bims-mascan 2021-03-28 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2021–03–28
27 papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh

The amino acid metabolism is essential for evading physical plasma-induced tumour cell death.
Spatially resolved metabolomics combined with multicellular tumor spheroids to discover cancer tissue relevant metabolic signatures.
Improved SILAC Quantification with Data-Independent Acquisition to Investigate Bortezomib-Induced Protein Degradation.
Adrenic Acid-Derived Epoxy Fatty Acids Are Naturally Occurring Lipids and Their Methyl Ester Prodrug Reduces Endoplasmic Reticulum Stress and Inflammatory Pain.
Circulating blood metabolite trajectories and risk of rheumatoid arthritis among military personnel in the Department of Defense Biorepository.
Serum metabolomics identifies dysregulated pathways and potential metabolic biomarkers for hyperuricemia and gout.
Serum metabolite signatures of epithelial ovarian cancer based on targeted metabolomics.
Separation and Characterization of Endogenous Nucleosomes by Native Capillary Zone Electrophoresis-Top-Down Mass Spectrometry.
GLUT5 (SLC2A5) enables fructose-mediated proliferation independent of ketohexokinase.
mTORC1 stimulates cell growth through SAM synthesis and m6A mRNA-dependent control of protein synthesis.
Methods to improve quantitative glycoprotein coverage from bottom-up LC-MS data.
The oncogene AAMDC links PI3K-AKT-mTOR signaling with metabolic reprograming in estrogen receptor-positive breast cancer.
Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.
Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics.
A small-molecule pan-class I glucose transporter inhibitor reduces cancer cell proliferation in vitro and tumor growth in vivo by targeting glucose-based metabolism.
SARS-CoV-2 infection rewires host cell metabolism and is potentially susceptible to mTORC1 inhibition.
A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics.
Ultra-fast proteomics with Scanning SWATH.
Mass spectrometry-based tear proteomics for noninvasive biomarker discovery.
Facile Preparation of Peptides for Mass Spectrometry Analysis in Bottom-Up Proteomics Workflows.
Signalling transduction of O-GlcNAcylation and PI3K/AKT/mTOR-axis in prostate cancer.
Adaptation of central metabolite pools to variations in growth rate and cultivation conditions in Saccharomyces cerevisiae.
Mutant IDH and non-mutant chondrosarcomas display distinct cellular metabolomes.
Visceral Adipose Tissue Phospholipid Signature of Insulin Sensitivity and Obesity.
Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins.
Obesity-Dependent Adipokine Chemerin Suppresses Fatty Acid Oxidation to Confer Ferroptosis Resistance.
Gut microbial metabolites facilitate anticancer therapy efficacy by modulating cytotoxic CD8+ T cell immunity.

Br J Cancer. 2021 Mar 25.

The amino acid metabolism is essential for evading physical plasma-induced tumour cell death.

Rajesh Kumar Gandhirajan, Dorothee Meyer, Sanjeev Kumar Sagwal, Klaus-Dieter Weltmann, Thomas von Woedtke, Sander Bekeschus.

BACKGROUND: Recent studies have emphasised the important role of amino acids in cancer metabolism. Cold physical plasma is an evolving technology employed to target tumour cells by introducing reactive oxygen species (ROS). However, limited understanding is available on the role of metabolic reprogramming in tumour cells fostering or reducing plasma-induced cancer cell death.
METHODS: The utilisation and impact of major metabolic substrates of fatty acid, amino acid and TCA pathways were investigated in several tumour cell lines following plasma exposure by qPCR, immunoblotting and cell death analysis.
RESULTS: Metabolic substrates were utilised in Panc-1 and HeLa but not in OVCAR3 and SK-MEL-28 cells following plasma treatment. Among the key genes governing these pathways, ASCT2 and SLC3A2 were consistently upregulated in Panc-1, Miapaca2GR, HeLa and MeWo cells. siRNA-mediated knockdown of ASCT2, glutamine depletion and pharmacological inhibition with V9302 sensitised HeLa cells to the plasma-induced cell death. Exogenous supplementation of glutamine, valine or tyrosine led to improved metabolism and viability of tumour cells following plasma treatment.
CONCLUSION: These data suggest the amino acid influx driving metabolic reprogramming in tumour cells exposed to physical plasma, governing the extent of cell death. This pathway could be targeted in combination with existing anti-tumour agents.

DOI: https://doi.org/10.1038/s41416-021-01335-8
Anal Chim Acta. 2021 Apr 22. pii: S0003-2670(21)00168-9. [Epub ahead of print]1155 338342

Spatially resolved metabolomics combined with multicellular tumor spheroids to discover cancer tissue relevant metabolic signatures.

Qingce Zang, Chenglong Sun, Xiaoping Chu, Limei Li, Wenqiang Gan, Zitong Zhao, Yongmei Song, Jiuming He, Ruiping Zhang, Zeper Abliz.

  Spatially resolved metabolomics offers unprecedented opportunities for elucidating metabolic mechanisms during cancer progression. It facilitated the discovery of aberrant cellular metabolism with clinical application potential. Here, we developed a novel strategy to discover cancer tissue relevant metabolic signatures by high spatially resolved metabolomics combined with a multicellular tumor spheroid (MCTS) in vitro model. Esophageal cancer MCTS were generated using KYSE-30 human esophageal cancer cells to fully mimic the 3D microenvironment under physiological conditions. Then, the spatial features and temporal variation of metabolites and metabolic pathways in MCTS were accurately mapped by using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) with a spatial resolution at ∼12 μm. Metabolites, such as glutamate, tyrosine, inosine and various types of lipids displayed heterogeneous distributions in different microregions inside the MCTS, revealing the metabolic heterogenicity of cancer cells under different proliferative states. Subsequently, through joint analysis of metabolomic data of clinical cancer tissue samples, cancer tissue relevant metabolic signatures in esophageal cancer MCTS were identified, including glutamine metabolism, fatty acid metabolism, de novo synthesis phosphatidylcholine (PC) and phosphatidylethanolamine (PE), etc. In addition, the abnormal expression of the involved metabolic enzymes, i.e., GLS, FASN, CHKA and cPLA2, was further confirmed and showed similar tendencies in esophageal cancer MCTS and cancer tissues. The MALDI-MSI combined with MCTS approach offers molecular insights into cancer metabolism with real-word relevance, which would potentially benefit the biomarker discovery and metabolic mechanism studies.

Keywords:  Cancer metabolism; Esophageal cancer; Mass spectrometry imaging; Multicellular tumor spheroids; Spatially resolved metabolomics

DOI:  https://doi.org/10.1016/j.aca.2021.338342
J Proteome Res. 2021 Mar 25.

Improved SILAC Quantification with Data-Independent Acquisition to Investigate Bortezomib-Induced Protein Degradation.

Lindsay K Pino, Josue Baeza, Richard Lauman, Birgit Schilling, Benjamin A Garcia.

  Stable isotope labeling by amino acids in cell culture (SILAC) coupled to data-dependent acquisition (DDA) is a common approach to quantitative proteomics with the desirable benefit of reducing batch effects during sample processing and data acquisition. More recently, using data-independent acquisition (DIA/SWATH) to systematically measure peptides has gained popularity for its comprehensiveness, reproducibility, and accuracy of quantification. The complementary advantages of these two techniques logically suggests combining them. Here we develop a SILAC-DIA-MS workflow using free, open-source software. We empirically determine that using DIA achieves similar peptide detection numbers as DDA and that DIA improves the quantitative accuracy and precision of SILAC by an order of magnitude. Finally, we apply SILAC-DIA-MS to determine protein turnover rates of cells treated with bortezomib, an FDA-approved 26S proteasome inhibitor for multiple myeloma and mantle cell lymphoma. We observe that SILAC-DIA produces more sensitive protein turnover models. Of the proteins determined to be differentially degraded by both acquisition methods, we find known proteins that are degraded by the ubiquitin-proteasome pathway, such as HNRNPK, EIF3A, and IF4A1/EIF4A-1, and a slower turnover for CATD, a protein implicated in invasive breast cancer. With improved quantification from DIA, we anticipate that this workflow will make SILAC-based experiments like protein turnover more sensitive.

Keywords:  data independent acquisition; protein degradation; protein turnover; pulse SILAC; quantitative proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.0c00938
ACS Omega. 2021 Mar 16. 6(10): 7165-7174

Adrenic Acid-Derived Epoxy Fatty Acids Are Naturally Occurring Lipids and Their Methyl Ester Prodrug Reduces Endoplasmic Reticulum Stress and Inflammatory Pain.

Nalin Singh, Bogdan Barnych, Karen M Wagner, Debin Wan, Christophe Morisseau, Bruce D Hammock.

Adrenic acid (AdA, 22:4) is an ω-6 polyunsaturated fatty acid (PUFA), derived from arachidonic acid. Like other PUFAs, it is metabolized by cytochrome P450s to a group of epoxy fatty acids (EpFAs), epoxydocosatrienoic acids (EDTs). EpFAs are lipid mediators with various beneficial bioactivities, including exertion of analgesia and reduction of endoplasmic reticulum (ER) stress, that are degraded to dihydroxy fatty acids by the soluble epoxide hydrolase (sEH). However, the biological characteristics and activities of EDTs are relatively unexplored, and, alongside dihydroxydocosatrienoic acids (DHDTs), they had not been detected in vivo. Herein, EDT and DHDT regioisomers were synthesized, purified, and used as standards for analysis with a selective and quantitative high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method. Biological verification in AdA-rich tissues suggests that basal metabolite levels are highest in the liver, with 16,17-EDT concentrations consistently being the greatest across the analyzed tissues. Enzyme hydrolysis assessment revealed that EDTs are sEH substrates, with greatest relative rate preference for the 13,14-EDT regioisomer. Pretreatment with an EDT methyl ester regioisomer mixture significantly reduced the onset of tunicamycin-stimulated ER stress in human embryonic kidney cells. Finally, administration of the regioisomeric mixture effectively alleviated carrageenan-induced inflammatory pain in rats. This study indicates that EDTs and DHDTs are naturally occurring lipids, and EDTs could be another therapeutically relevant group of EpFAs.

DOI: https://doi.org/10.1021/acsomega.1c00241
Ann Rheum Dis. 2021 Mar 22. pii: annrheumdis-2020-219682. [Epub ahead of print]

Circulating blood metabolite trajectories and risk of rheumatoid arthritis among military personnel in the Department of Defense Biorepository.

Karen H Costenbader, Michael DiIorio, Su H Chu, Jing Cui, Jeffrey A Sparks, Bing Lu, LauraKay Moss, Lindsay Kelmenson, Marie Feser, Jess Edison, Clary Clish, Jessica Lasky-Su, Kevin D Deane, Elizabeth W Karlson.

   OBJECTIVES: We sought to identify metabolic changes potentially related to rheumatoid arthritis (RA) pathogenesis occurring in the blood prior to its diagnosis.
METHODS: In a US military biorepository, serum samples collected at two timepoints prior to a diagnosis of RA were identified. These were matched to controls who did not develop RA by subject age, race and time between sample collections and RA diagnosis time to stored serum samples. Relative abundances of 380 metabolites were measured using liquid chromatography-tandem mass spectrometry. We determined whether pre-RA case versus control status predicted metabolite concentration differences and differences over time (trajectories) using linear mixed models, assessing for interactions between time, pre-RA status and metabolite concentrations. We separately examined pre-RA and pre-seropositive RA cases versus matched controls and adjusted for smoking. Multiple comparison adjustment set the false discovery rate to 0.05.
RESULTS: 291 pre-RA cases (80.8% pre seropositive RA) were matched to 292 controls, all with two serum samples (2.7±1.6 years; 1.0±0.9 years before RA/matched date). 52.0% were women; 52.8% were White, 26.8% Black and 20.4% other race. Mean age was 31.2 (±8.1) years at earliest blood draw. Fourteen metabolites had statistically significant trajectory differences among pre-RA subjects versus controls, including sex steroids, amino acid/lipid metabolism and xenobiotics. Results were similar when limited to pre seropositive RA and after adjusting for smoking.
CONCLUSIONS: In this military case-control study, metabolite concentration trajectory differences in pre-RA cases versus controls implicated steroidogenesis, lipid/amino acid metabolism and xenobiotics in RA pathogenesis. Metabolites may have potential as biomarkers and/or therapeutic targets preceding RA diagnosis.

Keywords:  arthritis; epidemiology; rheumatoid; rheumatoid arthritis

DOI:  https://doi.org/10.1136/annrheumdis-2020-219682
Arthritis Rheumatol. 2021 Mar 24.

Serum metabolomics identifies dysregulated pathways and potential metabolic biomarkers for hyperuricemia and gout.

Xia Shen, Can Wang, Ningning Liang, Zhen Liu, Xinde Li, Zheng-Jiang Zhu, Tony R Merriman, Nicola Dalbeth, Robert Terkeltaub, Changgui Li, Huiyong Yin.

   OBJECTIVE: To systemically profile metabolic alterations and dysregulated metabolic pathways in hyperuricemia (HU) and gout, and discover potential metabolite biomarkers to discriminate gout from asymptomatic HU.
METHODS: Serum samples of 330 participants, 109 gout, 102 asymptomatic HU, and 119 normouricemic (NU), were analyzed by high-resolution mass spectrometry-based metabolomics. Multivariate PCA and OPLS-DA analysis were performed to explore differential metabolites and pathways. MUVR (Multivariate methods with Unbiased Variable selection in R) algorithm was performed to identify potential biomarkers and build multivariate diagnostic models using three machine learning algorithms including Random Forest, Support Vector Machine and Logistic Regressions.
RESULTS: Univariate analysis demonstrated more distinct metabolic profiles between gout and NU than HU and NU, while gout and HU showed clear metabolomic differences. Pathway enrichment analysis found diverse significantly dysregulated pathways in HU and gout compared to NU, among which arginine metabolism appears to play a critical role. The multivariate diagnostic model using MUVR found thirteen metabolites as potential biomarkers to differentiate HU and gout from NU. By randomly selecting 2/3rd of the samples as training set and the remainder as validation set, receiver operating characteristic (ROC) analysis on seven metabolites yielded area under the curve of 0.83 to 0.87 in the training set and 0.78 to 0.84 in the validation set by three machine learning algorithms for distinguishing gout from asymptomatic HU.
CONCLUSION: Gout and HU have distinct serum metabolomic signatures. This diagnostic model has the potential to improve current gout care through early detection or prediction of gout progression from HU.

Keywords:  biomarkers; gout; hyperuricemia; machine learning algorithms; metabolomics

DOI:  https://doi.org/10.1002/art.41733
Clin Chim Acta. 2021 Mar 18. pii: S0009-8981(21)00088-7. [Epub ahead of print]

Serum metabolite signatures of epithelial ovarian cancer based on targeted metabolomics.

Xinyang Wang, Xinshu Zhao, Jinhui Zhao, Tongshu Yang, Fengmin Zhang, Liyan Liu.

   BACKGROUND: Epithelial ovarian cancer (EOC) is a common gynecological cancer with high mortality rates. The main objective of this study was to investigate the serum amino acid and organic acid profiles to distinguish key metabolites for screening EOC patients.
METHODS: In total, 39 patients with EOC and 31 healthy controls were selected as the training set. Serum amino acid and organic acid profiles were determined using the targeted metabolomics approach. Metabolite profiles were processed via multivariate analysis to identify potential metabolites and construct a metabolic network. Finally, a test dataset derived from 29 patients and 28 healthy controls was constructed to validate the potential metabolites.
RESULTS: Distinct amino acid and organic acid profiles were obtained between EOC and healthy control groups. Methionine, glutamine, asparagine, glutamic acid and glycolic acid were identified as potential metabolites to distinguish EOC from control samples. The areas under the curve for methionine, glutamine, asparagine, glutamic acid and glycolic acid were 0.775, 0 778, 0.955, 0.874 and 0.897, respectively, in the validation study. Metabolic network analysis of the training set indicated key roles of alanine, aspartate and glutamate metabolism as well as D-glutamine and D-glutamate metabolism in the pathogenesis of EOC.
CONCLUSIONS: Amino acid and organic acid profiles may serve as potential screening tools for EOC. Data from this study provide useful information to bridge gaps in the understanding of the amino acid and organic acid alterations associated with epithelial ovarian cancer.

Keywords:  Amino acid profiles; Epithelial ovarian cancer; Metabolomics; Organic acid profiles; Serum

DOI:  https://doi.org/10.1016/j.cca.2021.03.012
Anal Chem. 2021 Mar 22.

Separation and Characterization of Endogenous Nucleosomes by Native Capillary Zone Electrophoresis-Top-Down Mass Spectrometry.

Kevin Jooß, Luis F Schachner, Rachel Watson, Zachary B Gillespie, Sarah A Howard, Marcus A Cheek, Matthew J Meiners, Amin Sobh, Jonathan D Licht, Michael-Christopher Keogh, Neil L Kelleher.

We report a novel platform [native capillary zone electrophoresis-top-down mass spectrometry (nCZE-TDMS)] for the separation and characterization of whole nucleosomes, their histone subunits, and post-translational modifications (PTMs). As the repeating unit of chromatin, mononucleosomes (Nucs) are an ∼200 kDa complex of DNA and histone proteins involved in the regulation of key cellular processes central to human health and disease. Unraveling the covalent modification landscape of histones and their defined stoichiometries within Nucs helps to explain epigenetic regulatory mechanisms. In nCZE-TDMS, online Nuc separation is followed by a three-tier tandem MS approach that measures the intact mass of Nucs, ejects and detects the constituent histones, and fragments to sequence the histone. The new platform was optimized with synthetic Nucs to significantly reduce both sample requirements and cost compared to direct infusion. Limits of detection were in the low-attomole range, with linearity of over ∼3 orders of magnitude. The nCZE-TDMS platform was applied to endogenous Nucs from two cell lines distinguished by overexpression or knockout of histone methyltransferase NSD2/MMSET, where analysis of constituent histones revealed changes in histone abundances over the course of the CZE separation. We are confident the nCZE-TDMS platform will help advance nucleosome-level research in the fields of chromatin and epigenetics.

DOI: https://doi.org/10.1021/acs.analchem.0c04975
Cancer Metab. 2021 Mar 24. 9(1): 12

GLUT5 (SLC2A5) enables fructose-mediated proliferation independent of ketohexokinase.

Roger J Liang, Samuel Taylor, Navid Nahiyaan, Junho Song, Charles J Murphy, Ezequiel Dantas, Shuyuan Cheng, Ting-Wei Hsu, Shakti Ramsamooj, Rahul Grover, Seo-Kyoung Hwang, Bryan Ngo, Lewis C Cantley, Kyu Y Rhee, Marcus D Goncalves.

   BACKGROUND: Fructose is an abundant source of carbon and energy for cells to use for metabolism, but only certain cell types use fructose to proliferate. Tumor cells that acquire the ability to metabolize fructose have a fitness advantage over their neighboring cells, but the proteins that mediate fructose metabolism in this context are unknown. Here, we investigated the determinants of fructose-mediated cell proliferation.
METHODS: Live cell imaging and crystal violet assays were used to characterize the ability of several cell lines (RKO, H508, HepG2, Huh7, HEK293T (293T), A172, U118-MG, U87, MCF-7, MDA-MB-468, PC3, DLD1 HCT116, and 22RV1) to proliferate in fructose (i.e., the fructolytic ability). Fructose metabolism gene expression was determined by RT-qPCR and western blot for each cell line. A positive selection approach was used to "train" non-fructolytic PC3 cells to utilize fructose for proliferation. RNA-seq was performed on parental and trained PC3 cells to find key transcripts associated with fructolytic ability. A CRISPR-cas9 plasmid containing KHK-specific sgRNA was transfected in 293T cells to generate KHK-/- cells. Lentiviral transduction was used to overexpress empty vector, KHK, or GLUT5 in cells. Metabolic profiling was done with seahorse metabolic flux analysis as well as LC/MS metabolomics. Cell Titer Glo was used to determine cell sensitivity to 2-deoxyglucose in media containing either fructose or glucose.
RESULTS: We found that neither the tissue of origin nor expression level of any single gene related to fructose catabolism determine the fructolytic ability. However, cells cultured chronically in fructose can develop fructolytic ability. SLC2A5, encoding the fructose transporter, GLUT5, was specifically upregulated in these cells. Overexpression of GLUT5 in non-fructolytic cells enabled growth in fructose-containing media across cells of different origins. GLUT5 permitted fructose to flux through glycolysis using hexokinase (HK) and not ketohexokinase (KHK).
CONCLUSIONS: We show that GLUT5 is a robust and generalizable driver of fructose-dependent cell proliferation. This indicates that fructose uptake is the limiting factor for fructose-mediated cell proliferation. We further demonstrate that cellular proliferation with fructose is independent of KHK.

Keywords:  Fructose; GLUT5 (SLC2A5); Hexokinase; Ketohexokinase; Metabolism

DOI:  https://doi.org/10.1186/s40170-021-00246-9
Mol Cell. 2021 Mar 17. pii: S1097-2765(21)00177-5. [Epub ahead of print]

mTORC1 stimulates cell growth through SAM synthesis and m6A mRNA-dependent control of protein synthesis.

Elodie Villa, Umakant Sahu, Brendan P O'Hara, Eunus S Ali, Kathryn A Helmin, John M Asara, Peng Gao, Benjamin D Singer, Issam Ben-Sahra.

  The mechanistic target of rapamycin complex 1 (mTORC1) regulates metabolism and cell growth in response to nutrient, growth, and oncogenic signals. We found that mTORC1 stimulates the synthesis of the major methyl donor, S-adenosylmethionine (SAM), through the control of methionine adenosyltransferase 2 alpha (MAT2A) expression. The transcription factor c-MYC, downstream of mTORC1, directly binds to intron 1 of MAT2A and promotes its expression. Furthermore, mTORC1 increases the protein abundance of Wilms' tumor 1-associating protein (WTAP), the positive regulatory subunit of the human N6-methyladenosine (m6A) RNA methyltransferase complex. Through the control of MAT2A and WTAP levels, mTORC1 signaling stimulates m6A RNA modification to promote protein synthesis and cell growth. A decline in intracellular SAM levels upon MAT2A inhibition decreases m6A RNA modification, protein synthesis rate, and tumor growth. Thus, mTORC1 adjusts m6A RNA modification through the control of SAM and WTAP levels to prime the translation machinery for anabolic cell growth.

Keywords:  Cell growth; MAT2A; Methionine cycle; N(6)-methyladenosine; Protein Synthesis; RNA metabolism; S-adenosylmethionine; WTAP; mTOR; mTORC1

DOI:  https://doi.org/10.1016/j.molcel.2021.03.009
Mass Spectrom Rev. 2021 Mar 25.

Methods to improve quantitative glycoprotein coverage from bottom-up LC-MS data.

Deborah Chang, Joseph Zaia.

  Advances in mass spectrometry instrumentation, methods development, and bioinformatics have greatly improved the ease and accuracy of site-specific, quantitative glycoproteomics analysis. Data-dependent acquisition is the most popular method for identification and quantification of glycopeptides; however, complete coverage of glycosylation site glycoforms remains elusive with this method. Targeted acquisition methods improve the precision and accuracy of quantification, but at the cost of throughput and discoverability. Data-independent acquisition (DIA) holds great promise for more complete and highly quantitative site-specific glycoproteomics analysis, while maintaining the ability to discover novel glycopeptides without prior knowledge. We review additional features that can be used to increase selectivity and coverage to the DIA workflow: retention time modeling, which would simplify the interpretation of complex tandem mass spectra, and ion mobility separation, which would maximize the sampling of all precursors at a giving chromatographic retention time. The instrumentation and bioinformatics to incorporate these features into glycoproteomics analysis exist. These improvements in quantitative, site-specific analysis will enable researchers to assess glycosylation similarity in related biological systems, answering new questions about the interplay between glycosylation state and biological function.

Keywords:  glycoproteomics; ion mobility; mass spectrometry; quantification; retention time

DOI:  https://doi.org/10.1002/mas.21692
Nat Commun. 2021 Mar 26. 12(1): 1920

The oncogene AAMDC links PI3K-AKT-mTOR signaling with metabolic reprograming in estrogen receptor-positive breast cancer.

Emily Golden, Rabab Rashwan, Eleanor A Woodward, Agustin Sgro, Edina Wang, Anabel Sorolla, Charlene Waryah, Wan Jun Tie, Elisabet Cuyàs, Magdalena Ratajska, Iwona Kardaś, Piotr Kozlowski, Elizabeth K M Johnstone, Heng B See, Ciara Duffy, Jeremy Parry, Kim A Lagerborg, Piotr Czapiewski, Javier A Menendez, Adam Gorczyński, Bartosz Wasag, Kevin D G Pfleger, Christina Curtis, Bum-Kyu Lee, Jonghwan Kim, Joseph Cursons, Nathan J Pavlos, Wojciech Biernat, Mohit Jain, Andrew J Woo, Andrew Redfern, Pilar Blancafort.

Adipogenesis associated Mth938 domain containing (AAMDC) represents an uncharacterized oncogene amplified in aggressive estrogen receptor-positive breast cancers. We uncover that AAMDC regulates the expression of several metabolic enzymes involved in the one-carbon folate and methionine cycles, and lipid metabolism. We show that AAMDC controls PI3K-AKT-mTOR signaling, regulating the translation of ATF4 and MYC and modulating the transcriptional activity of AAMDC-dependent promoters. High AAMDC expression is associated with sensitization to dactolisib and everolimus, and these PI3K-mTOR inhibitors exhibit synergistic interactions with anti-estrogens in IntClust2 models. Ectopic AAMDC expression is sufficient to activate AKT signaling, resulting in estrogen-independent tumor growth. Thus, AAMDC-overexpressing tumors may be sensitive to PI3K-mTORC1 blockers in combination with anti-estrogens. Lastly, we provide evidence that AAMDC can interact with the RabGTPase-activating protein RabGAP1L, and that AAMDC, RabGAP1L, and Rab7a colocalize in endolysosomes. The discovery of the RabGAP1L-AAMDC assembly platform provides insights for the design of selective blockers to target malignancies having the AAMDC amplification.

DOI: https://doi.org/10.1038/s41467-021-22101-7
Nat Commun. 2021 03 25. 12(1): 1850

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.

Henry Gerdes, Pedro Casado, Arran Dokal, Maruan Hijazi, Nosheen Akhtar, Ruth Osuntola, Vinothini Rajeeve, Jude Fitzgibbon, Jon Travers, David Britton, Shirin Khorsandi, Pedro R Cutillas.

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman's rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.

DOI: https://doi.org/10.1038/s41467-021-22170-8
J Proteome Res. 2021 Mar 22.

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics.

Paolo Cifani, Zhi Li, Danmeng Luo, Mark Grivainis, Andrew M Intlekofer, David Fenyö, Alex Kentsis.

  Recent studies have revealed diverse amino acid, post-translational, and noncanonical modifications of proteins in diverse organisms and tissues. However, their unbiased detection and analysis remain hindered by technical limitations. Here, we present a spectral alignment method for the identification of protein modifications using high-resolution mass spectrometry proteomics. Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. Using synthetic standards and controlled chemical labeling experiments, we demonstrate its high specificity and sensitivity for the discovery of substoichiometric protein modifications in complex cellular extracts. SAMPEI mapping of mouse macrophage differentiation revealed diverse post-translational protein modifications, including distinct forms of cysteine itaconatylation. SAMPEI's robust parametrization and versatility are expected to facilitate the discovery of biological modifications of diverse macromolecules. SAMPEI is implemented as a Python package and is available open-source from BioConda and GitHub (https://github.com/FenyoLab/SAMPEI).

Keywords:  SAMPEI; functional regulation; itaconate; macrophages; mass spectrometry; metabolism; post-translational chemical modification; signaling; software; spectral alignment

DOI:  https://doi.org/10.1021/acs.jproteome.0c00638
Cancer Metab. 2021 Mar 26. 9(1): 14

A small-molecule pan-class I glucose transporter inhibitor reduces cancer cell proliferation in vitro and tumor growth in vivo by targeting glucose-based metabolism.

Pratik Shriwas, Dennis Roberts, Yunsheng Li, Liyi Wang, Yanrong Qian, Stephen Bergmeier, Jennifer Hines, Subhodip Adhicary, Corinne Nielsen, Xiaozhuo Chen.

   BACKGROUND: Cancer cells drastically increase the uptake of glucose and glucose metabolism by overexpressing class I glucose transporters (GLUT1-4) to meet their energy and biomass synthesis needs and are very sensitive and vulnerable to glucose deprivation. Although targeting glucose uptake via GLUTs has been an attractive anticancer strategy, the relative anticancer efficacy of multi-GLUT targeting or single GLUT targeting is unclear. Here, we report DRB18, a synthetic small molecule, is a potent anticancer compound whose pan-class I GLUT inhibition is superior to single GLUT targeting.
METHODS: Glucose uptake and MTT/resazurin assays were used to measure DRB18's inhibitory activities of glucose transport and cell viability/proliferation in human lung cancer and other cancer cell lines. Four HEK293 cell lines expressing GLUT1-4 individually were used to determine the IC50 values of DRB18's inhibitory activity of glucose transport. Docking studies were performed to investigate the potential direct interaction of DRB18 with GLUT1-4. Metabolomics analysis was performed to identify metabolite changes in A549 lung cancer cells treated with DRB18. DRB18 was used to treat A549 tumor-bearing nude mice. The GLUT1 gene was knocked out to determine how the KO of the gene affected tumor growth.
RESULTS: DRB18 reduced glucose uptake mediated via each of GLUT1-4 with different IC50s, which match with the docking glidescores with a correlation coefficient of 0.858. Metabolomics analysis revealed that DRB18 altered energy-related metabolism in A549 cells by changing the abundance of metabolites in glucose-related pathways in vitro and in vivo. DRB18 eventually led to G1/S phase arrest and increased oxidative stress and necrotic cell death. IP injection of DRB18 in A549 tumor-bearing nude mice at 10 mg/kg body weight thrice a week led to a significant reduction in the tumor volume compared with mock-treated tumors. In contrast, the knockout of the GLUT1 gene did not reduce tumor volume.
CONCLUSIONS: DRB18 is a potent pan-class I GLUT inhibitor in vitro and in vivo in cancer cells. Mechanistically, it is likely to bind the outward open conformation of GLUT1-4, reducing tumor growth through inhibiting GLUT1-4-mediated glucose transport and metabolisms. Pan-class I GLUT inhibition is a better strategy than single GLUT targeting for inhibiting tumor growth.

Keywords:  Anticancer therapeutics; Glycolysis; Metabolomics; TCA cycle; The Warburg effect; docking

DOI:  https://doi.org/10.1186/s40170-021-00248-7
Nat Commun. 2021 03 25. 12(1): 1876

SARS-CoV-2 infection rewires host cell metabolism and is potentially susceptible to mTORC1 inhibition.

Peter J Mullen, Gustavo Garcia, Arunima Purkayastha, Nedas Matulionis, Ernst W Schmid, Milica Momcilovic, Chandani Sen, Justin Langerman, Arunachalam Ramaiah, David B Shackelford, Robert Damoiseaux, Samuel W French, Kathrin Plath, Brigitte N Gomperts, Vaithilingaraja Arumugaswami, Heather R Christofk.

Viruses hijack host cell metabolism to acquire the building blocks required for replication. Understanding how SARS-CoV-2 alters host cell metabolism may lead to potential treatments for COVID-19. Here we profile metabolic changes conferred by SARS-CoV-2 infection in kidney epithelial cells and lung air-liquid interface (ALI) cultures, and show that SARS-CoV-2 infection increases glucose carbon entry into the TCA cycle via increased pyruvate carboxylase expression. SARS-CoV-2 also reduces oxidative glutamine metabolism while maintaining reductive carboxylation. Consistent with these changes, SARS-CoV-2 infection increases the activity of mTORC1 in cell lines and lung ALI cultures. Lastly, we show evidence of mTORC1 activation in COVID-19 patient lung tissue, and that mTORC1 inhibitors reduce viral replication in kidney epithelial cells and lung ALI cultures. Our results suggest that targeting mTORC1 may be a feasible treatment strategy for COVID-19 patients, although further studies are required to determine the mechanism of inhibition and potential efficacy in patients.

DOI: https://doi.org/10.1038/s41467-021-22166-4
Brief Bioinform. 2021 Mar 24. pii: bbab073. [Epub ahead of print]

A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics.

Christoph A Krettler, Gerhard G Thallinger.

  Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.

Keywords:  machine learning; mass spectrometry; metabolomics; quantum chemistry

DOI:  https://doi.org/10.1093/bib/bbab073
Nat Biotechnol. 2021 Mar 25.

Ultra-fast proteomics with Scanning SWATH.

Christoph B Messner, Vadim Demichev, Nic Bloomfield, Jason S L Yu, Matthew White, Marco Kreidl, Anna-Sophia Egger, Anja Freiwald, Gordana Ivosev, Fras Wasim, Aleksej Zelezniak, Linda Jürgens, Norbert Suttorp, Leif Erik Sander, Florian Kurth, Kathryn S Lilley, Michael Mülleder, Stephen Tate, Markus Ralser.

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

DOI: https://doi.org/10.1038/s41587-021-00860-4
Mass Spectrom Rev. 2021 Mar 24.

Mass spectrometry-based tear proteomics for noninvasive biomarker discovery.

Erika Ponzini, Carlo Santambrogio, Antonella De Palma, Pierluigi Mauri, Silvia Tavazzi, Rita Grandori.

  The lacrimal film has attracted increasing interest in the last decades as a potential source of biomarkers of physiopathological states, due to its accessibility, moderate complexity, and responsiveness to ocular and systemic diseases. High-performance liquid chromatography-mass spectrometry (LC-MS) has led to effective approaches to tear proteomics, despite the intrinsic limitations in sample amounts. This review focuses on the recent progress in strategy and technology, with an emphasis on the potential for personalized medicine. After an introduction on lacrimal-film composition, examples of applications to biomarker discovery are discussed, comparing approaches based on pooled-sample and single-tear analysis. Then, the most critical steps of the experimental pipeline, that is, tear collection, sample fractionation, and LC-MS implementation, are discussed with reference to proteome-coverage optimization. Advantages and challenges of the alternative procedures are highlighted. Despite the still limited number of studies, tear quantitative proteomics, including single-tear investigation, could offer unique contributions to the identification of low-invasiveness, sustained-accessibility biomarkers, and to the development of personalized approaches to therapy and diagnosis.

Keywords:  lacrimal film; liquid biopsies; peripheral body fluids; personalized medicine; single-tear analysis; tear collection and fractionation methods

DOI:  https://doi.org/10.1002/mas.21691
Curr Protoc. 2021 Mar;1(3): e85

Facile Preparation of Peptides for Mass Spectrometry Analysis in Bottom-Up Proteomics Workflows.

Melinda Wojtkiewicz, Linda Berg Luecke, Maia I Kelly, Rebekah L Gundry.

  Mass spectrometry (MS) is routinely used to identify, characterize, and quantify biological molecules. For protein analysis, MS-based workflows can be broadly categorized as top-down or bottom-up, depending on whether the proteins are analyzed as intact molecules or first digested into peptides. This article outlines steps for preparing peptide samples for MS as part of a bottom-up proteomics workflow, providing versatile methods suitable for discovery and targeted analyses in qualitative and quantitative workflows. Resulting samples contain peptides of suitable size for analysis by MS instrumentation generally available to modern research laboratories, including MS coupled to either liquid chromatography (LC) or matrix-assisted laser desorption/ionization (MALDI) interfaces. This article incorporates recent developments in methodologies and consumables to facilitate sample preparation. The protocols are well-suited to users without prior experience in proteomics and include methods for universally applicable suspension trap processing and for alternate in-solution processing to accommodate a range of sample types. Cleanup, quantification, and fractionation procedures are also described. © 2021 The Authors. Basic Protocol: Preparation of high-complexity peptide samples for mass spectrometry analysis using S-Trap™ processing Alternate Protocol 1: Preparation of low- to moderate-complexity peptide samples for mass spectrometry analysis using in-solution processing Alternate Protocol 2: Detergent, polymer, and salt removal from peptide samples before mass spectrometry analysis using SP2 processing Support Protocol 1: Protein quantification using Pierce 660 nm assay Support Protocol 2: Peptide quantification using Pierce quantitative fluorometric peptide assay Support Protocol 3: High-pH fractionation of complex peptide samples.

Keywords:  S-Trap™; bottom-up proteomics; detergent removal; mass spectrometry

DOI:  https://doi.org/10.1002/cpz1.85
Biochim Biophys Acta Mol Basis Dis. 2021 Mar 18. pii: S0925-4439(21)00062-4. [Epub ahead of print]1867(7): 166129

Signalling transduction of O-GlcNAcylation and PI3K/AKT/mTOR-axis in prostate cancer.

Vivek Makwana, Santosh Rudrawar, Shailendra Anoopkumar-Dukie.

  Hexosamine biosynthetic (HBP) and PI3K/AKT/mTOR pathways are found to predominate the proliferation and survival of prostate cancer cells. Both these pathways have their own specific intermediates to propagate the secondary signals in down-stream cascades and besides having their own structured network, also have shared interconnecting branches. These interconnections are either competitive or co-operative in nature depending on the microenvironmental conditions. Specifically, in prostate cancer HBP and mTOR pathways increases the expression and protein level of androgen receptor in order to support cancer cell proliferation, advancement and metastasis. Pharmacological inhibition of a single pathway is therefore insufficient to stop disease progression as the cancer cells manage to alter the signalling channel. This is one of the primary reasons for the therapeutic failure in prostate cancer and emergence of chemoresistance. Inhibition of these multiple pathways at their common junctures might prove to be of benefit in men suffering from an advanced disease state. Hence, a thorough understanding of these cellular intersecting points and their significance with respect to signal transduction mechanisms might assist in the rational designing of combinations for effective management of prostate cancer.

Keywords:  Androgen receptor; Chemoresistance; Combinatorial approach; HBP; mTOR

DOI:  https://doi.org/10.1016/j.bbadis.2021.166129
Microb Cell Fact. 2021 Mar 09. 20(1): 64

Adaptation of central metabolite pools to variations in growth rate and cultivation conditions in Saccharomyces cerevisiae.

Kanhaiya Kumar, Vishwesh Venkatraman, Per Bruheim.

   BACKGROUND: Saccharomyces cerevisiae is a well-known popular model system for basic biological studies and serves as a host organism for the heterologous production of commercially interesting small molecules and proteins. The central metabolism is at the core to provide building blocks and energy to support growth and survival in normal situations as well as during exogenous stresses and forced heterologous protein production. Here, we present a comprehensive study of intracellular central metabolite pool profiling when growing S. cerevisiae on different carbon sources in batch cultivations and at different growth rates in nutrient-limited glucose chemostats. The latest versions of absolute quantitative mass spectrometry-based metabolite profiling methodology were applied to cover glycolytic and pentose phosphate pathway metabolites, tricarboxylic acid cycle (TCA), complete amino acid, and deoxy-/nucleoside phosphate pools.
RESULTS: Glutamate, glutamine, alanine, and citrate were the four most abundant metabolites for most conditions tested. The amino acid is the dominant metabolite class even though a marked relative reduction compared to the other metabolite classes was observed for nitrogen and phosphate limited chemostats. Interestingly, glycolytic and pentose phosphate pathway (PPP) metabolites display the largest variation among the cultivation conditions while the nucleoside phosphate pools are more stable and vary within a closer concentration window. The overall trends for glucose and nitrogen-limited chemostats were increased metabolite pools with the increasing growth rate. Next, comparing the chosen chemostat reference growth rate (0.12 h-1, approximate one-fourth of maximal unlimited growth rate) illuminates an interesting pattern: almost all pools are lower in nitrogen and phosphate limited conditions compared to glucose limitation, except for the TCA metabolites citrate, isocitrate and α-ketoglutarate.
CONCLUSIONS: This study provides new knowledge-how the central metabolism is adapting to various cultivations conditions and growth rates which is essential for expanding our understanding of cellular metabolism and the development of improved phenotypes in metabolic engineering.

Keywords:  Bioreactor; Chemostat; Glycolysis; Mass Spectrometry; Metabolomics; Physiology; Yeast

DOI:  https://doi.org/10.1186/s12934-021-01557-8
Cancer Metab. 2021 Mar 24. 9(1): 13

Mutant IDH and non-mutant chondrosarcomas display distinct cellular metabolomes.

Sinthu Pathmanapan, Olga Ilkayeva, John T Martin, Adrian Kwan Ho Loe, Hongyuan Zhang, Guo-Fang Zhang, Christopher B Newgard, Jay S Wunder, Benjamin A Alman.

   BACKGROUND: Majority of chondrosarcomas are associated with a number of genetic alterations, including somatic mutations in isocitrate dehydrogenase 1 (IDH1) and IDH2 genes, but the downstream effects of these mutated enzymes on cellular metabolism and tumor energetics are unknown. As IDH mutations are likely to be involved in malignant transformation of chondrosarcomas, we aimed to exploit metabolomic changes in IDH mutant and non-mutant chondrosarcomas.
METHODS: Here, we profiled over 69 metabolites in 17 patient-derived xenografts by targeted mass spectrometry to determine if metabolomic differences exist in mutant IDH1, mutant IDH2, and non-mutant chondrosarcomas. UMAP (Uniform Manifold Approximation and Projection) analysis was performed on our dataset to examine potential similarities that may exist between each chondrosarcoma based on genotype.
RESULTS: UMAP revealed that mutant IDH chondrosarcomas possess a distinct metabolic profile compared with non-mutant chondrosarcomas. More specifically, our targeted metabolomics study revealed large-scale differences in organic acid intermediates of the tricarboxylic acid (TCA) cycle, amino acids, and specific acylcarnitines in chondrosarcomas. Lactate and late TCA cycle intermediates were elevated in mutant IDH chondrosarcomas, suggestive of increased glycolytic metabolism and possible anaplerotic influx to the TCA cycle. A broad elevation of amino acids was found in mutant IDH chondrosarcomas. A few acylcarnitines of varying carbon chain lengths were also elevated in mutant IDH chondrosarcomas, but with minimal clustering in accordance with tumor genotype. Analysis of previously published gene expression profiling revealed increased expression of several metabolism genes in mutant IDH chondrosarcomas, which also correlated to patient survival.
CONCLUSIONS: Overall, our findings suggest that IDH mutations induce global metabolic changes in chondrosarcomas and shed light on deranged metabolic pathways.

Keywords:  Acylcarnitines; Amino acids; Cancer; Chondrosarcoma; Genetic mutation; Glycolysis; Metabolism; Mutant IDH; TCA cycle

DOI:  https://doi.org/10.1186/s40170-021-00247-8
J Proteome Res. 2021 Mar 24.

Visceral Adipose Tissue Phospholipid Signature of Insulin Sensitivity and Obesity.

Magalí Palau-Rodriguez, Anna Marco-Ramell, Patricia Casas-Agustench, Sara Tulipani, Antonio Miñarro, Alex Sanchez-Pla, Mora Murri, Francisco J Tinahones, Cristina Andres-Lacueva.

  Alterations in visceral adipose tissue (VAT) are closely linked to cardiometabolic abnormalities. The aim of this work is to define a metabolic signature in VAT of insulin resistance (IR) dependent on, and independent of, obesity. An untargeted UPLC-Q-Exactive metabolomic approach was carried out on the VAT of obese insulin-sensitive (IS) and insulin-resistant subjects (N = 11 and N = 25, respectively) and nonobese IS and IR subjects (N = 25 and N = 10, respectively). The VAT metabolome in obesity was defined among other things by changes in the metabolism of lipids, nucleotides, carbohydrates, and amino acids, whereas when combined with high IR, it affected the metabolism of 18 carbon fatty acyl-containing phospholipid species. A multimetabolite model created by glycerophosphatidylinositol (18:0); glycerophosphatidylethanolamine (18:2); glycerophosphatidylserine (18:0); and glycerophosphatidylcholine (18:0/18:1), (18:2/18:2), and (18:2/18:3) exhibited a highly predictive performance to identify the metabotype of "insulin-sensitive obesity" among obese individuals [area under the curve (AUC) 96.7% (91.9-100)] and within the entire study population [AUC 87.6% (79.0-96.2)]. We demonstrated that IR has a unique and shared metabolic signature dependent on, and independent of, obesity. For it to be used in clinical practice, these findings need to be validated in a more accessible sample, such as blood.

Keywords:  biomarker; diabetes; discordant phenotypes; insulin resistance; lipid remodeling; metabolomics; metabotype; obesity; phospholipids

DOI:  https://doi.org/10.1021/acs.jproteome.0c00918
J Proteome Res. 2021 Mar 22.

Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins.

Seong Beom Ahn, Karthik S Kamath, Abidali Mohamedali, Zainab Noor, Jemma X Wu, Dana Pascovici, Subash Adhikari, Harish R Cheruku, Gilles J Guillemin, Matthew J McKay, Edouard C Nice, Mark S Baker.

  Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).

Keywords:  SWATH; cancer biomarkers; low abundance plasma protein identification; recombinant protein spectral DDA library (rPSL)

DOI:  https://doi.org/10.1021/acs.jproteome.0c00898
Cancer Discov. 2021 Mar 23. pii: candisc.1453.2020. [Epub ahead of print]

Obesity-Dependent Adipokine Chemerin Suppresses Fatty Acid Oxidation to Confer Ferroptosis Resistance.

Sze Kiat Tan, Iqbal Mahmud, Flavia Fontanesi, Michelle Puchowicz, Chase K A Neumann, Anthony J Griswold, Rutulkumar Patel, Marco Dispagna, Hamzah H Ahmed, Mark L Gonzalgo, J Mark Brown, Timothy J Garrett, Scott M Welford.

Clear cell renal cell carcinoma (ccRCC) is characterized by accumulation of neutral lipids and adipogenic trans-differentiation. We assessed adipokine expression in ccRCC and found that tumor tissues and patient plasma exhibit obesity-dependent elevations of the adipokine chemerin. Attenuation of chemerin by several approaches led to significant reduction in lipid deposition, and impairment of tumor cell growth in vitro and in vivo. A multi-omics approach revealed that chemerin suppresses fatty acid oxidation, preventing ferroptosis, and maintains fatty acid levels that activate HIF2a expression. The lipid CoQ and mitochondrial complex IV, whose biogenesis are lipid-dependent, were found decreased after chemerin inhibition, contributing to lipid reactive oxygen species production. Monoclonal antibody targeting chemerin led to reduced lipid storage and diminished tumor growth, demonstrating translational potential of chemerin inhibition. Collectively, the results suggest that obesity and tumor cells contribute to ccRCC through the expression of chemerin, which is indispensable in ccRCC biology.

DOI: https://doi.org/10.1158/2159-8290.CD-20-1453
Cell Metab. 2021 Mar 18. pii: S1550-4131(21)00109-1. [Epub ahead of print]

Gut microbial metabolites facilitate anticancer therapy efficacy by modulating cytotoxic CD8+ T cell immunity.

Yao He, Liuhui Fu, Yiping Li, Wenyan Wang, Mingli Gong, Jing Zhang, Xin Dong, Jiaoyan Huang, Quanbo Wang, Charles R Mackay, Yang-Xin Fu, Yun Chen, Xiaohuan Guo.

  Recent studies in both mice and humans have suggested that gut microbiota could modulate tumor responsiveness to chemo- or immunotherapies. However, the underlying mechanism is not clear yet. Here, we found that gut microbial metabolites, especially butyrate, could promote the efficacy of oxaliplatin by modulating CD8+ T cell function in the tumor microenvironment. Butyrate treatment directly boosted the antitumor cytotoxic CD8+ T cell responses both in vitro and in vivo in an ID2-dependent manner by promoting the IL-12 signaling pathway. In humans, the oxaliplatin responder cancer patients exhibited a higher amount of serum butyrate than did non-responders, which could also increase ID2 expression and function of human CD8+ T cells. Together, our findings suggest that the gut microbial metabolite butyrate could promote antitumor therapeutic efficacy through the ID2-dependent regulation of CD8+ T cell immunity, indicating that gut microbial metabolites could be effective as a part of cancer therapy.

Keywords:  CD8+ T cell; ID2; IL-12; antitumor therapy efficacy; butyrate; gut microbial metabolites

DOI:  https://doi.org/10.1016/j.cmet.2021.03.002