bims-mascan 2023-04-30 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2023–04–30
25 papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry-Mass Spectrometry Lipidomics Data.
Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools.
Automated Enrichment of Phosphotyrosine Peptides for High-Throughput Proteomics.
Comparison of TLC, HPLC, and direct-infusion ESI-MS methods for the identification and quantification of diacylglycerol molecular species.
Quantifying Cell Heterogeneity and Subpopulations Using Single Cell Metabolomics.
An automated proximity proteomics pipeline for subcellular proteome and protein interaction mapping.
Acylglycine Analysis by Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS).
MsImpute: Estimation of missing peptide intensity data in label-free quantitative mass spectrometry.
PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.
Quality Control of Targeted Plasma Lipids in a Large-Scale Cohort Study Using Liquid Chromatography-Tandem Mass Spectrometry.
Stable Isotope Tracing Reveals an Altered Fate of Glucose in N-Acetyltransferase 1 Knockout Breast Cancer Cells.
Advances in Metabolic Profiling of Biological Samples.
Effects of Different Storage Conditions on Lipid Stability in Mice Tissue Homogenates.
Branched-chain amino acid catabolism in muscle affects systemic BCAA levels but not insulin resistance.
Organelle interactions compartmentalize hepatic fatty acid trafficking and metabolism.
Metabolomic Profiling of Asparagine Deprivation in Asparagine Synthetase Deficiency Patient-Derived Cells.
A Practical Method for Amino Acid Analysis by LC-MS Using Precolumn Derivatization with Urea.
[Integrated analysis of serum untargeted metabolomics and targeted bile acid metabolomics for identification of diagnostic biomarkers for colorectal cancer].
FGF-21 Conducts a Liver-Brain-Kidney Axis to Promote Renal Cell Carcinoma.
Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis.
Sample Preparation Methods for Targeted Single-Cell Proteomics.
Mass spectrometric approaches in discovering lipid biomarkers for COVID-19 by lipidomics: future challenges and perspectives.
Machine learning estimation of human body time using metabolomic profiling.
Investigation of several chromatographic approaches for untargeted profiling of central carbon metabolism.
Measurement of kynurenine pathway metabolites by tandem mass spectrometry.

Molecules. 2023 Apr 14. pii: 3483. [Epub ahead of print]28(8):

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry-Mass Spectrometry Lipidomics Data.

Dylan H Ross, Jian Guo, Aivett Bilbao, Tao Huan, Richard D Smith, Xueyun Zheng.

  The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature.

Keywords:  ion mobility spectrometry; lipid identification; lipidomics; mass spectrometry; software

DOI:  https://doi.org/10.3390/molecules28083483
Metabolites. 2023 Mar 23. pii: 463. [Epub ahead of print]13(4):

Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools.

Elizabeth J Parker, Kathryn C Billane, Nichola Austen, Anne Cotton, Rachel M George, David Hopkins, Janice A Lake, James K Pitman, James N Prout, Heather J Walker, Alex Williams, Duncan D Cameron.

  Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

Keywords:  bioinformatics; mass-spectrometry; metabolomics; open-source; untargeted

DOI:  https://doi.org/10.3390/metabo13040463
J Proteome Res. 2023 Apr 25.

Automated Enrichment of Phosphotyrosine Peptides for High-Throughput Proteomics.

Alexis Chang, Mario Leutert, Ricard A Rodriguez-Mias, Judit Villén.

  Phosphotyrosine (pY) enrichment is critical for expanding the fundamental and clinical understanding of cellular signaling by mass spectrometry-based proteomics. However, current pY enrichment methods exhibit a high cost per sample and limited reproducibility due to expensive affinity reagents and manual processing. We present rapid-robotic phosphotyrosine proteomics (R2-pY), which uses a magnetic particle processor and pY superbinders or antibodies. R2-pY can handle up to 96 samples in parallel, requires 2 days to go from cell lysate to mass spectrometry injections, and results in global proteomic, phosphoproteomic, and tyrosine-specific phosphoproteomic samples. We benchmark the method on HeLa cells stimulated with pervanadate and serum and report over 4000 unique pY sites from 1 mg of peptide input, strong reproducibility between replicates, and phosphopeptide enrichment efficiencies above 99%. R2-pY extends our previously reported R2-P2 proteomic and global phosphoproteomic sample preparation framework, opening the door to large-scale studies of pY signaling in concert with global proteome and phosphoproteome profiling.

Keywords:  affinity purification; automated; high-throughput; phosphoproteomics; phosphorylation signaling; phosphotyrosine; sample preparation

DOI:  https://doi.org/10.1021/acs.jproteome.2c00850
Methods Enzymol. 2023 ;pii: S0076-6879(22)00392-5. [Epub ahead of print]683 191-224

Comparison of TLC, HPLC, and direct-infusion ESI-MS methods for the identification and quantification of diacylglycerol molecular species.

Prasad Parchuri, Matthew G Garneau, Mary R Roth, Pamela Tamura, Timothy P Durrett, Ruth Welti, Philip D Bates.

  Diacylglycerols (DAGs) are anabolic precursors to membrane lipid and storage triacylglycerol biosynthesis, metabolic intermediates of lipid catabolism, and potent cellular signaling molecules. The different DAG molecular species that accumulate over development or in different tissues reflect the changing aspects of cellular lipid metabolism. Consequently, an accurate determination of DAG molecular species in biological samples is essential to understand various metabolic processes and their diagnostic relevance. However, quantification of DAG molecular species in various biological samples represents a challenging task because of their low abundance, hydrophobicity, and instability. This chapter describes the most common chromatographic (TLC and HPLC) and mass spectrometry (MS) methods used to analyze DAG molecular species. In addition, we directly compared the three methods using DAG obtained by phospholipase C hydrolysis of phosphatidylcholine purified from a Nicotiana benthamiana leaf extract. We conclude that each method identified similar major molecular species, however, the exact levels of those varied mainly due to sensitivity of the technique, differences in sample preparation, and processing. This chapter provides three different methods to analyze DAG molecular species, and the discussion of the benefits and challenges of each technique will aid in choosing the right method for your analysis.

Keywords:  Argentation chromatography; Diacylglycerol; Electrospray ionization mass spectrometry (ESI-MS); Evaporative light scatter detector (ELSD); Flow liquid scintillation counting (LSC); High performance liquid chromatography (HPLC); Lipid; Molecular species; Radioisotope; Thin-layer chromatography (TLC)

DOI:  https://doi.org/10.1016/bs.mie.2022.09.011
Anal Chem. 2023 Apr 28.

Quantifying Cell Heterogeneity and Subpopulations Using Single Cell Metabolomics.

Renmeng Liu, Jiannong Li, Yunpeng Lan, Tra D Nguyen, Y Ann Chen, Zhibo Yang.

Mass spectrometry (MS) has become an indispensable tool for metabolomics studies. However, due to the lack of applicable experimental platforms, suitable algorithm, software, and quantitative analyses of cell heterogeneity and subpopulations, investigating global metabolomics profiling at the single cell level remains challenging. We combined the Single-probe single cell MS (SCMS) experimental technique with a bioinformatics software package, SinCHet-MS (Single Cell Heterogeneity for Mass Spectrometry), to characterize changes of tumor heterogeneity, quantify cell subpopulations, and prioritize the metabolite biomarkers of each subpopulation. As proof of principle studies, two melanoma cancer cell lines, the primary (WM115; with a lower drug resistance) and the metastatic (WM266-4; with a higher drug resistance), were used as models. Our results indicate that after the treatment of the anticancer drug vemurafenib, a new subpopulation emerged in WM115 cells, while the proportion of the existing subpopulations was changed in the WM266-4 cells. In addition, metabolites for each subpopulation can be prioritized. Combining the SCMS experimental technique with a bioinformatics tool, our label-free approach can be applied to quantitatively study cell heterogeneity, prioritize markers for further investigation, and improve the understanding of cell metabolism in human diseases and response to therapy.

DOI: https://doi.org/10.1021/acs.analchem.2c05245
bioRxiv. 2023 Apr 12. pii: 2023.04.11.536358. [Epub ahead of print]

An automated proximity proteomics pipeline for subcellular proteome and protein interaction mapping.

Xiaofang Zhong, Qiongyu Li, Benjamin J Polacco, Trupti Patil, Jeffrey F DiBerto, Rasika Vartak, Jiewei Xu, Aaron Marley, Helene Foussard, Bryan L Roth, Manon Eckhardt, Mark Von Zastrow, Nevan J Krogan, Ruth Hüttenhain.

Proximity labeling (PL) coupled with mass spectrometry has emerged as a powerful technique to map proximal protein interactions in living cells. Large-scale sample processing for proximity proteomics necessitates a high-throughput workflow to reduce hands-on time and increase quantitative reproducibility. To address this issue, we developed a scalable and automated PL pipeline, including generation and characterization of monoclonal cell lines, automated enrichment of biotinylated proteins in a 96-well format, and optimization of the quantitative mass spectrometry (MS) acquisition method. Combined with data-independent acquisition (DIA) MS, our pipeline outperforms manual enrichment and data-dependent acquisition (DDA) MS regarding reproducibility of protein identification and quantification. We apply the pipeline to map subcellular proteomes for endosomes, late endosomes/lysosomes, the Golgi apparatus, and the plasma membrane. Moreover, using serotonin receptor (5HT 2A ) as a model, we investigated agonist-induced dynamics in protein-protein interactions. Importantly, the approach presented here is universally applicable for PL proteomics using all biotinylation-based PL enzymes, increasing both throughput and reproducibility of standard protocols.

DOI: https://doi.org/10.1101/2023.04.11.536358
Curr Protoc. 2023 Apr;3(4): e758

Acylglycine Analysis by Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS).

Judith A Hobert, Rebecca Guymon, Tatiana Yuzyuk, Marzia Pasquali.

  Quantitative analysis of urine acylglycines has shown to be a highly sensitive and specific method with proven clinical utility for the diagnosis of several inherited metabolic disorders including: medium chain acyl-CoA dehydrogenase deficiency, multiple acyl-CoA dehydrogenase deficiency, short chain acyl-CoA dehydrogenase deficiency, 3-methylcrotonyl-CoA carboxylase deficiency, 2-methylbutyryl-CoA dehydrogenase deficiency, isovaleric acidemia, propionic academia, and isobutyryl-CoA dehydrogenase deficiency. Here, a method that is currently performed using ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) is described. © 2023 Wiley Periodicals LLC. Basic Protocol: Urinary acylglycine analysis by UPLC-MS/MS Support Protocol 1: Quality control preparation Support Protocol 2: Internal standard (ISTD) preparation Support Protocol 3: Standard (STD)/calibrator preparation.

Keywords:  UPLC-MS/MS; acylglycine; fatty acid β-oxidation; glycine N-acylase; organic acidemia

DOI:  https://doi.org/10.1002/cpz1.758
Mol Cell Proteomics. 2023 Apr 25. pii: S1535-9476(23)00068-3. [Epub ahead of print] 100558

MsImpute: Estimation of missing peptide intensity data in label-free quantitative mass spectrometry.

Soroor Hediyeh-Zadeh, Andrew I Webb, Melissa J Davis.

Mass spectrometry (MS) enables high throughput identification and quantification of proteins in complex biological samples and can provide insights into the global function of biological systems. Label-free quantification is cost effective and suitable for the analysis of human samples. Despite rapid developments in label-free data acquisition workflows, the number of proteins quantified across samples can be limited by technical and biological variability. This variation can result in missing values which can in turn challenge downstream data analysis tasks. General-purpose or gene expression-specific imputation algorithms are widely used to improve data completeness. Here, we propose an imputation algorithm designated for label-free mass spectrometry data that is aware of the type of missingness affecting data. On published datasets acquired by data-dependent and data-independent acquisition workflows with variable degrees of biological complexity, we demonstrate that the proposed missing value estimation procedure by barycenter computation competes closely with the state-of-the-art imputation algorithms in differential abundance tasks, while outperforming them in the accuracy of variance estimates of the peptide abundance measurements, and better controls the false discovery rate in label-free mass spectrometry experiments. The barycenter estimation procedure is implemented in the msImpute software package and is available from the Bioconductor repository.

DOI: https://doi.org/10.1016/j.mcpro.2023.100558
Nat Commun. 2023 Apr 28. 14(1): 2461

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.

Aivett Bilbao, Nathalie Munoz, Joonhoon Kim, Daniel J Orton, Yuqian Gao, Kunal Poorey, Kyle R Pomraning, Karl Weitz, Meagan Burnet, Carrie D Nicora, Rosemarie Wilton, Shuang Deng, Ziyu Dai, Ethan Oksen, Aaron Gee, Rick A Fasani, Anya Tsalenko, Deepti Tanjore, James Gardner, Richard D Smith, Joshua K Michener, John M Gladden, Erin S Baker, Christopher J Petzold, Young-Mo Kim, Alex Apffel, Jon K Magnuson, Kristin E Burnum-Johnson.

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

DOI: https://doi.org/10.1038/s41467-023-37031-9
Metabolites. 2023 Apr 13. pii: 558. [Epub ahead of print]13(4):

Quality Control of Targeted Plasma Lipids in a Large-Scale Cohort Study Using Liquid Chromatography-Tandem Mass Spectrometry.

Akiyoshi Hirayama, Takamasa Ishikawa, Haruka Takahashi, Sanae Yamanaka, Satsuki Ikeda, Aya Hirata, Sei Harada, Masahiro Sugimoto, Tomoyoshi Soga, Masaru Tomita, Toru Takebayashi.

  High-throughput metabolomics has enabled the development of large-scale cohort studies. Long-term studies require multiple batch-based measurements, which require sophisticated quality control (QC) to eliminate unexpected bias to obtain biologically meaningful quantified metabolomic profiles. Liquid chromatography-mass spectrometry was used to analyze 10,833 samples in 279 batch measurements. The quantified profile included 147 lipids including acylcarnitine, fatty acids, glucosylceramide, lactosylceramide, lysophosphatidic acid, and progesterone. Each batch included 40 samples, and 5 QC samples were measured for 10 samples of each. The quantified data from the QC samples were used to normalize the quantified profiles of the sample data. The intra- and inter-batch median coefficients of variation (CV) among the 147 lipids were 44.3% and 20.8%, respectively. After normalization, the CV values decreased by 42.0% and 14.7%, respectively. The effect of this normalization on the subsequent analyses was also evaluated. The demonstrated analyses will contribute to obtaining unbiased, quantified data for large-scale metabolomics.

Keywords:  cohort study; lipid; liquid chromatography–mass spectrometry; quality control; targeted lipidomics

DOI:  https://doi.org/10.3390/metabo13040558
Genes (Basel). 2023 Mar 31. pii: 843. [Epub ahead of print]14(4):

Stable Isotope Tracing Reveals an Altered Fate of Glucose in N-Acetyltransferase 1 Knockout Breast Cancer Cells.

James T F Wise, Xinmin Yin, Xipeng Ma, Xiang Zhang, David W Hein.

  Breast cancer is one of the leading causes of cancer death. Recent studies found that arylamine N-acetyltransferase 1 (NAT1) is frequently upregulated in breast cancer, further suggesting NAT1 could be a potential therapeutic target for breast cancer. Previous publications have established that NAT1 knockout (KO) in breast cancer cell lines leads to growth reduction both in vitro and in vivo and metabolic changes. These reports suggest that NAT1 contributes to the energy metabolism of breast cancer cells. Proteomic analysis and non-targeted metabolomics suggested that NAT1 KO may change the fate of glucose as it relates to the TCA/KREB cycle of the mitochondria of breast cancer cells. In this current study, we used [U-13C]-glucose stable isotope resolved metabolomics to determine the effect of NAT1 KO on the metabolic profile of MDA-MB-231 breast cancer cells. We incubated breast cancer cells (MDA-MB-231 cells) and NAT1 Crispr KO cells (KO#2 and KO#5) with [U-13C]-glucose for 24 h. Tracer incubation polar metabolites from the cells were extracted and analyzed by 2DLC-MS, and metabolite differences were compared between the parental and NAT1 KO cells. Differences consistent between the two KO cells were considered changes due to the loss of NAT1. The data revealed decreases in the 13C enrichment of TCA/Krebs cycle intermediates in NAT1 KO cells compared to the MDA-MB-231 cells. Specifically, 13C-labeled citrate, isocitrate, a-ketoglutarate, fumarate, and malate were all decreased in NAT1 KO cells. We also detected increased 13C-labeled L-lactate levels in the NAT1 KO cells and decreased 13C enrichment in some nucleotides. Pathway analysis showed that arginine biosynthesis, alanine, aspartate and glutamate metabolism, and the TCA cycle were most affected. These data provide additional evidence supporting the impacts of NAT1 knockout on cellular energy metabolism. The data suggest that NAT1 expression is important for the proper functioning of mitochondria and the flux of glucose through the TCA/Krebs cycle in breast cancer cells. The metabolism changes in the fate of glucose in NAT1 KO breast cancer cells offer more insight into the role of NAT1 in energy metabolism and the growth of breast cancer cells. These data provide additional evidence that NAT1 may be a useful therapeutic target for breast cancer.

Keywords:  N-acetyltransferase 1; breast cancer; breast cancer cells; metabolism; mitochondrial metabolism; stable isotope tracing

DOI:  https://doi.org/10.3390/genes14040843
Metabolites. 2023 Apr 09. pii: 534. [Epub ahead of print]13(4):

Advances in Metabolic Profiling of Biological Samples.

Joana Pinto.

Metabolomics constitutes a promising approach to clinical diagnostics, but its practical implementation in clinical settings is hindered by the requirement for rapid and efficient analytical methods [...].

DOI: https://doi.org/10.3390/metabo13040534
Metabolites. 2023 Mar 31. pii: 504. [Epub ahead of print]13(4):

Effects of Different Storage Conditions on Lipid Stability in Mice Tissue Homogenates.

Erika Dorochow, Robert Gurke, Samuel Rischke, Gerd Geisslinger, Lisa Hahnefeld.

  Lipids are biomolecules involved in numerous (patho-)physiological processes and their elucidation in tissue samples is of particular interest. However, tissue analysis goes hand in hand with many challenges and the influence of pre-analytical factors can intensively change lipid concentrations ex vivo, compromising the results of the whole research project. Here, we study the influence of pre-analytical factors on lipid profiles during the processing of homogenized tissues. Homogenates from four different mice tissues (liver, kidney, heart, spleen) were stored at room temperature as well as in ice water for up to 120 min and analyzed via ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). Lipid class ratios were calculated since their suitability as indicators for sample stability has been previously illustrated. Only approx. 40% of lipid class ratios were unchanged after 35 min, which was further reduced to 25% after 120 min during storage at room temperature. In contrast, lipids in tissue homogenates were generally stable when samples were kept in ice water, as more than 90% of investigated lipid class ratios remained unchanged after 35 min. Ultimately, swift processing of tissue homogenates under cooled conditions represents a viable option for lipid analysis and pre-analytical factors require more attention to achieve reliable results.

Keywords:  homogenization; lipidomics; lipids; lipolytic ratios; liquid chromatography; mass spectrometry; pre-analytics; stability; tissue

DOI:  https://doi.org/10.3390/metabo13040504
Nat Metab. 2023 Apr;5(4): 589-606

Branched-chain amino acid catabolism in muscle affects systemic BCAA levels but not insulin resistance.

Megan C Blair, Michael D Neinast, Cholsoon Jang, Qingwei Chu, Jae Woo Jung, Jessie Axsom, Marc R Bornstein, Chelsea Thorsheim, Kristina Li, Atsushi Hoshino, Steven Yang, Rachel J Roth Flach, Bei B Zhang, Joshua D Rabinowitz, Zoltan Arany.

Elevated levels of plasma branched-chain amino acids (BCAAs) have been associated with insulin resistance and type 2 diabetes since the 1960s. Pharmacological activation of branched-chain α-ketoacid dehydrogenase (BCKDH), the rate-limiting enzyme of BCAA oxidation, lowers plasma BCAAs and improves insulin sensitivity. Here we show that modulation of BCKDH in skeletal muscle, but not liver, affects fasting plasma BCAAs in male mice. However, despite lowering BCAAs, increased BCAA oxidation in skeletal muscle does not improve insulin sensitivity. Our data indicate that skeletal muscle controls plasma BCAAs, that lowering fasting plasma BCAAs is insufficient to improve insulin sensitivity and that neither skeletal muscle nor liver account for the improved insulin sensitivity seen with pharmacological activation of BCKDH. These findings suggest potential concerted contributions of multiple tissues in the modulation of BCAA metabolism to alter insulin sensitivity.

DOI: https://doi.org/10.1038/s42255-023-00794-y
Cell Rep. 2023 Apr 26. pii: S2211-1247(23)00446-1. [Epub ahead of print]42(5): 112435

Organelle interactions compartmentalize hepatic fatty acid trafficking and metabolism.

Charles P Najt, Santosh Adhikari, Timothy D Heden, Wenqi Cui, Erica R Gansemer, Adam J Rauckhorst, Todd W Markowski, LeeAnn Higgins, Evan W Kerr, Matthew D Boyum, Jonas Alvarez, Sophia Brunko, Dushyant Mehra, Elias M Puchner, Eric B Taylor, Douglas G Mashek.

  Organelle interactions play a significant role in compartmentalizing metabolism and signaling. Lipid droplets (LDs) interact with numerous organelles, including mitochondria, which is largely assumed to facilitate lipid transfer and catabolism. However, quantitative proteomics of hepatic peridroplet mitochondria (PDM) and cytosolic mitochondria (CM) reveals that CM are enriched in proteins comprising various oxidative metabolism pathways, whereas PDM are enriched in proteins involved in lipid anabolism. Isotope tracing and super-resolution imaging confirms that fatty acids (FAs) are selectively trafficked to and oxidized in CM during fasting. In contrast, PDM facilitate FA esterification and LD expansion in nutrient-replete medium. Additionally, mitochondrion-associated membranes (MAM) around PDM and CM differ in their proteomes and ability to support distinct lipid metabolic pathways. We conclude that CM and CM-MAM support lipid catabolic pathways, whereas PDM and PDM-MAM allow hepatocytes to efficiently store excess lipids in LDs to prevent lipotoxicity.

Keywords:  CP: Metabolism; MAM; cytosolic mitochondria; fatty acids; lipid anabolism; lipid catabolism; lipid droplets; organelle interactions; peridroplet mitochondria; perilipin 5; single-molecule localization microscopy

DOI:  https://doi.org/10.1016/j.celrep.2023.112435
Nutrients. 2023 Apr 18. pii: 1938. [Epub ahead of print]15(8):

Metabolomic Profiling of Asparagine Deprivation in Asparagine Synthetase Deficiency Patient-Derived Cells.

Mario C Chang, Stephen J Staklinski, Vinay R Malut, Geraldine L Pierre, Michael S Kilberg, Matthew E Merritt.

  The natural amino acid asparagine (Asn) is required by cells to sustain function and proliferation. Healthy cells can synthesize Asn through asparagine synthetase (ASNS) activity, whereas specific cancer and genetically diseased cells are forced to obtain asparagine from the extracellular environment. ASNS catalyzes the ATP-dependent synthesis of Asn from aspartate by consuming glutamine as a nitrogen source. Asparagine Synthetase Deficiency (ASNSD) is a disease that results from biallelic mutations in the ASNS gene and presents with congenital microcephaly, intractable seizures, and progressive brain atrophy. ASNSD often leads to premature death. Although clinical and cellular studies have reported that Asn deprivation contributes to the disease symptoms, the global metabolic effects of Asn deprivation on ASNSD-derived cells have not been studied. We analyzed two previously characterized cell culture models, lymphoblastoids and fibroblasts, each carrying unique ASNS mutations from families with ASNSD. Metabolomics analysis demonstrated that Asn deprivation in ASNS-deficient cells led to disruptions across a wide range of metabolites. Moreover, we observed significant decrements in TCA cycle intermediates and anaplerotic substrates in ASNS-deficient cells challenged with Asn deprivation. We have identified pantothenate, phenylalanine, and aspartate as possible biomarkers of Asn deprivation in normal and ASNSD-derived cells. This work implies the possibility of a novel ASNSD diagnostic via targeted biomarker analysis of a blood draw.

Keywords:  ASNSD; GC-MS; amino acids; asparagine; biogenic; cancer metabolism; deprivation; high-throughput; metabolomics; translational

DOI:  https://doi.org/10.3390/nu15081938
Int J Mol Sci. 2023 Apr 15. pii: 7332. [Epub ahead of print]24(8):

A Practical Method for Amino Acid Analysis by LC-MS Using Precolumn Derivatization with Urea.

Runjin Zhao, Biling Huang, Gang Lu, Songsen Fu, Jianxi Ying, Yufen Zhao.

  Amino acid (AA) analysis is important in biochemistry, food science, and clinical medicine. However, due to intrinsic limitations, AAs usually require derivatization to improve their separation and determination. Here, we present a liquid chromatography-mass spectrometry (LC-MS) method for the derivatization of AAs using the simple agent urea. The reactions proceed quantitatively under a wide range of conditions without any pretreatment steps. Urea-derivatized products (carbamoyl amino acids) of 20 AAs exhibit better separation on reversed-phase columns and increased response in a UV detector compared to underivatized ones. We applied this approach to AA analysis in complex samples using a cell culture media as a model, and it showed potential for the determination of oligopeptides. This fast, simple, and inexpensive method should be useful for AA analysis in complex samples.

Keywords:  LC-MS; amino acids; derivatization; quantitative analysis; urea

DOI:  https://doi.org/10.3390/ijms24087332
Nan Fang Yi Ke Da Xue Xue Bao. 2023 Mar 20. 43(3): 443-453

[Integrated analysis of serum untargeted metabolomics and targeted bile acid metabolomics for identification of diagnostic biomarkers for colorectal cancer].

X Wang, Y Zhu, H Zhou, Z Huang, H Chen, J Zhang, S Yang, G Chen, Q Zhang.

   OBJECTIVE: To identify potential diagnostic biomarkers of colorectal cancer (CRC) using serum metabolomic technology for minimally invasive and efficient screening for CRC.
METHODS: Serum samples from 79 healthy individuals and 82 CRC patients were analyzed by metabolomics using ultra-high-performance liquid chromatography-tandem highresolution mass spectrometry (UHPLC-HRMS). The differential metabolites between the two groups were analyzed using principal component analysis and orthogonal partial least squares discriminant analysis (OPLS-DA). Receiver operating characteristic curve (ROC) analysis was performed to identify the differential metabolites with good diagnostic performance (AUC>0.80) for CRC, and targeted bile acid metabolomics was used to verify the selected bile acids as biomarkers.
RESULTS: Serum metabolic profiles differed significantly between the healthy individuals and CRC patients, and a total of 82 differential metabolites (mostly fatty acids and glycerophospholipids) were selected. ROC analysis identified 10 differential metabolites, including adenine, bilirubin, ACar 12:0, ACar 10:1, ACar 9:0, PC 18:2e, deoxycholic acid, chenodeoxycholic acid, ACar 14:1 and palmitoylcarnitine. One of these metabolites was significantly up-regulated and 9 were down-regulated in the serum of CRC patients (P < 0.05). Multivariate ROC analysis with support vector machine algorithm showed that the biomarker panel consisting of 7 differential metabolites had an AUC of 0.94 for CRC diagnosis. The results of targeted bile acid metabolomics were consistent with those of untargeted metabolomics. The serum levels of deoxycholic acid and chenodeoxycholic acid were significantly down-regulated in patients with CRC as compared with the healthy individuals (P < 0.05).
CONCLUSION: Metabolic disorders of fatty acids and glycerophospholipids are closely related wigh tumorigenesis of CRC. Ten differential metabolites show good performance for CRC diagnosis, and the panel consisting 7 of these metabolites has important diagnostic value for CRC. Deoxycholic acid and chenodeoxycholic acid may serve as potential diagnostic biomarkers of CRC.

Keywords:  biomarkers; colorectal cancer; serum untargeted metabolomics; targeted bile acid metabolomics

DOI:  https://doi.org/10.12122/j.issn.1673-4254.2023.03.15
bioRxiv. 2023 Apr 13. pii: 2023.04.12.536558. [Epub ahead of print]

FGF-21 Conducts a Liver-Brain-Kidney Axis to Promote Renal Cell Carcinoma.

Zongyu Li, Xinyi Zhang, Wanling Zhu, Cuiling Zhang, Katherine Sadak, Alexandra A Halberstam, Jason R Brown, Curtis J Perry, Azia Bunn, David A Braun, Adebowale Adeniran, Sangwon Lee, Andrew Wang, Rachel J Perry.

Metabolic homeostasis is one of the most exquisitely tuned systems in mammalian physiology. Metabolic homeostasis requires multiple redundant systems to cooperate to maintain blood glucose concentrations in a narrow range, despite a multitude of physiological and pathophysiological pressures. Cancer is one of the canonical pathophysiological settings in which metabolism plays a key role. In this study, we utilized REnal Gluconeogenesis Analytical Leads (REGAL), a liquid chromatography-mass spectrometry/mass spectrometry-based stable isotope tracer method that we developed to show that in conditions of metabolic stress, the fasting hepatokine fibroblast growth factor-21 (FGF-21) 1, 2 coordinates a liver-brain-kidney axis to promote renal gluconeogenesis. FGF-21 promotes renal gluconeogenesis by enhancing β2 adrenergic receptor (Adrb2)-driven, adipose triglyceride lipase (ATGL)-mediated intrarenal lipolysis. Further, we show that this liver-brain-kidney axis promotes gluconeogenesis in the renal parenchyma in mice and humans with renal cell carcinoma (RCC). This increased gluconeogenesis is, in turn, associated with accelerated RCC progression. We identify Adrb2 blockade as a new class of therapy for RCC in mice, with confirmatory data in human patients. In summary, these data reveal a new metabolic function of FGF-21 in driving renal gluconeogenesis, and demonstrate that inhibition of renal gluconeogenesis by FGF-21 antagonism deserves attention as a new therapeutic approach to RCC.

DOI: https://doi.org/10.1101/2023.04.12.536558
Pharmaceutics. 2023 Mar 31. pii: 1121. [Epub ahead of print]15(4):

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis.

Atiqah Zaid, Norfarizah Hanim Hassan, Philip J Marriott, Yong Foo Wong.

  Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

Keywords:  COVID-19; GC×GC; biomarkers; cancer; drug discovery and development; mass spectrometry; metabolomics; psychiatric disorder; tuberculosis

DOI:  https://doi.org/10.3390/pharmaceutics15041121
J Proteome Res. 2023 Apr 24.

Sample Preparation Methods for Targeted Single-Cell Proteomics.

Azad Eshghi, Xiaofeng Xie, Darryl Hardie, Michael X Chen, Fabiana Izaguirre, Rachael Newman, Ying Zhu, Ryan T Kelly, David R Goodlett.

  We compared three cell isolation and two proteomic sample preparation methods for single-cell and near-single-cell analysis. Whole blood was used to quantify hemoglobin (Hb) and glycated-Hb (gly-Hb) in erythrocytes using targeted mass spectrometry and stable isotope-labeled standard peptides. Each method differed in cell isolation and sample preparation as follows: 1) FACS and automated preparation in one-pot for trace samples (autoPOTS); 2) limited dilution via microscopy and a novel rapid one-pot sample preparation method that circumvented the need for the solid-phase extraction, low-volume liquid handling instrumentation and humidified incubation chamber; and 3) CellenONE-based cell isolation and the same one-pot sample preparation method used for limited dilution. Only the CellenONE device routinely isolated single-cells from which Hb was measured to be 540-660 amol per red blood cell (RBC), which was comparable to the calculated SI reference range for mean corpuscular hemoglobin (390-540 amol/RBC). FACSAria sorter and limited dilution could routinely isolate single-digit cell numbers, to reliably quantify CMV-Hb heterogeneity. Finally, we observed that repeated measures, using 5-25 RBCs obtained from N = 10 blood donors, could be used as an alternative and more efficient strategy than single RBC analysis to measure protein heterogeneity, which revealed multimodal distribution, unique for each individual.

Keywords:  HbA1c; Single-cell proteomics; carboxymethyl hemoglobin; glycated hemoglobin; hemoglobin; one-pot; quantitative; red blood cell; targeted

DOI:  https://doi.org/10.1021/acs.jproteome.2c00429
Mass Spectrom Rev. 2023 Apr 27.

Mass spectrometric approaches in discovering lipid biomarkers for COVID-19 by lipidomics: future challenges and perspectives.

Siddabasave Gowda B Gowda, Chandra Shekhar, Divyavani Gowda, Yifan Chen, Hitoshi Chiba, Shu-Ping Hui.

  Coronavirus disease 2019 (COVID-19) has emerged as a global health threat and has rapidly spread worldwide. Significant changes in the lipid profile before and after COVID-19 confirmed the significance of lipid metabolism in regulating the response to viral infection. Therefore, understanding the role of lipid metabolism may facilitate the development of new therapeutics for COVID-19. Owing to their high sensitivity and accuracy, mass spectrometry (MS)-based methods are widely used for rapidly identifying and quantifying of thousands of lipid species present in a small amount of sample. To enhance the capabilities of MS for the qualitative and quantitative analysis of lipids, different platforms have been combined to cover a wide range of lipidomes with high sensitivity, specificity, and accuracy. Currently, MS-based technologies are being established as efficient methods for discovering potential diagnostic biomarkers for COVID-19 and related diseases. As the lipidome of the host cell is drastically affected by the viral replication process, investigating lipid profile alterations in patients with COVID-19 and targeting lipid metabolism pathways are considered to be crucial steps in host-directed drug targeting to develop better therapeutic strategies. This review summarizes various MS-based strategies that have been developed for lipidomic analyses and biomarker discoveries to combat COVID-19 by integrating various other potential approaches using different human samples. Furthermore, this review discusses the challenges in using MS technologies and future perspectives in terms of drug discovery and diagnosis of COVID-19. This article is protected by copyright. All rights reserved.

Keywords:  COVID-19; biomarker; diagnosis; lipid; mass spectrometry

DOI:  https://doi.org/10.1002/mas.21848
Proc Natl Acad Sci U S A. 2023 May 02. 120(18): e2212685120

Machine learning estimation of human body time using metabolomic profiling.

Tom Woelders, Victoria L Revell, Benita Middleton, Katrin Ackermann, Manfred Kayser, Florence I Raynaud, Debra J Skene, Roelof A Hut.

  Circadian rhythms influence physiology, metabolism, and molecular processes in the human body. Estimation of individual body time (circadian phase) is therefore highly relevant for individual optimization of behavior (sleep, meals, sports), diagnostic sampling, medical treatment, and for treatment of circadian rhythm disorders. Here, we provide a partial least squares regression (PLSR) machine learning approach that uses plasma-derived metabolomics data in one or more samples to estimate dim light melatonin onset (DLMO) as a proxy for circadian phase of the human body. For this purpose, our protocol was aimed to stay close to real-life conditions. We found that a metabolomics approach optimized for either women or men under entrained conditions performed equally well or better than existing approaches using more labor-intensive RNA sequencing-based methods. Although estimation of circadian body time using blood-targeted metabolomics requires further validation in shift work and other real-world conditions, it currently may offer a robust, feasible technique with relatively high accuracy to aid personalized optimization of behavior and clinical treatment after appropriate validation in patient populations.

Keywords:  circadian phase; dim light melatonin onset; human body time; machine learning; metabolomics

DOI:  https://doi.org/10.1073/pnas.2212685120
J Chromatogr A. 2023 Apr 14. pii: S0021-9673(23)00220-0. [Epub ahead of print]1697 463994

Investigation of several chromatographic approaches for untargeted profiling of central carbon metabolism.

Sergey Girel, Davy Guillarme, Szabolcs Fekete, Serge Rudaz, Víctor González-Ruiz.

  Monitoring the central carbon metabolism (CCM) network using liquid chromatography/mass spectrometry (LC-MS) analysis is hampered by the diverse chemical nature of its analytes, which are extremely difficult to analyze using single chromatographic conditions. Furthermore, CCM-related compounds present non-specific adsorption on metal surfaces, causing detrimental chromatographic effects and sensitivity loss. In this study, polar reversed-phase, mixed-mode (MMC), and zwitterionic hydrophilic interaction chromatography (HILIC) featuring low-adsorption hardware were investigated towards untargeted analysis of biological samples with a focus on energy metabolism-related analytes. Best results were achieved with sulfoalkylbetaine HILIC with different supports, where polymeric option featured the highest coverage and inert hybrid silica facilitated best throughput and kinetic performance at a cost of less selectivity for small carboxylic acids. MMC demonstrated excellent performance for strongly anionic analytes such as multiresidue phosphates. The obtained experimental data also suggested that an additional hydrophilic modulation might be necessary to facilitate better resolution of carboxylic acids in zHILIC mode, as found during the application of the developed method to study the effect of two different mutations on the energy metabolism of S. aureus.

Keywords:  Central carbon metabolism; Low adsorption column hardware; Mixed-mode chromatography; Untargeted metabolomics; Zwitterionic HILIC

DOI:  https://doi.org/10.1016/j.chroma.2023.463994
J Mass Spectrom Adv Clin Lab. 2023 Apr;28 114-121

Measurement of kynurenine pathway metabolites by tandem mass spectrometry.

Sedat Abusoglu, Duygu Eryavuz Onmaz, Gulsum Abusoglu, Fatma Humeyra Yerlikaya, Ali Unlu.

   Objectives: Recent studies have shown that derangements in kynurenine pathway metabolite levels are associated with various pathologies such as neurodegenerative diseases, schizophrenia, depression, bipolar disorder, rheumatoid arthritis, and cancer. Therefore, reliable, accurate, fast, and multiplex measurement methods for kynurenines have become increasingly important. This study aimed to validate a new mass spectrometric method for analyzing tryptophan metabolites.
Methods: A tandem mass spectrometric method, including protein precipitation and evaporation steps, was developed to measure serum levels of tryptophan, kynurenine, kynurenic acid, 3-hydroxykynurenine, and 3-hydroxyanthranilic acid. Samples were separated using a Phenomenex Luna C18 reversed-phase column. The kynurenine pathway metabolites were detected by tandem mass spectrometry. The developed method was validated according to Clinical & Laboratory Standards Institute (CLSI) guidelines and applied to hemodialysis samples.
Results: The developed method was linear at the concentrations of 48.8 - 25,000, 0.98 - 500, 1.2-5000, 1.2-5000, and 0.98-250 ng/mL for tryptophan, kynurenic acid, kynurenine, 3-hydroxyanthranilic acid, and 3-hydroxykynurenine, respectively. The imprecisions were less than 12 %. The median serum concentrations of tryptophan, kynurenine, kynurenic acid, 3-hydroxykynurenine, and 3-hydroxyanthranilic acid were 10530, 1100, 218, 17.6, and 25.4 ng/mL in pre-dialysis blood samples, respectively. They were 4560, 664, 135, 7.4, and 12.8 ng/mL in post-dialysis blood samples, respectively.
Conclusions: A fast, simple, cost-effective, accurate, robust, and validated tandem mass spectrometric method was developed, and the method was successfully used for the quantitation of kynurenine pathway metabolite concentrations in hemodialysis patients.

Keywords:  Kynurenine pathway; Mass spectrometry; Tryptophan; Validation

DOI:  https://doi.org/10.1016/j.jmsacl.2023.04.003