidylethanolamine, dimethyl phosphatidylethanolamine, ether-linked PC, ether-linked PE, free fatty acid, cholesteryl ester, diacylglycerol and triacylglycerol, and polar metabolites linked to several metabolic functions and gut microbial metabolism. Plasma also showed significantly positive correlations with muscle for several phospholipid species and polar metabolites linked to metabolic functions and gut microbial metabolism, and with AT for several triacylglycerol species. In conclusion, plasma metabolomic and lipidomic profiles were reflective more of the liver profile than any of the muscle or AT sites examined in the present study. Our findings highlighted the importance of taking into consideration the metabolomic relationship of various tissues with plasma when postulating plasma metabolites marker to underlying mechanisms occurring in a specific tissue.

Keywords:  biomarkers; clinical metabolomics; liquid chromatography-mass spectrometry; tissue metabolomics

DOI:  https://doi.org/10.1096/fj.202101812R
Methods Mol Biol. 2022 ;2528 127-143

Detection and Quantification of RNA Modifications on RNA-DNA Hybrids Using SID-UPLC-MS/MS.

Marta Starczak, Abdulkadir Abakir, Alexey Ruzov, Daniel Gackowski.

  R-loops are three-stranded nucleic acid structures consisting of an RNA-DNA hybrid and an unpaired strand of nontemplate DNA that represent a major source of genomic instability and are involved in regulation of several important biological processes in eukaryotic cells. A growing body of experimental evidence suggests that RNA moieties of RNA-DNA hybrids may convey RNA modifications influencing various aspects of R-loop biology. Here we present a protocol for quantitative analysis of RNA modifications on RNA-DNA hybrids using stable-isotope dilution ultraperformance liquid chromatography coupled with tandem mass spectrometry (SID-UPLC-MS/MS). Supplemented by other techniques, this method can be instrumental in deciphering the roles of RNA modifications in R-loop metabolism.

Keywords:  N6-methyladenosine; R-loops; RNA modifications; RNA–DNA hybrids; RNase H; Tandem mass spectrometry

DOI:  https://doi.org/10.1007/978-1-0716-2477-7_9
Metabolomics. 2022 Jun 14. 18(6): 40

PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management.

Nils Paulhe, Cécile Canlet, Annelaure Damont, Lindsay Peyriga, Stéphanie Durand, Catherine Deborde, Sandra Alves, Stephane Bernillon, Thierry Berton, Raphael Bir, Alyssa Bouville, Edern Cahoreau, Delphine Centeno, Robin Costantino, Laurent Debrauwer, Alexis Delabrière, Christophe Duperier, Sylvain Emery, Amelie Flandin, Ulli Hohenester, Daniel Jacob, Charlotte Joly, Cyril Jousse, Marie Lagree, Nadia Lamari, Marie Lefebvre, Claire Lopez-Piffet, Bernard Lyan, Mickael Maucourt, Carole Migne, Marie-Francoise Olivier, Estelle Rathahao-Paris, Pierre Petriacq, Julie Pinelli, Léa Roch, Pierrick Roger, Simon Roques, Jean-Claude Tabet, Marie Tremblay-Franco, Mounir Traïkia, Anna Warnet, Vanessa Zhendre, Dominique Rolin, Fabien Jourdan, Etienne Thévenot, Annick Moing, Emilien Jamin, François Fenaille, Christophe Junot, Estelle Pujos-Guillot, Franck Giacomoni.

  INTRODUCTION: Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories.OBJECTIVES: To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management.
METHODS: We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles.
RESULTS: PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases.
CONCLUSION: PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest .

Keywords:  Curation; Database; FAIR; Interoperability; Metabolite identification; Spectral library

DOI:  https://doi.org/10.1007/s11306-022-01899-3
Cell Mol Gastroenterol Hepatol. 2022 Jun 14. pii: S2352-345X(22)00102-3. [Epub ahead of print]

Deletion of Lactate Dehydrogenase-A Impairs Oncogene-Induced Mouse Hepatocellular Carcinoma Development.

Marina Serra, Mario Di Matteo, Jens Serneels, Rajesh Pal, Sarah Trusso Cafarello, Martina Lanza, Carlos Sanchez-Martin, Matthias Evert, Alessandra Castegna, Diego Francesco Calvisi, Massimiliano Mazzone, Amedeo Columbano.

  BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is a multistep process whereby abnormally proliferating cancer cells undergo extensive metabolic reprogramming. Metabolic alterations in hepatocarcinogenesis depend on the activation of specific oncogenes, thus partially explaining HCC heterogeneity. C-Myc oncogene overexpression, frequently observed in human HCCs, leads to a metabolic rewiring toward a Warburg phenotype and production of lactate, resulting in the acidification of the extracellular space, favoring the emergence of an immune-permissive tumor microenvironment. Here, we investigated whether Lactate dehydrogenase alpha (Ldha) genetic ablation interferes with metabolic reprogramming and HCC development in the mouse.METHODS: We characterized the metabolic reprogramming in tumors induced in C57BL/6J mice hydrodynamically co-transfected with c-Myc and h-Ras. Using the same experimental model, we investigated the effect of Ldha inhibition - gained through the inducible and hepatocyte-specific Ldha knockout - on cancer cell metabolic reprogramming, number and size of HCC lesions, and TME alterations.
RESULTS: C-Myc/h-Ras driven tumors display a striking glycolytic metabolism, suggesting a switch to a Warburg phenotype. The tumors also exhibited enhanced pentose phosphate pathway activity, the switch of glutamine to sustain glutathione synthesis instead of Tricarboxylic acid cycle, and the impairment of oxidative phosphorylation. In addition, Ldha abrogation significantly hampered tumor number and size together with an evident inhibition of the Warburg-like metabolic feature and a remarkable increase of CD4+ lymphocytes compared to Ldha wild-type livers.
CONCLUSIONS: These results demonstrate that Ldha deletion significantly impairs mouse HCC development and suggest LDH as a potential target to enhance the efficacy of the current therapeutic options.

Keywords:  C-Myc; HCC; Ldha; Metabolic Reprogramming; TME

DOI:  https://doi.org/10.1016/j.jcmgh.2022.06.003
Anal Chim Acta. 2022 Jul 11. pii: S0003-2670(22)00552-9. [Epub ahead of print]1216 339981

A derivatization strategy for comprehensive identification of 2- and 3-hydroxyl fatty acids by LC-MS.

Zhancheng Zhu, Xiaomei Li, Chunping Tang, Jianhua Shen, Jia Liu, Yang Ye.

  2/3-Hydroxy fatty acids (2/3-OHFAs) are a class of important biological molecules closely related to many diseases, of which the unsaturated ones (2/3-OHUFAs) obtain special recognition for their bioactivities. However, the comprehensive identification of 2/3-OHFAs has been a daunting task due to the similarity of isomeric structures and lack of authentic standards. Herein, we report a strategy for the 2/3-OHUFA identification by using an in-house synthesized derivatization reagent, 4-amino-1,1-dimethylpiperidin-1-ium iodide hydrochloride (ADMI). Through ADMI derivatization, the diagnostic ion m/z 155.1 or 171.1 were produced by liquid chromatography-mass spectrometry (LC-MS) analysis, which could distinguish the 2-OH or 3-OH group, respectively. Then the meta-chloroperoxybenzoic acid (mcpba) was used to resolve the locations of double bonds in 2/3-OHUFAs by the characteristic cleavage of the newly formed epoxides. Thus, the identification of 2/3-OHUFAs was enabled by all these characteristics. Moreover, in order to simplify the whole analysis, an isotope-labeled ADMI was designed to quickly pick out the low-abundant 2/3-OHFAs from complex biological matrices. Finally, a combined derivatization strategy was established to analyze mouse lung tissues with melanoma metastasis. Different long-chain 2-OHFAs and 3-OHFAs were identified, including FA 12:0-3OH, FA 12:1(Δ9)-3OH, FA 14:0-3OH, FA 14:1(Δ5)-3OH, FA 16:0-2/3OH, and FA 18:0-2/3OH. The results showed that the contents of most identified 2/3-OHFAs were lower in the cancer group than in the control group, suggesting the plausible role of 2/3-OHFAs in cancer development. In summary, the new derivatization method provides a powerful analysis strategy for 2/3-OHUFAs, which sheds light on a better understanding of the biological functions of 2/3-OHUFAs.

Keywords:  ADMI derivatization; Mcpba oxidation; Melanoma lung metastasis; Stable isotope labeling; Unsaturated 2/3-hydroxyl fatty acid

DOI:  https://doi.org/10.1016/j.aca.2022.339981
Proteomics. 2022 Jun 16. e2100394

Advances in ionisation techniques for mass spectrometry-based omics research.

Bob Challen, Rainer Cramer.

  Omics analysis by mass spectrometry (MS) is a vast field, with proteomics, metabolomics and lipidomics dominating recent research by exploiting biological mass spectrometry ionisation techniques. Traditional MS ionisation techniques such as electrospray ionisation have limitations in analyte-specific sensitivity, modes of sampling and throughput, leading to many researchers investigating new ionisation methods for omics research. In this review, we examine the current landscape of these new ionisation techniques, divided into the three groups of (electro)spray-based, laser-based, and other miscellaneous ionisation techniques. Due to the wide range of new developments, this review can only provide a starting point for further reading on each ionisation technique, as each have unique benefits, often for specialised applications, which promise beneficial results for different areas in the omics world. This article is protected by copyright. All rights reserved.

Keywords:  DESI; REIMS; acoustic droplet ejection; ionization techniques; liquid MALDI

DOI:  https://doi.org/10.1002/pmic.202100394