Concurrent lipidomics and proteomics on malignant plasma cells from multiple myeloma patients: Probing the lipid metabolome
Autoři:
Ahmed Mohamed aff001; Joel Collins aff003; Hui Jiang aff001; Jeffrey Molendijk aff001; Thomas Stoll aff001; Federico Torta aff007; Markus R. Wenk aff007; Robert J. Bird aff003; Paula Marlton aff003; Peter Mollee aff003; Kate A. Markey aff003; Michelle M. Hill aff001
Působiště autorů:
The University of Queensland Diamantina Institute, Faculty of Medicine, University of Queensland, Woolloongabba, Brisbane, Australia
aff001; QIMR Berghofer Medical Research Institute, Herston, Brisbane, Australia
aff002; Princess Alexandra Hospital, Division of Cancer Care Services, Department of Haematology, Woolloongabba, Brisbane, Australia
aff003; Toowoomba Hospital, Cancer Care Services, Toowoomba, Australia
aff004; The University of Queensland Faculty of Medicine, Brisbane, Australia
aff005; SLING, Department of Biochemistry, National University of Singapore, Singapore
aff006; Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
aff007
Vyšlo v časopise:
PLoS ONE 15(1)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0227455
Souhrn
Background
Multiple myeloma (MM) is a hematological malignancy characterized by the clonal expansion of malignant plasma cells. Though durable remissions are possible, MM is considered incurable, with relapse occurring in almost all patients. There has been limited data reported on the lipid metabolism changes in plasma cells during MM progression. Here, we evaluated the feasibility of concurrent lipidomics and proteomics analyses from patient plasma cells, and report these data on a limited number of patient samples, demonstrating the feasibility of the method, and establishing hypotheses to be evaluated in the future.
Methods
Plasma cells were purified from fresh bone marrow aspirates using CD138 microbeads. Proteins and lipids were extracted using a bi-phasic solvent system with methanol, methyl tert-butyl ether, and water. Untargeted proteomics, untargeted and targeted lipidomics were performed on 7 patient samples using liquid chromatography-mass spectrometry. Two comparisons were conducted: high versus low risk; relapse versus newly diagnosed. Proteins and pathways enriched in the relapsed group was compared to a public transcriptomic dataset from Multiple Myeloma Research Consortium reference collection (n = 222) at gene and pathways level.
Results
From one million purified plasma cells, we were able to extract material and complete untargeted (~6000 and ~3600 features in positive and negative mode respectively) and targeted lipidomics (313 lipids), as well as untargeted proteomics analysis (~4100 reviewed proteins). Comparative analyses revealed limited differences between high and low risk groups (according to the standard clinical criteria), hence we focused on drawing comparisons between the relapsed and newly diagnosed patients. Untargeted and targeted lipidomics indicated significant down-regulation of phosphatidylcholines (PCs) in relapsed MM. Although there was limited overlap of the differential proteins/transcripts, 76 significantly enriched pathways in relapsed MM were common between proteomics and transcriptomics data. Further evaluation of transcriptomics data for lipid metabolism network revealed enriched correlation of PC, ceramide, cardiolipin, arachidonic acid and cholesterol metabolism pathways to be exclusively correlated among relapsed but not in newly-diagnosed patients.
Conclusions
This study establishes the feasibility and workflow to conduct integrated lipidomics and proteomics analyses on patient-derived plasma cells. Potential lipid metabolism changes associated with MM relapse warrant further investigation.
Klíčová slova:
Lipid analysis – Lipid metabolism – Lipids – Plasma cells – Proteomic databases – Proteomics – Transcriptome analysis
Zdroje
1. Rajkumar SV, Dimopoulos MA, Palumbo A, Blade J, Merlini G, Mateos MV, et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. The Lancet Oncology. 2014;15(12):e538–48. Epub 2014/12/03. doi: 10.1016/S1470-2045(14)70442-5 25439696.
2. Fonseca R, Abouzaid S, Bonafede M, Cai Q, Parikh K, Cosler L, et al. Trends in overall survival and costs of multiple myeloma, 2000–2014. Leukemia. 2017. doi: 10.1038/leu.2016.380 28008176
3. Palumbo A, Avet-Loiseau H, Oliva S, Lokhorst HM, Goldschmidt H, Rosinol L, et al. Revised International Staging System for Multiple Myeloma: A Report From International Myeloma Working Group. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2015;33(26):2863–9. Epub 2015/08/05. doi: 10.1200/jco.2015.61.2267 26240224; PubMed Central PMCID: PMC4846284.
4. Mancini R, Noto A, Pisanu ME, De Vitis C, Maugeri-Sacca M, Ciliberto G. Metabolic features of cancer stem cells: the emerging role of lipid metabolism. Oncogene. 2018;37(18):2367–78. doi: 10.1038/s41388-018-0141-3 29445137.
5. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K, et al. Body Fatness and Cancer—Viewpoint of the IARC Working Group. N Engl J Med. 2016;375(8):794–8. doi: 10.1056/NEJMsr1606602 27557308.
6. Teras LR, Kitahara CM, Birmann BM, Hartge PA, Wang SS, Robien K, et al. Body size and multiple myeloma mortality: a pooled analysis of 20 prospective studies. Br J Haematol. 2014;166(5):667–76. doi: 10.1111/bjh.12935 24861847; PubMed Central PMCID: PMC4134758.
7. Morris EV, Edwards CM. Adipokines, adiposity, and bone marrow adipocytes: Dangerous accomplices in multiple myeloma. J Cell Physiol. 2018. doi: 10.1002/jcp.26884 29943829.
8. Nagata Y, Ishizaki I, Waki M, Ide Y, Hossen MA, Ohnishi K, et al. Palmitic acid, verified by lipid profiling using secondary ion mass spectrometry, demonstrates anti-multiple myeloma activity. Leukemia research. 2015;39(6):638–45. Epub 2015/04/08. doi: 10.1016/j.leukres.2015.02.011 25846050.
9. Jurczyszyn A, Czepiel J, Gdula-Argasinska J, Pasko P, Czapkiewicz A, Librowski T, et al. Plasma fatty acid profile in multiple myeloma patients. Leukemia research. 2015;39(4):400–5. Epub 2015/02/11. doi: 10.1016/j.leukres.2014.12.010 25666255.
10. Guang MHZ, McCann A, Bianchi G, Zhang L, Dowling P, Bazou D, et al. Overcoming multiple myeloma drug resistance in the era of cancer 'omics'. Leuk Lymphoma. 2018;59(3):542–61. doi: 10.1080/10428194.2017.1337115 28610537.
11. Franco D, Trusso S, Fazio E, Allegra A, Musolino C, Speciale A, et al. Raman spectroscopy differentiates between sensitive and resistant multiple myeloma cell lines. Spectrochim Acta A Mol Biomol Spectrosc. 2017;187:15–22. doi: 10.1016/j.saa.2017.06.020 28645097.
12. Zub KA, Sousa MM, Sarno A, Sharma A, Demirovic A, Rao S, et al. Modulation of cell metabolic pathways and oxidative stress signaling contribute to acquired melphalan resistance in multiple myeloma cells. PLoS One. 2015;10(3):e0119857. doi: 10.1371/journal.pone.0119857 25769101; PubMed Central PMCID: PMC4358942.
13. Cajka T, Fiehn O. Increasing lipidomic coverage by selecting optimal mobile-phase modifiers in LC–MS of blood plasma. Metabolomics. 2015;12:34.
14. Koenig AM, Karabatsiakis A, Stoll T, Wilker S, Hennessy T, Hill MM, et al. Serum profile changes in postpartum women with a history of childhood maltreatment: a combined metabolite and lipid fingerprinting study. Sci Rep. 2018;8(1):3468. doi: 10.1038/s41598-018-21763-6 29472571; PubMed Central PMCID: PMC5823924.
15. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research. 2015;43(7):e47-e.
16. Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 2015;12(6):523–6. doi: 10.1038/nmeth.3393 25938372; PubMed Central PMCID: PMC4449330.
17. Cajka T, Fiehn O. LC-MS-Based Lipidomics and Automated Identification of Lipids Using the LipidBlast In-Silico MS/MS Library. Methods Mol Biol. 2017;1609:149–70. doi: 10.1007/978-1-4939-6996-8_14 28660581.
18. MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics. 2010;26(7):966–8. doi: 10.1093/bioinformatics/btq054 20147306
19. Dieterle F, Ross A, Schlotterbeck G, Senn H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem. 2006;78(13):4281–90. doi: 10.1021/ac051632c 16808434.
20. Sergushichev A. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. bioRxiv. 2016:060012.
21. Dave KA, Norris EL, Bukreyev AA, Headlam MJ, Buchholz UJ, Singh T, et al. A comprehensive proteomic view of responses of A549 type II alveolar epithelial cells to human respiratory syncytial virus infection. Molecular & Cellular Proteomics. 2014;13(12):3250–69.
22. Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26(12):1367–72. doi: 10.1038/nbt.1511 19029910.
23. Välikangas T, Suomi T, Elo LL. A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation. Briefings in bioinformatics. 2017.
24. Fabregat A, Sidiropoulos K, Garapati P, Gillespie M, Hausmann K, Haw R, et al. The reactome pathway knowledgebase. Nucleic acids research. 2015;44(D1):D481–D7. doi: 10.1093/nar/gkv1351 26656494
25. Mohamed A, Hancock T, Nguyen CH, Mamitsuka H. NetPathMiner: R/Bioconductor package for network path mining through gene expression. Bioinformatics. 2014;30(21):3139–41. doi: 10.1093/bioinformatics/btu501 25075120
26. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research. 2003;13(11):2498–504. doi: 10.1101/gr.1239303 14597658
27. Steiner N, Muller U, Hajek R, Sevcikova S, Borjan B, Johrer K, et al. The metabolomic plasma profile of myeloma patients is considerably different from healthy subjects and reveals potential new therapeutic targets. PLoS One. 2018;13(8):e0202045. doi: 10.1371/journal.pone.0202045 30096165; PubMed Central PMCID: PMC6086450 Sciences AG. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.
28. Hanna VS, Hafez EAA. Synopsis of arachidonic acid metabolism: A review. J Adv Res. 2018;11:23–32. doi: 10.1016/j.jare.2018.03.005 30034873; PubMed Central PMCID: PMC6052663.
29. Wang D, Dubois RN. Eicosanoids and cancer. Nat Rev Cancer. 2010;10(3):181–93. doi: 10.1038/nrc2809 20168319; PubMed Central PMCID: PMC2898136.
30. Xu S, Zhao L, Larsson A, Venge P. The identification of a phospholipase B precursor in human neutrophils. FEBS J. 2009;276(1):175–86. doi: 10.1111/j.1742-4658.2008.06771.x 19019078.
31. Nikesitch N, Lee JM, Ling S, Roberts TL. Endoplasmic reticulum stress in the development of multiple myeloma and drug resistance. Clin Transl Immunology. 2018;7(1):e1007. doi: 10.1002/cti2.1007 29484184; PubMed Central PMCID: PMC5822402.
Článek vyšel v časopise
PLOS One
2020 Číslo 1
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Je libo čepici místo mozkového implantátu?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
Nejčtenější v tomto čísle
- Severity of misophonia symptoms is associated with worse cognitive control when exposed to misophonia trigger sounds
- Chemical analysis of snus products from the United States and northern Europe
- Calcium dobesilate reduces VEGF signaling by interfering with heparan sulfate binding site and protects from vascular complications in diabetic mice
- Effect of Lactobacillus acidophilus D2/CSL (CECT 4529) supplementation in drinking water on chicken crop and caeca microbiome
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy