bims-mascan 2022-01-30 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2022‒01‒30
thirteen papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh

Metabolic regulation of ferroptosis in the tumor microenvironment.
LC-HRMS based Targeted Metabolomics for High-throughput and Quantitative Analysis of 21 Growth Inhibition Related Metabolites in CHO Cell Fed-Batch Cultures.
The Development of Single-Cell Metabolism and Its Role in Studying Cancer Emergent Properties.
Lipidomic profiles as a tool to search for new biomarkers.
multiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets.
A simple and rapid method for extraction and measurement of circulating sphingolipids using LC-MS/MS: a targeted lipidomic analysis.
Endothelial Lipid Metabolism.
Oxygen Isotope Exchange Reaction for Untargeted LC-MS Analysis.
Lipidomics Analysis of Outer Membrane Vesicles and Elucidation of the Inositol Phosphoceramide Biosynthetic Pathway in Bacteroides thetaiotaomicron.
Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts.
APOE ε2 resilience for Alzheimer's disease is mediated by plasma lipid species: Analysis of three independent cohort studies.
RawHummus: an R Shiny App for Automated Raw Data Quality Control in Metabolomics.
MetaNetwork Enhances Biological Insights from Quantitative Proteomics Differences by Combining Clustering and Enrichment Analyses.

J Biol Chem. 2022 Jan 20. pii: S0021-9258(22)00057-6. [Epub ahead of print] 101617

Metabolic regulation of ferroptosis in the tumor microenvironment.

Nneka E Mbah, Costas A Lyssiotis.

  Ferroptosis is an iron-dependent, non-apoptotic form of regulated cell death triggered by impaired redox and antioxidant machinery and propagated by the accumulation of toxic lipid peroxides. A compendium of experimental studies suggest that ferroptosis is tumor-suppressive. Sensitivity or resistance to ferroptosis can be regulated by cell-autonomous and non-cell-autonomous metabolic mechanisms. This includes a role for ferroptosis that extends beyond the tumor cells themselves, mediated by components of the tumor microenvironment, including T cells and other immune cells. Herein, we review the intrinsic and extrinsic factors that promote the sensitivity of cancer cells to ferroptosis and conclude by describing approaches to harness the full utility of ferroptotic agents as therapeutic options for cancer therapy.

Keywords:  cancer; ferroptosis; metabolism; tumor immunity; tumor microenvironment

DOI:  https://doi.org/10.1016/j.jbc.2022.101617
Biomed Chromatogr. 2022 Jan 26.

LC-HRMS based Targeted Metabolomics for High-throughput and Quantitative Analysis of 21 Growth Inhibition Related Metabolites in CHO Cell Fed-Batch Cultures.

Zijuan Lai, Feroza K Choudhury, Danming Tang, Xiaorong Liang, Brian Dean, Shahram Misaghi, Dewakar Sangaraju.

  Chinese hamster ovary (CHO) cells have been widely used in the biopharmaceutical industry for production of therapeutic proteins. CHO cells in fed-batch cultures produce various amino acid-derived intermediate metabolites. These small molecule metabolic byproducts have proven to be critical to cell growth, culture performance, and more interestingly antibody drug productivity. Herein, we developed a liquid chromatography - high resolution mass spectrometry (LC-HRMS) based targeted metabolomics approach for comprehensive quantification of total 21 growth inhibition related metabolites generated from 14 different amino acids in CHO cell fed-batch cultures. High throughput derivatization procedures, matrix-matched calibration curves, stable isotope-labeled internal standards, and accurate mass Full MS scan were utilized in order to achieve our goal for wide scope of metabolite screening as well as validity and reliability of metabolite quantification. We further present a novel analytical strategy for extending the assay's dynamic range by utilizing naturally occurring isotope M+1 ion as a quantification analogue in the circumstances where the principal M ion is beyond its calibration range. The integrated method was qualified for selectivity, sensitivity, linearity, accuracy, precision, isotope analysis and other analytical aspects to demonstrate assay robustness. We then applied this metabolomics approach to characterize metabolites of interest in a CHO cell based monoclonal antibody (mAb) production process with fed-batch bioreactor culture mode. Absolute quantification combined with multivariate statistical analysis illustrated that our target analytes derived from amino acids, especially from branched-chain amino acids, closely correlated with cell viability and significantly differentiated cellular stages in production process.

Keywords:  Bioprocessing; CHO Cell Culture; LC-HRMS; Quantitative; Targeted Metabolomics

DOI:  https://doi.org/10.1002/bmc.5348
Front Oncol. 2021 ;11 814085

The Development of Single-Cell Metabolism and Its Role in Studying Cancer Emergent Properties.

Dingju Wei, Meng Xu, Zhihua Wang, Jingjing Tong.

  Metabolic reprogramming is one of the hallmarks of malignant tumors, which provides energy and material basis for tumor rapid proliferation, immune escape, as well as extensive invasion and metastasis. Blocking the energy and material supply of tumor cells is one of the strategies to treat tumor, however tumor cell metabolic heterogeneity prevents metabolic-based anti-cancer treatment. Therefore, searching for the key metabolic factors that regulate cell cancerous change and tumor recurrence has become a major challenge. Emerging technology--single-cell metabolomics is different from the traditional metabolomics that obtains average information of a group of cells. Single-cell metabolomics identifies the metabolites of single cells in different states by mass spectrometry, and captures the molecular biological information of the energy and substances synthesized in single cells, which provides more detailed information for tumor treatment metabolic target screening. This review will combine the current research status of tumor cell metabolism with the advantages of single-cell metabolomics technology, and explore the role of single-cell sequencing technology in searching key factors regulating tumor metabolism. The addition of single-cell technology will accelerate the development of metabolism-based anti-cancer strategies, which may greatly improve the prognostic survival rate of cancer patients.

Keywords:  cancer metabolism; metabolic heterogeneity; metabolic reprogramming; single-cell metabolomics; tumor drug resistance

DOI:  https://doi.org/10.3389/fonc.2021.814085
Int J Occup Med Environ Health. 2022 Jan 20. pii: 142527. [Epub ahead of print]

Lipidomic profiles as a tool to search for new biomarkers.

Anna Pisarska, Wojciech Wąsowicz, Jolanta Gromadzińska.

  Lipidomics belongs to the family of the so-called omics domains, which, based on modern chemical technologies, strive to explain the biological principles of the organism's functioning. Main biological functions of lipids include energy storage, the formation of cell membranes, and participation in the transmission of biological signals, and their dysregulation is responsible for the development of pathological states. Thanks to lipid profiling, potential biomarkers for disease diagnosis and prognosis can be identified. This paper discusses selected examples of the use of lipidomic tests in the diagnosis of the kidney, metabolic and neoplastic diseases based on research papers published over the last few years (since 2016). Only works based on the study of human biological material by mass spectrometry methods were taken into account. The examples of lipidomics application presented in this publication are only a few of the possibilities of this technique. As potential possibilities have already been discovered, the next step for the research community is to work on standardization of the approach to lipidomic research and to develop bioinformatics methods that allow efficient processing and analysis of large amounts of data generated in this technique.

Keywords:  biomarkers; cancer; kidney disease; lipidomics; lipids; metabolomic disease

DOI:  https://doi.org/10.13075/ijomeh.1896.01857
J Proteome Res. 2022 Jan 27.

multiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets.

Pauline Hiort, Christoph N Schlaffner, Judith A Steen, Bernhard Y Renard, Hanno Steen.

  In liquid-chromatography-tandem-mass-spectrometry-based proteomics, information about the presence and stoichiometry of protein modifications is not readily available. To overcome this problem, we developed multiFLEX-LF, a computational tool that builds upon FLEXIQuant, which detects modified peptide precursors and quantifies their modification extent by monitoring the differences between observed and expected intensities of the unmodified precursors. multiFLEX-LF relies on robust linear regression to calculate the modification extent of a given precursor relative to a within-study reference. multiFLEX-LF can analyze entire label-free discovery proteomics data sets in a precursor-centric manner without preselecting a protein of interest. To analyze modification dynamics and coregulated modifications, we hierarchically clustered the precursors of all proteins based on their computed relative modification scores. We applied multiFLEX-LF to a data-independent-acquisition-based data set acquired using the anaphase-promoting complex/cyclosome (APC/C) isolated at various time points during mitosis. The clustering of the precursors allows for identifying varying modification dynamics and ordering the modification events. Overall, multiFLEX-LF enables the fast identification of potentially differentially modified peptide precursors and the quantification of their differential modification extent in large data sets using a personal computer. Additionally, multiFLEX-LF can drive the large-scale investigation of the modification dynamics of peptide precursors in time-series and case-control studies. multiFLEX-LF is available at https://gitlab.com/SteenOmicsLab/multiflex-lf.

Keywords:  LC−MS/MS; PTM quantification; bioinformatics tool; label-free quantification; modification stoichiometry; post-translational modification

DOI:  https://doi.org/10.1021/acs.jproteome.1c00669
Anal Bioanal Chem. 2022 Jan 23.

A simple and rapid method for extraction and measurement of circulating sphingolipids using LC-MS/MS: a targeted lipidomic analysis.

Yifan Xu, Haonan Li, Yiqun Han, Teng Wang, Yanwen Wang, Jicheng Gong, Ke Gao, Wu Chen, Weiju Li, Hongyin Zhang, Junxia Wang, Xinghua Qiu, Tong Zhu.

  Sphingolipids are a class of lipids with high structural diversity and biological pleiotropy. Mounting evidence supports a role for sphingolipids in regulating pathophysiology of cardiometabolic diseases, and they have been proposed as potential cardiometabolic biomarkers. Current methods for quantifying sphingolipids require laborious pretreatment and relatively large sample volumes, and cover limited species, hindering their application in epidemiological studies. Herein, we applied a time-, labor-, and sample-saving protocol simply using methanol for plasma sphingolipid extraction. It was compared with classical liquid-liquid extraction methods and showed significant advantages in terms of simplicity, sphingolipid coverage, and sample volume. By coupling the protocol with liquid chromatography using a wide-span mobile phase polarity parameter and tandem mass spectrometry operated in dynamic multiple reaction monitoring mode, 37 sphingolipids from 8 classes (sphingoid base, sphingoid base phosphate, ceramide-1-phosphate, lactosylceramide, hexosylceramide, sphingomyelin, ceramide, and dihydroceramide) were quantified within 16 min, using only 10 μL of human plasma. The current method showed good performance in terms of linearity (R2 > 0.99), intra- and interbatch accuracy (70-123%) and precision (RSD < 12%), matrix effect (91-121%), recovery (96-101%), analyte chemical stability (deviation < 19%), and carryover (< 16%). We successfully applied this method to quantify 33 detectable sphingolipids from 579 plasma samples of an epidemiological study within 10 days. The quantified sphingolipid concentrations were comparable with previous studies. Positive associations of ceramide C22:0/C24:0 and their precursors with homeostasis model assessment of insulin resistance suggested that the synthesis of the ceramides might be involved in insulin resistance. This novel method constitutes a simple and rapid approach to quantify circulating sphingolipids for epidemiological studies using targeted lipidomic analysis, which will help elucidate the sphingolipid-regulated pathways underlying cardiometabolic diseases.

Keywords:  Cardiometabolic diseases; LC–MS/MS; Lipidomics; Plasma; Sphingolipids

DOI:  https://doi.org/10.1007/s00216-021-03853-z
Cold Spring Harb Perspect Med. 2022 Jan 24. pii: a041162. [Epub ahead of print]

Endothelial Lipid Metabolism.

Boa Kim, Zolt Arany.

Endothelial cells (ECs) line all vessels of all vertebrates and are fundamental to organismal metabolism. ECs rely on their metabolism both to transport nutrients in and out of underlying parenchyma, and to support their own cellular activities, including angiogenesis. ECs primarily consume glucose, and much is known of how ECs transport and consume glucose and other carbohydrates. In contrast, how lipids are transported, and the role of lipids in normal EC function, has garnered less attention. We review here recent developments on the role of lipids in endothelial metabolism, with a focus on lipid uptake and transport in quiescent endothelium, and the use of lipid pathways during angiogenesis.

DOI: https://doi.org/10.1101/cshperspect.a041162
J Am Soc Mass Spectrom. 2022 Jan 25.

Oxygen Isotope Exchange Reaction for Untargeted LC-MS Analysis.

Sergey Osipenko, Alexander Zherebker, Lidiia Rumiantseva, Oxana Kovaleva, Evgeny N Nikolaev, Yury Kostyukevich.

  LC-MS is a key technique for the identification of small molecules in complex samples. Accurate mass, retention time, and fragmentation spectra from LC-MS experiments are compared to reference values for pure chemical standards. However, this information is often unavailable or insufficient, leading to an assignment to a list of candidates instead of a single hit; therefore, additional features are desired to filter candidates. One such promising feature is the number of specific functional groups of a molecule that can be counted via derivatization or isotope-exchange techniques. Hydrogen/deuterium exchange (HDX) is the most widespread implementation of isotope exchange for mass spectrometry, while oxygen 16O/18O exchange is not applied as frequently as HDX. Nevertheless, it is known that some functional groups may be selectively exchanged in 18O enriched media. Here, we propose an implementation of 16O/18O isotope exchange to highlight various functional groups. We evaluated the possibility of using the number of exchanged oxygen atoms as a descriptor to filter database candidates in untargeted LC-MS-based workflows. It was shown that 16O/18O exchange provides 62% (median, n = 45) search space reduction for a panel of drug molecules. Additionally, it was demonstrated that studying the fragmentation spectra after 16O/18O can aid in eliminating false positives and, in some cases, help to annotate fragments formed with water traces in the collisional cell.

Keywords:  LC−MS; drugs; identification; isotope exchange; mass spectrometry; metabolomics; stable isotopes

DOI:  https://doi.org/10.1021/jasms.1c00383
Microbiol Spectr. 2022 Jan 26. e0063421

Lipidomics Analysis of Outer Membrane Vesicles and Elucidation of the Inositol Phosphoceramide Biosynthetic Pathway in Bacteroides thetaiotaomicron.

Mariana G Sartorio, Ezequiel Valguarnera, Fong-Fu Hsu, Mario F Feldman.

  Approximately one-third of the human colonic microbiome is formed by bacteria from the genus Bacteroides. These bacteria produce a large amount of uniformly sized outer membrane vesicles (OMVs), which are equipped with hydrolytic enzymes that play a role in the degradation of diet- and host-derived glycans. In this work, we characterize the lipid composition of membranes and OMVs from Bacteroides thetaiotaomicron VPI-5482. Liquid chromatography-mass spectrometry (LC-MS) analysis indicated that OMVs carry sphingolipids, glycerophospholipids, and serine-dipeptide lipids. Sphingolipid species represent more than 50% of the total lipid content of OMVs. The most abundant sphingolipids in OMVs are ethanolamine phosphoceramide (EPC) and inositol phosphoceramide (IPC). Bioinformatics analysis allowed the identification of the BT1522-1526 operon putatively involved in IPC synthesis. Mutagenesis studies revealed that BT1522-1526 is essential for the synthesis of phosphatidylinositol (PI) and IPC, confirming the role of this operon in the biosynthesis of IPC. BT1522-1526 mutant strains lacking IPC produced OMVs that were indistinguishable from the wild-type strain, indicating that IPC sphingolipid species are not involved in OMV biogenesis. Given the known role of sphingolipids in immunomodulation, we suggest that OMVs may act as long-distance vehicles for the delivery of sphingolipids in the human gut. IMPORTANCE Sphingolipids are essential membrane lipid components found in eukaryotes that are also involved in cell signaling processes. Although rare in bacteria, sphingolipids are produced by members of the phylum Bacteroidetes, human gut commensals. Here, we determined that OMVs carry sphingolipids and other lipids of known signaling function. Our results demonstrate that the BT1522-1526 operon is required for IPC biosynthesis in B. thetaiotaomicron.

Keywords:  Bacteroides; OMV; ceramide; sphingolipids

DOI:  https://doi.org/10.1128/spectrum.00634-21
Brain. 2022 Jan 28. pii: awac025. [Epub ahead of print]

Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts.

Stephen A Goutman, Kai Guo, Masha G Savelieff, Adam Patterson, Stacey A Sakowski, Hani Habra, Alla Karnovsky, Junguk Hur, Eva L Feldman.

  Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease lacking effective treatments. This is due, in part, to a complex and incompletely understood pathophysiology. To shed light, we conducted untargeted metabolomics on plasma from two independent cross-sectional ALS cohorts versus control participants to identify recurrent dysregulated metabolic pathways. Untargeted metabolomics was performed on plasma from two ALS cohorts (cohort 1, n = 125; cohort 2, n = 225) and healthy controls (cohort 1, n = 71; cohort 2, n = 104). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon, adjusted logistic regression, and partial least squares-discriminant analysis, while group lasso explored sub-pathway level differences. Adjustment parameters included age, sex, and body mass index. Metabolomics pathway enrichment analysis was performed on metabolites selected by the above methods. Additionally, we conducted a sex sensitivity analysis due to sex imbalance in the cohort 2 control arm. Finally, a data-driven approach, differential network enrichment analysis (DNEA), was performed on a combined dataset to further identify important ALS metabolic pathways. Cohort 2 ALS participants were slightly older than controls (64.0 vs. 62.0 years, p = 0.009). Cohort 2 controls were over-represented in females (68%, p < 0.001). The most concordant cohort 1 and 2 pathways centered heavily on lipid sub-pathways, including complex and signaling lipid species and metabolic intermediates. There were differences in sub-pathways that enriched in ALS females versus males, including in lipid sub-pathways. Finally, DNEA on the merged metabolite dataset of both ALS and control cohorts identified nine significant subnetworks; three centered on lipids and two encompassed a range of sub-pathways. In our analysis, we saw consistent and important shared metabolic sub-pathways in both ALS cohorts, particularly in lipids, further supporting their importance as ALS pathomechanisms and therapeutics targets.

Keywords:  amyotrophic lateral sclerosis (ALS); differential network enrichment analysis; lipidomics; metabolomics; sphingolipids

DOI:  https://doi.org/10.1093/brain/awac025
Alzheimers Dement. 2022 Jan 25.

APOE ε2 resilience for Alzheimer's disease is mediated by plasma lipid species: Analysis of three independent cohort studies.

Tingting Wang, Kevin Huynh, Corey Giles, Natalie A Mellett, Thy Duong, Anh Nguyen, Wei Ling Florence Lim, Alex At Smith, Gavriel Olshansky, Gemma Cadby, Joseph Hung, Jennie Hui, John Beilby, Gerald F Watts, Pratishtha Chatterjee, Ian Martins, Simon M Laws, Ashley I Bush, Christopher C Rowe, Victor L Villemagne, David Ames, Colin L Masters, Kevin Taddei, Vincent Doré, Jürgen Fripp, Matthias Arnold, Gabi Kastenmüller, Kwangsik Nho, Andrew J Saykin, Rebecca Baillie, Xianlin Han, Ralph N Martins, Eric K Moses, Rima Kaddurah-Daouk, Peter J Meikle.

  INTRODUCTION: The apolipoprotein E (APOE) genotype is the strongest genetic risk factor for late-onset Alzheimer's disease. However, its effect on lipid metabolic pathways, and their mediating effect on disease risk, is poorly understood.METHODS: We performed lipidomic analysis on three independent cohorts (the Australian Imaging, Biomarkers and Lifestyle [AIBL] flagship study, n = 1087; the Alzheimer's Disease Neuroimaging Initiative [ADNI] 1 study, n = 819; and the Busselton Health Study [BHS], n = 4384), and we defined associations between APOE ε2 and ε4 and 569 plasma/serum lipid species. Mediation analysis defined the proportion of the treatment effect of the APOE genotype mediated by plasma/serum lipid species.
RESULTS: A total of 237 and 104 lipid species were associated with APOE ε2 and ε4, respectively. Of these 68 (ε2) and 24 (ε4) were associated with prevalent Alzheimer's disease. Individual lipid species or lipidomic models of APOE genotypes mediated up to 30% and 10% of APOE ε2 and ε4 treatment effect, respectively.
DISCUSSION: Plasma lipid species mediate the treatment effect of APOE genotypes on Alzheimer's disease and as such represent a potential therapeutic target.

Keywords:  APOE ε2; APOE ε4; Alzheimer's disease; lipid species; lipidomics; mass spectrometry

DOI:  https://doi.org/10.1002/alz.12538
Bioinformatics. 2022 Jan 26. pii: btac040. [Epub ahead of print]

RawHummus: an R Shiny App for Automated Raw Data Quality Control in Metabolomics.

Yonghui Dong, Yana Kazachkova, Meng Gou, Liat Morgan, Tal Wachsman, Ehud Gazit, Rune Isak Dupont Birkler.

MOTIVATION: Robust and reproducible data is essential to ensure high-quality analytical results and is particularly important for large-scale metabolomics studies where detector sensitivity drifts, retention time and mass accuracy shifts frequently occur. Therefore, raw data needs to be inspected before data processing in order to detect measurement bias and verify system consistency.RESULTS: Here we present RawHummus, an R Shiny app for an automated raw data quality control in metabolomics studies. It produces a comprehensive quality control report, which contains interactive plots and tables, summary statistics and detailed explanations. The versatility and limitations of RawHummus are tested with thirteen metabolomics/lipidomics datasets and one proteomics dataset obtained from five different liquid chromatography mass spectrometry platforms.
AVAILABILITY: RawHummus is released on CRAN repository (https://cran.r-project.org/web/packages/RawHummus), with source code being available on GitHub (https://github.com/YonghuiDong/RawHummus). The web application can be executed locally from the R console using the command "runGui()". Alternatively, it can be freely accessed at https://bcdd.shinyapps.io/RawHummus/.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btac040
J Proteome Res. 2022 Jan 24.

MetaNetwork Enhances Biological Insights from Quantitative Proteomics Differences by Combining Clustering and Enrichment Analyses.

Austin V Carr, Brian L Frey, Mark Scalf, Anthony J Cesnik, Zach Rolfs, Kyndal A Pike, Bing Yang, Mark P Keller, David F Jarrard, Michael R Shortreed, Lloyd M Smith.

  Interpreting proteomics data remains challenging due to the large number of proteins that are quantified by modern mass spectrometry methods. Weighted gene correlation network analysis (WGCNA) can identify groups of biologically related proteins using only protein intensity values by constructing protein correlation networks. However, WGCNA is not widespread in proteomic analyses due to challenges in implementing workflows. To facilitate the adoption of WGCNA by the proteomics field, we created MetaNetwork, an open-source, R-based application to perform sophisticated WGCNA workflows with no coding skill requirements for the end user. We demonstrate MetaNetwork's utility by employing it to identify groups of proteins associated with prostate cancer from a proteomic analysis of tumor and adjacent normal tissue samples. We found a decrease in cytoskeleton-related protein expression, a known hallmark of prostate tumors. We further identified changes in module eigenproteins indicative of dysregulation in protein translation and trafficking pathways. These results demonstrate the value of using MetaNetwork to improve the biological interpretation of quantitative proteomics experiments with 15 or more samples.

Keywords:  informatics; prostate cancer; weighted correlation network analysis

DOI:  https://doi.org/10.1021/acs.jproteome.1c00756