bims-mascan 2022-10-02 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2022‒10‒02
thirty-two papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh

Metabolomics: Going Deeper, Going Broader, Going Further.
Relative quantification of proteins in formalin-fixed paraffin-embedded breast cancer tissue using multiplexed mass spectrometry assays.
Glutamine stabilizes myc via alpha-ketoglutarate and regulates paclitaxel sensitivity.
Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets.
Extracellular vesicles as central regulators of blood vessel function in cancer.
DIA label-free proteomic analysis of murine bone-marrow-derived macrophages.
TurboPutative: A web server for data handling and metabolite classification in untargeted metabolomics.
Quantitative Cell Proteomic Atlas: Pathway-Scale Targeted Mass Spectrometry for High-Resolution Functional Profiling of Cell Signaling.
Single cell metabolism: current and future trends.
Analysis of the Human Pineal Proteome by Mass Spectrometry.
Optimization of a GC-MS Injection-Port Derivatization Methodology to Enhance Metabolomics Analysis Throughput in Biological Samples.
Transient Systemic Autophagy Inhibition is Selectively and Irreversibly Deleterious to Lung Cancer.
De novo sphingolipid biosynthesis necessitates detoxification in cancer cells.
Obtaining clean and informative mass spectra from complex chromatographic and high-resolution all-ions-fragmentation data by nonnegative parallel factor analysis 2.
TurnoveR: A Skyline External Tool for Analysis of Protein Turnover in Metabolic Labeling Studies.
In-situ sampling of lipids in tissues using a porous membrane microprobe for direct mass spectrometry analysis.
compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets.
Liquid Chromatography-Tandem Mass Spectrometry with Online, In-Source Droplet-Based Phenylboronic Acid Derivatization for Sensitive Analysis of Saccharides.
In vivo metabolic imaging identifies lipid vulnerability in a preclinical model of Her2+/Neu breast cancer residual disease and recurrence.
Uncommon posttranslational modifications in proteomics: ADP-ribosylation, tyrosine nitration, and tyrosine sulfation.
Microbial containment device: A platform for comprehensive analysis of microbial metabolism without sample preparation.
Validation of metabolomic and lipidomic analyses of human tears using ultra-high-performance liquid chromatography tandem mass spectrometry.
Ferroptosis: A potential opportunity for intervention of pre-metastatic niche.
Homologue Series Detection and Management in LC-MS data with homologueDiscoverer.
Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications.
Slow-Off-Rate-Modified Aptamer Labeling for Fluorescence Microscopy and DNA-PAINT.
Multiomics analysis reveals the mechanical stress-dependent changes in trabecular meshwork cytoskeletal-extracellular matrix interactions.
A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery.
Limited Proteolysis-Mass Spectrometry to Identify Metabolite-Protein Interactions.
Proteome Integral Solubility Alteration (PISA) for High-Throughput Ligand Target Deconvolution with Increased Statistical Significance and Reduced Sample Amount.
Dihydroceramide desaturase 1 (DES1) promotes anchorage-independent survival downstream of HER2-driven glucose uptake and metabolism.
TFEB regulates sulfur amino acid and coenzyme A metabolism to support hepatic metabolic adaptation and redox homeostasis.

Methods Mol Biol. 2023 ;2554 155-178

Metabolomics: Going Deeper, Going Broader, Going Further.

Sofia Moco, Joerg M Buescher.

  Metabolomics is a continuously dynamic field of research that is driven by demanding research questions and technological advances alike. In this review we highlight selected recent and ongoing developments in the area of mass spectrometry-based metabolomics. The field of view that can be seen through the metabolomics lens can be broadened by adoption of separation techniques such as hydrophilic interaction chromatography and ion mobility mass spectrometry (going broader). For a given biospecimen, deeper metabolomic analysis can be achieved by resolving smaller entities such as rare cell populations or even single cells using nano-LC and spatially resolved metabolomics or by extracting more useful information through improved metabolite identification in untargeted metabolomic experiments (going deeper). Integration of metabolomics with other (omics) data allows researchers to further advance in the understanding of the complex metabolic and regulatory networks in cells and model organisms (going further). Taken together, diverse fields of research from mechanistic studies to clinics to biotechnology applications profit from these technological developments.

Keywords:  Chromatography; Coverage; Identification; Mass spectrometry; Metabolomics; Omics integration

DOI:  https://doi.org/10.1007/978-1-0716-2624-5_11
Mol Cell Proteomics. 2022 Sep 21. pii: S1535-9476(22)00224-9. [Epub ahead of print] 100416

Relative quantification of proteins in formalin-fixed paraffin-embedded breast cancer tissue using multiplexed mass spectrometry assays.

Carine Steiner, Pierre Lescuyer, Paul Cutler, Jean-Christophe Tille, Axel Ducret.

  The identification of clinically relevant biomarkers represents an important challenge in oncology. This problem can be addressed with biomarker discovery and verification studies performed directly in tumor samples using formalin-fixed paraffin-embedded (FFPE) tissues. However, reliably measuring proteins in FFPE samples remains challenging. Here, we demonstrate the use of liquid chromatography coupled to multiple reaction monitoring mass spectrometry (LC-MRM/MS) as an effective technique for such applications. An LC-MRM/MS method was developed to simultaneously quantify hundreds of peptides extracted from FFPE samples and was applied to the targeted measurement of 200 proteins in 48 triple-negative (TNBC), 19 HER2-overexpressing, and 20 luminal A breast tumors. Quantitative information was obtained for 185 proteins, including known markers of breast cancer such as HER2, hormone receptors, Ki-67, or inflammation-related proteins. LC-MRM/MS results for these proteins matched IHC or CISH data. In addition, comparison of our results with data from the literature showed that several proteins representing potential biomarkers were identified as differentially expressed in TNBC samples. These results indicate that LC-MRM/MS assays can reliably measure large sets of proteins using the analysis of surrogate peptides extracted from FFPE samples. This approach allows to simultaneously quantify the expression of target proteins from various pathways in tumor samples. LC-MRM/MS is thus a powerful tool for the relative quantification of proteins in FFPE tissues and for biomarker discovery.

Keywords:  biomarker; breast cancer; formalin-fixed paraffin-embedded; mass spectrometry; protein assay; selected reaction monitoring

DOI:  https://doi.org/10.1016/j.mcpro.2022.100416
Med Oncol. 2022 Sep 29. 39(12): 227

Glutamine stabilizes myc via alpha-ketoglutarate and regulates paclitaxel sensitivity.

Vikas Bhardwaj, Jun He, Aditi Jain.

  Metabolic reprogramming wherein the cancer cells exhibit altered energetics is a hallmark of cancer. Although recent discoveries have enhanced our understanding of tumor metabolism, the therapeutic utility of targeting tumor metabolism is not yet realized. Glutamine, a non-essential amino acid, plays a critical role in regulating tumor metabolism and provides an alternative tumor energy source. In this study, we investigate the molecular mechanism regulated by glutamine and elucidate if targeting glutamine metabolism would enhance the efficacy of cancer chemotherapy. Using clonogenic and cell cycle analysis, we found that deprivation of glutamine suppress the growth of cancer cells. Mechanistically we demonstrate that glutamine stabilizes myc by preventing its ubiquitination through alpha-ketoglutarate. Inhibition of glutamine metabolism enhanced the sensitivity of tumor cells to chemotherapeutic agent paclitaxel. Our results delineate the mechanism behind glutamine-induced myc stabilization, and they provide a viable strategy to target cancer with a glutamine metabolism inhibitor in the clinic.

Keywords:  Cancer; Glutamine; Metabolism; Myc

DOI:  https://doi.org/10.1007/s12032-022-01834-5
Anal Chim Acta. 2022 Oct 09. pii: S0003-2670(22)00923-0. [Epub ahead of print]1229 340352

Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets.

Yasin El Abiead, Christoph Bueschl, Lisa Panzenboeck, Mingxun Wang, Maria Doppler, Bernhard Seidl, Jürgen Zanghellini, Pieter C Dorrestein, Gunda Koellensperger.

  Covalent or non-covalent heterogeneous multimerization of molecules associated with extracts from biological samples analyzed via LC-MS are quite difficult to recognize/annotate and therefore the prevalence of multimerization remains largely unknown. In this study, we utilized 13C labeled and unlabeled Pichia pastoris extracts to recognize heterogeneous multimers. More specifically, between 0.8% and 1.5% of the biologically-derived features detected in our experiments were confirmed to be heteromers, about half of which we could successfully annotate with monomeric partners. Interestingly, we found specific chemical classes such as nucleotides to disproportionately contribute to heteroadducts. Furthermore, we compiled these compounds into the first MS/MS library that included data from heteromultimers to provide a starting point for other labs to improve the annotation of such ions in other metabolomics data sets. Then, the detected heteromers were also searched in publicly accessible LC-MS datasets available in Metabolights, Metabolomics WB and GNPS/MassIVE to demonstrate that these newly annotated ions are also relevant to other public datasets. Furthermore, in additional datasets (Triticum aestivum, Fusarium graminearum, and Trichoderma reesei) our developed workflow also detected 0.5%-4.9% of metabolite features to originate from heterodimers, demonstrating heteroadducts to be present in metabolomics studies at a low percentage.

Keywords:  Adduct; Annotation; Identification; Liquid chromatography; Mass spectrometry; Metabolomics

DOI:  https://doi.org/10.1016/j.aca.2022.340352
Sci Signal. 2022 Sep 27. 15(753): eaaz4742

Extracellular vesicles as central regulators of blood vessel function in cancer.

Fernanda G Kugeratski, Alice Santi, Sara Zanivan.

Blood vessels deliver oxygen and nutrients that sustain tumor growth and enable the dissemination of cancer cells to distant sites and the recruitment of intratumoral immune cells. In addition, the structural and functional abnormalities of the tumor vasculature foster the development of an aggressive tumor microenvironment and impair the efficacy of existing cancer therapies. Extracellular vesicles (EVs) have emerged as major players of tumor progression, and a growing body of evidence has demonstrated that EVs derived from cancer cells trigger multiple responses in endothelial cells that alter blood vessel function in tumors. EV-mediated signaling in endothelial cells can occur through the transfer of functional cargos such as miRNAs, lncRNAs, cirRNAs, and proteins. Moreover, membrane-bound proteins in EVs can elicit receptor-mediated signaling in endothelial cells. Together, these mechanisms reprogram endothelial cells and contribute to the sustained exacerbated angiogenic signaling typical of tumors, which, in turn, influences cancer progression. Targeting these angiogenesis-promoting EV-dependent mechanisms may offer additional strategies to normalize tumor vasculature. Here, we discuss the current knowledge pertaining to the contribution of cancer cell-derived EVs in mechanisms regulating blood vessel functions in tumors. Moreover, we discuss the translational opportunities in targeting the dysfunctional tumor vasculature using EVs and highlight the open questions in the field of EV biology that can be addressed using mass spectrometry-based proteomics analysis.

DOI: https://doi.org/10.1126/scisignal.aaz4742
STAR Protoc. 2022 Sep 26. pii: S2666-1667(22)00605-0. [Epub ahead of print]3(4): 101725

DIA label-free proteomic analysis of murine bone-marrow-derived macrophages.

Christa P Baker, Iain R Phair, Alejandro J Brenes, Abdelmadjid Atrih, Dylan G Ryan, Roland Bruderer, Albena T Dinkova-Kostova, Douglas J Lamont, J Simon C Arthur, Andrew J M Howden.

  Here, we describe an optimized protocol to analyze murine bone-marrow-derived macrophages using label-free data-independent acquisition (DIA) proteomics. We provide a complete step-by-step protocol describing sample preparation utilizing the S-Trap approach for on-column digestion and peptide purification. We then detail mass spectrometry data acquisition and approaches for data analysis. Single-shot DIA protocols achieve comparable proteomic depth with data-dependent MS approaches without the need for fractionation. This allows for better scaling for large sample numbers with high inter-experimental reproducibility. For complete details on the use and execution of this protocol, please refer to Ryan et al. (2022).

Keywords:  Bioinformatics; Immunology; Mass spectrometry; Protein biochemistry; Proteomics

DOI:  https://doi.org/10.1016/j.xpro.2022.101725
Front Mol Biosci. 2022 ;9 952149

TurboPutative: A web server for data handling and metabolite classification in untargeted metabolomics.

Rafael Barrero-Rodríguez, Jose Manuel Rodriguez, Rocío Tarifa, Jesús Vázquez, Annalaura Mastrangelo, Alessia Ferrarini.

  Untargeted metabolomics aims at measuring the entire set of metabolites in a wide range of biological samples. However, due to the high chemical diversity of metabolites that range from small to large and more complex molecules (i.e., amino acids/carbohydrates vs. phospholipids/gangliosides), the identification and characterization of the metabolome remain a major bottleneck. The first step of this process consists of searching the experimental monoisotopic mass against databases, thus resulting in a highly redundant/complex list of candidates. Despite the progress in this area, researchers are still forced to manually explore the resulting table in order to prioritize the most likely identifications for further biological interpretation or confirmation with standards. Here, we present TurboPutative (https://proteomics.cnic.es/TurboPutative/), a flexible and user-friendly web-based platform composed of four modules (Tagger, REname, RowMerger, and TPMetrics) that streamlines data handling, classification, and interpretability of untargeted LC-MS-based metabolomics data. Tagger classifies the different compounds and provides preliminary insights into the biological system studied. REname improves putative annotation handling and visualization, allowing the recognition of isomers and equivalent compounds and redundant data removal. RowMerger reduces the dataset size, facilitating the manual comparison among annotations. Finally, TPMetrics combines different datasets with feature intensity and relevant information for the researcher and calculates a score based on adduct probability and feature correlations, facilitating further identification, assessment, and interpretation of the results. The TurboPutative web application allows researchers in the metabolomics field that are dealing with massive datasets containing multiple putative annotations to reduce the number of these entries by 80%-90%, thus facilitating the extrapolation of biological knowledge and improving metabolite prioritization for subsequent pathway analysis. TurboPutative comprises a rapid, automated, and customizable workflow that can also be included in programmed bioinformatics pipelines through its RESTful API services. Users can explore the performance of each module through demo datasets supplied on the website. The platform will help the metabolomics community to speed up the arduous task of manual data curation that is required in the first steps of metabolite identification, improving the generation of biological knowledge.

Keywords:  LC-MS; lipids; metabolite ID prioritize; putative annotations; simplification

DOI:  https://doi.org/10.3389/fmolb.2022.952149
J Proteome Res. 2022 Sep 26.

Quantitative Cell Proteomic Atlas: Pathway-Scale Targeted Mass Spectrometry for High-Resolution Functional Profiling of Cell Signaling.

Paolo Cifani, Alex Kentsis.

  In spite of extensive studies of cellular signaling, many fundamental processes such as pathway integration, cross-talk, and feedback remain poorly understood. To enable integrated and quantitative measurements of cellular biochemical activities, we have developed the Quantitative Cell Proteomics Atlas (QCPA). QCPA consists of panels of targeted mass spectrometry assays to determine the abundance and stoichiometry of regulatory post-translational modifications of sentinel proteins from most known physiologic and pathogenic signaling pathways in human cells. QCPA currently profiles 1 913 peptides from 469 effectors of cell surface signaling, apoptosis, stress response, gene expression, quiescence, and proliferation. For each protein, QCPA includes triplets of isotopically labeled peptides covering known post-translational regulatory sites to determine their stoichiometries and unmodified protein regions to measure total protein abundance. The QCPA framework incorporates analytes to control for technical variability of sample preparation and mass spectrometric analysis, including TrypQuant, a synthetic substrate for accurate quantification of proteolysis efficiency for proteins containing chemically modified residues. The ability to precisely and accurately quantify most known signaling pathways should enable improved chemoproteomic approaches for the comprehensive analysis of cell signaling and clinical proteomics of diagnostic specimens. QCPA is openly available at https://qcpa.mskcc.org.

Keywords:  PTM stoichiometry; biochemical regulation; cancer; open access; phosphorylation; post-translational modification; targeted mass spectrometry

DOI:  https://doi.org/10.1021/acs.jproteome.2c00223
Metabolomics. 2022 Oct 01. 18(10): 77

Single cell metabolism: current and future trends.

Ahmed Ali, Shawn Davidson, Ernest Fraenkel, Ian Gilmore, Thomas Hankemeier, Jennifer A Kirwan, Andrew N Lane, Ingela Lanekoff, Mioara Larion, Laura-Isobel McCall, Michael Murphy, Jonathan V Sweedler, Caigang Zhu.

  Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.

Keywords:  Metabolic imaging; Single cell metabolism; Spatial metabolomics

DOI:  https://doi.org/10.1007/s11306-022-01934-3
Methods Mol Biol. 2022 ;2550 123-132

Analysis of the Human Pineal Proteome by Mass Spectrometry.

Mariette Matondo, Guillaume Dumas, Erik Maronde.

  The human pineal gland regulates the day-night dynamics of multiple physiological processes, especially through the secretion of melatonin. Recently, using mass spectrometry-based proteomics and dedicated analysis tools, we have identified regulated proteins and signaling pathways that differ between day and night and/or between control and autistic pineal glands. This large-scale proteomic approach is the method of choice to study proteins in a biological system globally. This chapter proposes a protocol for large-scale analysis of the pineal gland proteome.

Keywords:  Autism; Human; Mass spectrometry; Pineal gland; Protein

DOI:  https://doi.org/10.1007/978-1-0716-2593-4_16
J Proteome Res. 2022 Sep 30.

Optimization of a GC-MS Injection-Port Derivatization Methodology to Enhance Metabolomics Analysis Throughput in Biological Samples.

Elisabet Foguet-Romero, Iris Samarra, Maria Guirro, Marc Riu, Jorge Joven, Javier A Menendez, Núria Canela, Antoni DelPino-Rius, Salvador Fernández-Arroyo, Pol Herrero.

  Advances in metabolomics analysis and data treatment increase the knowledge of complex biological systems. One of the most used methodologies is gas chromatography-mass spectrometry (GC-MS) due to its robustness, high separation efficiency, and reliable peak identification through curated databases. However, methodologies are not standardized, and the derivatization steps in GC-MS can introduce experimental errors and take considerable time, exposing the samples to degradation. Here, we propose the injection-port derivatization (IPD) methodology to increase the throughput in plasma metabolomics analysis by GC-MS. The IPD method was evaluated and optimized for different families of metabolites (organic acids, amino acids, fatty acids, sugars, sugar phosphates, etc.) in terms of residence time, injection-port temperature, and sample/derivatization reagent ratio. Finally, the method's usefulness was validated in a study consisting of a cohort of obese patients with or without nonalcoholic steatohepatitis. Our results show a fast, reproducible, precise, and reliable method for the analysis of biological samples by GC-MS. Raw data are publicly available at MetaboLights with Study Identifier MTBLS5151.

Keywords:  gas chromatography; injection-port derivatization; mass spectrometry; metabolomics; nonalcoholic steatohepatitis

DOI:  https://doi.org/10.1021/acs.jproteome.2c00119
Cancer Res. 2022 Sep 26. pii: CAN-22-1039. [Epub ahead of print]

Transient Systemic Autophagy Inhibition is Selectively and Irreversibly Deleterious to Lung Cancer.

Khoosheh Khayati, Vrushank Bhatt, Taijin Lan, Fawzi Alogaili, Wenping Wang, Enrique Lopez, Zhixian Sherrie Hu, Samantha Gokhale, Liam Cassidy, Masashi Narita, Ping Xie, Eileen White, Jessie Yanxiang Guo.

Autophagy is a conserved catabolic process that maintains cellular homeostasis. Autophagy supports lung tumorigenesis and is a potential therapeutic target in lung cancer. A better understanding of the importance of tumor cell-autonomous versus systemic autophagy in lung cancer could facilitate clinical translation of autophagy inhibition. Here, we exploited inducible expression of Atg5 shRNA to temporally control Atg5 levels and generate reversible tumor-specific and systemic autophagy loss mouse models of KrasG12D/+;p53-/- (KP) non-small cell lung cancer (NSCLC). Transient suppression of systemic but not tumor Atg5 expression significantly reduced established KP lung tumor growth without damaging normal tissues. In vivo 13C isotope tracing and metabolic flux analyses demonstrated that systemic Atg5 knockdown specifically led to reduced glucose and lactate uptake. As a result, carbon flux from glucose and lactate to major metabolic pathways, including the tricarboxylic acid cycle, glycolysis, and serine biosynthesis, was significantly reduced in KP NSCLC following systemic autophagy loss. Furthermore, systemic Atg5 knockdown increased tumor T cell infiltration, leading to T cell-mediated tumor killing. Importantly, intermittent transient systemic Atg5 knockdown, which resembles what would occur during autophagy inhibition for cancer therapy, significantly prolonged lifespan of KP lung tumor-bearing mice, resulting in recovery of normal tissues but not tumors. Thus, systemic autophagy supports the growth of established lung tumors by promoting immune evasion and sustaining cancer cell metabolism for energy production and biosynthesis, and the inability of tumors to recover from loss of autophagy provides further proof of concept that inhibition of autophagy is a valid approach to cancer therapy.

DOI: https://doi.org/10.1158/0008-5472.CAN-22-1039
Cell Rep. 2022 Sep 27. pii: S2211-1247(22)01256-6. [Epub ahead of print]40(13): 111415

De novo sphingolipid biosynthesis necessitates detoxification in cancer cells.

Meghan E Spears, Namgyu Lee, Sunyoung Hwang, Sung Jin Park, Anne E Carlisle, Rui Li, Mihir B Doshi, Aaron M Armando, Jenny Gao, Karl Simin, Lihua Julie Zhu, Paul L Greer, Oswald Quehenberger, Eduardo M Torres, Dohoon Kim.

  Sphingolipids play important signaling and structural roles in cells. Here, we find that during de novo sphingolipid biosynthesis, a toxic metabolite is formed with critical implications for cancer cell survival. The enzyme catalyzing the first step in this pathway, serine palmitoyltransferase complex (SPT), is upregulated in breast and other cancers. SPT is dispensable for cancer cell proliferation, as sphingolipids can be salvaged from the environment. However, SPT activity introduces a liability as its product, 3-ketodihydrosphingosine (3KDS), is toxic and requires clearance via the downstream enzyme 3-ketodihydrosphingosine reductase (KDSR). In cancer cells, but not normal cells, targeting KDSR induces toxic 3KDS accumulation leading to endoplasmic reticulum (ER) dysfunction and loss of proteostasis. Furthermore, the antitumor effect of KDSR disruption can be enhanced by increasing metabolic input (via high-fat diet) to allow greater 3KDS production. Thus, de novo sphingolipid biosynthesis entails a detoxification requirement in cancer cells that can be therapeutically exploited.

Keywords:  CP: Cancer; CP: Metabolism; cancer metabolism; cancer therapy; endoplasmic reticulum; ketodihydrosphingosine reductase; serine palmitoyltransferase

DOI:  https://doi.org/10.1016/j.celrep.2022.111415
J Chromatogr A. 2022 Sep 16. pii: S0021-9673(22)00693-8. [Epub ahead of print]1682 463501

Obtaining clean and informative mass spectra from complex chromatographic and high-resolution all-ions-fragmentation data by nonnegative parallel factor analysis 2.

Oskar Munk Kronik, Xiaomeng Liang, Nikoline Juul Nielsen, Jan H Christensen, Giorgio Tomasi.

  A major challenge in processing of complex data obtained from chromatography hyphenated to mass spectrometry is to resolve chromatographically co-eluting compounds. In this study, we present a workflow for the resolution of ultra-high pressure liquid chromatography high-resolution mass spectrometry data obtained by the broadband data-independent acquisition MSE operation (UHPLCHRMSE). The workflow is based on a recently introduced algorithm for Parallel Factor Analysis 2 (PARAFAC2) that allows to enforce non-negativity on all the model coefficients. The workflow was tested on three sets of UHPLC-HRMSE measurements from a Lupinus angustifolius L. crop field study, which included plant tissue samples, soil samples and samples from drainage water as well as stream water close to the field. The three datasets included 93, 59, and 75 chromatographic runs in total for the plant, soil and water batches, respectively. Nonnegative-PARAFAC2 models were fitted on the summed high and low energy (HE and LE) traces on chromatographic intervals corresponding to spiked standard for the three sample sets independently. In soil and plant samples, 13 out of 14 spiked standards were resolved by NN-PARAFAC2 even in presence of chromatographic co-elution, and their mass spectral loadings could be matched to a reference spectrum. In contrast, only seven spiked standards were correctly resolved and matched for the water samples because a higher chromatographic baseline rendered the data noisier. The results show that the workflow we present can provide improved mass spectral selectivity for data-independent acquisition compared to using the raw mass spectra and can be used to match fragment ions from the HE trace, and precursor and adduct ions from the LE trace even in presence of co-eluting compounds.

Keywords:  Data processing; Lupinus angustifolius l; Metabolomics; Multiway curve resolution; Non-target screening

DOI:  https://doi.org/10.1016/j.chroma.2022.463501
J Proteome Res. 2022 Sep 27.

TurnoveR: A Skyline External Tool for Analysis of Protein Turnover in Metabolic Labeling Studies.

Nathan Basisty, Nicholas Shulman, Cameron Wehrfritz, Alexandra N Marsh, Samah Shah, Jacob Rose, Scott Ebert, Matthew Miller, Dao-Fu Dai, Peter S Rabinovitch, Christopher M Adams, Michael J MacCoss, Brendan MacLean, Birgit Schilling.

  In spite of its central role in biology and disease, protein turnover is a largely understudied aspect of most proteomic studies due to the complexity of computational workflows that analyze in vivo turnover rates. To address this need, we developed a new computational tool, TurnoveR, to accurately calculate protein turnover rates from mass spectrometric analysis of metabolic labeling experiments in Skyline, a free and open-source proteomics software platform. TurnoveR is a straightforward graphical interface that enables seamless integration of protein turnover analysis into a traditional proteomics workflow in Skyline, allowing users to take advantage of the advanced and flexible data visualization and curation features built into the software. The computational pipeline of TurnoveR performs critical steps to determine protein turnover rates, including isotopologue demultiplexing, precursor-pool correction, statistical analysis, and generation of data reports and visualizations. This workflow is compatible with many mass spectrometric platforms and recapitulates turnover rates and differential changes in turnover rates between treatment groups calculated in previous studies. We expect that the addition of TurnoveR to the widely used Skyline proteomics software will facilitate wider utilization of protein turnover analysis in highly relevant biological models, including aging, neurodegeneration, and skeletal muscle atrophy.

Keywords:  Skyline; aging; mass spectrometry; metabolic labeling; protein degradation; protein synthesis; protein turnover; proteostasis; quantitative proteomics; stable isotope labeling

DOI:  https://doi.org/10.1021/acs.jproteome.2c00173
Mater Today Bio. 2022 Dec;16 100424

In-situ sampling of lipids in tissues using a porous membrane microprobe for direct mass spectrometry analysis.

Bin Jiao, Wei Zhou, Yikun Liu, Wenpeng Zhang, Zheng Ouyang.

  Direct sampling of lipids from tissues for direct mass spectrometry (MS) analysis allows a quick profiling of lipidome, which is important for biomedical applications. In this work, we developed a polyporous polymeric membrane (PPM) microprobe for highly efficient sampling of lipids directly from tissue samples. The PPM was prepared by polypropylene with pores as large of 10 μm, facilitating the permeation of lipids from tissue surfaces. The PPM was coated onto a stainless steel wire with a thickness of ∼100 μm. The entire analysis procedure includes sampling of the lipids in tissue, washing the probe, and extraction spray ionization for MS analysis. The effectiveness was validated by analyzing mouse brain tissue samples. It showed high recoveries for a series of lipid classes in comparison with total lipid extraction method. Further demonstration was carried out with analysis of tissue samples from mouse liver, stomach, kidney and legs. With high physical strength and good chemical stability, the microprobe was also demonstrated for sampling lipids inside mouse kidney tissue samples. By incorporating a photochemical derivatization, a workflow was also developed for fast detection of lipid C[bond, double bond]C isomers in tissue samples. Finally, a microprobe array was also developed for simultaneous sampling of lipids from multiple sites on tissue surfaces.

Keywords:  Biomarkers; Lipids; Mass spectrometry; Microprobe; Sampling

DOI:  https://doi.org/10.1016/j.mtbio.2022.100424
J Proteome Res. 2022 Sep 29.

compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets.

Rob Marissen, Madhushri S Varunjikar, Jeroen F J Laros, Josef D Rasinger, Benjamin A Neely, Magnus Palmblad.

  It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.

Keywords:  compareMS2; distance metric; molecular phylogenetics; quality control; tandem mass spectrometry

DOI:  https://doi.org/10.1021/acs.jproteome.2c00457
Anal Chem. 2022 Sep 30.

Liquid Chromatography-Tandem Mass Spectrometry with Online, In-Source Droplet-Based Phenylboronic Acid Derivatization for Sensitive Analysis of Saccharides.

Derik R Heiss, Abraham K Badu-Tawiah.

The ability to identify abnormalities in the body's saccharide profile is a promising means for early disease detection but requires analytical tools capable of detecting saccharides at low concentrations and/or for volume-limited samples. The preferred analysis approach for these compounds, liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS), often lacks sensitivity due to poor ionization efficiency. In this work, we employ a modified electrospray interface-termed contained-electrospray (contained-ESI) to couple accelerated droplet chemistry to conventional LC-MS for the online and automated separation, derivatization, and detection of saccharides. The chromatographic component enables complex sample and mixtures analysis with low sample volume requirements, while the enhanced reaction kinetics afforded by electrosprayed microdroplets facilitates rapid, on-the-fly derivatization to boost sensitivity. Derivatization occurs during ion formation as analytes elute from the column, eliminating the need for superfluous post-column derivatization hardware or complicated benchtop protocols. A grounded coupler was incorporated to shield the LC from the high-voltage ion source, and method conditions were optimized to accommodate the low flow rates preferred for microdroplet reactions. The new LC-contained-ESI-MS/MS platform was demonstrated for the analysis of several mono-, di-, and oligosaccharides using in-source droplet-based phenylboronic acid derivatization. Femtomole limits of detection were achieved for a 1 μL injection, representing sensitivity enhancement of 1-2 orders of magnitude over conventional LC-ESI-MS/MS without derivatization. In addition, isobaric saccharides that are difficult to differentiate by MS alone were easily distinguished. Method precision, accuracy, and linearity were established, and the ability to detect oligosaccharides at trace levels in human urine and plasma was demonstrated.

DOI: https://doi.org/10.1021/acs.analchem.2c03736
NPJ Breast Cancer. 2022 Sep 26. 8(1): 111

In vivo metabolic imaging identifies lipid vulnerability in a preclinical model of Her2+/Neu breast cancer residual disease and recurrence.

Megan C Madonna, Joy E Duer, Brock J McKinney, Enakshi D Sunassee, Brian T Crouch, Olga Ilkayeva, Matthew D Hirschey, James V Alvarez, Nirmala Ramanujam.

Recurrent cancer cells that evade therapy is a leading cause of death in breast cancer patients. This risk is high for women showing an overexpression of human epidermal growth factor receptor 2 (Her2). Cells that persist can rely on different substrates for energy production relative to their primary tumor counterpart. Here, we characterize metabolic reprogramming related to tumor dormancy and recurrence in a doxycycline-induced Her2+/Neu model of breast cancer with varying times to recurrence using longitudinal fluorescence microscopy. Glucose uptake (2-NBDG) and mitochondrial membrane potential (TMRE) imaging metabolically phenotype mammary tumors as they transition to regression, dormancy, and recurrence. "Fast-recurrence" tumors (time to recurrence ~55 days), transition from glycolysis to mitochondrial metabolism during regression and this persists upon recurrence. "Slow-recurrence" tumors (time to recurrence ~100 days) rely on both glycolysis and mitochondrial metabolism during recurrence. The increase in mitochondrial activity in fast-recurrence tumors is attributed to a switch from glucose to fatty acids as the primary energy source for mitochondrial metabolism. Consequently, when fast-recurrence tumors receive treatment with a fatty acid inhibitor, Etomoxir, tumors report an increase in glucose uptake and lipid synthesis during regression. Treatment with Etomoxir ultimately prolongs survival. We show that metabolic reprogramming reports on tumor recurrence characteristics, particularly at time points that are essential for actionable targets. The temporal characteristics of metabolic reprogramming will be critical in determining the use of an appropriate timing for potential therapies; namely, the notion that metabolic-targeted inhibition during regression reports long-term therapeutic benefit.

DOI: https://doi.org/10.1038/s41523-022-00481-3
Mass Spectrom Rev. 2022 Sep 27. e21811

Uncommon posttranslational modifications in proteomics: ADP-ribosylation, tyrosine nitration, and tyrosine sulfation.

Aarti Bashyal, Jennifer S Brodbelt.

  Posttranslational modifications (PTMs) are covalent modifications of proteins that modulate the structure and functions of proteins and regulate biological processes. The development of various mass spectrometry-based proteomics workflows has facilitated the identification of hundreds of PTMs and aided the understanding of biological significance in a high throughput manner. Improvements in sample preparation and PTM enrichment techniques, instrumentation for liquid chromatography-tandem mass spectrometry (LC-MS/MS), and advanced data analysis tools enhance the specificity and sensitivity of PTM identification. Highly prevalent PTMs like phosphorylation, glycosylation, acetylation, ubiquitinylation, and methylation are extensively studied. However, the functions and impact of less abundant PTMs are not as well understood and underscore the need for analytical methods that aim to characterize these PTMs. This review focuses on the advancement and analytical challenges associated with the characterization of three less common but biologically relevant PTMs, specifically, adenosine diphosphate-ribosylation, tyrosine sulfation, and tyrosine nitration. The advantages and disadvantages of various enrichment, separation, and MS/MS techniques utilized to identify and localize these PTMs are described.

Keywords:  ADP-ribosylation; ADP-ribosylome; nitroproteome; proteomics; sulfoproteome; tandem mass spectrometry; tyrosine nitration; tyrosine sulfation

DOI:  https://doi.org/10.1002/mas.21811
Front Microbiol. 2022 ;13 958785

Microbial containment device: A platform for comprehensive analysis of microbial metabolism without sample preparation.

Mehdi Mohammadi, Stephanie L Bishop, Raied Aburashed, Saad Luqman, Ryan A Groves, Dominique G Bihan, Thomas Rydzak, Ian A Lewis.

  Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes - the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system-the Microbial Containment Device (MCD)-that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical.

Keywords:  antibiotic susceptibility testing (AST); bacteria identification; fabrication; liquid chromatography-mass spectrometry; metabolic boundary fluxes; metabolic preference assay; metabolomics

DOI:  https://doi.org/10.3389/fmicb.2022.958785
Talanta. 2022 Sep 20. pii: S0039-9140(22)00728-7. [Epub ahead of print]253 123932

Validation of metabolomic and lipidomic analyses of human tears using ultra-high-performance liquid chromatography tandem mass spectrometry.

Sophie Catanese, Raoul K Khanna, Antoine Lefevre, Hugo Alarcan, Pierre-Jean Pisella, Patrick Emond, Hélène Blasco.

  To facilitate application in ophthalmological and systemic diseases, there is a need to standardize preanalytical and analytical steps for metabo-lipidomics in human tears. We assessed different methods for each step of the workflow, from sampling to omics profiles acquisition, to provide the largest metabo-lipidomic coverage with the most robust analytical criteria in human tears. We compared reproducibility according to different extraction methods, two sampling techniques, three volumes (2 μL, 5 μL, 10 μL) and eye laterality using ultra-high-performance liquid chromatography coupled with tandem high-resolution mass spectrometry for metabolomic and lipidomic application. The effect of age on the tear metabo-lipidome was also investigated in healthy subjects. The extraction method using methanol/water provided the best results for Schirmer strip metabolomics, while Folch extraction was superior for lipidomics, whatever the sampling method used. When comparing both sampling methods, microcapillary glass tube was superior to Schirmer strip for metabolomics but comparable for lipidomics. The 5 μL volume provided a satisfying metabo-lipidomic coverage. There was no significant difference in tear metabo-lipidome between both eyes in healthy subjects. While most metabolites and lipids where not influenced by age, the phenylalanine-tyrosine-tryptophan pathway, aminoacyl t-RNA biosynthesis, and alanine-aspartate-glutamate metabolism were the 3 principal pathways associated with the 15 most variable metabolites according to age. The current findings will contribute to improve metabo-lipidomic workflow in human tears for the identification of new biomarkers. Preanalytical and analytical standardization is mandatory in order to perform better between-study comparisons and increase the chances of transferring laboratory findings into clinical practice.

Keywords:  Lipidomic; Liquid chromatography; Mass spectrometry; Metabolomic; Tears

DOI:  https://doi.org/10.1016/j.talanta.2022.123932
Front Oncol. 2022 ;12 980620

Ferroptosis: A potential opportunity for intervention of pre-metastatic niche.

Shenghua Zhuo, Liangwang Yang, Shenbo Chen, Caiying Tang, Weicheng Li, Zhenzhong Gao, Jigao Feng, Kun Yang.

  It is widely thought that the tumor microenvironment (TME) provides the "soil" for malignant tumors to survive. Prior to metastasis, the interaction at the host site between factors secreted by primary tumors, bone-marrow-derived cells, with stromal components initiates and establishes a pre-metastatic niche (PMN) characterized by immunosuppression, inflammation, angiogenesis and vascular permeability, as well as lymphangiogenesis, reprogramming and organotropism. Ferroptosis is a non-apoptotic cell death characterized by iron-dependent lipid peroxidation and metabolic constraints. Ferroptotic cancer cells release various signal molecules into the TME to either suppress or promote tumor progression. This review highlights the important role played by ferroptosis in PMN, focusing on the relationship between ferroptosis and PMN characteristics, and discusses future research directions.

Keywords:  ferroptosis; immune escape; pre-metastatic niche; therapeutic strategies; tumor

DOI:  https://doi.org/10.3389/fonc.2022.980620
Bioinformatics. 2022 Sep 27. pii: btac647. [Epub ahead of print]

Homologue Series Detection and Management in LC-MS data with homologueDiscoverer.

Kevin Mildau, Justin J J van der Hooft, Mira Flasch, Benedikt Warth, Yasin El Abiead, Gunda Koellensperger, Jürgen Zanghellini, Christoph Büschl.

SUMMARY: Untargeted metabolomics data analysis is highly labor intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series are a particular case of features that can either represent large numbers of noise features, or alternatively represent features of interest with large peak redundancy. Here we present homologueDiscoverer, an R package which allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities.AVAILABILITY: homologueDiscoverer is freely available at github https://github.com/kevinmildau/homologueDiscoverer.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btac647
Mass Spectrom Rev. 2022 Sep 26. e21808

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications.

Sander R Piersma, Andrea Valles-Marti, Frank Rolfs, Thang V Pham, Alex A Henneman, Connie R Jiménez.

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

DOI: https://doi.org/10.1002/mas.21808
Methods Mol Biol. 2023 ;2570 177-185

Slow-Off-Rate-Modified Aptamer Labeling for Fluorescence Microscopy and DNA-PAINT.

Sebastian Strauss, Ralf Jungmann.

  Super-resolution microscopy methods enable the visualization of biological processes on the level of a few nanometers. However, the application of these techniques in biological systems is limited by the availability of small affinity reagents. Slow off-rate-modified aptamers as nucleic acid analogues to antibodies have been successfully applied to improve the resolution and quantification of DNA-PAINT. In this chapter, we describe a protocol for using SOMAmers as labeling reagents for super-resolution microscopy.

Keywords:  Aptamer; DNA-PAINT; Fluorescence microscopy; Labeling probes; SOMAmer; Super-resolution

DOI:  https://doi.org/10.1007/978-1-0716-2695-5_13
Front Cell Dev Biol. 2022 ;10 874828

Multiomics analysis reveals the mechanical stress-dependent changes in trabecular meshwork cytoskeletal-extracellular matrix interactions.

Avinash Soundararajan, Ting Wang, Rekha Sundararajan, Aruna Wijeratne, Amber Mosley, Faith Christine Harvey, Sanjoy Bhattacharya, Padmanabhan Paranji Pattabiraman.

  Trabecular meshwork (TM) tissue is subjected to constant mechanical stress due to the ocular pulse created by the cardiac cycle. This brings about alterations in the membrane lipids and associated cell-cell adhesion and cell-extracellular matrix (ECM) interactions, triggering intracellular signaling responses to counter mechanical insults. A loss of such response can lead to elevated intraocular pressure (IOP), a major risk factor for primary open-angle glaucoma. This study is aimed to understand the changes in signaling responses by TM subjected to mechanical stretch. We utilized multiomics to perform an unbiased mRNA sequencing to identify changes in transcripts, mass spectrometry- (MS-) based quantitative proteomics for protein changes, and multiple reaction monitoring (MRM) profiling-based MS and high-performance liquid chromatography (HPLC-) based MS to characterize the lipid changes. We performed pathway analysis to obtain an integrated map of TM response to mechanical stretch. The human TM cells subjected to mechanical stretch demonstrated an upregulation of protein quality control, oxidative damage response, pro-autophagic signal, induction of anti-apoptotic, and survival signaling. We propose that mechanical stretch-induced lipid signaling via increased ceramide and sphingomyelin potentially contributes to increased TM stiffness through actin-cytoskeleton reorganization and profibrotic response. Interestingly, increased phospholipids and diacylglycerol due to mechanical stretch potentially enable cell membrane remodeling and changes in signaling pathways to alter cellular contractility. Overall, we propose the mechanistic interplay of macromolecules to bring about a concerted cellular response in TM cells to achieve mechanotransduction and IOP regulation when TM cells undergo mechanical stretch.

Keywords:  cytoskeleton; extracellular matrix; glaucoma; lipid signaling; mechanical stretch; multiomics analysis; ocular hypertension; trabecular meshwork (TM)

DOI:  https://doi.org/10.3389/fcell.2022.874828
Med Oncol. 2022 Sep 29. 39(12): 206

A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery.

Gayathri Ashok, Sudha Ramaiah.

  Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.

Keywords:  Cancer Biomarkers; Epigenomics; Gene interaction networks; Genomics; MicroRNA; Multi-omics tools; Systems biology

DOI:  https://doi.org/10.1007/s12032-022-01815-8
Methods Mol Biol. 2023 ;2554 69-89

Limited Proteolysis-Mass Spectrometry to Identify Metabolite-Protein Interactions.

Aleš Holfeld, Jan-Philipp Quast, Roland Bruderer, Lukas Reiter, Natalie de Souza, Paola Picotti.

  Metabolite-protein interactions regulate diverse cellular processes, prompting the development of methods to investigate the metabolite-protein interactome at a global scale. One such method is our previously developed structural proteomics approach, limited proteolysis-mass spectrometry (LiP-MS), which detects proteome-wide metabolite-protein and drug-protein interactions in native bacterial, yeast, and mammalian systems, and allows identification of binding sites without chemical modification. Here we describe a detailed experimental and analytical workflow for conducting a LiP-MS experiment to detect small molecule-protein interactions, either in a single-dose (LiP-SMap) or a multiple-dose (LiP-Quant) format. LiP-Quant analysis combines the peptide-level resolution of LiP-MS with a machine learning-based framework to prioritize true protein targets of a small molecule of interest. We provide an updated R script for LiP-Quant analysis via a GitHub repository accessible at https://github.com/RolandBruderer/MiMB-LiP-Quant .

Keywords:  LiP–Quant; LiP–SMap; Limited proteolysis; Machine learning; Mass spectrometry; Metabolite; Protein interactions; Proteomics; Structural proteomics

DOI:  https://doi.org/10.1007/978-1-0716-2624-5_6
Methods Mol Biol. 2023 ;2554 91-106

Proteome Integral Solubility Alteration (PISA) for High-Throughput Ligand Target Deconvolution with Increased Statistical Significance and Reduced Sample Amount.

Massimiliano Gaetani, Roman A Zubarev.

  Proteome Integral Solubility Alteration (PISA) is a recently developed mass spectrometry-based, deep proteomics method for unbiased, proteome-wide target deconvolution of ligands, requiring no chemical ligand modification. PISA can be applied to living cells for studying target engagement in vivo or alternatively to protein extracts to identify in vitro ligand-interacting proteins. Here we describe the PISA workflow optimized in our lab. PISA improves the target discovery throughput 10-100 folds compared to the previously used proteomics methods and provides higher statistical significance for target candidates by enabling several biological replicates. Sample multiplexing makes all-in-one analysis of multiple ligands simultaneously possible. PISA dramatically reduces analysis costs, allowing many research questions in need of target deconvolution to be addressed, and unlocks the potential of miniaturizing biological models, including primary cells.

Keywords:  Chemoproteomics; Ligand; Ligand target deconvolution; PISA; Protein–metabolite interactions; Solubility alteration

DOI:  https://doi.org/10.1007/978-1-0716-2624-5_7
FASEB J. 2022 Oct;36(10): e22558

Dihydroceramide desaturase 1 (DES1) promotes anchorage-independent survival downstream of HER2-driven glucose uptake and metabolism.

Ryan W Linzer, Danielle L Guida, Jonathan Aminov, Justin M Snider, Gabrielle Khalife, A Burak Buyukbayraktar, Charbel Alhaddad, Andrew E Resnick, Pule Wang, Chun-Hao Pan, Janet J Allopenna, Christopher J Clarke.

  Oncogenic reprogramming of cellular metabolism is a hallmark of many cancers, but our mechanistic understanding of how such dysregulation is linked to tumor behavior remains poor. In this study, we have identified dihydroceramide desaturase (DES1)-which catalyzes the last step in de novo sphingolipid synthesis-as necessary for the acquisition of anchorage-independent survival (AIS), a key cancer enabling biology, and establish DES1 as a downstream effector of HER2-driven glucose uptake and metabolism. We further show that DES1 is sufficient to drive AIS and in vitro tumorigenicity and that increased DES1 levels-found in a third of HER2+ breast cancers-are associated with worse survival outcomes. Taken together, our findings reveal a novel pro-tumor role for DES1 as a transducer of HER2-driven glucose metabolic signals and provide evidence that targeting DES1 is an effective approach for overcoming AIS. Results further suggest that DES1 may have utility as a biomarker of aggressive and metastasis-prone HER2+ breast cancer.

Keywords:  breast cancer; cell survival; metastasis; oncogene; sphingolipid

DOI:  https://doi.org/10.1096/fj.202200748R
Nat Commun. 2022 Sep 28. 13(1): 5696

TFEB regulates sulfur amino acid and coenzyme A metabolism to support hepatic metabolic adaptation and redox homeostasis.

David Matye, Sumedha Gunewardena, Jianglei Chen, Huaiwen Wang, Yifeng Wang, Mohammad Nazmul Hasan, Lijie Gu, Yung Dai Clayton, Yanhong Du, Cheng Chen, Jacob E Friedman, Shelly C Lu, Wen-Xing Ding, Tiangang Li.

Fatty liver is a highly heterogenous condition driven by various pathogenic factors in addition to the severity of steatosis. Protein insufficiency has been causally linked to fatty liver with incompletely defined mechanisms. Here we report that fatty liver is a sulfur amino acid insufficient state that promotes metabolic inflexibility via limiting coenzyme A availability. We demonstrate that the nutrient-sensing transcriptional factor EB synergistically stimulates lysosome proteolysis and methionine adenosyltransferase to increase cysteine pool that drives the production of coenzyme A and glutathione, which support metabolic adaptation and antioxidant defense during increased lipid influx. Intriguingly, mice consuming an isocaloric protein-deficient Western diet exhibit selective hepatic cysteine, coenzyme A and glutathione deficiency and acylcarnitine accumulation, which are reversed by cystine supplementation without normalizing dietary protein intake. These findings support a pathogenic link of dysregulated sulfur amino acid metabolism to metabolic inflexibility that underlies both overnutrition and protein malnutrition-associated fatty liver development.

DOI: https://doi.org/10.1038/s41467-022-33465-9